https://jicse.sbu.ac.ir/ Journal of Innovations in Computer Science and Engineering (JICSE) en daily 1 Sun, 01 Jun 2025 00:00:00 +0330 Sun, 01 Jun 2025 00:00:00 +0330 https://jicse.sbu.ac.ir/article_106066.html With the advent of new technologies, using various formats of digital gadgets is becoming widespread. In today's world, where everyday tasks are inevitable without technology, this extensive use of computers paves the way for malicious activity. Malware is one of the well-known and widely used means utilized for doing destructive activities by malicious attackers. Producing malware from scratch is somewhat difficult, so attackers tend to obfuscate existing malware and prepare it to become an unrecognizable program. Since creating new malware from an old one using obfuscation is a creative task, there are some drawbacks to identifying obfuscated malwares. In this research, we propose a solution to overcome this problem by converting the code to an image in the first step and then using a semi-supervised approach combined with contrastive learning. In this case, an obfuscation in the malware bytecode corresponds to an augmentation in the image. Hence, by utilizing meaningful augmentations, which simulate some obfuscation changes and combine them to generate complex ambiguity procedures, our proposed solution is able to construct, learn, and detect a wide range of obfuscations. This work addresses two issues: 1) malware classification despite the data deficiency and 2) obfuscated malware detection by training on non-obfuscated malwares. According to the results, the proposed method overcomes the data shortage problem in malware classification, as its accuracy is 90.1% when just 10% of data is used for training the model. Moreover, training on basic malwares without obfuscation achieved 96.21 percent accuracy in detecting obfuscated malware. https://jicse.sbu.ac.ir/article_106201.html Population imaging studies rely on good-quality medical imagery before quantifying downstream images. This study provides an automated approach for image quality assessment (IQA) from cardiovascular magnetic resonance (CMR) imaging at scale. We identify four common CMR imaging artefacts: respiratory motion, cardiac motion, Gibbs ringing, and aliasing. Four datasets, including UK Biobank, York University (YU), the Universidad Carlos III (UCIII) and CMR-Tehran, were used to perform the experiments. This study proposes two deep-learning models for CMR IQA in spatial and frequency domains. The presented spatial-domain model also has domain adaptation. The accuracies of supervised 4-fold cross-validation experiments for UK Biobank, YU, UCIII and CMR-Tehran datasets are 99.41%, 75.78%, 89.46% and 67.87% for the spatial-domain and 87.46%, 63.76%, 80.25% and 58.48% for the frequency-domain. Domain adaptation results, considering UK Biobank as the source set and YU, UCIII and CMR-Tehran as the target sets, show the domain shift gap coverage between the datasets to the extent of +11.91%, +3.93% and +16.57%, respectively. Besides, by training and testing the spatial-domain model on 30,125 images from the UK Biobank, an accuracy of 89.56% was obtained in a training time of 394.80 seconds. Meanwhile, the frequency-domain model with training and testing on 180,750 images achieves an accuracy of 87.99% in a training time of 255.04 seconds. Thus, the frequency-domain model can achieve almost the same accuracy yet 1.548 times faster than the spatial model. The proposed models can detect four common CMR imaging artefacts by receiving images or the corresponding k-spaces. https://jicse.sbu.ac.ir/article_106202.html Abstract— Sea Surface Temperature (SST) data reveal the temporal and spatial distribution of warm anticyclonic eddies and cold cyclonic eddies, impacting ocean behavior. The SST combined products attained adequate decisions to permit the recognition of mesoscale eddies with the introduction of altimeter operations and the availability of two or more altimeters at the same time. Climate change impacts ocean circulation and atmospheric anomalies linked to SST variations. Ocean eddies, vital for material and energy transport, require precise identification to advance oceanography. This study uses SST data from CMEMS in the Atlantic to introduce EddyNet, a deep-learning model for automatic eddy detection and classification. EddyNet's encoder-decoder architecture includes a pixel-wise classification layer, labeling each pixel as "0" (non-eddy), "1" (anticyclonic), or "2" (cyclonic). The high-resolution feature representation outperforms existing models, marking a significant leap in eddy detection accuracy and reliability. This study introduces EddyNet, a deep-learning model based on the U-Net architecture for automatic eddy detection and classification using Sea Surface Temperature (SST) data. The model was trained and evaluated on satellite imagery from the Copernicus Marine Environment Monitoring Service (CMEMS), achieving a training accuracy of 78.55%, a Dice score of 31.99%, and a precision of 0.9259. The recall values for different classes indicate that the model correctly identifies 99.51% of non-eddy pixels, 51.57% of anticyclonic eddy pixels, and 57.19% of cyclonic eddy pixels. These results demonstrate the effectiveness of deep learning in mesoscale eddy detection and highlight the potential for further optimization in classifying eddy structures with higher precision. https://jicse.sbu.ac.ir/article_106267.html Abstract—Understanding individual motor signatures (IMS) is essential for personalized treatment and performance optimization. This study investigates the effectiveness of Fuzzy C-Means (FCM) clustering for identifying individual motor signatures from graphomotor tasks. We analyze various kinematic and geometric features, such as movement duration, velocity, and trajectory length, to reveal which aspects of motor behavior are most effective in distinguishing individuals. The results show that features like length of movement are particularly discriminative, while others, such as beta and velocity, offer weaker clustering outcomes. Understanding individual motor signatures (IMS) is essential for personalized treatment and performance optimization. This study investigates the effectiveness of Fuzzy C-Means (FCM) clustering for identifying individual motor signatures from graphomotor tasks. We analyze various kinematic and geometric features, such as movement duration, velocity, and trajectory length, to reveal which aspects of motor behavior are most effective in distinguishing individuals. The results show that features like length of movement are particularly discriminative, while others, such as beta and velocity, offer weaker clustering outcomes. https://jicse.sbu.ac.ir/article_106266.html Abstract—The study employed Deep Learning Frailty (DLF), a compelling neural modeling framework for predicting heart failure patient survival. The DLF embeds a notion of multiplicative frailty from classical survival analysis that deals with unobserved heterogeneity while exploiting the neural structure's strong capabilities in approximating any non-linear covariate relationship. The results showed that Incorporating frailty leads to significant improvements, and the DLF model performs better on average. Abstract—The study employed Deep Learning Frailty (DLF), a compelling neural modeling framework for predicting heart failure patient survival. The DLF embeds a notion of multiplicative frailty from classical survival analysis that deals with unobserved heterogeneity while exploiting the neural structure's strong capabilities in approximating any non-linear covariate relationship. The results showed that Incorporating frailty leads to significant improvements, and the DLF model performs better on average. Abstract—The study employed Deep Learning Frailty (DLF), a compelling neural modeling framework for predicting heart failure patient survival. The DLF embeds a notion of multiplicative frailty from classical survival analysis that deals with unobserved heterogeneity while exploiting the neural structure's strong capabilities in approximating any non-linear covariate relationship. The results showed that Incorporating frailty leads to significant improvements, and the DLF model performs better on average. https://jicse.sbu.ac.ir/article_106296.html Abstract— Activation functions are essential for extracting meaningful relationships from real-world data in deep learning models. The design of activation functions is critical, as they directly influence the performance of these models. Nonlinear activation functions are commonly preferred since linear functions can limit a model’s learning capacity. Nonlinear activation functions can either have fixed parameters, which are predefined before training, or adjustable ones that modify during training. Fixed-parameter activation functions require the user to set the parameter values prior to model training. However, finding suitable parameters can be time-consuming and may slow down the convergence of the model. In this study, a novel fixed-parameter activation function is proposed and its performance is evaluated using benchmark MNIST datasets, demonstrating improvements in both accuracy and convergence speed.Abstract— Activation functions are essential for extracting meaningful relationships from real-world data in deep learning models. The design of activation functions is critical, as they directly influence the performance of these models. Nonlinear activation functions are commonly preferred since linear functions can limit a model’s learning capacity. Nonlinear activation functions can either have fixed parameters, which are predefined before training, or adjustable ones that modify during training. Fixed-parameter activation functions require the user to set the parameter values prior to model training. However, finding suitable parameters can be time-consuming and may slow down the convergence of the model. In this study, a novel fixed-parameter activation function is proposed and its performance is evaluated using benchmark MNIST datasets, demonstrating improvements in both accuracy and convergence speed. https://jicse.sbu.ac.ir/article_106297.html Abstract—Nowadays, advances in technology and medical science have led to significant changes in the field of healthcare. Consequently, an effort has been taken to develop an intelligent health assistant in the Persian language, focusing on the emergency department. To achieve this goal, a labeled dataset was prepared. Subsequently, an intelligent assistant architecture was developed, utilizing slot filling and speech act classification for natural language understanding. A dialogue manager was designed to address negation in patient statements, resulting in the classification of triage patients. Evaluation revealed that the assistant's performance matched that of emergency staff in 83% of cases.Abstract—Nowadays, advances in technology and medical science have led to significant changes in the field of healthcare. Consequently, an effort has been taken to develop an intelligent health assistant in the Persian language, focusing on the emergency department. To achieve this goal, a labeled dataset was prepared. Subsequently, an intelligent assistant architecture was developed, utilizing slot filling and speech act classification for natural language understanding. A dialogue manager was designed to address negation in patient statements, resulting in the classification of triage patients. Evaluation revealed that the assistant's performance matched that of emergency staff in 83% of cases. https://jicse.sbu.ac.ir/article_106530.html In recent years, one of the most widely studied areas in computer vision and natural language processing (NLP) is the interdisciplinary problem of Visual Question Answering (VQA), which involves the integration of computer vision and NLP.Important challenges in this field include the need for large and suitable datasets as well as powerful hardware for training the model. Key factors to improve the performance of these models include selecting the appropriate neural network for processing the inputs, selecting the appropriate dataset, and the method of combining the features extracted from the inputs. Also, using different attention mechanisms can improve the overall performance of the system. Furthermore, incorporating various attention mechanisms into the model can significantly enhance the overall performance of VQA systems. In these systems, different neural networks are employed to process inputs: convolutional neural networks (CNNs) with various architectures are used for image processing, and different types of recurrent neural networks (RNNs) are used for text processing.In this research, the architecture of the convolutional neural network is changed and the self-attention mechanism is used in text processing and the Skipgram language model is used for embedding the input text. The performance of the proposed model is evaluated on two datasets, VQA 1.0 and VQA 2.0. The results show that the proposed model has been able to increase the overall accuracy in the VQA 1.0 dataset to 67.25% and in the VQA 2.0 dataset to 61.57%, which show a significant improvement over the baseline models. https://jicse.sbu.ac.ir/article_106942.html Developer expertise is a critical component of community-based question-answering platforms like Stack Overflow. However, expertise is not monolithic. Developers exhibit different "shapes" of expertise, such as deep specialists (I-shaped) or broad generalists with one specialty (T-shaped). While prior research has examined developer expertise and answer quality independently, how different expertise structures influence preferences for specific answer characteristics remains insufficiently understood. This paper investigates the relationship between a developer's expertise shape and their preference for specific answer characteristics.We present a large-scale empirical study of over 48,000 Stack Overflow users, classifying them into I-shaped, T-shaped, Pi-shaped, and Comb-shaped profiles based on the distribution of tag-level reputation, following established expertise-shape modeling approaches. We then analyze the types of answers these user profiles tend to upvote and accept as solutions, focusing on characteristics such as answer length, inclusion of code snippets, use of images, and citation of external references.Using separate analyses for community-level (upvotes) and task-resolution-level (accepted answers) preference signals, our findings reveal distinct and systematic differences across expertise shapes. I-shaped specialists favor technically deep, code-heavy answers, while T-shaped and Comb-shaped experts show a preference for more summarized, conceptual answers that include diagrams or references. These patterns are consistent across robustness checks and sensitivity analyses.The results highlight that answer usefulness is user-dependent rather than universal, and they can help improve expertise-aware answer recommendation systems and foster more effective knowledge sharing on collaborative platforms. https://jicse.sbu.ac.ir/article_106941.html Spectrum-Based Fault Localization (SBFL) techniques are widely used and studied. These techniques are straightforward, low-cost, and fast in comparison to other fault localization techniques, such as Mutation-Based and Learning-Based ones. However, the accuracy of these techniques is always controversial. The main criticism of these techniques refers to the mere use of coverage and spectra information. Hence, this study seeks to improve SBFL techniques by enhancing their accuracy while maintaining simplicity, aiming to make them applicable for regression faults. To achieve this, the first step involves integrating SBFL techniques with two features: (1) Change and (2) Test Case Weight, which utilizes the same coverage and spectrum data in a novel way. Additionally, the study examines the impact of incorporating Data and Control Dependency into the formula for fault localization accuracy. It also considers the suspiciousness scores generated by SBFL formulae as a feature. Three SBFL techniques—Tarantula, Ochiai, and Jaccard—are applied in this study both as a feature and as a baseline for result comparison. The findings reveal that combining SBFL suspiciousness scores with Change and Test Case Weight significantly enhances performance by identifying at least 27% more faults at the Top-3 ranking. Furthermore, integrating these features with Data Dependency leads to even greater improvements, locating minimum 45% more faults at the Top-3 ranking compared to aforementioned SBFL formulae. Overall, this study emphasizes the limitations of existing SBFL formulae while highlighting the advantages of augmenting SBFL with additional features and repurposing spectral information to achieve greater accuracy in fault localization. https://jicse.sbu.ac.ir/article_107013.html Digital data production speeds up dramatically every day, and the need to store extensive data rises consequently. Many storage media are used today, but only a few are capable of the required features in terms of high density, data duration, storage lifetime, and reliability. DNA-based digital data storage systems are one of the new and feature-rich media to store a massive amount of data in a very tiny volume with a strange lifetime. However, building dense and straightforward DNA storage comes with some challenges. Using manual methods to transfer and mix liquids containing DNA molecules makes this storage more error-prone. Moreover, the encoding method of digital data into DNA molecules is very crucial. This paper proposes a novel method for automatic and controllable DNA strand manipulation using a customized Digital Microfluidic Biochip (DMFB) corresponding to the CAD algorithm. Then, we offer a new approach to encoding the digital data into DNA molecules that helps error detection and correction in DNA storage while it brings efficient encoding density. Experimental results show that we can write 1.6 bits on each nucleotide with a 200-base strand which is a good density compared with other methods. In contrast, the encoding method is much simpler and easier to implement. https://jicse.sbu.ac.ir/article_107040.html Blockchain technology has revolutionized various industries by enabling decentralized, transparent, and secure systems for applications such as supply chain management, Internet of Things (IoT), and smart contracts. However, evaluating the performance of diverse blockchain systems remains challenging due to the absence of standardized metrics. This systematic review synthesizes findings from 55 peer-reviewed studies to develop a comprehensive taxonomy of Key Performance Indicators (KPIs). Our analysis reveals a critical trade-off: while Proof-of-Work systems (e.g., Bitcoin) offer superior security with hash rates exceeding 500 EH/s, they consume approximately 700 kWh per transaction—a sustainability gap of nearly two orders of magnitude compared to more efficient alternatives. Furthermore, we identify the IoT domain as the most vulnerable to the lack of standardization, where inconsistent KPIs for latency and throughput currently hinder real-time device coordination. We categorize 30 general KPIs into four dimensions—technical, security, economic, and user-centric—while identifying domain-specific KPIs tailored to applications like supply chain logistic, IoT, and smart contracts. Additionally, we map blockchain features required for measuring these KPIs, ensuring traceability to system properties. Derived from a rigorous systematic review of blockchain performance literature, our findings provide a holistic framework for assessing blockchain performance. This framework bridges technical and application- specific needs, offering actionable insights for researchers, developers, and industry practitioners to optimize blockchain systems across public, private, and hybrid architectures. The taxonomy and measurement features support standardized evaluation, paving the way for scalable and sustainable blockchain adoption in emerging domains like decentralized finance.