@article{ 
author = {Sarabi, Homeyra and AbdaliMohammadi, Fardi},  
title = {Diagnosing Children\'s Developmental Disorders By A Transfer Learning Based Architecture Using Knowledge Distillation}, 
abstract ={Enhancing medical devices with Internet of Things (IOT) and diagnostic artificial intelligence technology, while considering the constraints of these systems, has the potential to modernize and enhance the diagnostic approach of future generations of Internet of Things systems in healthcare. The radiography device, commonly used in paraclinical settings, is widely used in various hospital departments. The automated assessment of abnormalities and bone age from radiographic images of the left hand assists radiologists, pediatricians, and forensic experts in determining the developmental stage of young individuals. The IoT devices in medicine are unable to process large amounts of data due to limited resources. This article uses a teacher-student network for bone age classification, using the decomposed knowledge distillation model of convolutional neural networks. This approach minimizes the computational resources needed for edge devices. The proposed method is comprised of two sequential steps.  In the preprocessing step, the initial phase involves the elimination of non-clinical data and artifacts. This is followed by the extraction of region of interest (ROI). In this phase of the procedure, only the hand portion of the patient's X-ray remains for further evaluation. The subsequent phase involves the delineation of the boundaries of the region of interest. This is necessary because, in certain age groups, some bones are not ossified. Consequently, reliance on bones as landmarks is precluded.  In the second step, The extracted ROI from the preceding step is utilized to train the teacher model. The student model utilizes the teacher model's knowledge to learn how to predict patient age. Therefore, the present study puts forth transfer learning methodologies founded on the distillation of knowledge, with the aim of facilitating the transference of knowledge between teacher and student models.  The proposed method is based on the data set of the Digital Hand Atlas (DHA) database. The evaluation criteria used in this work are Accuracy, recall, permission and mean absolute error (MAE). The proposed model achieves 96/47% test accuracy for bone age classification.},  
Keywords = {Developmental Disorders, Knowledge Distillation, Bone Age, Deep Learning},
volume = {21},
Number = {4}, 
pages = {1-14}, 
publisher = {Research Center on Developing Advanced Technologies},

doi = {10.61186/jsdp.21.4.1},
url = {http://jsdp.rcisp.ac.ir/article-1-1399-en.html},  
eprint = {http://jsdp.rcisp.ac.ir/article-1-1399-en.pdf},  
journal = {Signal and Data Processing},  
issn = {2538-4201}, 
eissn = {2538-421X}, 
year = {2025}  
}

@article{ 
author = {RahimiResketi, Mahsa and Motameni, Homayun and Akbari, Ebrahim and Nematzadeh, Hossei},  
title = {Tag recommendation in social networks with the help of text summarization and KNN}, 
abstract ={In recent years, the utilization of social networks has surged markedly, with interest in their use escalating daily. A pivotal concern is augmenting the number of views for individuals' posts or messages to enhance their popularity. The most effective means to achieve this objective is through the use of tags. Tags significantly contribute to the organization and retrieval of existing data, and the automatic generation of tags has garnered substantial attention. Tag recommendation from textual sources can be approached as a text extraction issue. This paper endeavors to propose a comprehensive set of suggested keywords derived from data via advanced text summarization techniques, culminating in the presentation of a sophisticated tag recommender. Consequently, this research introduces an innovative and robust solution by integrating clustering, summarization, and recommendation methodologies. Initially, utilizing the Bag of Words (BoW) model, comprehensive word parsing and extraction of word roots are performed. This process yields a bag of words capable of facilitating deep semantic exploration. The data is meticulously simplified to its core elements, with prepositions and repetitions omitted. Verbs, due to their high frequency and significance depending on the context of the sentence or post, are mined separately. Other words are judiciously selected based on their frequency and importance, and stored with their repetition counts. Subsequently, employing the K-Nearest Neighbor (KNN) clustering algorithm, the data is clustered, and the cluster representatives serve as the output tags. A slight modification is made to the KNN algorithm by incorporating the Explicit Semantic Analysis (ESA) method for precise scale calculations. The proposed solution was rigorously evaluated on two public datasets: TPA, extracted by Aminer, and AG, extracted by ComeToMyHead. The AG dataset comprises 127,600 news articles, categorized into four distinct tag types. Each category contains 30,000 training samples and 1,900 test samples, with a total of 31,900 tags representing global, sports, business, and scientific concepts. The findings of this study were compared with those from 13 similar research papers, which fall into four distinct categories: machine learning, long-short-term memory (LSTM), convolutional neural network (CNN), and capsule-based models. The comparative analysis revealed that the proposed method demonstrates superior accuracy, comprehensive coverage, and an enhanced F-measure. The integration of advanced text analytics techniques underscores the significance of this study in the broader context of information retrieval and data mining. By harnessing the power of semantic analysis and machine learning, this research provides a novel framework that not only enhances the efficiency of tag recommendation systems but also contributes to the theoretical foundation of automated keyword extraction. The implications of these findings are far-reaching, with potential applications extending beyond social networks to other domains requiring efficient data organization and retrieval.},  
Keywords = {label recommendation, text summarization, word embedding, k-nearest neighbor, BoW},
volume = {21},
Number = {4}, 
pages = {15-28}, 
publisher = {Research Center on Developing Advanced Technologies},

doi = {10.61186/jsdp.21.4.15},
url = {http://jsdp.rcisp.ac.ir/article-1-1326-en.html},  
eprint = {http://jsdp.rcisp.ac.ir/article-1-1326-en.pdf},  
journal = {Signal and Data Processing},  
issn = {2538-4201}, 
eissn = {2538-421X}, 
year = {2025}  
}

@article{ 
author = {Abdolrazzagh-Nezhad, Majid and Kherad, Mehdi},  
title = {Discovering Influential Nodes in Social Networks based on modified independent cascade model and Fuzzy Multi-Objective Genetic Algorithm}, 
abstract ={In recent years, social networks have become an integral part of people's lives and play a significant role in the real world. The primary aim of influence maximization problem is finding a set of nodes in the network which can maximize the influence if the diffusion process starts from them. Therefore, the problem’s goal is to find influential people in large scale real social networks. The penetration phenomenon is carried out according to an influence model in the network. Two independent cascade and linear threshold influence models, the most common of which is the independent cascade model, are utilized for broadcasting in the network. Theoreticaly, optimizing the selection influential nodes problem is NP-hard in both models. The problem will start by considering the social network’s graph, a specific influence model and a given number k. The problem’s goal is to select k nodes (users) from the graph (network) as influential nodes, so that the number of active nodes is maximized at the end of the diffusion process. Due to the influence maximization problem and finding influential people is an NP-hard optimization problem in the social network, meta-heuristic algorithms can be used to solve the problem. With regard to the privous researches, there is just one objective function as the problem’s goal and it is maximizing the number of effective nodes of the diffusion model. While other objective functions such as maximizing the number of effective nodes in the diffusion model and minimizing the budget value k (the number of initial nodes as seed) are not considered, minimizing the time required for effective diffusion can be achieved by having k initial nodes. Although various models for the problem and various optimization algorithms have been presented to discover influential nodes in social networks, paying attention to the multi-objective nature of the problem and improving the performance of the proposed optimization algorithms are a serious research challenge in this field. In this paper. A fuzzy version of the NSGA-II as a multi-objective genetic algorithm, whose mutation and crossover rates are adjusted by Fuzzy Inference System, is utilized to simultaneously optimize the three objectives of maximizing the number of effective nodes, minimizing the number of initial nodes and minimizing the required diffusion time. In the proposed method, the Expected Diffusion Value (EDV) of the diffusion model is replaced instead the simulation of the independent cascade diffusion model with heavy calculations to calculate the diffusion spread (the number of effective nodes of the diffusion model). Therefore, the EDV function is satisfied as the thied objective (minimizing the required diffusion time). The second objective function can also be converted into a maximization function by considering N-k nodes, where k is the number of selected initial nodesand N is the total graph nodes. The decimal numerical coding with fixed length is used in the proposed method. Based on the coding, each chromosome has k genes in the search space. The integer part of each gene is the selected node number. The decimal part is also used to determine whether that initial node exists or not. In the maximizing influence process using the fuzzy NSGA-II algorithm, the solution space (chromosomes) consists of k number of initial nodes, which should be encoded into the fuzzy NSGA-II comprehensible space. Also, a Fuzzy Inference System is proposed to adjust the mutation and recombination rates for filling up a serious challenge of genetic algorithms. In the fuzzy system, NF and FitBest are considered as two input variables, and Pm (mutation rate) and Pc (crossover rate) are returned as outputs NF is the number of chromosomes in the first frontier (F1) of the multi-objective genetic algorithm and FitBest is the average of the normalized objective functions for the chromosomes in F1. To analysis the efficiency of the proposed method, the obtained exprimental results have been compared with conventional maximizing influence methods, i.e., degree centrality, distance centrality, closeness centerality, betweenness, eigenvector and page rank methods, non-fuzzy version of NSGA-II, the latest methods presented for maximizing penetration based on multi-objective meta-heuristic algorithms, i.e. µGP multi-objective evolutionary algorithm, multi-objective crow search algorithm (MOCSA), greedy randomized adaptive search process algorithm (GRASP) and the Multi-Transformation Evolutionary Framework (MTEF) on five benchmark graph datasets Arenasjazz, Canetscience, EgoFacebook, Higgs-Reply and Slashdot. This comparison is based on four criteria: EDV, cost (the number of nodes selected as seed), the influence expansion criterion σ(s), i.e. the number of active nodes with the independent cascade (IC) propagation model, and the execution time of the method in seconds. The obtained results show the superiority of the proposed method over the other methods.},  
Keywords = {Influence Maximization Problem, Social Network, Genetic Algorithm with Non-Dominant Sorting, Fuzzy System},
volume = {21},
Number = {4}, 
pages = {29-48}, 
publisher = {Research Center on Developing Advanced Technologies},

doi = {10.61186/jsdp.21.4.29},
url = {http://jsdp.rcisp.ac.ir/article-1-1380-en.html},  
eprint = {http://jsdp.rcisp.ac.ir/article-1-1380-en.pdf},  
journal = {Signal and Data Processing},  
issn = {2538-4201}, 
eissn = {2538-421X}, 
year = {2025}  
}

@article{ 
author = {Gholizade, Masoume and soltanizadeh, hadi and Rahmanimanesh, Mohamm},  
title = {Multi-Source Transfer Learning Based on Fuzzy Rules for Improving Image Classification Accuracy}, 
abstract ={Image classification tasks often involve the challenge of acquiring a sufficient number of labeled training samples, a process that is not only expensive but also time-consuming. In response to this issue, researchers have focused on transfer learning algorithms, which capitalize on prior knowledge to enhance a model training. While numerous existing transfer learning methods concentrate on knowledge transfer between a single-source domain and a single-target domain, the complexity of real-world scenarios is often underestimated. Limited studies have delved into domain adaptation within multi-source environments, where transferring knowledge from multiple sources introduces ambiguity and uncertainty into the learning process. To address this challenge, this study proposes the application of fuzzy rule-based transfer learning, leveraging the inherent ability of fuzzy rules to effectively handle uncertainty. One notable aspect of fuzzy transfer learning, and transfer learning in general, is the unresolved question of effectively combining and utilizing knowledge when multiple source domains are available. This issue is particularly pertinent in scenarios involving diverse datasets from various sources. Consequently, the present study introduces a novel approach to multi-source transfer learning anchored in fuzzy rules. By integrating fuzzy logic, the proposed method aims to provide a robust solution to the challenges posed by knowledge transfer in scenarios with multiple source domains. This research contributes to advancing transfer learning methodologies, offering a nuanced perspective on handling uncertainty in multi-source environments by applying fuzzy rule-based techniques. In conclusion, the significance of transfer learning in image classification tasks is underscored by the inherent challenges of acquiring labeled training data. The conventional focus on single-source to single-target domain transfer has limitations, prompting a shift towards addressing the more realistic and challenging scenarios of multi-source domain adaptation. This study introduces a pioneering approach to multi-source transfer learning, utilizing fuzzy rule-based techniques to effectively navigate the complexities introduced by knowledge transfer from multiple sources. Through this contribution, the research aims to propel advancements in transfer learning methodologies and foster a more comprehensive understanding of handling uncertainty in multi-source environments.},  
Keywords = {Machine learning, transfer learning, fuzzy rules, multi-source domain adaptation.},
volume = {21},
Number = {4}, 
pages = {49-66}, 
publisher = {Research Center on Developing Advanced Technologies},

doi = {10.61186/jsdp.21.4.49},
url = {http://jsdp.rcisp.ac.ir/article-1-1401-en.html},  
eprint = {http://jsdp.rcisp.ac.ir/article-1-1401-en.pdf},  
journal = {Signal and Data Processing},  
issn = {2538-4201}, 
eissn = {2538-421X}, 
year = {2025}  
}

@article{ 
author = {ghorbani, masoumeh and esmaeili, leil},  
title = {Application of web usage mining to investigate online shopping behavior via PC versus mobile devices: evidence from click-stream data}, 
abstract ={In recent years, the widespread use of smartphones and the rapid growth of mobile technologies have significantly transformed e-commerce, leading to the rise of mobile commerce (m-commerce). Mobile commerce services such as mobile banking, mobile payments, and mobile shopping have gained substantial traction. According to Statista, mobile retail sales in the United States exceeded $360 billion in 2021 and are projected to nearly double to approximately $710 billion by 2025. Furthermore, by the end of 2021, nearly one-third of U.S. internet users reported making weekly purchases online via their mobile devices. This unprecedented growth underscores the need to explore user behavior in online shopping, particularly through mobile platforms. Several factors influence online shopping behavior, with the device used for browsing and purchasing playing a critical role. As smartphones become the dominant means of internet access, understanding the behavioral differences between mobile and desktop users becomes increasingly important. Although mobile commerce is a subset of e-commerce and shares similarities such as convenience and speed, notable differences exist due to device characteristics. These include the constant availability of smartphones, their lower computational power compared to desktops, and their smaller screen sizes, which can negatively impact the user experience during complex transactions. Research has shown that mobile-specific features, including screen size, speed, security, and website optimization for mobile users, influence browsing and shopping behaviors. Despite the growing recognition of these differences, limited studies have compared user behavior between mobile and desktop platforms in e-commerce settings. This study addresses this gap by analyzing user behavior on the Basalam platform, a prominent Iranian social e-commerce marketplace that supports both desktop and mobile shopping. The primary objective is to empirically examine whether and how user browsing behaviors differ between mobile and desktop platforms. The analysis adopts a novel approach inspired by the work of Orit Raphaeli et al., utilizing sequential association rule mining to uncover frequent navigation patterns and their implications for user interaction and purchase likelihood. Unlike Raphaeli’s dataset, which focuses on specific web page content, this study employs server-side event logs from Basalam to generalize findings and enhance applicability across e-commerce platforms. The Basalam dataset represents user interactions captured through server logs, documenting user activities on the platform. The preprocessing steps differ from those in Raphaeli’s study due to variations in data structure, features, and timeframes. The proposed methodology creatively applies sequential association rule mining to episodes of user activity rather than specific web pages, identifying patterns that influence purchase outcomes without focusing on content-specific details. The findings reveal distinct behavioral trends between desktop and mobile users. Desktop sessions are task-oriented, resulting in higher conversion rates, while mobile users demonstrate exploratory browsing patterns. Notably, certain navigation sequences were associated with higher purchase probabilities across both platforms. This research contributes to the field in several ways: It is the first study of its kind focusing on the browsing behavior of Iranian e-commerce platforms, comparing mobile and desktop interactions. The methodology adapts and extends prior approaches to accommodate differences in dataset characteristics, providing a scalable framework for behavioral analysis. The results hold significant implications for e-commerce strategies, offering insights for enhancing user experience, optimizing platform design, and improving conversion rates across devices. By analyzing Basalam’s event logs, this study provides a comprehensive understanding of user behavior in mobile and desktop contexts, highlighting the strategic importance of platform-specific design in the evolving landscape of digital commerce.},  
Keywords = {M-commerce, E-commerce, Online browsing behavior, Navigation patterns, Footstep graph, Web usage mining, Sequential association rule mining},
volume = {21},
Number = {4}, 
pages = {67-84}, 
publisher = {Research Center on Developing Advanced Technologies},

doi = {10.61186/jsdp.21.4.67},
url = {http://jsdp.rcisp.ac.ir/article-1-1366-en.html},  
eprint = {http://jsdp.rcisp.ac.ir/article-1-1366-en.pdf},  
journal = {Signal and Data Processing},  
issn = {2538-4201}, 
eissn = {2538-421X}, 
year = {2025}  
}

@article{ 
author = {rastgoo, mohammad and Ghaffari, Hamid Rez},  
title = {A location recommender in social networks based on location based on deep learning}, 
abstract ={The potential of social networks to extract valuable insights into user behavior has become a focal point of research. With the proliferation of social media platforms, people are increasingly sharing their experiences online. This wealth of user-generated data provides unique opportunities to understand movement patterns and predict future behavior. Location-based social networks like Foursquare exemplify this, allowing users to check in at various locations and enabling researchers to analyze these data points.By analyzing the data collected from these platforms, we can uncover patterns in user behavior, such as frequently visited locations and the factors influencing these choices. This information can be invaluable for businesses and urban planners.To improve the accuracy of predicting a user's next location, this study focuses on identifying the most influential friends or individuals in a user's social network. Factors such as the strength of these relationships, historical visit data, and temporal-spatial characteristics are considered. Additionally, the study emphasizes the importance of data quality, focusing on locations that have been visited more than 100 times to ensure reliability. A key aspect of this research is understanding the influence of social connections on individual behavior. By analyzing the overlap in visited locations between friends, the study aims to identify the most influential friends for each user. These influential friends are then used to predict the user's next location. The proposed method employs machine learning techniques, specifically RandomForest and recurrent neural networks (LSTM, RNN, and GRU), to predict user behavior. RandomForest is used to analyze the data and identify the most significant features, while recurrent neural networks are employed to model the sequential nature of user behavior. Among these, LSTM achieved the highest accuracy of 71% in predicting users' next locations.This research demonstrates that combining artificial intelligence with spatial-temporal data can provide profound insights into human behavior in urban and digital environments. By understanding these patterns, businesses can tailor their offerings to individual customers, and urban planners can design more efficient and user-friendly cities.},  
Keywords = {Location-based social networks, recommender systems, spatial data mining},
volume = {21},
Number = {4}, 
pages = {85-96}, 
publisher = {Research Center on Developing Advanced Technologies},

doi = {10.61186/jsdp.21.4.85},
url = {http://jsdp.rcisp.ac.ir/article-1-1365-en.html},  
eprint = {http://jsdp.rcisp.ac.ir/article-1-1365-en.pdf},  
journal = {Signal and Data Processing},  
issn = {2538-4201}, 
eissn = {2538-421X}, 
year = {2025}  
}

@article{ 
author = {Haghparast, Daniyal and Fotouhi, Ali Mohamm},  
title = {Fatigue and drowsiness detection of the car driver based on image processing and artificial intelligence on the mobile phone}, 
abstract ={One of the important factors in traffic accidents is the fatigue and drowsiness of the driver. In this paper, by using the driver's face detection and eye state recognition based on image processing and artificial intelligence, the driver's drowsiness is detected, and appropriate alarms sound to wake up the driver. The proposed method is implemented on the driver's mobile phone and uses the facilities of the phone, including processor, camera, and alarm, so it requires no additional hardware in the car. The method used and implemented in order to detect and determine the position of the face is based on the Hare-Cascade algorithm. In order to further speed up the algorithm by combining the two stages of eye detection and eye state detection, the Hare-Cascade method has been used to detect open eyes in the face area. The proposed algorithm, while providing the necessary accuracy, unlike the existing numerous and advanced algorithms, including algorithms based on deep learning, has a low computational cost and can be implemented in real time on different types of smart mobile phones. Also, by adjusting the sensitivity of the software by the user, based on the detection of one or two open eyes in the area of the face and the time between two consecutive frames of not detecting open eyes, increasing the number of correct alarms and reducing the number of false alarms can be controlled. In this research to train and increase the accuracy of the intelligent model used, a database of 500 suitable images in different driving situations was prepared and used. Experimental results on 20 test videos in different driving situations show the proper performance of the designed system by creating 95% of the expected alarms. Based on the results of numerous and various experimental tests with the acceptable performance of the product of this applied research in detecting driver drowsiness and creating correct alarms, it seems that if used by drivers, it can prevent many car accidents.},  
Keywords = {Driver fatigue and drowsiness, image processing, artificial intelligence, face and eye detection, mobile phone application},
volume = {21},
Number = {4}, 
pages = {97-112}, 
publisher = {Research Center on Developing Advanced Technologies},

doi = {10.61186/jsdp.21.4.97},
url = {http://jsdp.rcisp.ac.ir/article-1-1377-en.html},  
eprint = {http://jsdp.rcisp.ac.ir/article-1-1377-en.pdf},  
journal = {Signal and Data Processing},  
issn = {2538-4201}, 
eissn = {2538-421X}, 
year = {2025}  
}

@article{ 
author = {Taheri, Mohammad Amin and Shenassa, Mohammad Ebrahim and Minaei-Bidgoli, Behrouz and Hossayni, Sayyed Ali},  
title = {Noor-Vajeh: A Benchmark Dataset for Keyword Extraction from Persian Papers}, 
abstract ={There are various ways to express the overall intention and focal points of a text, and keywords might be the most appropriate choice. Keywords are defined as the most prominent phrases in a document that convey its main message. By extracting relevant words and phrases from a text, keyword extraction can help to uncover meaningful patterns in the text and provide an overview of the content. It can also help highlight the most significant concepts in a text and focus the attention of a machine learning algorithm on them. Keyword extraction is an imperative subtask of natural language processing. By reducing the complexity of the text and making it easier to process, keyword extraction can be used as the basis for many other processing tasks such as text classification, clustering, and summarization. By extracting keywords from text, a machine can better understand the meaning and context of the text. This enables it to better analyze the text, recognize patterns, and make more accurate decisions. It can also reduce the amount of time it takes to process the text by eliminating unnecessary words and focusing on the most important words. Many datasets are proposed for evaluating keyword extraction methods in Persian, most of which only contain authors’ keywords and do not cover all potential ones. Thus, using such datasets leads to incorrect judgments about the accuracy of the suggested supervised and unsupervised methods. In this paper, we introduce Noor-Vajeh, a Persian keyword extraction dataset of about 1400 scientific papers. We asked experts to extract potential keywords besides the authors’ keywords to complete the keywords set for each article. The resulting dataset is a valuable resource for ongoing research into Persian keyword extraction. To evaluate the dataset to be used as a benchmark, we tested several unsupervised keyword extraction methods. We used these methods because, compared to supervised methods, they take less time to execute and require minimal to no manual tuning. Moreover, they are able to extract keywords with a high degree of accuracy and generalize well to different articles. Furthermore, due to the wide variety of categories of unsupervised learning methods, graph-based methods have been regularly applied in different projects, so we describe and use some of their most famous ones, such as TextRank, SingleRank, and PositionRank. These algorithms can identify important words and phrases in a given text, as well as identify relationships between them. Doing so can provide insights into the overall structure and meaning of the text. This makes them especially useful for finding patterns and making predictions in a variety of tasks, such as machine translation, text summarization, and sentiment analysis. Furthermore, graph-based methods are highly versatile and can be adapted to different datasets and tasks. This makes them ideal for use in benchmark datasets. The results inferred from these methods confirm the comparisons made between the methods employed in other papers.},  
Keywords = {Keyword Extraction, Persian Dataset, Unsupervised Learning, Graph-Based Methods, Information Retrieval},
volume = {21},
Number = {4}, 
pages = {113-123}, 
publisher = {Research Center on Developing Advanced Technologies},

doi = {10.61186/jsdp.21.4.113},
url = {http://jsdp.rcisp.ac.ir/article-1-1340-en.html},  
eprint = {http://jsdp.rcisp.ac.ir/article-1-1340-en.pdf},  
journal = {Signal and Data Processing},  
issn = {2538-4201}, 
eissn = {2538-421X}, 
year = {2025}  
}