@article{ author = {Karimi, Seyedehfatemeh and Khodabakhsh, Maryam}, title = {Unsupervised Methods for Predicting Query Performance}, abstract ={With the rapid increase in the use of search engines, the need for developing more effective information retrieval and ranking methods has become critical. One of the key challenges in information retrieval is predicting query performance, which involves estimating how well a search engine can fulfill a user's information need. Accurate prediction of query performance allows search engines to take adaptive actions, such as query reformulation or ranking adjustment, to enhance retrieval effectiveness. Query Performance Prediction (QPP) methods fall into two main categories: pre-retrieval prediction and post-retrieval prediction. Pre-retrieval predictors estimate query difficulty before the retrieval process, relying on linguistic and statistical query features rather than retrieved documents. In contrast, post-retrieval prediction methods assess query performance based on the ranking list and document collection, providing deeper insights into retrieval effectiveness. In this study, we propose a novel unsupervised post-retrieval QPP method that evaluates query performance by analyzing the clustering behavior of retrieved documents. Our method defines five new metrics—CC, DCIC, DCNIC, DCNICR, and CCR— to measure the distribution and coherence of retrieved documents. These metrics help assess query difficulty by capturing how documents group into clusters, identifying outlier documents that do not fit well into clusters, and evaluating the overall structure of retrieved results. By leveraging these metrics, our approach provides a more fine-grained estimation of query performance without requiring human-labeled data. To evaluate the effectiveness of the proposed method, we conduct experiments on three datasets: TREC DL 2019, TREC DL 2020, and DL-Hard. The results demonstrate that our approach improves Spearman's correlation coefficient by 0.009 and 0.163 on the TREC DL 2019 and DL-Hard datasets, respectively. Additionally, it increases Pearson’s correlation coefficient by 0.037 on the TREC DL 2020 dataset compared to state-of-the-art unsupervised QPP methods. These improvements indicate that clustering-based QPP methods can effectively capture query difficulty and retrieval quality without the need for external supervision.}, Keywords = {Query Performance Prediction, Information Retrieval, Search Engines}, volume = {22}, Number = {1}, pages = {3-12}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.22.1.3}, url = {http://jsdp.rcisp.ac.ir/article-1-1407-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1407-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {Nazari, Ali and Fallah, Mostafa and Taheri, Mohammad Javad and Diyanat, Abolfazl}, title = {Drive Test Route Optimization in Mobile Networks}, abstract ={The design and maintenance of the radio access network (RAN) in mobile telecommunications requires meticulous monitoring of network performance to ensure optimal quality and coverage. Drive testing is a prevalent methodology for collecting data on network status across delineated geographical areas. This process involves systematically traversing various routes, including streets and pathways, to assess network performance metrics such as Reference Signal Received Power (RSRP). Although drive testing provides essential insights for identifying regions with inadequate signal quality, it is inherently resource-intensive, involving considerable time and financial expenditures. This paper proposes an innovative optimization methodology aimed at enhancing the efficiency of data collection during drive tests. The proposed approach is organized into four fundamental steps: (1) partitioning the map into smaller sections, (2) selecting critical points within each section, (3) employing map-matching techniques to accurately align these points with actual streets and pathways, and (4) determining the optimal route for traversing the critical points. A rectangular area of interest is selected and divided into K smaller sub-regions, within which M critical points are identified according to a predefined criterion. These points, which may not initially correspond with the existing street network, are corrected through map-matching techniques to ensure feasible traversal paths. Lastly, an optimization algorithm is utilized to compute the shortest route that encompasses all identified critical points. The efficacy of the proposed method is assessed through experimental studies that manipulate key parameters K (denoting the number of sub-regions) and M (indicating the maximum critical points per zone). The evaluation emphasizes two critical metrics: the total distance traveled and the success rate in detecting areas with RSRP values below -100 dBm. Results indicate that the proposed approach significantly decreases the distance required for drive testing while achieving substantial coverage of areas with weak signals. For example, in an experiment where the optimized route covered only 18.54 kilometers—equivalent to 34% of the distance of an entire drive test—it successfully identified 70% of regions with poor RSRP. Furthermore, this paper introduces a criterion for assessing the effectiveness of drive test routes, highlighting the balance between route length and coverage of low-signal areas. The findings substantiate that adequate data for network performance analysis can be secured with considerable savings in both cost and time compared to conventional exhaustive drive testing. While this study concentrates on RSRP measurements within 4G networks, the proposed methodology is adaptable for other metrics, including Reference Signal Received Quality (RSRQ). Future research may also examine the integration of alternative data sources, such as satellite imagery, to further refine map partitioning and critical point selection, thereby enhancing the overall efficacy of the proposed method.}, Keywords = {Mobile Networks, Drive Test, Route Optimization, Reference Signal Received Power (RSRP)}, volume = {22}, Number = {1}, pages = {13-24}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.22.1.13}, url = {http://jsdp.rcisp.ac.ir/article-1-1428-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1428-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {Rastari, Soheil and Mohajjelkafshdooz, Morteza and Shamsi, Mahboubeh}, title = {Resource-Aware Neural Architecture Search for Multicore Embedded Real-Time Systems}, abstract ={Creating neural networks in a non-automatic way is a slow process based on trial and error. When the number of network parameters or the number of layers increases, the non-automatic method becomes very expensive and the final result may be suboptimal. Automatic network architecture search algorithms are used to solve this problem. Recently, these algorithms have been able to achieve high accuracies on various datasets such as CIFAR-10, ImageNet, and Penn Tree Bank. These algorithms have the ability to search a wide space of architectures with different characteristics such as network depth, width, connection method, and operations in order to discover architectures with appropriate accuracy. However, one of the traditional challenges of these algorithms is their high search time (Approximately tens of thousands of GPU hours), which has been reduced to tens of hours with new research. Another challenge that usually exists in these methods is their focus on improving network accuracy, while other criteria such as network speed and consumed resources are not taken into account. As a result, these methods cannot be used directly to find the optimal architecture in embedded systems that have limited resources such as processing power, memory, and energy consumption. Therefore, search methods should be devised that are aware of these limitations. Research has been done in this field in recent years, but these methods do not focus specifically on coarse-grained multi-core architectures that do not have a GPU. In this article, we present a method for the automatic design of networks that are suitable for running on multi-core processors. In this method, based on gradient descent, a SuperNet with parallel paths and computational blocks is created. The number of parallel paths is equal to or less than the number of cores. We use a series of decision variables to select appropriate operations in each block of the path. In addition to deciding on the operations performed in each block, deciding is also made regarding synchronization points to utilize the intermediate results of parallel paths and improve the network's accuracy. Then, by training the decision variables (block type and synchronization points) simultaneously with the main network weights, an appropriate subnetwork is selected. Due to the use of the gradient descent method in this approach, the training process is performed only twice, resulting in the final structure of the network. As a result, it has a much lower execution time compared to other methods based on evolutionary search and reinforcement learning. Additionally, considering the constraints of the target system, such as the number of cores and memory consumption, can lead to a more suitable architecture compared to other methods. Experiments conducted on the CIFAR-10 dataset demonstrate that the proposed method can achieve satisfactory accuracy with very little search time.}, Keywords = {Neural network architecture search, embedded systems, parallelization, multi-core processors, gradient descent method.}, volume = {22}, Number = {1}, pages = {25-38}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.22.1.25}, url = {http://jsdp.rcisp.ac.ir/article-1-1418-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1418-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {moradbeiki, pardis and basiri, alirez}, title = {Recognizing request and non-request messages in social networks with combined approaches}, abstract ={The aim of the request recognition task in social networks is to understand the intent behind the posts, comments, or messages shared by users. Many businesses are actively present on various social networks, making it crucial to identify user needs for marketers in this space to foster the growth of online businesses and e-commerce. Detecting request messages automatically and filtering them is essential. However, social network messages often contain slang and numerous spelling errors, posing challenges for research in this domain. While extensive research has been conducted in English, studies on this task in Persian are limited. Telegram stands out as the most popular social network in Iran, with a large Persian-speaking user base. This study utilized a standard labeled Persian dataset from Telegram for training and testing purposes, comprising 85741 messages from the platform, evenly split between request and non-request categories. To tackle the significant challenges posed by sarcastic messages and spelling mistakes on social media platforms, we devised a multi-step hybrid strategy. The initial step involves preprocessing. Social media data typically consists of unstructured and slang-ridden user messages, necessitating preprocessing to enhance Persian text processing and reduce slang usage. The pre-processing phase is crucial when dealing with social media platforms. Because Telegram is unique compared to other platforms the data cleaning process varies. This study's accomplishment includes developing a unique dataset and filtering out noise from Telegram enhancing improvement in the pre-processing phase. Also, this involves normalizing different word forms, such as "beautiful" and "beauty," to maintain the integrity of word meanings. The subsequent step focuses on feature extraction. Various approaches to feature extraction come with their own set of advantages and drawbacks. Hence, we employed hybrid feature extraction methods to address this complexity. While Tf-Idf methods assess word importance without considering meaning, FastText retains semantic similarity. By combining the bag of words and FastText methods, our research aims to enhance accuracy. The final step involves classification, where deep learning networks are utilized to evaluate these features. Experimental findings indicate that our final model achieves precision, recall, and f-score rates of nearly 90%, representing a 5% improvement on average compared to previous methodologies.}, Keywords = {e-commerce, request detection, social networks, messaging, deep-learning based method.}, volume = {22}, Number = {1}, pages = {39-52}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.22.1.39}, url = {http://jsdp.rcisp.ac.ir/article-1-1425-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1425-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {asadzadeh, samira and parvinnia, Elham}, title = {Intelligent routing of the money-carrying vehicle in the urban traffic network of Shiraz}, abstract ={With the rapid growth and development of urban areas, the demand for secure and efficient transportation systems in urban logistics has become increasingly critical. One of the most pressing challenges in this domain is the routing of bank cash-in-transit (CIT) vehicles, which, due to their sensitive and high-risk nature, require precise and intelligent planning. The primary challenges for CIT vehicles include ensuring security, optimizing timing, managing traffic congestion, and selecting the most efficient routes. This paper proposes an innovative method for the intelligent routing of CIT vehicles by mapping the urban routing problem to the Traveling Salesman Problem (TSP). The proposed approach leverages the Ant Colony Optimization (ACO) algorithm, enhanced with real-world constraints such as heavy traffic, low-security areas, and road hazards, which are incorporated as additional weights in the optimization process. The data used in this study includes the urban traffic map of Shiraz, Iran, and the locations of various banks. The results demonstrate that the proposed method effectively selects routes that avoid high-traffic zones, crime-prone areas, and hazardous roads while optimizing travel time. This approach not only enhances security and operational efficiency but also contributes to reducing operational costs. The core innovation of this research lies in its ability to map the urban routing problem to the TSP, a well-known combinatorial optimization problem, and to utilize the ACO algorithm, which is inspired by the foraging behavior of ants. In nature, ants leave pheromone trails to communicate and find the shortest path between their nest and food sources. Similarly, the ACO algorithm employs artificial ants to explore possible routes, leaving virtual pheromones to guide subsequent ants toward optimal paths. In this study, the ACO algorithm is further enhanced by incorporating heuristic information such as traffic volume, security rates, and unsafe driving conditions, which are treated as critical factors in the routing process. The implementation of the proposed method utilizes real-world data from the urban traffic map of Shiraz, including the locations of eight major banks and the routes connecting them. The distances between these locations are calculated using the Haversine formula, which accounts for the Earth's curvature to provide accurate geographical distances. The algorithm is tested with various parameters, including different numbers of artificial ants (ranging from 10 to 200), evaporation rates (0.1 to 0.5), and exploration-exploitation trade-offs (alpha and beta values). The results show that the proposed method can effectively identify routes that minimize travel time while avoiding high-traffic areas, crime-prone zones, and hazardous roads. One of the key contributions of this research is the integration of multiple heuristic factors into the ACO algorithm. Traditional routing algorithms often focus solely on minimizing distance or travel time, neglecting critical real-world constraints. In contrast, the proposed method assigns weights to factors such as traffic volume, security levels, and unsafe driving conditions, allowing the algorithm to prioritize safer and more efficient routes. For example, routes passing through areas with high crime rates or heavy traffic are penalized, reducing their likelihood of being selected. This approach ensures that the final route is not only the shortest but also the safest and most reliable. Comparative evaluations indicate that the proposed algorithm offers a more realistic and comprehensive solution compared to other models. By balancing multifaceted aspects of routing such as safety, timeliness, and cost, the method proves to be highly effective. The algorithm's ability to avoid routes with heavy traffic, low security, and poor road conditions significantly enhances the safety and time efficiency of CIT vehicles. Furthermore, the proposed method represents a significant step forward in improving the efficiency and security of banking operations by providing an innovative approach to intelligent routing for CIT vehicles. By mapping the urban routing problem to the TSP and utilizing the ACO algorithm with real-world constraints, the method delivers optimal and secure routes. In comparison to other studies, the proposed method demonstrates superior performance in terms of accuracy, execution time, energy consumption, and security. The use of real-time traffic data and the algorithm's ability to adapt to dynamic changes further enhance the practicality and reliability of the proposed solution. This adaptability makes the method particularly suitable for urban environments with fluctuating traffic patterns and evolving security challenges. In conclusion, this paper presents a novel and effective approach to intelligent routing for CIT vehicles in urban environments. By combining the strengths of the ACO algorithm with real-world constraints, the proposed method offers a comprehensive solution that balances efficiency, security, and reliability. Future work could explore the integration of machine learning techniques to further enhance the algorithm's predictive capabilities, enabling it to anticipate and respond to emerging traffic and security challenges proactively. This research marks a significant advancement in the field of urban logistics, providing a robust framework for the safe and efficient routing of high-risk transportation systems.}, Keywords = {Intelligent routing, Ant Colony Optimization, Traveling Salesman Problem, route optimization, traffic security}, volume = {22}, Number = {1}, pages = {53-70}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.22.1.53}, url = {http://jsdp.rcisp.ac.ir/article-1-1424-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1424-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {FiruzMahjanabadi, Zahra and Jafari, Pouria and Rezaei, Mehdi}, title = {Automated Classification of Steel Phases in Scanning Electron Microscope Images}, abstract ={The properties of steels are intrinsically dependent on their microstructural components, known as phases, which form during the manufacturing process. Different steel phases can be observed in microscopic images of steel surfaces. Automatic detection and classification of these phases from images can significantly enhance the understanding of steel properties with improved speed and accuracy. This paper introduces, for the first time, an intelligent and automated method for classifying steel phases from microscopic images. This process requires defining and extracting suitable texture features unique to these images and segmenting the images into highly irregular regions based on the extracted features. To achieve this, the input image is initially divided into blocks, and texture features are extracted independently for each block. The dimensionality of these features is then reduced using Principal Component Analysis, and the refined features are subsequently fed into a Softmax neural network for classification. The implementation results indicate that the proposed method achieves an accuracy of over 99% in distinguishing between two phases: acicular ferrite and granular ferrite. Furthermore, it attains an accuracy exceeding 86% when classifying three phases: granular ferrite, acicular ferrite, and Widmanstätten ferrite. This suggests that the widely used and conventional k-means clustering method, as a traditional machine learning approach, is incapable of effectively distinguishing microscopic steel phase blocks using extracted texture features. Notably, as of the writing of this paper, no prior research has been conducted on the automatic classification of different ferrite phases, making this study a novel contribution to the field. In this research, an automated classification algorithm for ferrite phase structures in SEM images of steel is proposed using texture feature extraction methods and machine learning models. The dataset comprises images of 1024×768 resolution, which were divided into 128×128 blocks, with classification performed independently for each block. Due to the limited number of blocks available for training machine learning models, data augmentation techniques such as rotation and scaling were applied to increase the dataset size. Various image processing methods were used to extract 128 texture features. These extracted features were then used to classify different ferrite phases using two machine learning models: k-means clustering and the Softmax neural network. Additionally, PCA was employed to reduce feature dimensionality, which positively impacted the classification of granular and acicular ferrite. While k-means clustering, as a conventional and widely used machine learning method, failed to achieve satisfactory classification accuracy, the proposed approach using a smooth maximum neural network demonstrated exceptional performance. Despite the complex and irregular nature of ferrite shapes, the selected features and the proposed algorithm successfully achieved over 99% accuracy for two-phase classification and over 86% accuracy for three-phase classification.}, Keywords = {Scanning Electron Microscope, Automated Classification, Steel Phases, Neural Networks, K-Means Algorithm.}, volume = {22}, Number = {1}, pages = {71-82}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.22.1.71}, url = {http://jsdp.rcisp.ac.ir/article-1-1400-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1400-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {Bayat, Mohammad Hosein and Tarvirdizadeh, Bahram and Shahbazi, Mohamm}, title = {A Review of Vision-Based Tracking Methods: Temporal and Spatial Features}, abstract ={Vision-based object tracking, as one of the most challenging fields in machine vision, means following the target(s) in the sequence of image frames in the presence of various challenges. In general, tracking algorithms can be classified to the single-target and multi-target based on the number of objects that should be tracked in frames. Trackers use two basic features in tracking: the appearance and motion. The appearance features are extracted from independent images but the motion features are produced through sequence of frames. According to the evaluations, motion models improve the tracking performance and take less process compared to the appearance features. Our investigations show that in contrast of single-target algorithms, the multi-target algorithms consider more contribution for the motion models, and due to the multiplicity of objectives in the scene they focus less on the appearance features. Despite the wide range of methods and significant progress in machine vision, reliable and flawless performance cannot be expected in the use of tracking algorithms with real-time criteria. This will be aggravated if one of the challenges occurs. Challenges such as sudden and fast movements by the target, occlusion by obstacles or other targets in the scene, extreme changes in the appearance and dimensions of the target, as well as entering and exiting the scene, which cause tracking algorithms to fail. Having a good trade-off between the accuracy and the execution speed is one of the main problems for applied tracking algorithms. Detection algorithms, which are known to detect different targets in an independent image, have shown acceptable accuracy, but it is not possible to use them in every frame for a real-time tracking, because either due to the high processing volume of these algorithms, the execution speed of the detector is limited or they are only able to identify certain classes. But the purpose of a general tracking is to follow an object in a sequence of images regardless of its type and class as well as considering temporal and spatial dependencies among successive frames. With the development of recurrent neural networks and their great ability to process sequential data such as text, audio and video, their use in tracking algorithms is increasing. The use of these networks has helped to improve the performance of tracking algorithms due to their short-term and long-term memory in maintaining important features during tracking. Different methods of integrating convolutional and recurrent neural networks are presented and showed grate performance in tracking, but the main drawback of most of them is the low execution speed of the algorithms. Our studies show that direct feeding the high-dimensional inputs, such as features extracted from images, to the recurrent networks greatly reduces their processing speed. Therefore, in some methods with the approach of real-time execution, the dimensions of the recurrent networks input are downsampled and reduced to the smaller size, although the accuracy is also slightly reduced. Our investigations show that the use of motion models in single-target tracking algorithms is less explored compared to the multi-target methods. Meanwhile, the studies show the success of these models in improving tracking performance. Before the introduction of convolutional networks and their remarkable success in extracting deep features from the image, motion models were mostly used, but in recent methods, especially in single-target trackers, appearance features are used more. In single-target algorithms, the presence of only one object in the image and less computational volume compared to multi-target algorithms allows for more free use of appearance features, but this is not possible in multi-target tracking due to the multiplicity of targets so the motion models are more useful in these algorithms. Therefore, in this paper, a more detailed investigation of motion models and their effect on tracking performance is done. The results show that motion models have a profound effect on improving tracking performance while being simple and impose low processing volume. In this paper, a comprehensive review and implementation of different tracking algorithms is discussed and appropriate methods are introduced for practical implementations. On the other hand, different tracking structures are investigated and categorized based on spatial, temporal, appearance and motion features. Also, due to the development of deep learning methods and their impact on tracking, deep architectures, training datasets and standard evaluation methods are studied and the future horizon of this field is discussed. Our studies show that temporal and motion features have received less attention despite their favorable impact on tracking performance. With the development of deep memory networks, the use of these features is increasing and they have taken a greater portion in tracking.}, Keywords = {Vision-Based Object Tracking, Appearance Features, Motion Features, Deep Learning, Machine Vision.}, volume = {22}, Number = {1}, pages = {83-112}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.22.1.83}, url = {http://jsdp.rcisp.ac.ir/article-1-1409-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1409-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {Haji-Esmaeili, Mohammad Mahdi and Montazer, Gholamali}, title = {a Critical Survey on Content-Based & Semantic Image Retrieval – Abstract}, abstract ={The rapid increase in the volume, diversity, and complexity of visual content in the digital world has made the need for designing and implementing visual content search and retrieval systems highly evident. Currently, we are facing a massive scale of visual data on the web, for which the conventional approaches based on manual and human-generated metadata are not sufficient to handle the diversity and sheer volume. The enormous volume of data generated on the web, without a high-accuracy and high-speed solution for understanding and retrieving it, will join the digital archives forever and never be found again. Recently, there have been significant efforts for retrieving these images, particularly in the fields of Content-Based Image Retrieval (CBIR) and Semantic Image Retrieval (SIR). Content-based and semantic image retrieval systems have the capability to search and retrieve images based on their internal content and high-level human-understandable semantics, rather than just the metadata that may be associated with them. This paper provides a comprehensive review of the latest advancements in the field of content-based image retrieval in recent years. It aims to critically discuss the strengths and weaknesses of each research area in content-based retrieval, and provide an overall framework of this process and the progress made in areas such as image preprocessing, feature extraction and embedding, machine learning, benchmark datasets, similarity matching, and performance evaluation. Finally, the paper presents novel research approaches, challenges, and suggestions for better advancing research in this field. The sections of the paper are organized as follows: After the introduction, Section 2 describes the components of a CBIR system framework, and with a cursory look at classical and traditional methods, it will delve into the workings of modern approaches and their associated challenges. In Section 3, we will provide an overview of the concept of "relevance feedback" and explain the need for this method to enhance the retrieval performance in CBIR systems, followed by an introduction to the prominent solutions in this domain. Finally, in Section 4, we will present a review of the image datasets commonly used in the field of content-based image retrieval, along with a discussion of their characteristics. IGiven the recent advancements in the field of computer vision and image processing, especially in the area of "image-text relationship" and how to integrate the two to improve retrieval performance, the focus of a large part of this study has been on the solutions in this area and the performance of the prominent methods. The current main research in this field is monopolized by large companies and organizations with access to vast financial resources, which has slowed down the progress of research and academic work in this field. These companies, with access to unimaginable data and financial resources, have trained well-known and sometimes unknown models on a very large scale (billions of images and texts), and after the training is complete, they have placed the final model in various web services without publishing the details of the research conducted. The important point is that the scale law applies in this field, and any entity that has more access to computational and storage resources will be able to train better and more accurate models, which has made it less possible for small research units and universities to enter this field and wait for the publication of research by the aforementioned organizations and companies. There is a dire need to introduce effective solutions in this field that require limited resources and are capable of achieving high accuracy and competitiveness with the massive models, with a fraction of the budget required to train them. This has happened in the field of large language models, and after two years, multiple research groups have been able to achieve the accuracy of the Chat-GPT4 language model from OpenAI and with the ability to run on home devices, and it is necessary for research in this field to shift from focusing on achieving accuracy with greater scale to focusing on achieving accuracy with lower cost, otherwise this field will remain in the monopoly of companies focused on greater profits}, Keywords = {Content-Based Image Retrieval, Image Processing, Computer Vision, Machine Learning, Deep Learning, Semantic Gap.}, volume = {22}, Number = {1}, pages = {113-141}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.22.1.113}, url = {http://jsdp.rcisp.ac.ir/article-1-1432-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1432-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {SadeghiMoghadam, Mahdi and nejatian, samad and Parvin, Hamid and BagheriFard, Karamullah and Yagoubian, Seyed Hadi}, title = {Solving dynamic optimization problems with an Improved Imperialis Competition Algorithm}, abstract ={Optimization issues are often defined statically, assuming constant environmental conditions. However, in many real-world scenarios, problem environments are dynamic and continuously changing. Thus we need optimization algorithms that could solves those issues in dynamic environments as well. Dynamic optimization problems are change(s) that may occur through the time. Such environments are characterized by uncertainty, temporal changes, and structural complexities, which makes the optimization process a significant challenge. In addressing these challenges, evolutionary algorithms have emerged as one of the most effective approaches for solving dynamic optimization problems (DOPs). Among these algorithms, the Imperialist Competitive Algorithm (ICA), designed based on swarm intelligence and competition among imperialist countries, has garnered considerable attention due to its capability in solving static optimization issues. In this research, Imperialist Competitive Algorithms, inspired by the historical and political processes of colonization and assimilation, have been known as one of the efficient evolutionary algorithms. These algorithms face numerous challenges when dealing with dynamic problems , including reduced population diversity, performance degradation in conditions of rapid environmental changes, and limitations in optimal convergence. These cases indicate the need to develop improved and more adaptable versions of these algorithms. Using concepts such as memory, population clustering, and repulsion mechanisms, this algorithm has been able to maintain population diversity at all stages while increasing the speed of convergence in the face of environmental changes. The key feature of the proposed algorithm is the use of memory to store previous optimal solutions, a clustering mechanism to manage population diversity, and repulsion to prevent unnecessary accumulation of solutions in specific regions. Nevertheless, ICA exhibits poor performance in dynamic environments because it lacks mechanisms to maintain diversity, quick adaptation to environmental changes, and new optima track. This study presents an improved version of ICA aimed at overcoming these limitations. The proposed algorithm incorporates a combination of a memory mechanism and a clustering strategy to enhance its adaptability to environmental changes and preserve diversity within the population. The memory mechanism stores information about previous optima and utilizes it under appropriate conditions to accelerate the optimization process.for clustering method is used for clustering. Clustering in the proposed method ensures that diversity is maintained for the population during the execution of the algorithm. In this study, our goal is to solve problems that change the environment in a global way. That means, the fitness of all points in the environment changes. By testing just one point in the environment and comparing the fitness obtained with its previously stored value, we can detect a change in the environment. On the other hand, the clustering strategy, particularly the k-means technique, to maintain maintain population diversity and prevents the convergence of solutions to specific regions. Together, these two components create a balance between exploration and exploitation, thereby improving the algorithm's performance in dynamic environments. To evaluate the performance of the proposed algorithm, the Moving Peaks Benchmark (MPB) was used as a standard metric. Due to its capability to simulate complex and diverse changes in dynamic environments—particularly in Branke's second scenario—MPB is one of the most recognized tools for assessing the performance of dynamic optimization algorithms. The proposed algorithm was evaluated alongside advanced algorithms such as FTmPSO (TMO), RAmQSO-s4, RmNAFSA-s4, TFTmPSO, RFTmPSO, mQSO10 (5+5q), FMSO, CellularPSO, Multi-SwarmPSO, mCPSO, AmQSO*, FTMPSO, almPSO, and CDEPSA. Experimental results demonstrated that the proposed algorithm outperformed other methods in areas such as convergence speed, adaptability to environmental changes, and population diversity preservation. A key feature of the proposed algorithm is its ability to retain identified optima even after environmental changes. Additionally, the use of the k-means clustering technique has ensured that the algorithm effectively avoids excessive focus on specific regions and maintains population diversity while facing complex environmental changes. Another advantage of this algorithm is its scalability in handling dynamic optimization issues with high dimensions and complexities. These findings indicate that the proposed algorithm is not only effective in laboratory settings but also suitable for real-world applications with fast and dynamic changes.}, Keywords = {Dynamic Optimization, Dynamic Environments, Memory, Imperialist Competition Algorithm, Clustering, Moving Peaks Benchmark.}, volume = {22}, Number = {2}, pages = {3-30}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61882/jsdp.22.2.3}, url = {http://jsdp.rcisp.ac.ir/article-1-1394-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1394-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {Shaeeri, Zahra and Kazemitabar, Javad and Haghverdi, Soroush}, title = {Stock Market Anomaly Detection Using Behavioral Analysis}, abstract ={Stock market fraud, particularly front-running, is a deceptive practice in which traders exploit prior knowledge of significant orders placed by others to profit from stock price movements. Front-running is considered illegal because it involves using confidential or non-public information to manipulate the market for personal gain. This paper tries ti propose a novel, unsupervised, and real-time anomaly detection method based on behavioral analysis, specifically designed to identify front-running fraud within stock market transactions. The method focuses on building individual behavioral profiles for each trader, capturing their specific traits and patterns in stock buying and selling. These profiles serve as baselines for what is considered as 'normal' trading behavior for each trader. To detect anomalies, we introduce a statistical framework where the risk of each transaction will be calculated by evaluating the deviation from the expected behavior based on the trader's historical actions. This deviation is a measure of how unusual the current transaction is in comparison to the trader’s typical actions. The risk calculation involves the use of the log-likelihood ratio, a concept derived from detection theory, which compares the likelihood of a transaction being normal or fraudulent. The conditional probability of a transaction being either fraudulent or non-fraudulent is computed, and the ratio of these probabilities has been taken on a logarithmic scale to define the transaction risk. This risk metric is then utilized to flag potentially suspicious behavior for further investigations. Bayesian probability theory underpins the model, specifically employing Bayes' rule to update the likelihood of fraud as more data will be accumulated over time. The model assumes the independence of risk components, which simplifies the complexity of the system and improves computational efficiency. Despite the potential limitation of assuming independence, empirical studies have shown that this assumption often yields reliable results for detecting anomalous behavior, making the approach both practical and effective. Behavioral profiling plays a key role in this method. By observing the individual’s trading history—such as the frequency, timing, and amounts of trades—the system learns a trader’s typical behavior. This behavioral information is critical because it accounts for the natural variance in a trader's actions over time, allowing the model to distinguish between normal fluctuations and abnormal activities that might indicate fraud. Key behavioral indicators include the timing of trades, the volume of trades, the frequency of transactions with specific counterparties, and the trader’s overall market engagement. Traders whose actions significantly deviate from their established patterns—such as purchasing large quantities of stocks at unusual times or interacting with the same trader excessively—are flagged as high-risk. The simulation section of the paper uses 16 months of stock market transaction data, where features such as transaction amounts, time of trade, urgency, and consistency in trading with particular traders are analyzed to calculate the risk profile. The system ranks traders based on the risk scores of their transactions, enabling the detection of front-running activities in near real-time. The results from the simulation indicate that the proposed method is highly effective in identifying front-running fraud. The use of behavioral profiling ensures that the system is adaptive to individual trading patterns, making it resistant to the evolving nature of fraud in financial markets. The methodology also provides a significant advantage over traditional rule-based systems, which often struggle to adapt to new fraud techniques as they emerge. Furthermore, this approach can be applied in live trading environments, making it a practical tool for regulatory bodies and market surveillance. This paper contributes to the growing field of financial fraud detection by introducing an innovative approach that combines behavioral analysis with advanced statistical techniques. The findings underline the importance of real-time monitoring and adaptive fraud detection systems in maintaining market integrity. In the simulation section, stock market data of 16 months is used. Features related to amounts, hours, urgency, and trading with one trader in buying/selling have been used to obtain the ranking. Results show that the proposed method is effective in detecting front running cases}, Keywords = {Stock market fraud detection, behavioral profiling, data analytics, Front running, log-likelihood, Bayes Rule, anomaly detection.}, volume = {22}, Number = {2}, pages = {31-42}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61882/jsdp.22.2.31}, url = {http://jsdp.rcisp.ac.ir/article-1-1203-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1203-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {sarhadi, ZAHRA and khazaiepoor, mehdi}, title = {A detection system for smart cities Using a neural network and Sailfish Optimizer algorithm}, abstract ={“The Internet of Things” is an extensive network of intelligent objects that has a large number of objects connected to the Internet. One of the applications of the IOT network is in smart cities. In smart cities, all parts of the city, such as the transportation system, electricity network, health network, etc., are interconnected with IOT support. One of the critical challenges of the IOT network is the occurrence of attacks against this network, which causes the network services to be disrupted. Intrusion detection systems are used to detect attacks on the IOT. The role of an IoT network intrusion detection system is to analyze the network traffic, detect abnormal traffic, and send necessary warning to the firewall. One of the methods of detecting attacks on the IOT and smart cities is to use machine learning methods such as support vector machines(SVM). One method to reduce the error of the support vector machine in detecting attacks on the IOT network and the smart city is to use feature selection methods and optimize its parameters. By selecting the feature and optimizing the parameters of the support vector machine, the attack detection error will be reduced. In this article, an intrusion detection method with an artificial neural network and a swordfish optimization algorithm is presented to detect attacks on the smart city. The proposed method includes three different phases: data set balancing with game theory and GAN network, feature selection with Sailfish Optimizer algorithm, and optimization of SVM parameters with Archimedes optimization algorithm (AOA) algorithm. The role of a multilayer neural network in the proposed method of evaluating feature vectors and the role of the support vector machine is to classify network traffic into two categories: attack and normal. The evaluation and tests performed in MATLAB software and on the NSL-KDD data set show that the accuracy, sensitivity, and precision of the proposed method are 99.12%, 98.92%, and 98.96%, respectively, and the support vector machine with Gaussian kernel seems to be more accurate. The results of the experiments showed that the proposed method is more accurate than meta-heuristic algorithms, such as gray wolf optimization and genetic algorithms in detecting attacks on the smart city}, Keywords = {Internet of Things, Smart Cities, Intrusion Detection System, Machine Learning, Sailfish Optimizer Algorithm, Feature Selection}, volume = {22}, Number = {2}, pages = {43-64}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61882/jsdp.22.2.43}, url = {http://jsdp.rcisp.ac.ir/article-1-1378-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1378-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {Amoli, Fatemeh and Bastam, Mostafa and Ataei, Ehs}, title = {A Blockchain-Driven Approach to Automating Event Log Data Integrity and Confidentiality}, abstract ={With the rapid rise of cybersecurity threats and the increasing complexity of digital security, event log data serves as a critical source for identifying and analyzing cyberattacks and threats. This data provide key insights into system activities, essential for detecting unauthorized intrusions, analyzing suspicious behaviors, and conducting security investigations. However, any alteration or tampering with the data can disrupt the analysis and detection processes, leading to incorrect security decisions. Blockchain technology, with its unique features such as decentralization, immutability, and transparency, has been recognized as a reliable and secure platform for storing and protecting data. This technology enables the storage of data hashes in a way that any changes can be easily detected. However, directly storing the vast volume of event log data on the blockchain faces challenges such as high costs and storage space limitations. In this research, an innovative model has been presented to automate the assurance of event log data integrity and confidentiality using the public Ethereum blockchain and smart contracts. Instead of storing event log data directly, only their hashes have been saved on the blockchain. This approach not only reduces storage costs but also ensures data confidentiality. The automated data integrity assurance process in this model occurs in two stages: Stage One: Event log data hashes have been periodically stored on the blockchain and compared with previous hashes. Stage Two: Over longer intervals, all stored hashes have been reviewed and validated to prevent any potential tampering. In this study, the costs associated with implementing this model on the Ethereum Sepolia test network had been precisely calculated. The analysis indicates that operational costs and computational overhead have been optimized across different time intervals, demonstrating the model's feasibility for large-scale deployment. Ultimately, this research tries to introduce a novel and practical model, taking a significant step toward automating the assurance of event log data integrity and confidentiality, providing a reliable solution for real-world applications.}, Keywords = {Log management, Data integrity, Blockchain, Ethereum, Smart Contract.}, volume = {22}, Number = {2}, pages = {65-78}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61882/jsdp.22.2.65}, url = {http://jsdp.rcisp.ac.ir/article-1-1451-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1451-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {Azarnavid, Babak and Abdolhosseinzadeh, Mohsen and Emami, Hojjat}, title = {Stacking machine learning model for classification and prediction of liver diseases}, abstract ={Liver diseases are among the leading causes of mortality worldwide, deeply influencing individuals' lives, often at younger ages when they are in the prime of their personal and professional lives. The insidious nature of these diseases lies in their early initial symptoms, which frequently goes unnoticed until the condition has progressed to an advanced stage. This delay in diagnosis not only diminishes the chances of successful treatment but also places an immense emotional and financial burden on patients as well as families. Early detection is therefore critical, as it can significantly alter the course of the disease, improving survival rates and quality of life. However, traditional diagnostic methods often fall short in terms of speed, accuracy, and accessibility, particularly in resource-limited settings. This underscores the urgent need for innovative approaches to liver disease detection and its management. Machine learning (ML) has been emerged as a powerful tool in this regard, offering the potential to revolutionize how we diagnose and predict liver diseases. By leveraging vast datasets—ranging from clinical records and laboratory results to imaging data—ML algorithms can uncover complex patterns and correlations that may elude human experts. These insights can lead to earlier and more accurate diagnoses, enabling timely interventions that can save lives. Among the various ML approaches, stacked machine learning (SML) models stand out for their ability to combine the strengths of multiple algorithms, mitigating the limitations of individual models and enhancing overall performance. This research focuses on developing and evaluating an SML model specifically designed for the accurate diagnosis, classification, and prediction of liver diseases, with the goal of addressing some of the most pressing challenges in this field. The proposed SML model employs a sophisticated two-layer architecture to tackle common issues such as overfitting and improving prediction accuracy. In the first layer, the model integrates four robust base learner algorithms: Extremely Randomized Trees (ET), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB). Each of these algorithms contributes unique strengths, such as handling high-dimensional data, capturing non-linear relationships, and reducing variance. The predictions generated by these base learners are then fed into the second layer, where a Logistic Regression (LR) algorithm synthesizes the outputs to produce the final prediction. This layered approach ensures that the model benefits from the collective intelligence of multiple algorithms, resulting in more reliable and precise outcomes. To further optimize performance, the Grid Search (GS) algorithm was employed to fine-tune the parameters of the learning algorithms, ensuring that the model operates at its full potential. This study employs dataset from the University of California, Irvine (UCI) Machine Learning Repository. A sample size of 615 instances has been utilized to implement the proposed methodologies, with a stratified division of 70% for training and 30% allocated for testing purposes. The results of this research seems to be highly promising. Evaluation based on 5-fold cross-validation demonstrates that the proposed SML model outperforms existing methods, achieving an impressive 0.9940 accuracy and a 0.9880 F1-score on the test data. These metrics not only highlight the model's exceptional predictive capabilities but also underscore its potential to serve as a valuable tool for clinicians in real-world settings. By providing accurate and timely diagnoses, the SML model can help reduce the mortality and morbidity associated with liver diseases, offering hope to patients and their families. Beyond the technical achievements, the human impact of this research cannot be overstated. For patients, the SML model represents a lifeline—a chance to detect liver diseases early, when treatment seems most effective, and to avoid the devastating consequences of late-stage diagnoses. For healthcare providers, it offers a reliable and efficient diagnostic tool that can enhance decision-making and improve patient outcomes. Also, for society as a whole, it signifies a step forward in the fight against a disease that disproportionately affects vulnerable populations, including those in underserved regions where access to advanced medical care is limited. In essence, this research is not just about developing a sophisticated algorithm; it is also about harnessing the power of machine learning to make a tangible difference in people's lives. By bridging the gap between cutting-edge technology and human care, the proposed SML model embodies the potential of computer science to address some of the most critical health challenges of our time. It is a testament to the transformative power of innovation, compassion, and collaboration in the pursuit of better health for all.}, Keywords = {Liver diseases, Early Diagnosis, Machine Learning, Cumulative Machine Learning Model, Cross-Validation}, volume = {22}, Number = {2}, pages = {79-96}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61882/jsdp.22.2.79}, url = {http://jsdp.rcisp.ac.ir/article-1-1454-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1454-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {Goshvarpour, Ateke}, title = {Classifying Various Stages of Typing Learning through EEG Rhodonea Curve Asymmetry Indices: A Focus on the Optimal Number of Petals and Brain Channels}, abstract ={Background: Electroencephalography (EEG) is a cornerstone in cognitive neuroscience, providing critical insights into the neural mechanisms underlying skill acquisition. Despite significant advancements in signal processing techniques, extracting meaningful patterns from EEG data —especially in the context of dynamic neural shifts during learning—remains a persistent challenge. Traditional analytical approaches often fail to account for the nonlinear temporal dynamics inherent in learning processes, which limits their ability to decode subtle neural reorganizations. This study addresses this gap by proposing an innovative computational framework based on Rhodonea curves—sinusoidal patterns resembling flower petals—to analyze EEG signals during the acquisition of a complex motor skill: touch-typing by the Colemak keyboard layout. Objective and Innovation: The study aims to develop and validate a computationally efficient algorithm for classifying EEG data across distinct stages of skill learning. Central to this approach is the introduction of asymmetry indices derived from Rhodonea curves, which quantify nonlinear features of brain activity. This work represents the first application of Rhodonea-based analysis in EEG signal processing, providing a geometrically intuitive and computationally lightweight alternative to conventional nonlinear methods, such as entropy or fractal dimension analysis. Methodology: The dataset, available on IEEEDataPort, consisted of EEG recordings from 10 participants (6 females and 4 males), focusing on 9 channels (F3, Fz, F4, C3, Cz, C4, P3, POz, P4) collected during 12 typing sessions. Data from sessions 4, 8, and 11—representing the early, intermediate, and advanced learning phases—were analyzed, with each session repeated five times to capture intra-session variability. For the first time, a Rhodonea curve-based method has been introduced for signal analysis, featuring a structure resembling a flower with an adjustable number of petals. The Rhodonea model was parameterized with one to ten petals, and three new indices based on asymmetry in the Rhodonea curve were computed to characterize spatiotemporal variations in EEG signals. A Support Vector Machine (SVM) utilizing a one-vs-all strategy was employed to classify 15 classes (5 repetitions × 3 sessions). Channel-specific optimizations and petal-count analyses were conducted to identify discriminative brain regions and optimal model configurations. Key Findings: The analysis revealed robust classification performance, with two-class classification achieving accuracies ranging from 79.3% to 93.3%. Optimal results were observed in channels F3, Fz, C3, C4, and POz using a 4-petal Rhodonea configuration. In the three-session classification, the highest accuracy was recorded for the advanced learning phase (Session 11: 92%), followed by the early phase (Session 4: 90%) and the intermediate phase (Session 8: 72.6%). The lower accuracy in Session 8 suggests a transitional neural state marked by unstable skill consolidation, where neither novice nor expert patterns dominate. Neuroanatomically, the frontal (F3, Fz), central (C3, C4), and parieto-occipital (POz) regions demonstrated heightened discriminative power, consistent with prior studies implicating these areas in cognitive control, motor planning, and visuospatial integration during learning. Session-specific activation patterns indicated early-phase prefrontal engagement for attention allocation and advanced-phase parietal consolidation for skill automatization. Comparative Analysis: This study diverges from prior work by integrating geometric asymmetry metrics—rather than spectral or entropy-based features—to model learning-induced neural plasticity. The computational efficiency and interpretability of Rhodonea-based features (e.g., petal-count visualization) offer distinct advantages for real-time brain-computer interface (BCI) applications. Notably, the intermediate phase’s lower accuracy (72.6%) highlights the methodological challenge of decoding transitional neural states, a limitation underrepresented in earlier literature. Limitations and Future Directions: This research had limitations that should be considered in future studies. First, the small sample size (N=10) and fixed signal length (1280 samples) may limit generalizability; future work should incorporate larger datasets and variable-length signal analysis. Second, although the non-linear features presented are computationally simple and low-cost, using other complex features might enhance the model's performance. Third, while SVM demonstrated efficacy, comparative studies with deep learning models (e.g., CNNs, LSTMs) could further validate the method’s robustness. Fourth, physiological validation via multimodal neuroimaging (e.g., fMRI/fNIRS) is needed to spatially localize the observed dynamics. Finally, statistical refinements—such as ANOVA or t-tests for feature selection—could enhance model rigor and mitigate overfitting risks. Conclusion: This research pioneers the application of Rhodonea curves in EEG analysis, establishing a novel framework for decoding the neural correlation of skill learning. The high classification accuracies and neuroanatomically consistent results underscore the method’s potential for both academic research and applied domains, including adaptive learning systems and neurorehabilitation. Future efforts should prioritize large-scale validation and integration with multimodal neuroimaging to advance our understanding of learning-related brain plasticity and refine real-world applications.}, Keywords = {Electroencephalography, Learning, Rhodonea curve, Signal processing, Classification, Asymmetry}, volume = {22}, Number = {2}, pages = {97-108}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61882/jsdp.22.2.97}, url = {http://jsdp.rcisp.ac.ir/article-1-1376-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1376-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {AnbaeeFarimani, Saeede and Ghouchannezhadnoornia, Raheleh and VAFAEIJAHAN, MAJID}, title = {Review on Large Language Models in Finance: Text and Time Series Analysis for Investor Behavior and Market Prediction}, abstract ={The onset of social media venues, online news media, and digital content allowed a vast volume of text and time series data to be generated which plays significant role in investors' decision-making and financial market volatility. Data extracted from these platforms provide information on public sentiments, immediate reactions to news, and informal analyses, which, if processed appropriately, can be very useful indicators in forecasting financial market trends. Billions of dollars are invested and lost, depending on correct forecasting. However, advances in deep learning, especially in large language models (LLMs) and novel time series analysis algorithms, have opened new windows to processing and analyzing this complex data. The advanced language models identify hidden patterns and nonlinear dependencies, always taking into account the context and semantic details of the text between news, market sentiments, and price fluctuations, as well as utilizing them via intelligent market analysis systems. This review analyzes the existing research trends on the relationship of text data available on websites and social networks with the behavior of financial markets, having reviewed more than 200 scientific papers published between 2006 and 2024 in a systematic manner. This study focuses on identifying advanced methods within text representation, sentiment analysis, predictive modeling, and language model applications for analyzing real-time and unstructured data. More than one information source has to be taken into consideration: (Twitter, news agencies, blogs, and specialized forums) from a perspective of credibility, data structure, and influence-on market decisions. Given the complexity of financial markets, such as stocks and forex, there is an ever-increasing demand for hybrid models capable of carrying out analyses across time-series and text data simultaneously. This paper aims to analyze the current research accomplishments, identify gaps in the research, and ultimately put forward future directions for the fields of text mining, AI, and deep learning. These directions can open up the path for the next generation of real-time and adaptive recommender, predictor, and correlation analyzer systems in the financial markets.}, Keywords = {Large Language Models, Text Mining, Sentiment Analysis, Financial Market Prediction, News, Social Media}, volume = {22}, Number = {2}, pages = {109-126}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61882/jsdp.22.2.109}, url = {http://jsdp.rcisp.ac.ir/article-1-1252-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1252-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {Nasiri, Mohsen and Daneshpour, Negi}, title = {Presenting a new method for multi label classification based on neural network}, abstract ={The problem of classification can be divided into two categories: single-label and multi-label. Single-label classification consists of binary and multi-class classification. In binary classification, the task is to predict one in two possible classes, such as distinguishing between spam and non-spam emails. In multi-class classification, the goal is to classify instances into more than two classes, such as identifying different species of flowers based on petal measurements. In contrast to single-label classification, multi-label classification is more complex because each instance could belong to multiple categories simultaneously. In multi-label learning, instead of assigning a single label to each instance, a set of labels is assigned. This means that each sample may have zero, one, or more than one associated label. For example, in a text classification task, a news article about technology and business might be labeled as both "Technology" and "Business". To handle multi-label classification, several approaches have been developed. One of the simplest methods is Binary Relevance (BR), which transforms the multi-label problem into multiple independent binary classification tasks—one for each label. Although this approach is easy to implement, it treats each label independently and ignores possible relationships among them. However, in real-world applications, labels are often correlated; for instance, in medical diagnosis, certain diseases frequently appear together. In another approach, Label Powerset (LP), considers label dependencies by treating each unique combination of labels as a separate class. While this method captures relationships between labels, it suffers from scalability issues while dealing with a large number of labels, as the number of possible label combinations increases exponentially. To address these challenges, the proposed method incorporates k-means constraint clustering to group both labels and features prior to applying classification. In the first step, clustering is performed to group similar labels together, ensuring that label correlations are preserved. This also helps to mitigate the issue of imbalanced classification, where certain labels may be underrepresented in the dataset. Once the labels are being clustered, a separate multi-layer neural network would be assigned to each cluster. Instead of using a single large neural network for all labels, multiple smaller networks would be trained for different label clusters. This approach enhances learning efficiency and improves accuracy by focusing on relevant label groups. However, using multiple classifiers increases computational costs and training time. To mitigate this issue, a scatter-add dimension reduction technique is applied. Using scatter-add, attributes are efficiently assigned to the input of each neural network, ensuring that each classifier receives only the relevant feature subset. Each neural network then predicts labels within its designated cluster. Eventually, the predictions from all classifiers are combined to generate the final multi-label output for each instance. To evaluate the effectiveness of the proposed method, experiments were conducted on various text datasets. The results were compared with traditional multi-label classification methods, including Binary Relevance and Label Powerset. The evaluation has been based on several performance metrics, such as accuracy, precision, and hamming-loss. The results demonstrated that the proposed approach achieved superior performance across multiple datasets, ranking first in several evaluation criteria. Notably, it outperformed existing methods by a margin of approximately 1% in accuracy. These findings suggest that clustering-based multi-label classification using k-means constraint clustering and multi-layer neural networks is a promising approach. By leveraging label correlations and reducing dimensionality, the proposed method effectively improves classification performance while addressing issues such as label imbalance and computational inefficiency. Future research may further explore optimization techniques to reduce training time while maintaining high accuracy.}, Keywords = {Classification, Multi-Label Classification, Clustering, Neural Networks}, volume = {22}, Number = {2}, pages = {127-138}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61882/jsdp.22.2.127}, url = {http://jsdp.rcisp.ac.ir/article-1-1433-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1433-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {Zarei, Ahmadreza and Moallem, Paym}, title = {Improvement of SIFT matching algorithm for matching visible satellite images using Siamese deep neural network}, abstract ={Matching remote sensing images is a fundamental step in many image processing applications. Unlike regular images, remote sensing images often undergo complex and nonlinear background changes, making them difficult to match. They also pose challenges such as scale variations, rotation, and different viewing angles. One commonly used method for finding corresponding points between images is the Scale-Invariant Feature Transform (SIFT) algorithm; however, it often produces many incorrect matches when applied to such data. In contrast, deep learning-based approaches can extract and compare medium and high-level features for more accurate matching. Inspired by these advances, this work introduces a method that combines the SIFT algorithm with a Siamese deep neural network to improve the matching of remote sensing images. The proposed method modifies the conventional SIFT by adjusting its parameters to increase the proportion of correct to incorrect correspondences. After keypoints are extracted and described, initial correspondences are established. Then, for each matched point, a local patch is extracted based on the keypoint’s position, scale, and orientation. These patch pairs are input to a trained Siamese network that estimates the probability of a correct match. Matches with confidence below a threshold are rejected. This hybrid approach leverages the strengths of both traditional and deep learning-based techniques to enhance accuracy. The proposed approach introduces several key innovations, including optimized keypoint extraction to maximize true matches, patch-based feature representation aligned with local image geometry, and a neural network-based verification step to suppress incorrect matches. Based on experiments conducted on a dataset of 35 pairs of remote sensing images, and comparing the results with the SIFT algorithm and deep learning-based methods, the proposed approach achieved an accuracy of 0.849 by reducing false matches and increasing correct ones.}, Keywords = {Image Keywords: Image Matching, Remote Sensing, SITF Algorithm, Deep Learning, Siamese Convolutional Neural Networks Matching, Remote Sensing, SITF Algorithm, Deep Learning, Siamese Convolutional Neural Networks.}, volume = {22}, Number = {3}, pages = {3-18}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.66224/jsdp.22.3.3}, url = {http://jsdp.rcisp.ac.ir/article-1-1415-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1415-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {Ahrari, Vahideh and Afshari, Robab}, title = {Analysis of the traffic structure of the roads of Chahar Mahal and Bakhtiari province using data-mining approaches}, abstract ={Road traffic management is a fundamental and multidimensional challenge within transportation systems, exerting a direct impact on public safety, economic efficiency, and environmental sustainability. The province of Chaharmahal and Bakhtiari, due to its strategic geographical location, plays a critical role in connecting various regions of Iran. Therefore, a precise analysis of the traffic structure of this province’s roadways is essential for improving the quality of data-driven planning and decision-making in the transportation sector. In this study, road traffic-counter data collected during September were utilized. These data include key variables such as the average number of vehicles in five different classes, instances of traffic violations (namely speeding, tailgating, and illegal overtaking), as well as the average speed on various road segments. The data were analyzed using three unsupervised learning algorithms: k-means clustering, hierarchical clustering, and autoencoder-based clustering. To assess the accuracy and performance of these algorithms in segment clustering, three well-established evaluation metrics—Silhouette score, Davies-Bouldin index, and Calinski-Harabasz index—were employed. The results demonstrate that the autoencoder and hierarchical clustering models offer a more accurate classification of road segments compared to the conventional k-means method, revealing latent traffic structures more effectively. Based on the findings, the roads in the province were categorized into two distinct clusters: the first cluster includes segments with the highest traffic volume and the highest rates of speeding and tailgating violations—indicative of risky driving behaviors and elevated accident risk. The second cluster encompasses segments characterized by safer traffic patterns. The primary contribution of this research lies in the integrated application of multiple advanced clustering algorithms alongside diverse performance evaluation metrics, which significantly enhance the precision and robustness of the analysis. Furthermore, the combination of technical and behavioral traffic variables within a unified data-driven framework enables the extraction of deeper insights into traffic behavior patterns. The proposed framework is generalizable to other regions and can serve as a novel model for the intelligent management of roadway networks.By providing accurate and actionable analytical tools, this study has the potential to support decision-makers in raising awareness, optimizing resource allocation, designing safety strategies, and ultimately reducing accident rates. To extend and enrich this line of research, future studies are encouraged to incorporate temporal (e.g., seasonal) analyses, integrate human and environmental variables, and develop hybrid predictive models in the transportation domain.}, Keywords = {Unsupervised learning, clustering, traffic structure analysis, autoencoder, speeding violation, tailgating, Chaharmahal and Bakhtiari Province.}, volume = {22}, Number = {3}, pages = {19-34}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.66224/jsdp.22.3.19}, url = {http://jsdp.rcisp.ac.ir/article-1-1435-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1435-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {Mosayebi, Elyas and EbrahimiAtani, Rez}, title = {A Novel Privacy-Preserving Distributed Data Publishing Protocol Based on Probabilistic Models}, abstract ={In the era of digital transformation, government agencies and corporations increasingly rely on electronic services, generating vast volumes of sensitive data stored in distributed databases. While these records hold immense potential for knowledge discovery through data mining, their publication or sharing raises critical privacy concerns, particularly when sensitive individual information is at risk. Traditional Privacy-Preserving Distributed Data Publishing (PPDDP) methods rely heavily on Trusted Third-Party (TTP) intermediaries and Secure Multi-Party Computation (SMC), which introduce systemic vulnerabilities such as communication bottlenecks, synchronization failures, insider attacks, and inherent distrust in centralized entities. In healthcare analytics, hospitals leverage patient data to enhance diagnostic precision, optimize clinical workflows, and advance preventive and precision medicine. Yet, reliance on siloed datasets from individual institutions often restricts model generalizability and impedes comprehensive insights into health outcomes. Patient health is a multidimensional construct influenced not only by genetic and biological factors but also by behavioral patterns and socio-environmental determinants. Cross-institutional collaboration integrating diverse datasets from geographically distributed sources is essential to develop robust analytical models. However, such collaboration raises critical privacy concerns, as centralized aggregation of sensitive data risks exposure to breaches or misuse. Our probabilistic framework for privacy-preserving distributed data publishing directly addresses this challenge. By eliminating dependencies on trusted third parties and secure multi-party computation, our approach enables secure, decentralized integration of heterogeneous healthcare data. Through uncertainty-aware probabilistic anonymization and adaptive noise injection, the framework ensures compliance with stringent privacy regulations (e.g., GDPR, CPRA, HIPAA) while preserving the analytical utility required for accurate, actionable health outcome predictions. This balance of utility and privacy empowers researchers to harness the full potential of distributed datasets without compromising individual confidentiality, ultimately fostering innovation in precision medicine and population health management. This paper introduces a novel probabilistic framework for privacy preservation in distributed environments, eliminating dependencies on TTP and SMC. Unlike existing approaches, this method leverages uncertainty-aware probabilistic models to dynamically anonymize and perturb data across distributed nodes while preserving global data utility. First a survey of privacy preservation data publishing methods is presented in this paper and then we discuss about prose and cons of the techniques. After this we present the model and its implementation details. The results obtained by security evaluations shows that the presented method will balance out the privacy security and the accuracy of distributed data better, using the probability model without needing a Trusted Third-Party and Secure Multi-party Computation.}, Keywords = {Data Mining, Data Publishing, Data sharing, Privacy Preserving, Security.}, volume = {22}, Number = {3}, pages = {35-58}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.66224/jsdp.22.3.35}, url = {http://jsdp.rcisp.ac.ir/article-1-1467-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1467-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {eslamifar, omid and soltani, Mohammadreza and RastegarFatemi, seyed Mohamadjalal}, title = {Identification, detection and classification and of multiclass heterogeneous blood cell series based on the council algorithm and aggregation of tissue descriptors}, abstract ={Understanding the structural and morphological characteristics of blood cells plays a crucial role in the early diagnosis and treatment of hematological disorders. Manual inspection of blood smears under a microscope is still the standard approach in many laboratories; however, this process is subjective, time-consuming, and highly dependent on the expertise of the hematologist. To overcome these limitations, the present study introduces an intelligent hybrid framework for multiclass classification of heterogeneous blood cells based on the integration of deep learning and metaheuristic optimization techniques. In the proposed approach, the wavelet coefficients of microscopic images are first extracted to capture discriminative frequency-domain features. These coefficients are then fed into a YOLO-based convolutional neural network to detect candidate cell regions and identify spatial characteristics. A customized CNN architecture is subsequently employed for hierarchical feature learning, while a Golden Eagle Optimization (GEO) algorithm is utilized to perform feature selection and dimensionality reduction by eliminating redundant and less informative attributes. To achieve robust decision-making, three classical classifiers Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbors (KNN) are combined through a weighted voting ensemble strategy. The model was trained and validated on a dataset consisting of microscopic images of five major white blood cell types: lymphocytes, monocytes, eosinophils, basophils, and neutrophils. Quantitative evaluation was performed using precision, recall, F1-score, and accuracy metrics. Experimental results demonstrate that the proposed CNN GEO ensemble model achieves an overall accuracy of 95.7% and an average F1-score of 94.9%, outperforming comparable state-of-the-art methods such as CNN+SVM, PSO+KNN, and VGG-16 in both accuracy and computational efficiency. The findings highlight the capability of the proposed system to accurately distinguish among multiple blood cell categories, thereby providing a reliable and automated decision-support tool for early hematological diagnosis. Future work will focus on expanding the dataset and integrating domain adaptation mechanisms to further enhance cross-laboratory generalization.}, Keywords = {blood cell type, classification, YOLO neural network, golden eagle optimization.}, volume = {22}, Number = {3}, pages = {59-76}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.66224/jsdp.22.3.59}, url = {http://jsdp.rcisp.ac.ir/article-1-1393-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1393-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {Elyasi, Naiereh and HosseiniMoghadam, Mehdi}, title = {An application of the topological data analysis approach in Persian poetry classification}, abstract ={This research delves into authorship attribution through an avant-garde lens, employing Topological Data Analysis (TDA) as a potent instrument to unravel intricate patterns within classical Persian poetry. The focal point of this study is the distinguished works of Ferdowsi and Hafez, two preeminent Persian poets, exploring the latent structures in their verses through the lenses of Persistent Homology and Mapper a pair of TDA methodologies. The discernment between Non-Semantic and Semantic authorship attribution methodologies lays the groundwork, elucidating the significance of capturing structural nuances in textual data. The main focus of this investigation revolves around the deployment of Persistent Homology a cutting-edge technique that transcends traditional text analysis methodologies. It operates in high-dimensional spaces, extracting topological features, and rendering them comprehensible through persistent diagrams. This paper meticulously unpacks the mathematical underpinnings of Persistent Homology, providing a stepwise exposition of its application, focusing on Homology, Simplicial Complex, and Group Theory. These foundational elements converge to empower extracting meaningful topological signatures from the poetic corpus. In tandem, Mapper, another TDA tool, unfolds as a pivotal player in this explorative journey. This algorithmic entity facilitates dimensionality reduction and simplicial complex construction to portray an accurate depiction of the intrinsic topological architecture residing in the dataset. The intricacies of Mapper's workflow from filter function selection to binning and clustering are meticulously detailed, forming a coherent narrative of its operational dynamics. Transitioning from theoretical discourse to practical implementation, this research adopts a case study approach, weaving Ferdowsi and Hafez's poetic masterpieces into the TDA tapestry. Beyond the mere application of algorithms, the study delves into the realm of accuracy assessments, subjecting the Mapper algorithm to rigorous tests, and gauging the precision of its poem classifications within identified clusters. An additional layer of complexity unfolds as the research embraces semantic clustering, elucidating thematic resonances embedded within the verses. The results borne out of this meticulous exploration not only underscore the efficacy of TDA methodologies in unveiling the intricate structures of Persian poetry but also offer a nuanced perspective on their interpretability and utility in the realm of authorship attribution. The poetic narrative, with its semantic richness and structural subtleties, emerges as a fertile ground for the application of TDA, pushing the boundaries of text classification methodologies. This research, therefore, contributes significantly to the evolving discourse on the intersection of literature and data science, offering a profound understanding of how TDA can be wielded as a transformative lens to decipher the profound threads of authorial expression.}, Keywords = {Topological data analysis, Persistent Homology, Mapper, Persian poems.}, volume = {22}, Number = {3}, pages = {77-90}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.66224/jsdp.22.3.77}, url = {http://jsdp.rcisp.ac.ir/article-1-1395-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1395-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {Balouchi, Ali and Bagmohmmadi, Mozafar and Karmi, Mojtab}, title = {Using Majority Voting in Graph Neural Networks for Aspect-Based Sentiment Analysis}, abstract ={Aspect-Based Sentiment Analysis (ABSA) is a detailed subdomain of sentiment analysis that focuses on detecting sentiments toward specific aspects of entities, such as product features or service attributes, rather than providing a general sentiment polarity. This granular understanding is essential in domains such as customer feedback evaluation, social media opinion mining, and intelligent recommendation systems. However, capturing the syntactic and semantic dependencies required for accurate ABSA remains a challenge for conventional models. In this study, we propose an ensemble-based approach utilizing Graph Convolutional Networks (GCNs), which are particularly effective in learning structural relationships from sentence-level dependency trees. Our methodology involves the integration of four advanced GCN-based models: DualGCN, RDGCN, SSEGCN, and R-GAT. Each model offers distinct strengths, ranging from dual-graph encoding and reinforcement-driven attention mechanisms to syntax-aware semantic enhancements. These models are trained individually and then aggregated through a majority voting mechanism to create a robust ensemble capable of improved sentiment prediction at the aspect level. The models were evaluated on benchmark datasets including SemEval-2014 (Rest14 and Laptops subsets) and Twitter, covering both formal and informal texts. Extensive preprocessing was conducted to standardize input formats and ensure fair comparison across models. Moreover, training was performed using both GLoVE and BERT embeddings, allowing the ensemble to benefit from a diverse range of semantic features. The proposed majority voting strategy aggregates the predictions of individual models and determines the final sentiment class based on the most frequent output. In case of a tie, the model with the highest validation accuracy takes precedence. This strategy effectively combines the complementary capabilities of multiple GCN variants, leading to improved performance and stability across diverse datasets. Experimental results show that the proposed ensemble method significantly outperforms both baseline models and recent state-of-the-art methods. On the Rest14 dataset, the ensemble achieved an accuracy of 88.47%, improving upon the best recent model (SAGCN + BERT) by +1.34%. On the Laptops dataset, it attained 85.44%, exceeding SAGCN’s 85.12% by +0.32%. Similarly, on the Twitter dataset, our model reached 82.12%, surpassing SAGCN’s 81.45% by +0.67%. Additionally, compared to individual baseline models, the proposed method improved accuracy by 2.15% and F1-score by 2.8% on Rest14, 9.2% and 11.74% on Laptops, and 7.8% and 8.7% on Twitter, respectively. These improvements highlight the robustness of the ensemble in handling varying linguistic structures and domains. We also explored alternative ensemble strategies including weighted voting, neural fusion, and combined embedding approaches, yet none outperformed the majority voting strategy in consistency or accuracy. This further reinforces the effectiveness and simplicity of our proposed method .In conclusion, this research introduces a novel and practical ensemble technique for ABSA using multiple GCN models and a majority voting strategy. The method achieves state-of-the-art accuracy across multiple benchmarks and demonstrates strong generalization, making it a valuable contribution to aspect-level sentiment analysis. Future work may extend this approach to multilingual and domain-specific contexts or integrate large pretrained language models such as RoBERTa or GPT to further enhance contextual understanding.}, Keywords = {Natural Language Processing, Deep Learning, Graph Neural Networks, Aspect Level Sentiment Analysis, Ensemble Learning.}, volume = {22}, Number = {3}, pages = {91-102}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.66224/jsdp.22.3.91}, url = {http://jsdp.rcisp.ac.ir/article-1-1431-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1431-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2025} } @article{ author = {rahnema, ali and akhoodad, zahr}, title = {KANFlow: A Novel Approach to Encrypted Traffic Identification Using Kolmogorov-Arnold Network}, abstract ={With the growing usage of encryption protocols like VPN, and Tor in digital communication, identification and classification of encrypted traffic has been one of the core issues in network security and traffic management. It is a major contributor to quality of service (QoS) assurance, resource allocation, user identification, and anomaly detection. But the sophistication of encrypted traffic structure and the vagueness of behavioral patterns have drastically decreased the effectiveness of conventional approaches like deep packet inspection (DPI). In spite of the progress, typical deep learning models also encounter great difficulties in dealing with encrypted data; they typically need a huge amount of labeled data and lack the capacity to analyze unbalanced data. To tackle these difficulties, this study proposes a novel hybrid architecture named seqKAN with enhanced interpretability and high accuracy. seqKAN integrates the temporal modeling capability of sequential networks like LSTM with the distinctive characteristics of Kolmogorov-Arnold networks (KAN), such as examining nonlinear relationships and intrinsic mathematical transparency. The framework also enjoys high flexibility and generalizability with the use of modules like Reproducible Hilbert Space Mapping (RKHS) and Neural Ordinary Differential Equations (ODE). Experiments are performed on benchmark datasets comprising Tor and VPN traffic (ISCXTor2016 and ISCXVPN2016). In this context, by meticulously filtering out the streams and addressing unbalanced data via class weighting, the model's stable performance is guaranteed. Ablation Study demonstrate that the inclusion of the RKHS layer significantly contributes to the improvement of the model's accuracy and robustness, particularly in encrypted settings. Among the models compared, the seqKAN approach delivered the best performance in F1 score and demonstrated clear superiority in the classification of encrypted traffic. In addition, the interpretability of the model was quantitatively and qualitatively demonstrated with standard feature importance analysis techniques (SHAP and LIME) and KAN's inherent visual analysis. seqKAN successfully automatically extracted key features and patterns in the flow packets and clearly explained each decision; this transparency evidently illustrates the model's superiority over typical methods. Finally, this research shows that the seqKAN architecture provides a comprehensive, efficient, and reliable solution for intelligent traffic analysis in complex network environments by creating a smart balance between accuracy, computational efficiency, and interpretability. The findings of this research highlight the high potential of KAN-based hybrid models as the basis for the next generation of transparent and reliable network security tools}, Keywords = {Encrypted Traffic Classification, Kolmogorov-Arnold Network (KAN), Deep Learning, Model Interpretability, Ablation Study}, volume = {22}, Number = {4}, pages = {18-3}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-1464-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1464-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2026} } @article{ author = {AlidoustGhadikolayi, Ali Akbar and Nasrabadi, Ali Motie and Malayeri, Saee}, title = {ADHD recognition by processing nonlinear features of ABR based on a new innovative method for extracting Geometry features from phase space trajectory}, abstract ={ADHD recognition in first years after birth is essential to consider a better treatment plan for patients and consequently helps children who suffer from this disorder to promote their communication abilities. According to the latest researches, ADHD recognition through ABR is a useful method for this purpose instead of surveys or other oral methods. Considering the basis of ABR which is nonlinear, so nonlinear processing methods can be more effective for classification normal and ADHD groups from each other than other linear classifications. In this paper ABR signal of two groups of children including 37 normal and 31 with ADHD disorder have been recorded in a rehabilitation center. To that end, in this study firstly by using embedding method, the two-dimensional ABR signal converted to three-dimensional signals to be ready for drawing and calculating the phase space arguments. In the phase space the accuracy of this hypothesis was checked that if ADHD and normal signals in different time would show different behavior in occupying and movement through the voxels in the phase space, for this purpose at first the phase space data should be produced, but it is important to have access to the raw data of phase space because if the methods that only print the phase space were been used the access of raw data would be impossible, Actually using these method for drawing phase space is in conflict with this study’s objectives, because it is not important to just draw the phase space of normal or ADHD signals but it is necessary to divide the phase space of individual signals to equal voxels and then determine that any sample of trajectory points is located in which of these voxels over the time. So, it is crucial to access the raw data of each axis of phase space of any signal to use them for diving that volume to voxels and then trace the attendance of trajectory in these voxels and make a new mapped signal that introduce the voxel number of each point of trajectory against time.  Therefore the source data of any axis of any signal has been produced by embedding method that it is discussed before, it means that we replace any x(t) by {x(t), x(t+lag), x(t+2 lag)} and they are three axis of new three dimensional space, so we can use these axis for diving this space to different voxels. The phase space should be divided to equal voxels depending on the length of each signal, then a novel method of classification has been developed by extracting new geometry features in the phase space. This approach includes dividing three-dimensional phase space to equal voxels and checking space voxel’s occupation by trajectory points in these voxels. As trajectory points has the same time sequence as time series of original signals so trajectories can be used in mentioned new method to check time occupation of trajectories in phase space. Then by changing each sample value by its voxel number of that sample, a mapping method was developed, this new mapped signal is the basis of later analysis and feature extractions in this study. Briefly, this mapped signal has been formed by taking some steps including producing the phase space signal from ABR, dividing the phase space to voxels and predicating voxel numbers to each sample. Then to find some distinctions between ADHD and normal group mapped signal, four groups of features have been developed in this study including temporal features, extremum features, histogram features, spatial occupational, Lyapunov features. Temporal features are some common features including min, max, mean, median, variance, skewness, kurtosis. But further in accuracy analysis of project it has been cleared that time or sample limited min and max (called extremum features) are more effective and can make a better distinction result. Histogram features focus is on the most repeated voxel numbers which means which voxels has been most occupied by trajectory during its journey through the phased space. Spatial occupational features deal with the span or extension of total trajectory path by introducing some features like the biggest or smallest voxel number which has been occupied by at least one point of trajectory, as it is can be interpreted from these two latter features, the subtraction of the biggest and smallest occupied voxel number can represent a vision of extension of trajectory. Another feature in this category is the total voxels quantity which are occupied by at least one of the points of the trajectory. The Lyapunov related feature also calculate the total number of non-zero voxels in a specific duration of time than can represent the chaotic grade of the signal, beside the difference of two adjacent time duration Lyapunov feature and show the gradient of chaotic level of the signal that shows the Lyapunov gradient. Finally, efficiency of this method and features have been evaluated and best result observed in local minimum feature extracted by the new method and using KNN and SVM classifiers, the best accuracy is about 98.53 that shows a significant increase in accuracy in comparison with linear processing approaches. In the other words, this distinction gained from the minimum feature shows that there are some places among the lower voxel numbers in trajectories which Normal group of signals desire to occupy more than ADHD group. According to anatomical data which is extractable from ABR signal in different points of time is reachable in the final mapped data also, because the time sequence of first samples is saved in the final data. It means that we can check the distinction areas in phase space and adopt them with time or their anatomical generating source. From the physiologic point of view, these distinction areas in the phase space are correspond to the activity of the primary auditory neurons in the cochlear nerve and lower levels of the brainstem.}, Keywords = {Attention-Deficit Hyperactivity Disorder, Auditory Brainstem Response, Phase Space, Trajectory}, volume = {22}, Number = {4}, pages = {38-19}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-1436-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1436-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2026} } @article{ author = {Mirkamali, Seyedsaei}, title = {Unsupervised Semantic Segmentation of RGB-D Images Using Combination of Conditional Random Field with Graph Cuts}, abstract ={Semantic segmentation seeks to give a set of pixels depicting an object in an image suitable labels depending on their appearance and semantic characteristics. Though it is still one of the most difficult issues in image processing and computer vision, this work has attracted a lot of interest recently. The availability of RGBD sensors has introduced new possibilities for segmentation by incorporating depth information alongside color. However, effectively combining these modalities presents challenges due to misalignments and depth inaccuracies. This paper proposes CRFCut, a novel unsupervised segmentation method that utilizes a Conditional Random Field (CRF) model optimized with graph cuts to segment RGBD images into coherent regions. The method recursively divides regions into foreground and background layers, employing superpixel-based appearance segmentation for the RGB component and integrating depth cues to refine results. This approach enables robust segmentation, even in the presence of noisy or incomplete depth information. The CRFCut algorithm begins by separating the depth image into foreground and background regions using a median depth threshold. This initial step requires no preprocessing and provides the basis for further segmentation. Simultaneously, the RGB image is segmented into superpixels using an appearance-based approach, such as the mean-shift algorithm. These superpixels and the depth regions are combined within a CRF model, where labels are assigned by minimizing the energy function using the graph-cut α-expansion algorithm. The algorithm is applied recursively to subdivided regions, allowing finer segmentation in a parallelizable manner. The proposed method was evaluated on two datasets: the NYUv2 dataset and the MIT dataset. The NYUv2 dataset, which includes 1449 RGBD images with annotated object classes, demonstrated the superior performance of CRFCut compared to five state-of-the-art segmentation techniques in Table 1. In the MIT dataset, which provides human-labeled sequences of indoor and outdoor scenes, CRFCut achieved comparable or better results, even with depth maps generated from 2D images using existing estimation methods (Table 2). The RandIndex metric was used to evaluate segmentation accuracy, and qualitative results, as shown in Figures 3 and 4, highlight CRFCut’s  robustness, particularly with noisy or imprecise depth data. In summary, CRFCut introduces an unsupervised CRF-based approach that integrates RGB and depth information for accurate scene segmentation. By leveraging graph-cut optimization and a recursive structure, the method achieves high-quality segmentation results with minimal preprocessing. Despite some limitations, such as challenges in distinguishing adjacent objects with similar features, CRFCut offers a promising framework for real-time segmentation of RGBD images. Future work will address these limitations by incorporating supervised techniques and improving depth data quality for enhanced performance.  }, Keywords = {Semantic Segmentation, RGB-D Image, Combination of Conditional Random Field, Graph Cuts}, volume = {22}, Number = {4}, pages = {52-39}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-1448-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1448-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2026} } @article{ author = {OlyaeiTorqabeh, Ali and Rasoolzadegan, Abbas}, title = {A Deep Learning Based Method for Android Malware Detection}, abstract ={The Android operating system, an open-source platform supported by Google, has become a cornerstone of modern technology due to its widespread adoption in diverse devices, including smartphones, smart TVs, and wearables. This extensive reach has established Android as a dominant force in the global market but simultaneously made it a primary target for malware developers. The growing sophistication and frequency of mobile malware attacks pose significant challenges for users and Android app distribution platforms. These attacks exploit the open nature of the Android ecosystem and increasingly employ advanced techniques such as obfuscation, rendering traditional detection methods less effective. In response to these challenges, this study introduces an innovative approach to malware detection leveraging image and audio processing in combination with deep learning techniques. Our proposed methodology addresses the limitations of existing methods by providing a scalable, high-accuracy solution suitable for industrial deployment. The research is based on static analysis. During the static analysis, executable file bytes are transformed into audio signals, and features extracted from these signals are used to train a deep learning model. This model achieved an impressive accuracy of 99.3%, with a precision of 99.8% and a recall of 99.1%. The novelty of our approach lies in its ability to detect obfuscated malware, a critical and challenging aspect of modern malware detection. By mapping executable files to the audio domain in static analysis, our method effectively reduces computational complexity while enhancing detection accuracy. The proposed framework was validated on a diverse and comprehensive dataset, showcasing its capability to distinguish between benign and malicious applications with high reliability. Furthermore, the method's design ensures practical applicability in real-world scenarios, particularly in app distribution platforms where rapid and accurate malware detection is crucial. This research contributes a novel, efficient, and scalable malware detection solution that addresses the challenges posed by obfuscation and computational demands. The proposed framework not only advances the state-of-the-art in Android malware detection but also lays the groundwork for future research exploring hybrid analysis techniques and real-time detection capabilities.  }, Keywords = {Android Malware Detection, Deep Learning, Static Analysis, Image Processing, Audio Signal Processing}, volume = {22}, Number = {4}, pages = {70-53}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-1455-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1455-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2026} } @article{ author = {khosravirad, niloufar and ahsan, reza and sharif, ahmad and karimi, ali}, title = {Presenting a model for establishing trust in inter-vehicle networks based on blockchain using fuzzy inference and Chord structure}, abstract ={With the swift advancement of the Internet of Things (IoT), Vehicular Ad Hoc Networks (VANETs) have become a crucial component in enabling smart transportation systems by supporting real-time communication between vehicles and roadside units (RSUs). In these networks, vehicles function as mobile nodes that generate and transmit data across the system. A major challenge in VANETs is ensuring the integrity and trustworthiness of shared messages, as any malicious or inaccurate information could severely impact safety and system performance. This research introduces a trust management framework that integrates VANET with blockchain technology and fuzzy logic to improve the reliability of vehicle-to-vehicle communication. When an event is detected, a vehicle instantly broadcasts a corresponding message. RSUs then evaluate the sender’s trust level and verify the message before validation. To minimize communication overhead and avoid duplication, repeated messages are filtered prior to distribution. Unlike conventional trust models that depend on computationally heavy consensus mechanisms such as Proof of Work (PoW), the proposed system adopts a Chord-based distributed architecture. This approach significantly lowers processing times and boosts scalability. The framework utilizes a multi-phase trust evaluation process involving message scoring, dynamic trust calculation, and formation of evaluator groups by RSUs. Simulations reveal notable gains in message credibility: a 6% increase compared to the Score-Based Trust Management System (SBTMS) and an 11% improvement over PoW-based approaches. These results underline the effectiveness of the proposed model in achieving a balance between security, scalability, and low latency in VANET environments. By merging VANET architecture with decentralized trust mechanisms and soft computing techniques, this study presents an innovative and pragmatic solution to one of the key challenges in vehicular communications—facilitating secure, efficient, and trustworthy message exchange in highly dynamic, distributed networks.}, Keywords = {Blockchain, Chord structure, fuzzy inference, Inter-vehicle network, reliability}, volume = {22}, Number = {4}, pages = {100-71}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-1462-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1462-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2026} } @article{ author = {Emami, Hojjat and Azarnavid, Babak and Abdolhosseinzadeh, Mohse}, title = {Predicting glioma brain tumor grades using ensemble machine learning}, abstract ={Gliomas, or in other words, aggressive and progressive brain tumors, lead to great complexity in the diagnosis and treatment of patients. While recent machine learning models provided encouraging results in glioma diagnosis and grading, the topic is open, and more efforts are needed. Existing models, despite encouraging results, often fall short of the ideal diagnostic state, highlighting the need for further research to develop robust and high-performing predictive models . This study introduces an optimized ensemble machine learning (EML) model designed to maximize classification (grading) performance and mitigate the pervasive issue of overfitting in glioma grading. Our approach employs a two-layer architecture that synergistically combines diverse weak and base learners. In the first layer, a diverse set of learners, including support vector machine (SVM), categorical boosting (CatBoost), extremely randomized trees (ERT), and random forest (RF), is integrated. This initial ensemble aims to capture a broad spectrum of grading patterns and enhance the overall accuracy by leveraging the complementary strengths of each base model. The outputs from this first layer, representing diversified classification probabilities, are then fed into a second-layer logistic regression (LR) model. This layer refines the predictions, performing the ultimate classification while explicitly addressing and eliminating the overfitting problem, thereby promoting better generalization to unseen data. To rigorously evaluate the performance of the proposed ELM model, a comprehensive comparison was conducted against its constituent base learners and counterpart machine learning models. All models were assessed using a standard, publicly available glioma dataset. To prevent overfitting, examine the robustness of models, and evaluate models fairly, a 5-fold cross-validation strategy is used in experiments. The effectiveness of models was measured using four performance metrics, including accuracy, recall, precision, and F1-score. The experimental results demonstrate the superior performance of the proposed EML model. Across all evaluated metrics, our model consistently outperformed the individual base learners and other benchmarked algorithms, securing the top rank in terms of accuracy. Specifically, the LR model operating on the first-layer ensemble predictions proved highly effective in both enhancing accuracy and preventing overfitting. Following our proposed model, the standalone LR and RF models demonstrated commendable performance, ranking second and third, respectively . The findings of this study underscore the significant potential of an optimized EML model for advancing the field of glioma tumor grading. The proposed model generated promising results and mitigated overfitting through integrating diverse base learners and using an LR model as a meta-model. The results reveal that the proposed model is a reliable and robust tool that can aid Clinical specialists in effectively diagnosing and classifying gliomas, ultimately paving the way for improved patient satisfaction.}, Keywords = {Brain tumor, tumor detection, glioma, data mining, group learning, parameter optimization}, volume = {22}, Number = {4}, pages = {122-101}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-1484-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1484-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2026} }