@article{ author = {Mir, Marziye and Noferesti, Samir}, title = {Using Data Augmentation Techniques for Sentiment Analysis of Users’ Opinions on Reopening of Schools During the Covid-19 Epidemic}, abstract ={Sentiment analysis, also called opinion mining, is one of the sub-areas of natural language processing that aims to classify texts according to the sentiments, beliefs and attitudes expressed in them. In the most current research, texts are divided into two "positive" and "negative" categories. However, there are also other categories such as good/bad" and agree/disagree, every one of which has its applications. The purpose of this paper is to analyze the opinions expressed by users on social media about the reopening of schools during the Covid-19 outbreak using supervised machine learning techniques, and to classify them into two "agree" and "disagree" categories. Users' opinions, in this paper, are in Persian. The lack of sufficient datasets and also the low accuracy of natural language processing tools are the most important problems of text processing in Persian. Due to the mentioned limitations, the use of supervised machine learning algorithms and also the extraction of effective features for training machine learning classifiers in Persian are facing a serious challenge. In this paper, first, a small dataset of the users' opinions about the reopening of schools was collected and manually labeled. Then, a combined method was used for data augmentation of the dataset. In the proposed method, first, Persian sentences were translated into English. Then nouns, verbs and adjectives of the English sentences were replaced with their synonyms. Next, the English sentences were translated into Persian again. The new sentence with the class label of the initial sentence was added to the training set. Thus, the size of the training set increased by 97 percent. After that, the efficiency of employing the common pre-processing steps and using common feature sets in sentiment analysis of the English texts for Persian were evaluated and the best of them were selected. Considering the low accuracy of the Persian natural language processing tools, it was tried to select those features that were less dependent on the tools. Finally, machine learning classification was used to determine agree/disagree class of the user opinions of the test sets. The results of the experiments indicated that by applying the proposed method for data augmentation and using selected features in this paper, 81 and 79 percent precision was obtained for the polarity classification of opinions using SVM and CNN algorithms, respectively.}, Keywords = {Sentiment Analysis, Opinion mining, Supervised learning, Deep learning, Data augmentation, Covid-19}, volume = {21}, Number = {2}, pages = {3-14}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.21.2.3}, url = {http://jsdp.rcisp.ac.ir/article-1-1385-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1385-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2024} } @article{ author = {sametomrani, moslem and sanieeabadeh, mohammad and moghaddamcharkari, nasrollah}, title = {Rumor Detection on Twitter using tweet and user features}, abstract ={When every news item is posted on social media, reactions to it are different and arouse curiosity from different viewpoints. The most important part is to understand the accuracy of the news. A rumor is invalid news, meaning it has not yet been confirmed and it may cause irreparable damage if it is not valid. Therefore, it is very important to detect it. Rumor detection, or in other words, determining its validity, plays an essential role in preventing fake news. Naturally, every phenomenon of normal and anomaly is transmitted to people through social networks. Every News Reactions to that news are different. Depending on the importance of the news, it may be widely covered or it may not have a specific reaction. But if the news spreads widely, it arouses curiosity from different angles. The news is false or true, or the news is valid or invalid. In this work, an attempt was made to identify rumors on social networks by using Hand-Crafted features based on tweets, users and a combination of the two, oversampling and normalization, and by using machine learning classification. Using 4 machine learning classifiers, including Support vector machine, Logistic regression, K-nearest neighbors and Random forest, the two rumors on social networks were detected. Two data sets, PHEME 2017 and PHEME 2018, have been used. The results on these two datasets show that in PHEME 2017, the random forest classifier shows an accuracy of 0.988 using tweet and combination features. Also, these features show a precision of 0.987, which is better than other classifiers used in this work. This classifier has a better recall than other classifiers along with logistic regression with a value of 0.986. Also, this classifier obtained better results with the two mentioned features, with 0.987. In the PHEME 2018 dataset, it obtained the RF classifier with an accuracy of 0.969 using tweet and combination features, and it has better performance in precision, recall and F1. In addition, the user feature in the classifier of k nearest neighbors brings better results than the other two features.}, Keywords = {rumor detection, machine learning, user Feature, tweet Feature, Hand-Crafted Feature}, volume = {21}, Number = {2}, pages = {15-28}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.21.2.15}, url = {http://jsdp.rcisp.ac.ir/article-1-1354-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1354-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2024} } @article{ author = {Haj-Hosseini, Zeinab and Doostari, Mohammad-Ali and Yusefi, Hame}, title = {Implementation of a countermeasure method against DPA on McEliece Post Quantum Cryptosystem}, abstract ={In recent years, embedded systems have continuously gained importance. This ubiquity is accompanied by an increased need for embedded security. Cryptography can address these security requirements. Many symmetric and asymmetric algorithms, such as AES, DES, RSA, ElGamal, and ECC, have been implemented on embedded devices. All frequently implemented public-key cryptosystems rely on the presumed hardness of either factoring the product of two large primes (FP) or computing discrete logarithms (DLP). These two problems are closely related. Therefore, solving these problems would have significant ramifications for classical public-key cryptography and, consequently, for all embedded devices that utilize these algorithms. Currently, both problems are believed to be computationally infeasible with a conventional computer. However, a quantum computer capable of performing computations on a few thousand qubits could solve both problems using Shor's algorithm[1]. Although a quantum computer of this scale has not been reported, it could become a reality within the next one to three decades. Consequently, the development and cryptanalysis of alternative post-quantum cryptosystems are crucial. Post-quantum cryptosystems refer to cryptosystems that are not susceptible to the critical security loss or complete compromise caused by quantum computers. One of the major security challenges is the development of quantum computers and the potential compromise of current cryptosystems in the future. Therefore, it is essential to consider post-quantum cryptosystem algorithms and the challenges of implementing and attacking them. Post-quantum cryptosystems encompass various types, including hash-based cryptography, multivariate-quadratic-equations cryptography, lattice-based cryptography, and code-based cryptography. In this study, our focus is on the QC-MDPC McEliece code-based algorithm. Post-quantum public keys must be designed to gain popularity in practice; they should be optimized for implementation and efficient in execution. McEliece encryption and decryption do not require computationally expensive processing, making it more suitable for implementation[2]. One of the implementation challenges for these algorithms is the large key length, which poses an important issue for implementation on embedded systems. Additionally, countering side-channel attacks caused by information leakage from hardware equipment is crucial. We have addressed this by reducing the key length from 1200 bytes to 180 bytes, providing 80-bit security, and introducing a new method for implementing the QC-MDPC McEliece cryptosystem. Differential power analysis attacks (DPA) exploit the relationship between power consumption and intermediate data to recover the key. In this study, we have used a masking technique for multiplication in the finite field in the syndrome computation part of the decryption algorithm. We have implemented the Threshold Implementation (TI) masking countermeasure for DPA to eliminate information leaks from the previous implementation.}, Keywords = {Post-Quantum Cryptosystem, DPA, McEliece, QC-MDPC Codes}, volume = {21}, Number = {2}, pages = {29-42}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.21.2.29}, url = {http://jsdp.rcisp.ac.ir/article-1-1222-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1222-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2024} } @article{ author = {mihandoost, sar}, title = {Atrial Fibrillation Classification Using PiCA-ESN Algorithm and Stockwell Transform}, abstract ={Atrial fibrillation (AF) is a prevalent cardiac arrhythmia characterized by irregular heartbeats, often without noticeable symptoms in patients. Diagnosing AF is challenging for cardiologists, requiring advanced methods for accurate identification using electrocardiogram (ECG) signals. Automated AF diagnosis can significantly aid cardiologists in prompt identification, potentially reducing the risks associated with acute heart disease and stroke. Various non-invasive techniques based on ECG signal processing have been suggested to better understand the mechanisms by analyzing the atrial fibrillatory waves (f-waves). Different signal processing methods for f-wave extraction have been explored, which may be classified as follows: average beat subtraction and its advanced variants, QT-interval interpolation, principal and independent component analysis, nonlinear adaptive filtering using an echo state network, diffusion geometry, and extended Kalman filtering. This study aims to extract the f-wave from the ECG signal using the PiCA-ESN algorithm, which yields better results compared to other methods. Additionally, the f-wave's time-frequency behavior was analyzed using the Stockwell transform to differentiate between terminated and non-terminated AF states for the first time in this study. First, the PiCA-ESN algorithm facilitated the extraction of the f-wave from the ECG signal. Subsequently, the Stockwell transform was used to compute the time-frequency maps of the extracted f-wave. Various features were derived from the amplitude of the Stockwell transform and utilized in conjunction with three classifiers: MLP, SVM, and AdaBoost. The findings reveal that the proposed method outperforms selected methodologies from the Physionet Challenge 2004, achieving an impressive 100% accuracy in both tasks. Additionally, an experiment was conducted to assess the robustness of the proposed features across consecutive signal segments, validating their stability during signal analysis.}, Keywords = {Electroencephalogram (ECG), Atrial fibrilation (AF), f-wave, PiCA-ESN algorithm, Stockwell transform (S transform)}, volume = {21}, Number = {2}, pages = {43-54}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.21.2.43}, url = {http://jsdp.rcisp.ac.ir/article-1-1374-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1374-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2024} } @article{ author = {soleimani, mohsen and ChehelAmirani, Mahdi and Kabodian, Seied Jahanshah}, title = {Steganalysis of Compressed Audio Files Based on Machine Learning}, abstract ={The science of hiding a message containing information in a carrier medium is called steganography, and the attempt to detect the presence or absence of a hidden message in a cover medium is called steganalysis. The MP3 compression format has been used among audio data as a suitable and comprehensive host for information encryption, and various encryption methods have been designed for this purpose. In this research, the aim is to present an algorithm for audio ateganalysis, specifically for compressed audio files in MP3 format, in which some data has been embedded using MP3stego software. To prepare encrypted data, text files with random texts have been used. First, by using the side information extracted from MP3 files, the necessary features are extracted and the audio data, which includes two categories of stego files and clean files, is divided into two parts: training data and test data. And then, using machine learning techniques (support vector machine), the detection system of infected files and clean files is designed, and finally, the efficiency of the system is measured using the test data. In this paper, a new feature called spectral peakiness (SPK) is extracted from the side information of MP3 file. The proposed system was tested using separate test data, which includes clean files and stego files with various encryption capacities, and it distinguished clean and stego files with 100% accuracy and without error. The results indicate the perfect classification of stego and clean files while reducing the computational complexity and increasing the speed of steganalysis compared to other methods. Instead of using the audio signal information stored in the MP3 file, the proposed method uses the side information of the MP3 file, which is less dependent on the audio content of the file. In this method, the MDB side information in the compressed audio file is assumed as a sequence, and then, using a feature extraction method, a new feature in the frequency domain called spectral peakiness is calculated. This simple yet powerful feature is combined with features such as temporal average and spectral average of the MDB sequence and forms a low-dimensional (three-dimensional) feature vector. This feature vector will then be classified by a support vector machine (SVM) classifier as a suspicious file or a normal file. The feature extraction method, while being simple and having very few calculations, has 100% accuracy (recognition without any error) for MP3 files, even when the amount of the hidden information in the audio file is very low.}, Keywords = {Compressed Audio File, Audio Steganography, Audio Steganalysis, MP3, MP3stego}, volume = {21}, Number = {2}, pages = {55-66}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.21.2.55}, url = {http://jsdp.rcisp.ac.ir/article-1-1273-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1273-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2024} } @article{ author = {saneiarani, hassan and esmaili, mahdi and Afsharkazimi, Mohmmad ali}, title = {Choosing the Best Installation Paths In the Development of Urban CCTV Cameras}, abstract ={Optimizing camera placement is a two-decade-old research problem. Many researches have solved the problem with different approaches. Some different methods such as genetic algorithm, reinforcement learning, and greedy algorithm have been developed to obtain the maximum surface coverage. Some researchers have considered specific applications in order to optimally cover a certain area such as a coastal area or a protected area under the coverage of CCTV cameras. Some researchers have also considered the camera's capabilities of vertical rotation or horizontal rotation or zooming in order to use these capabilities for optimization. With the development of drone manufacturing technology, this tool is also proposed for specific applications. But what is less discussed is the optimization of the placement of urban surveillance cameras in a real city map. Usually, due to the high cost, all city cameras are not installed at once, and cameras are added annually to develop the city traffic monitoring system. Therefore, it is necessary to prioritize the selection of the route and a very important factor in prioritization is traffic. Traffic is the most important factor in choosing the route for the placement of urban surveillance cameras because the streets with more traffic are exposed to more traffic accidents and should be the priority for video monitoring. Traffic data is usually big data, not available for all cities, and on the other hand, providing traffic data may violate citizens' privacy. Therefore, there are many methods for creating virtual traffic, which are classified into two categories: macro and micro. Macro methods model traffic as a physical phenomenon such as fluid or gas, but micro models, which are mostly used in artificial intelligence methods, consider traffic as a set of individual trips. In this work, we use the second method to create virtual traffic so that routes with more traffic are prioritized for installation. Citizens usually make a lot of intra-city trips, and the function of city monitoring systems is to monitor these routes. Therefore, the placement of surveillance cameras should also be in such a way that it considers the observation of these routes. In the proposed method, the real map of the city is selected as a model. Then, by separating the main paths and obtaining the skeleton of the path, a graph of the paths is obtained, the intersection point of the paths will be its vertex and the distance between the vertices will be the weight of the connecting edges. Now by randomly selecting two vertices from the graph as the origin and destination of an intra-city trip and routing between them with Dijkstra's algorithm, a trip is made. By repeating this process, virtual traffic is simulated. To create virtual traffic similar to real traffic, the probability of choosing high-traffic points is considered more than other points. Therefore, the probability of selecting vertices in the graph is different according to their location in the city. By creating one hundred thousand paths for the studied model, the edges with the highest repetition can be found as the final results and suggested for camera installation. The evaluation of the final results is done by repeating random experiments and using the Jaccard similarity coefficient, and the degree of similarity of the output results is checked. The reliability of the proposed method is expressed by mathematical analysis and by drawing graphs, and the impact of influential parameters such as the number of city trips, the probability of choosing points, the impact of city topology, and the number of output results are expressed analytically, and the similarity of the results is 98%. The advantage of the proposed method is not depending on special tools such as special cameras for traffic measurement, as well as not depending on a specific location and topology.}, Keywords = {placement of urban CCTV camera - Virtual traffic - Jaccard similarity coefficient - Dijkstra's algorithm - smart city}, volume = {21}, Number = {2}, pages = {67-78}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.21.2.67}, url = {http://jsdp.rcisp.ac.ir/article-1-1402-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1402-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2024} } @article{ author = {Jaberi, Pouyan and Nemati, Shahla and Basiri, Mohammad Ehs}, title = {Classification of skin cancer images using two-level ensemble deep learning}, abstract ={Today, despite the tremendous advances in medical science and technology, access to a specialist doctor is still considered a major challenge. This challenge is of great importance for diseases such as cancer. Skin cancer is the 13th most common cancer in men and the 15th most common cancer in women. While some skin problems are benign and harmless, some of them can be malignant masses, which will remain harmless if they are diagnosed in time. When consulting a specialist doctor may be time-consuming and expensive, an intelligent system can be a fast alternative or, at least, an efficient preliminary treatment solution. For skin cancer, such intelligent system may utilize the images of suspicious skin masses labeled according to their benign or malignant state by specialist physicians. These labeled images are useful for training intelligent systems which should diagnose the potential problems in unseen new images. In this research, a novel deep learning-based approach is proposed for the problem of classifying skin cancer images into two categories of benign and malignant images. In the proposed model, powerful deep learning models for image classification including VGG, ResNet, and Inception are used in two levels. Specifically, we formed two ensembles; VGG ensemble which consists of VGG-16 and VGG-19 models and ResNet ensemble which consists of ResNet152, ResNet50, and Inception models. CatBoost algorithm is used in each level to combine the models on that ensemble. Finally, at the next level, two ensembles were combined using the CatBoost algorithm. The proposed ensemble model tries to improve the accuracy and consistency of the results by aggregating the deep models at its two levels. In order to show the utility of the proposed model, a subset of ISIC public dataset for skin cancer images is used for training and evaluation of models. The performance of the proposed ensemble model is compared with several deep neural networks and previous similar researches. Specifically, we compared the results achieved by the proposed model with those obtained by existing similar deep models and those used as building blocks of the proposed model. The results show that the proposed model performs better in classifying skin cancer images. The performance of the proposed model, both in each of the classes and in general, has been better than all independent deep learning models. It has also been shown that using VGG ensemble along with this proposed model by combining its results with the help of CatBoost and forming a two-level ensemble has improved its independent performance in each class.}, Keywords = {Deep Learning, Ensemble Learning, Skin Cancer, Benign, Malignant}, volume = {21}, Number = {2}, pages = {79-90}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.21.2.79}, url = {http://jsdp.rcisp.ac.ir/article-1-1350-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1350-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2024} } @article{ author = {Ahmadnia, Mahdi and Maghrebi, Mojtaba and Ghanbari, Rez}, title = {Providing an effective way to enhance low-light images: Enhanced Illumination Map Optimally}, abstract ={Low-light images often suffer from low brightness and contrast, which makes some scene details hard to see. This can affect the performance of many computer vision tasks, such as object recognition, tracking, scene understanding, and occlusion detection. Therefore, it is important and useful to enhance low-light images. One technique to enhance low-light images is based on the Retinex theory, which decomposes images into two components: reflection and illumination. Several mathematical models have been recently developed to estimate the illumination map using this theory. These methods first compute an initial illumination map and then refine it by solving a mathematical model. This paper introduces a novel method based on the Retinex theory to estimate the illumination map. The proposed method employs a new mathematical model with a differentiable objective function, unlike other similar models. This allows us to use more diverse methods to solve the proposed model, as classical optimization methods such as Newton, Gradient, and Trust-Region methods need the objective function to be differentiable. The proposed model also has linear constraints and is convex, which are desirable properties for optimization. We use the CPLEX solver to solve the proposed model, as it performs well and exploits the features of the model. Finally, we improve the illumination map obtained from the mathematical model using a simple linear transformation. This paper introduces a new method based on the Retinex theory for enhancing low-light images. The proposed method improves the illumination and the visibility of the scene details. We compare the performance of our method with six existing methods: AMSR, NPE, SRIE, DONG, MF, and LIME. We use four common metrics to evaluate the visual quality of the enhanced images: AMBE, LOE, SSIM, and NIQE. The results demonstrate that our method is competitive with many of the state-of-the-art methods for low-light image enhancement.}, Keywords = {Enhance illumination, Enhance low-light images, Illumination map, Retinex theory, Optimization model, Image Processing}, volume = {21}, Number = {2}, pages = {91-104}, publisher = {Research Center on Developing Advanced Technologies}, doi = {10.61186/jsdp.21.2.91}, url = {http://jsdp.rcisp.ac.ir/article-1-1256-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-1256-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2024} }