International Journal of Informatics and Communication Technology (IJ-ICT)
The International Journal of Informatics and Communication Technology (IJ-ICT), p-ISSN 2252-8776 , e-ISSN 2722-2616 , is a common platform for publishing quality research papers as well as other intellectual outputs. This journal is aimed at promoting the dissemination of scientific knowledge and technology in the information and communication technology areas in front of an international audience of the scientific community, to encourage the progress and innovation of technology for human life, and also to be the best platform for the proliferation of ideas and thoughts for all scientists, regardless of their locations or nationalities. The IJ-ICT covers all areas of informatics and communication technology (ICT) and focuses on integrating hardware and software solutions for storage, retrieval, sharing, manipulation management, analysis, visualization, interpretation, and their applications for human services programs and practices. It publishes refereed original research articles and technical notes. It is designed to serve researchers, developers, managers, strategic planners, graduate students, and others interested in state-of-the-art research activities in ICT. This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU). Now, this journal is accredited by the Indonesian Ministry of Education, Culture, Research, and Technology. The journal was accepted for Scopus indexing on November 9, 2023, and it is now available on the Scopus source page, with coverage starting in 2023. Submit your paper now through online submission ONLY.
Institute of Advanced Engineering and Science
AI-based federated learning for heart disease prediction: a collaborative and privacy-preserving approach
People with symptoms like diabetes, high BP, and high cholesterol are at an increased risk for heart disease and stroke as they get older. To mitigate this threat, predictive fashions leveraging machine learning (ML) and artificial intelligence (AI) have emerged as a precious gear; however, heart disease prediction is a complicated task, and diagnosis outcomes are hardly ever accurate. Currently, the existing ML tech says it is necessary to have data in certain centralized locations to detect heart disease, as data can be found centrally and is easily accessible. This review introduces federated learning (FL) to answer data privacy challenges in heart disease prediction. FL, a collaborative technique pioneered by Google, trains algorithms across independent sessions using local datasets. This paper investigates recent ML methods and databases for predicting cardiovascular disease (heart attack). Previous research explores algorithms like region-based convolutional neural network (RCNN), convolutional neural network (CNN), and federated logistic regressions (FLRs) for heart and other disease prediction. FL allows the training of a collaborative model while keeping patient info spread out among various sites, ensuring privacy and security. This paper explores the efficacy of FL, a collaborative technique, in enhancing the accuracy of cardiovascular disease (CVD) prediction models while preserving data privacy across distributed datasets.
Copyright (c) 2025 Stuti Bhatt, Surender Reddy Salkuti, Seong-Cheol Kim http://creativecommons.org/licenses/by-sa/4.0
Attitude and intention to use chatbots in e-commerce: the moderating role of personal innovativeness
Internet-based retailers employ artificial intelligence (AI) chatbots to facilitate customer communication. This research endeavored to evaluate consumers' intentions regarding the utilization of chatbots for customer service interactions, building upon the technology acceptance model (TAM). TAM-based chatbot adoption is the subject of an abundance of research. Conversely, the extent to which users' perception of the chatbot's response quality influences their intention to adopt remains uncertain. In addition to investigating the potential influence of chatbot response accuracy and completeness on users' intention to adopt the system, this study explored the relationship between users' personal innovativeness and adoption intention. A total of 312 usable responses were analyzed with PLS-SEM from survey data collected via convenience sampling from e-commerce customers. Perceived usefulness, convenience of use, accuracy, and completeness all influenced attitudes toward chatbots, as shown by hypothesis testing result. Attitude formation toward chatbots is most strongly influenced by perceived completeness. Personal innovativeness has a negative influence, which contradicts the hypothesis despite the fact that its moderating effect is statistically significant. Further comprehension of the key determinants of attitude towards chatbots is enhanced by these findings. It is advisable for organizations to empower the chatbot with the capability to conduct thorough and precise responses to inquiries.
Copyright (c) 2025 Ahmad Maki, Indah Oktaviani Hardi, Evi Rinawati Simanjuntak http://creativecommons.org/licenses/by-sa/4.0
Scaling of Facebook architecture and technology stack with heavy workload: past, present and future
Leading social media Facebook has improved its architecture to meet user needs. Facebook has improved its systems to handle millions of users with heavy workloads and large datasets using innovative architectural solutions and adaptive strategies. The study examines Facebook’s architectural and technological advances in heavy workload and big data. To understand how Facebook scaled with a growing user base and data volume, history and system architecture will be examined. It will also examine how cloud storage and high-performance computing optimize resource utilization and maintain performance during peak user activity. Facebook is managing big data and heavy workloads with new technologies like the hybrid communication model that uses PULL and PUSH strategies for real-time messaging. Facebook switched from HBase to MyRocks for message storage to improve performance as data grew. Architectural scaling and technology stack research must prioritize data storage innovations and optimized communication protocols to handle heavy workloads and big data. The messenger Sync protocol reduces network congestion and improves synchronous communication, reducing resource consumption and maintaining performance under high load. High-performance computing (HPC) and cloud storage should be studied together to support complex compute workflows. This convergence may improve large-scale application infrastructures and encourage interdisciplinary collaboration for scalable and resilient systems.
Copyright (c) 2025 Tole Sutikno, Laksana Talenta Ahmad http://creativecommons.org/licenses/by-sa/4.0
Unit commitment problem solved with adaptive particle swarm optimization
This article presents an innovative approach that solves the problem of generation scheduling by supplying all possible operating states for generating units for the given time schedule over the day. The scheduling variables are set up to code the load demand as an integer each day. The proposed adaptive particle swarm optimization (APSO) technique is used to solve the generation scheduling issue by a method of optimization considering production as well as transitory costs. The system and generator constraints are considered when solving the problem, which includes minimum and maximum uptime and downtime as well as the amount of energy produced by each producing unit (like capacity reserves). This paper describes the suggested algorithm that can be applied for unit commitment problems with wind and heat units. Test systems with 26 and 10 units are used to validate the suggested algorithm.
Copyright (c) 2025 Ramesh Babu M, Venkatesh Kumar C, Bharathraj M, Dashagireevan S http://creativecommons.org/licenses/by-sa/4.0
State space controller of SLCC and design analysis with MPPT approaches
Power systems with standalone properties like remote unit telecommunication network requires high negative DC supply voltage. In such remote places, solar photovoltaic (PV) are used to generate power. Maximum power point tracking techniques (MPPT) gives unregulated voltage from solar panel. This unregulated voltage is converted into regulated voltage by providing proper pulse width modulation (PWM) signal to self-lift cuk converter (SLCC). In comparison with classic cuk converter, SLCC reduces load voltage and load current ripples. This paper focuses on state space controller design and implementation of SLCC used in MPPT based PV system. The switching pulse of SLCC can be generated by perturb and observe (P&O), incremental conductance (IC) and also using fuzzy control. The simulation of SLCC has been performed using MATLAB/Simulink and its specifications in time domain has been compared.
Copyright (c) 2025 JEYAPRAKASH N, NIVETHITHA DEVI M, MOHANA SANTHOSH M, LOKESHWAR REDDY K V, SUDHAKAR T D http://creativecommons.org/licenses/by-sa/4.0
Solar-powered boost-fly back converter for efficient warehouse monitoring with flack droid
Warehouses serve as essential infrastructure for storing a wide array of goods and are utilized by various entities. Implementing a sophisticated warehouse management system (WMS) represents a pinnacle of technological advancement. Effective warehouse maintenance is paramount, benefiting both consumers and producers alike. Typically, warehouses store items such as medicine, chemicals, food, and electronics, requiring controlled conditions of temperature and humidity. Monitoring these factors is essential to comply with regulations and maintain internal quality standards. This paper focuses on optimizing warehouse management to meet customer demands and streamline processes for packaging and production teams. Additionally, it proposes the integration of droid technology within warehouses to monitor the parameters and mitigate fire hazards, thereby enhancing the efficiency and safety of goods storage. This proactive approach not only ensures the integrity of stored products but also contributes to cost-saving measures within the warehouse. This paper introduces an innovative method to achieve a substantial increase in voltage output in a DC-DC converter while avoiding the need for excessively high duty ratios. The converter’s operation is governed by a single pulse width modulation (PWM) signal, employing a fractional-order proportional-integral-derivative controller (FOPID) for regulating the power switch. By merging boost-forward-fly back (BFF) converter topologies, the design achieves a remarkable voltage gain. Moreover, the converter efficiently recycles energy stored in the leakage inductance of the coupled inductor, thereby reducing voltage stress and minimizing power losses and thus enhancing overall converter efficiency.
Copyright (c) 2025 Sivajothi Kavitha S, Usha D, Jamuna V http://creativecommons.org/licenses/by-sa/4.0
Performance analysis of D2D network in heterogeneous multitier interference scenarios
The trade-off between boosting network throughput and minimizing interference is a critical issue in fifth generation (5G) networks. Diverting the data traffic around the network access point in device-to-device (D2D) communication is an important step in realizing the vision of 5G. Though the D2D network improves the network performance, they complicate the interference management process. Interference is an invisible physical phenomenon occurring in wireless communication which happens when multiple transmissions happen simultaneously over a general wireless medium. Enormous growth in usage of mobile phone and other wireless gadgets in recent years has paved the way for significant role in Interference analysis over multi-tier network. Interference could affect communication systems performance and it might even prevent systems functioning properly. 3G and 4G wireless devices coexisted with reverse compatibility in a coverage area. Also, after their widespread adoption, 5G devices have become prevalent across the globe and this reaffirms interference coexistence as a significant field of research. Multiple systems operating in a region will cause severe interference and ultimately reduce the quality of received signal. A simulation environment for cellular standards coexistence considering real-time parameters is created and experimented. Various research works earlier addresses the interference mitigation techniques in multi-tier networks but none of them present the analysis of scenarios and interfering signal power levels in the respective contexts. In this paper various scenarios with different network interference coexistence were studied, simulated, and analyzed quantitatively.
Copyright (c) 2025 DHILIPKUMAR S, ARUNACHALAPERUMAL C, JENIFER SURIYA L.J, JERLIN A http://creativecommons.org/licenses/by-sa/4.0
Modeling chemical kinetics of geopolymers using physics informed neural network
Using a physics informed neural network for the analysis of geopolymers as an alternate material for cement can be a viable approach, as neural networks are capable of modeling complex, nonlinear relationships in data, which can be beneficial for representing the dynamics of chemical properties. If you have a substantial amount of theoretical data, a neural network can learn patterns and relationships in the data, even when the underlying system dynamics are not well-defined or are difficult to model analytically. A welltrained neural network can generalize from the training data to make predictions for unseen scenarios, which can be useful for real-time analysis of the material.
Copyright (c) 2025 Blesso Abraham, Sudhakar T.D http://creativecommons.org/licenses/by-sa/4.0
Electric load forecasting using ARIMA model for time series data
Any country's economic progress is heavily reliant on its power infrastructure, network, and availability, as energy has become an essential component of daily living in today's globe. Electricity's distinctive quality is that it cannot be stored in huge quantities, which explains why global demand for home and commercial electricity has grown at an astonishing rate. On the other hand, electricity costs have varied in recent years, and there is insufficient electricity output to meet global and local demand. The solution is a series of case studies designed to forecast future residential and commercial electricity demand so that power producers, transformers, distributors, and suppliers may efficiently plan and encourage energy savings for consumers. However, load prognosticasting has been one of the most difficult issues confronting the energy business since the inception of electricity. This study covers a new one–dimensional approach algorithm that is essential for the creation of a short–term load prognosticasting module for distribution system design and operation. It has numerous operations, including energy purchase, generation, and infrastructure construction. We have numerous time series forecasting methods of which autoregressive integrated moving average (ARIMA) outperforms the others. The auto–regressive integrated moving average model, or ARIMA, outperforms all other techniques for load forecasting.
Copyright (c) 2025 Belshanth B, Sudhakar T D http://creativecommons.org/licenses/by-sa/4.0
Novel multilevel local binary texture descriptor for oral cancer detection
Categorizing texture medical images is an extensive job in most of the fields of computer vision, pattern recognition and biomedical imaging. For the past few years, the texture analysis system model, especially for biological images, has been brought to attention because of its ever-growing requirements and characteristics. This research shows its novelty by using a multilevel local binary texture descriptor (MLBTD) algorithm with support vector machine (SVM), k-nearest neighbor (KNN), and CT Classifiers to investigate the texture features of the oral cancer samples. The simulation work is done in MATLAB2021a environment by employing the MLBTD algorithm. A Mendeley dataset, containing 89 oral cavity histopathological images and 439 OSCC images in 100x magnification, is used. A statistical comparative study of local binary pattern (LBP) and MLBTD with linear SVM, KNN, CT classifier is performed in which results show the better performance of MLBTD and linear SVM with 89.94% of accuracy and by applying MLBTD algorithm over 90.57% accuracy is obtained whereas LBP algorithm only provides 86.16% of accuracy.
Copyright (c) 2025 Vijaya Yaduvanshi, Murugan R http://creativecommons.org/licenses/by-sa/4.0
Spth-FCM: decision support tool for speech therapist based on fuzzy cognitive mapping
The development and integration of medical information systems into a unified information space is a significant focus in the field of information technologies. It is essential to develop decision support systems (DSS) to enhance the effectiveness of medical and diagnostic procedures. This article presents a novel decision support tool for speech therapists, which is based on fuzzy cognitive maps (FCM). The latter is a method of modeling complex systems using knowledge of human existence and experience. The proposed tool is composed of three phases. The first phase focuses on entering patient information into the graphical interface developed in JAVA based on the most precise observations. An FCM will be automatically constructed, describing the type of disorder and the patient’s case during the second phase. Finally, in the third phase, FCM-based scenarios were built during the execution of the inference process under FCM expert. The system is presented and demonstrated using a real cases study for eight weeks. The results show that the tool makes it possible to display, guide, assist, and confirm the medical decision of the speech therapist for an appropriate diagnosis and treatment.
Copyright (c) 2025 Asma Maziz, Cherif Taouche http://creativecommons.org/licenses/by-sa/4.0
Does empathy and awareness of bullying affect the performance of Moroccan students in PISA?
Socioemotional skills, such as empathy and bullying awareness, play a pivotal role in shaping students' personal and academic development. These skills are increasingly recognized as critical factors influencing educational outcomes, particularly in addressing challenges like bullying that can hinder learning. This study examines the impact of empathy and bullying awareness on the academic performance of Moroccan students, using data from the 2018 Programme for International Student Assessment (PISA). To ensure robust causal inference in high-dimensional data, the double/debiased machine learning (DML) technique is employed. The findings reveal that higher levels of empathy and awareness of bullying significantly enhance performance across reading, mathematics, and science, with the most notable improvements observed in reading. These results remain consistent across various demographic and socioeconomic groups, highlighting their robustness. The study underscores the importance of integrating socioemotional learning into educational practices to foster academic success and create supportive school environments. By contributing to the growing evidence on non-cognitive skills in education, this research offers valuable insights for educators and policymakers seeking to improve student outcomes.
Copyright (c) 2025 ilyas tammouch, Abdelamine Elouafi, Ibtissam Essadik, soumaya nouna http://creativecommons.org/licenses/by-sa/4.0
Comparative study of traditional edge detection methods and phase congruency based method
Finding relevant and crucial details from images and effectively interpreting what they represent are two of image processing's main goals. An edge is the line that separates an object from its backdrop and shows where two things meet. Mining the picture's borders for extracting useful data remains one of the trickiest steps in understanding of an image. The borders of the objects may be used to build the image's edges, which are its basic characteristics. There are different types of traditional edge retrieval techniques that are conventionally categorized as first order and second gradient based methods such as Roberts, Prwitt, Kirsch, Robinson, canny, Laplacian and Laplacian of gaussian. The majority of research and review work on edge detection algorithms focuses on conventional algorithms and soft computing based methods, neglecting illumination invariant phase congruency based edge detector. This study aims to compare traditional derivative based edge detection algorithms with log Gabor wavelet based edge detector phase congruency. This work does a thorough examination of various edgedetecting approaches, including traditional boundary detection methods and log Gabor wavelet based method. To test effectiveness of edge detection algorithms, experimental results are obtained on images from DRIVE, STARE, and BSDS500 dataset.
Copyright (c) 2025 Rajendra Patil http://creativecommons.org/licenses/by-sa/4.0
Review of NLP in EMR: abbreviation, diagnosis, and ICD classification
This review explores state-of-the-art natural language processing (NLP) methods applied to electronic medical records (EMRs) for key tasks such as expanding medical abbreviations, automated diagnosis generation, international classification of diseases (ICD) classification, and explaining model outcomes. With the growing digitization of healthcare data, the complexity of EMR analysis presents a significant challenge for accurate and interpretable results. This paper evaluates various methodologies, highlighting their strengths, limitations, and potential for improving clinical decision-making. Special attention is given to abbreviation expansion as a crucial step for disambiguating terms in the clinical text, followed by an exploration of auto-diagnosis models and ICD code assignment techniques. Finally, interpretability methods like integrated gradients and attention-based approaches are reviewed to understand model predictions and their applicability in healthcare. This review aims to provide a comprehensive guide for researchers and practitioners interested in leveraging NLP for clinical text analysis.
Copyright (c) 2025 Nurul Anis Balqis Iqbal Basheer, Sharifalillah Nordin, Sazzli Shahlan Kassim, Azliza Mohd Ali, Nurzeatul Hamimah Abdul Hamid http://creativecommons.org/licenses/by-sa/4.0
Predictive insights into student online learning adaptability: elevating e-learning landscape
In Morocco’s rapidly transforming educational landscape, this study delves into students’ adaptability to online learning environments by integrating sophisticated artificial intelligence (AI) algorithms and hyperparameter optimization techniques. This research uses the comprehensive “online learning adaptivity” dataset to identify pivotal factors influencing student flexibility and effectiveness in e-learning platforms. We applied various AI models, with a particular emphasis on the CatBoost classifier, which exhibited exceptional predictive performance, achieving an accuracy rate near 98%. This high precision in predicting student adaptiveness offers essential insights into tailoring digital education systems. The results underscore the significant potential of machine learning technologies to enhance educational methodologies by catering to the diverse needs of students. Such capabilities are instrumental for educators and policymakers dedicated to refining e-learning strategies that effectively accommodate individual learning styles, ultimately improving the broader educational outcomes in Moroccan tertiary education. These findings advocate for a more nuanced understanding of the interplay between student behavior and technological solutions, providing a roadmap for developing more responsive and effective educational platforms.
Copyright (c) 2025 Mohamed El Ghali http://creativecommons.org/licenses/by-sa/4.0
A hybrid approach using VGG16-EffcientNetV2B3-FCNets for accurate indoor vs outdoor and animated vs natural image classification
The paper introduces a hybrid approach that synergistically combines the strengths of VGG16, EfficientNetV2B3, and fully connected networks (FCNets) to achieve precise image classification. Specifically, our focus lies in discerning between basic indoor and outdoor scenes, further extended to distinguish between animated and natural images. Our proposed hybrid architecture harnesses the unique characteristics of each component to significantly enhance the model’s overall performance in fine-grained image categorization. In our methodology, we utilize VGG16 and EfficientNetV2B3 as the feature extractors. During evaluation, we examined various classification algorithms, such as VGG16, EfficientNet, Feature_Aggr_Avg, and Feature_Aggr_max, among others. Notably, our hybrid feature aggregation approach demonstrates a remarkable improvement of 0.5% in accuracy compared to existing solutions employing VGG16 and EfficientNet as feature extractors. Notably, for indoor versus outdoor image classification, feature_aggr_avgachieves an accuracy of 98.51%. Similarly, when distinguishing between animated and natural images, Feature_Aggr_Avgachieves an impressive accuracy of 99.20%. Our findings demonstrate improved accuracy with the hybrid model, proving its adaptability across diverse classification tasks. The model is promising for applications like automated surveillance, content filtering, and intelligent visual recognition, with its robustness and precision making it ideal for realworld scenarios requiring nuanced categorization.
Copyright (c) 2025 Meghana Anant Deshmukh, Amit Gaikwad http://creativecommons.org/licenses/by-sa/4.0
A healthy population is vital for societal prosperity and happiness. Amidst busy lifestyles and the challenges posed by the COVID-19 pandemic, individuals often neglect their health needs. To address this, we introduce a novel approach utilizing a chatbot for virtual medical assistance. Tailored for individuals confined indoors or hesitant to visit hospitals for minor ailments, our chatbot offers personalized medical support by diagnosing ailments based on user-reported symptoms and engaging in interactive conversations. Leveraging a robust dataset containing 132 symptoms, 41 diseases, and corresponding medications, our chatbot employs a systematic approach for symptom refinement, enhancing diagnostic precision. Upon identifying a disease, the chatbot promptly suggests basic medications tailored to the specific ailment. Furthermore, our system integrates user demographics to evaluate medication history and current state, allowing for personalized medication recommendations based on individual needs. Through extensive testing and validation, we demonstrate the effectiveness of our chatbot in accurately predicting ailments and providing timely treatment advice. Our study introduces a novel paradigm for medicine recommendation and disease prediction, with the potential to enhance healthcare accessibility and effectiveness.
Copyright (c) 2025 Sainath Chaithanya Aravalli http://creativecommons.org/licenses/by-sa/4.0
Shellcode classification analysis with binary classification-based machine learning
The internet enables people to connect through their devices. While it offers numerous benefits, it also has adverse effects. A prime example is malware, which can damage or even destroy a device or harm its users, highlighting the importance of cyber security. Various methods can be employed to prevent or detect malware, including machine learning techniques. The experiments are based on training and testing data from the UNSW_NB15 dataset. K-nearest neighbor (KNN), decision tree, and Naïve Bayes classifiers determine whether a record in the test data represents a Shellcode attack or a non-Shellcode attack. The KNN, decision tree, and Naïve Bayes classifiers reached accuracy rates of 96.26%, 97.19%, and 57.57%, respectively. This study's findings aim to offer valuable insights into the application of machine learning to detect or classify malware and other forms of cyberattacks.
Copyright (c) 2025 Jaka Naufal Semendawai http://creativecommons.org/licenses/by-sa/4.0
Classification of breast cancer using a precise deep learning model architecture
Breast cancer is an important topic in medical image analysis because it is a high-risk disease and the leading cause of death in women. Early detection of breast cancer improves treatment outcomes, which can be achieved by identifying it using mammography images. Computer-aided diagnostic systems detect and classify medical images of breast lesions, allowing radiologists to make accurate diagnoses. Deep learning algorithms improved the performance of these diagnoses systems. We utilized efficient deep learning approaches to propose a system that can detect breast cancer in mammograms. The proposed approach adopted relies on two main elements: improving image contrast to enhance marginal information and extracting discriminatory features sufficient to improve overall classification quality, these improvements achieved based on a new model from scratch to focus on enhancing the accuracy and reliability of breast cancer detection. The model trained on the digital database for screening mammography (DDSM) dataset and compared with different convolutional neural network (CNN) models, namely EfficientNetB1, EfficientNetB5, ResNet-50, and ResNet101. Moreover, to enhance the feature selection process, we have integrated adam optimizer in our methodology. In evaluation, the proposed method achieved 96.5% accuracy across the dataset. These results show the effectiveness of this method in identifying breast cancer through images.
Copyright (c) 2025 Mohammed Talal Ghazal http://creativecommons.org/licenses/by-sa/4.0
Empowering low-resource languages: a machine learning approach to Tamil sentiment classification
Sentiment analysis is essential for deciphering public opinion, guiding decisions, and refining marketing strategies. It plays a crucial role in monitoring public sentiment, fostering customer engagement, and enhancing relationships with businesses' target audiences by analyzing emotional tones and attitudes in vast textual data. Sentiment analysis is extremely limited, particularly for languages like Tamil, due to limited application in diverse linguistic contexts with fewer resources. Given its global impact and linguistic diversity, addressing this gap is crucial for a more nuanced understanding of sentiments in India. In the context of Tamil, the need for sentiment analysis models is particularly crucial due to its status as one of the classical languages spoken by millions. The cultural, social, and historical nuances embedded in Tamil language usage require tailored sentiment analysis approaches that can capture the subtleties of sentiment expression. This paper introduces a novel method that assesses the performance of various text embedding methods in conjunction with a range of machine learning (ML) algorithms to enhance sentiment classification for Tamil text, with a specific focus on lyrics. Experiments notably emphasize FastText word embedding as the most effective method, showcasing superior results with a remarkable 78% accuracy when coupled with the support vector classification (SVC) model.
Copyright (c) 2025 Saleem Raja Abdul Samad http://creativecommons.org/licenses/by-sa/4.0
An artificial intelligent system for cotton leaf disease detection
This study aims to develop a deep learning-based system for the detection and classification of diseases in cotton leaves, with the goal of aiding in early diagnosis and disease management, thereby enhancing agricultural productivity in India. The study utilizes a dataset of cotton leaf images, classified into four categories: Fusarium wilt, Curl virus, Bacterial blight, and Healthy leaves. The dataset is used to train and evaluate various CNN models such as basic CNN, VGG19, Xception, InceptionV3, and ResNet50. These models were evaluated on their accuracy in identifying the presence of diseases and classifying cotton leaf images into the respective categories. The models were trained using standard deep learning frameworks and optimized for high performance. The results indicated that ResNet50 achieved the highest accuracy of 100%, followed by InceptionV3 with 98.75%, and VGG19 and Xception both with 97.50%. The basic CNN model showed an accuracy of 96.25%. These models demonstrated strong potential for accurate multi-class classification of cotton leaf diseases. This study emphasizes the potential of deep learning in agricultural diagnostics. Future research can focus on improving model robustness, incorporating larger datasets, and deploying the system for real-time field use to assist farmers in disease management and improving cotton production.
Copyright (c) 2025 Nitesh Sureja http://creativecommons.org/licenses/by-sa/4.0
Legal challenges of artificial intelligence in the European Union’s digital economy
This article critically examines the legal and regulatory challenges posed by artificial intelligence (AI) within the European Union’s (EU) digital economy, focusing on the recently adopted EU Ai Act (Regulation 2024/1689). While previous studies have addressed AI's ethical and theoretical dimensions, this research fills a gap by analyzing the Act’s practical application across its temporal, personal, material, and territorial scopes. The study adopts a qualitative legal methodology, integrating doctrinal interpretation, comparative analysis, and systemic review of EU and international legal instruments. Key findings reveal that the EU AI Act establishes a pioneering risk-based regulatory framework, distinguishing itself through strong safeguards for fundamental rights, transparency, and human oversight. However, critical limitations remain, including ambiguous high-risk classifications, reliance on provider self-assessment, and exemptions for national security that may undermine human rights protections. The article compares the EU approach with those of the United States and China, illustrating divergent models of AI regulation and their global implications. It argues that while the EU AI Act sets an ambitious precedent, its success depends on effective enforcement, stakeholder compliance, and international regulatory convergence. By addressing these challenges, the EU can shape a globally influential framework for ethical and responsible AI deployment. This study contributes to the evolving academic and policy debate on AI governance by offering practical insights and recommendations for future research and legal development.
Copyright (c) 2025 Volodymyr Kudin, Tamara Kortukova, Maryna Dei, Andrii Onyshchenko, Petro Kravchuk http://creativecommons.org/licenses/by-sa/4.0
Comparative analysis of u-net architectures and variants for hand gesture segmentation in parkinson’s patients
U-Net is a well-known method for image segmentation, and has proven effective for a variety of segmentation challenges. A deep learning architecture for segmenting hand gestures in parkinson’s disease is explored in this paper. We prepared and compared four custom models: a simple U-Net, a three-layer U-Net, an auto encoder-decoder architecture, and a U-Net with dense skip pathways, using a custom dataset of 1,000 hand gesture images and their corresponding masks. Our primary goal was to achieve accurate segmentation of parkinsonian hand gestures, which is crucial for automated diagnosis and monitoring in healthcare. Using metrics including accuracy, precision, recall, intersection over union (IoU), and dice score, we demonstrated that our architectures were effective in delineating hand gestures under different conditions. We also compared the performance of our custom models against pretrained deep learning architectures such as ResNet and VGGNet. Our findings indicate that the custom models effectively address the segmentation task, showcasing promising potential for practical applications in medical diagnostics and healthcare. This work highlights the versatility of our architectures in tackling the unique segmentation challenges associated with parkinson’s disease research and clinical practice.
Copyright (c) 2025 Avadhoot Ramgonda Telepatil, Jayashree s Vaddin http://creativecommons.org/licenses/by-sa/4.0
A recommendation system for teaching strategies according to learning styles
Teaching strategies (TS) are resources, procedures, techniques, and/or methods that teachers use as instruments to promote meaningful learning in students and that have proven to be efficient as support in classroom teaching. This paper describes a recommendation system (RS) for teaching strategies according to learning styles (RSTSLS) that helps to determine the most appropriate TS to use according to the learning style (LS) of the students based on Felder and Silverman’s learning styles model (FSLSM). A working example of the system is provided, as well as the results of its functional and non-functional tests, which were satisfactory. It is concluded that the system can be useful as a support tool for teachers, allowing them to adapt their TS according to the LS of their students.
Copyright (c) 2025 Juan Francisco Figueroa Pérez, Manuel de Jesus Rodríguez Guerrero, Alan David Ramírez Noriega, Yobani Martínez Ramírez http://creativecommons.org/licenses/by-sa/4.0
Design of miniaturized dual-band bandpass filter with enhanced selectivity for GPS and RFID applications
This article presents a miniaturized interdigital coupled dual-band bandpass filter with multiple transmission zeros/poles. Stepped impedance resonators, interdigital coupled lines, and series coupled lines make up the proposed filter design. A circuit simulator is used to analyze a proposed filter, and the magnitude and bandwidth shifts have been investigated. To confirm the proposed filter design, equations for transmission zero frequencies have been constructed and verified based on even-odd mode analysis and lossless transmission line theory. A working prototype for 2.2 GHz (RFID) and 1.38 GHz (GPS) applications is made and tested. With λg representing the guided wavelength at the first band (1.38GHz), the finished prototype is compact, measuring 0.32 λg×0.27 λg. According to the experimental findings, there is strong selectivity in the first and second passbands, with roll-off rates of 190 and 168 dB/GHz, respectively. Good isolation between the two passbands is indicated by an insertion loss of less than 20 dB.
Copyright (c) 2025 Thupalli shaik Mahammed Basha, Arun Raaza, Vishakha Bhujbal, Meena Mathivanan http://creativecommons.org/licenses/by-sa/4.0
Advancements in brain tumor classification: a survey of transfer learning techniques
This survey article presents a critical review of the state-of-the-art transfer learning (TL) methodologies applied in the field of brain tumor classification, with a special emphasis on their various contributions and associated performance metrics. We will discuss various pre-processing approaches, the underlying fine-tuning strategies, whether used purely or in an end-to-end training manner, and multi-modal applications. The current study specifically highlights the application of VGG16 and residual network (ResNet) methods for feature extraction, demonstrating that leveraging highorder features in magnetic resonance imaging (MRI) images can enhance accuracy while reducing training. We further analyze fine-tuning methods in relation to their role in optimizing model layers for small, domain-specific datasets, finding them particularly effective in enhancing performance on the small brain tumor dataset. It will look into end-to-end training, which means fine-tuning models that have already been trained on large datasets to make them better. It will also present multimodal TL as a way to use both MRI and computed tomography (CT) scan data to get better classification results. Comparing different pre-trained models can provide a better understanding of the strengths and weaknesses associated with the particular brain tumor classification task. This review aims to analyze the advancements in TL for medical image analysis and explore potential avenues for future research and development in this crucial field of medical diagnostics.
Copyright (c) 2025 Smita Dinesh Bharne http://creativecommons.org/licenses/by-sa/4.0
Multilingual hate speech detection using deep learning
The rise of social media has enabled public expression but also fueled the spread of hate speech, contributing to social tensions and potential violence. Natural language processing (NLP), particularly text classification, has become essential for detecting hate speech. This study develops a hate speech detection model on Twitter using FastText with bidirectional long short-term memory (Bi-LSTM) and explores multilingual bidirectional encoder representations from transformers (M-BERT) for handling diverse languages. Data augmentation techniques-including easy data augmentation (EDA) methods, back translation, and generative adversarial networks (GANs)-are employed to enhance classification, especially for imbalanced datasets. Results show that data augmentation significantly boosts performance. The highest F1-scores are achieved by random insertion for Indonesian (F1-score: 0.889, Accuracy: 0.879), synonym replacement for English (F1-score: 0.872, Accuracy: 0.831), and random deletion for German (F1-score: 0.853, Accuracy: 0.830) with the FastText + Bi-LSTM model. The M-BERT model performs best with random deletion for Indonesian (F1-score: 0.898, Accuracy: 0.880), random swap for English (F1 score: 0.870, Accuracy: 0.866), and random deletion for German (F1-score: 0.662, Accuracy: 0.858). These findings underscore that data augmentation effectiveness varies by language and model. This research supports efforts to mitigate hate speech’s impact on social media by advancing multilingual detection capabilities.
Copyright (c) 2025 vincent vincent http://creativecommons.org/licenses/by-sa/4.0
Quality of service optimization for 4G LTE upload and download throughput
Demand for mobile data services and people’s dependence on 4G LTE networks continue to increase. However, the quality of service (QoS) of this network still requires improvement, especially regarding the effect of QoS on throughput at specific frequencies. The research gap lies in the lack of indepth analysis of the impact of QoS parameters on network performance at frequencies of 2,100 MHz and 2,300 MHz. This study evaluates the effect of QoS parameters, such as delay, jitter, and packet loss, on throughput in 4G LTE networks at both frequencies. The research methodology uses an experimental approach with throughput, delay, jitter, and packet loss measurements in various network conditions. The results showed that delay (17.2174 ms to 37.0322 ms), jitter, and packet loss significantly influence throughput, ranging from 624.5 Kbps to 1,322.4 Kbps. The 2,100 MHz frequency tends to show better performance than 2,300 MHz. This study concludes that optimizing QoS parameters, especially delay and jitter, can significantly improve 4G LTE network performance. These findings provide practical contributions for mobile operators in improving network quality and customer satisfaction and open opportunities for further research on other frequencies or newer network technologies.
Copyright (c) 2025 Afrizal - Yuhanef http://creativecommons.org/licenses/by-sa/4.0
Indonesian automated short-answer grading using transformers-based semantic similarity
Automatic short answer grading (ASAG) systems offer a promising solution for improving the efficiency of reading literacy assessments. While promising, current Indonesian artificial intelligence (AI) grading systems still have room for improvement, especially when dealing with different domains. This study explores the effectiveness of large language models, specifically bidirectional encoder representations from transformers (BERT) variants, in conjunction with traditional hand-engineered features, to improve ASAG accuracy. We conducted experiments using various BERT models, hand-engineered features, text pre-processing techniques, and dimensionality reduction. Our findings show that BERT models consistently outperform traditional methods like term frequency-inverse document frequency (TF-IDF). IndoBERTLite-Base-P2 achieved the highest quadratic weighted kappa (QWK) score among the BERT variants. Integrating handengineered features with BERT resulted in a substantial enhancement of the QWK score. Utilizing comprehensive text pre-processing is a critical factor in achieving optimal performance. In addition, dimensionality reduction should be carefully used because it potentially removes semantic information.
Copyright (c) 2025 Samuel Indra Gunawan Situmeang http://creativecommons.org/licenses/by-sa/4.0
Optimized ultra-low power and reduced delay GNR Ternary SRAM using a 7-transistor architecture
Greater need and evolution in electronics require a memory device that can go with a decreased power delay, SRAM plays an important role as a storage element in digital circuit design. Power and delay are vital problems faced by today’s RAM technology. It is necessary to lessen the power and increase the speed. There is a need to reduce power utilization and time delay. The proposed method is seen in the Electronics technical tool H-Spice technology. The technique proposed on DRG 7T- transistors SRAM consumes less power and delay. After the analysis and enhancement of the circuit, this approach gives the power delay product of the graphene nanoribbon (GNR) 7T SRAM as 80% at 0.7 V, 59% at 0.8 V, 34 % at 0.9 V, which is much less when compared to conventional SRAM power delay product.
Copyright (c) 2025 Ravi Kishore G http://creativecommons.org/licenses/by-sa/4.0
Parameter-optimized routing protocols for targeted broadcast messages in smart campus environments
The spread of handheld mobile devices integrated with multiple sensors makes it easy for these devices to interact with each other. These interactions are useful in a variety of applications such as monitoring and notification systems that can be adopted in smart campuses. The performance of these applications depends primarily on the network infrastructure and network protocols. In cases of failure, smart campus requires the provision of effective alternatives that can handle essential services. Hence, this work uses the Wi-Fi mobile ad hoc network (MANET) as an alternative backup to the traditional infrastructure. The dynamic nature of such a network relies on individuals' mobility, this leads to a lack of end-to-end connectivity. To overcome this challenge, delay-tolerant networking (DTN) has been adopted as its primary approach to routing information inside campus. Spray and wait, binary spray and wait (BSW), and probabilistic flooding protocols are deeply assessed to ensure sustained communications in the working environment. The protocols’ parameters are comprehensively investigated and optimized. Moreover, the performance metrics that are used in the evaluation are messages consumption, node responsiveness, and coverage. The findings showed that the optimal protocol and its parameters is reliant upon the specific application and resources available.
Copyright (c) 2025 Karam Mheide Al-sofy, Jalal K Jalal, Fajer F Fadhil, Basim Mahmood http://creativecommons.org/licenses/by-sa/4.0
Digital control of plant development through sensors and microcontrollers in Kosova
The plant monitoring system aims to develop an automated solution for optimizing plant growth. Using the Arduino Uno ATMEGA328P microcontroller module and various sensors, this system regulates environmental conditions to promote optimal plant development. It requires adequate software to operate effectively, enabling the microcontroller to monitor and regulate climatic conditions. The primary goal of this paper is to present a comprehensive system that continuously measures parameters such as light intensity, air humidity, and soil moisture in real time within a vegetable greenhouse or a plastic-covered plant environment. This scientific paper provides an in-depth description of the hardware components used, their electronic connections, and the implementation of program code written in C++. Based on the measured physical parameters, the plant monitoring system performs specific actions, such as watering the plants and regulating the ambient temperature. In conclusion, this system effectively supports healthy plant growth and enhances the quality and yield of plant products. The paper serves as a practical example for improving plant cultivation in the agricultural sector in the Republic of Kosova.
Copyright (c) 2025 Ragmi M Mustafa, Kujtim R Mustafa, Refik Ramadani http://creativecommons.org/licenses/by-sa/4.0
A survey on ransomware detection using AI models
Data centers and cloud environments are compromised as they are at great risk from ransomware attacks, which attack data integrity and security. Through this survey, we explore how AI, especially machine learning and deep learning (DL), is being used to improve ransomware detection capabilities. It classifies ransomware types, highlights active groups such as Akira, and evaluates new DL techniques effective at real-time data analysis and encryption handling. Feature extraction, selection methods, and essential parameters for effective detection, including accuracy, precision, recall, F1-score and receiver operating characteristic (ROC) curve, are identified. The findings point to the state of the art and the state of the art in AI based ransomware detection and underscore the need for robust, real-time models and collaborative research. The statistical and graphical analyses help researchers and practitioners understand existing trends and directions for future development of efficient ransomware detection systems to strengthen cybersecurity in data centers and cloud infrastructures.
Copyright (c) 2025 Goteti Badrinath, Arpita Gupta http://creativecommons.org/licenses/by-sa/4.0
Advanced control techniques for performance improvement of axial flux machines
The topological advancements in twin rotor axial flux induction motors (TRAxFIMs) have spurred the interest in performance optimization and control strategies for electric vehicle (EV) applications in particular. This paper investigates for the enhanced performance of multi-level inverters (MLIs) fed TRAxFIMs with different advanced control techniques. The performance evaluation is done under variable speed conditions at constant torque and vice versa. The TRAxFIMs offer unique advantages like high power density, high efficiency and most suitable for EV applications. The performance analysis of MLIs fed TRAxFIM has been carried out with proportional-integral (PI), fuzzy controllers, and artificial neural network (ANN) controllers. The PI controller provides a conventional control approach, while the fuzzy and ANN controllers serve as advanced control strategies. The integration of MLIs and advanced control techniques with TRAxFIMs aims to enhance dynamic response, stability and efficiency. The proposed control strategies are evaluated through extensive MATLAB simulations and the potential of MLIs fed TRAxFIMs is emphasized for EV applications.
Copyright (c) 2025 KALPANA ANUMALA, ramesh babu veligatla http://creativecommons.org/licenses/by-sa/4.0
Revolutionizing human activity recognition with prophet algorithm and deep learning
Various industries, such as healthcare and surveillance, depend heavily on the ability to recognize human activity. The “human activity recognition (HAR) using smartphones data set” can be found in the UCI online repository and includes accelerometer and gyroscope readings recorded during a variety of human activities. The accelerometer and gyroscope signals are also subjected to a band-pass filter to eliminate unwanted frequencies and background noise. This method effectively decreases the dimensionality of the feature space while improving the model's accuracy and efficiency. “Convolutional neural networks (CNNs)” and “long shortterm memory (LSTM)” networks are combined to create pyramidal dilated convolutional memory network (PDCMN), which is the final proposal. Results from experiments demonstrate the effectiveness and reliability of the suggested method, demonstrating its potential for precise and effective HAR in actuality schemes.
Copyright (c) 2025 Jaykumar S. Dhage, Avinash K. Gulve http://creativecommons.org/licenses/by-sa/4.0
Enhancing biodegradable waste management in Mauritius through real-time computer vision-based sorting
Mauritius faces a significant waste management challenge due to the indiscriminate mixing of biodegradable and non-biodegradable waste. This practice hinders proper recycling and composting efforts, contributing to environmental pollution and resource depletion. This research proposes a computer vision-based system for real-time classification of waste into biodegradable and non-biodegradable categories. Transfer learning approach based on deep learning models, specifically DenseNet121, MobileNet, InceptionV3, VGG16 and VGG19 were evaluated with two different classifiers, the K-nearest neighbors (KNN) and support vector machine (SVM). Our experiments demonstrate that the MobileNet paired with SVM achieves a classification accuracy of 97% for detection in realtime. Compared to other studies, our results demonstrate better performance and realtime classification capabilities through the implementation of a prototype, facilitating automatic sorting of waste.
Copyright (c) 2025 Geerish Suddul, Avitah babajee, Nundjeet Rambarun, Sandhya Armoogum http://creativecommons.org/licenses/by-sa/4.0
Real-time posture monitoring prediction for mitigating sedentary health risks using deep learning techniques
Sedentary behavior has become a pressing global public health issue. This study introduces an innovative method for monitoring and addressing posture changes during inactivity, offering real-time feedback to individuals. Unlike our prior research, which focused on post-analysis, this approach emphasizes real-time monitoring of upper body posture, including hands, shoulders, and head positioning. Image capture techniques document sedentary postures, followed by preprocessing with bandpass filters and morphological operations such as dilation, erosion, and opening to enhance image quality. Texture feature extraction is employed for comprehensive analysis, and deep neural networks (DNN) are used for precise predictions. A key innovation is a feedback system that alerts individuals through an alarm, enabling immediate posture adjustments. Implemented in MATLAB, the method achieved accuracy, sensitivity, and specificity rates of 98.2%, 90.7%, and 99.2%, respectively. Comparative analysis with established methods, including support vector machine (SVM), random forest, and K-nearest neighbors (KNN), demonstrate the superiority of our approach in accuracy and performance metrics. This real-time intervention strategy has the potential to mitigate the adverse effects of sedentary behavior, reducing risks associated with cardiovascular and musculoskeletal diseases. By providing immediate corrective feedback, the proposed system addresses a critical gap in sedentary behavior research and offers a practical solution for improving public health outcomes.
Copyright (c) 2025 Shanmugam D.B http://creativecommons.org/licenses/by-sa/4.0
The bootstrap procedure for selecting the number of principal components in PCA
The initial step in determining the number of principal components for both classification and regression involves evaluating how much each component contributes to the total variance in the data. Based on this analysis, a subset of components that explains the highest percentage of variance is typically selected. However, multiple valid combinations may exist, and the final choice is often made manually by the researcher. This study introduces a novel yet straightforward algorithm for the automatic selection of the number of principal components. By integrating ANOVA and bootstrapping with principal component analysis (PCA), the proposed method enables automatic component selection in classification tasks. The algorithm is evaluated using three publicly available datasets and applied with both decision tree and support vector machine (SVM) classifiers. Results indicate that this automated procedure not only eliminates researcher bias in selecting components but also improves classification accuracy. Unlike traditional methods, it selects a single optimal combination of principal components without manual intervention, offering a new and efficient approach to PCAbased model development.
Copyright (c) 2025 Borislava Petrova Toleva http://creativecommons.org/licenses/by-sa/4.0
Navigating predictive landscapes of cloud burst prediction approaches: insights from comparative research
Cloud burst forecasting remains an evolving field that grapples with the complexities of atmospheric phenomena and their impact on local environments. Cloud bursts in hilly regions demand robust predictive models to mitigate risks. This study addresses the challenge of imbalanced cloud burst occurrences, emphasizing the need for accurate predictions to minimize damage. It develops and evaluates a machine learning-based forecasting approach that includes several weather factors such as temperature, humidity, wind speed, and atmospheric pressure. The study also tackles the imbalance in cloud burst data. A dual-axis chart visually merges cloud burst occurrences with weather parameters, providing insights into their relationships over time. The model’s overall accuracy is 0.68, with precision and recall for cloud burst events at 0.25 and 0.07, respectively, and an F1-score of 0.11. However, when it comes to forecasting non-cloud burst occurrences, it shows a high precision of 0.72. This study evaluates machine learning models for cloud burst prediction, highlighting random forest as the top performer with an accuracy of 85.43%, effectively balancing true positives and true negatives while minimizing misclassifications. This research contributes to cloud burst prediction, offering performance insights and suggesting avenues for future exploration.
Copyright (c) 2025 Anil Hingmire http://creativecommons.org/licenses/by-sa/4.0
Leveraging IoT with LoRa and AI for predictive healthcare analytics
Progress in mobile technology, the internet, cloud computing, digital platforms, and social media has substantially facilitated interpersonal connections following the COVID-19 pandemic. As individuals increasingly prioritise health, there is an escalating desire for novel methods to assess health and well-being. This study presents an internet of things (IoT)-based system for remote monitoring utilizing a long range (LoRa), a low-cost and LoRa wireless network for the early identification of health issues in home healthcare environments. The project has three primary components: transmitter, receiver, and alarm systems. The transmission segment captures data via sensors and transmits it to the reception segment, which then uploads it to the cloud. Additionally, machine learning (ML) methods, including convolutional neural networks (CNN), artificial neural networks (ANN), Naïve Bayes (NB), and long short-term memory (LSTM), were utilized on the acquired data to forecast heart rate, blood oxygen levels, body temperature patterns. The forecasting models are trained and evaluated using data from various health parameters from five diverse persons to ascertain the architecture that exhibits optimal performance in modeling and predicting dynamics of different medical parameters. The models' accuracy was assessed using mean absolute error (MAE) and root mean square error (RMSE) measures. Although the models performed similarly, the ANN model outperformed them in all conditions.
Copyright (c) 2025 Lavanya Pillalamarri http://creativecommons.org/licenses/by-sa/4.0
Soil moisture prototype soil moisture sensor YL-69 for Gaharu (Aquilaria malaccensis) tree planting media
Soil moisture, defined as the amount of water present in the spaces between soil particles, plays a critical role in plant growth. Excessive soil moisture can lead to issues such as root rot, deviating from the ideal conditions required for root absorption. To address this, we developed a prototype tool using the YL-69 soil moisture sensor to monitor and control the soil moisture levels in Agarwood/Gaharu tree planting media. The prototype was designed to activate a water pump when soil moisture exceeded 80%, ensuring optimal humidity for plant growth. Once the moisture level dropped below 80%, the pump was deactivated to prevent overwatering. The YL-69 sensor demonstrated an accuracy of 88.76% under controlled conditions. This study highlights the potential of using low-cost sensors for automated soil moisture management in small-scale Gaharu cultivation.
Copyright (c) 2025 Rikie Kartadie http://creativecommons.org/licenses/by-sa/4.0
Pneumonia detection system using convolutional neural network with DenseNet201 architecture
The diagnosis of pneumonia remains a significant challenge for medical practitioners worldwide, particularly in regions with limited healthcare resources. Traditional interpretation of chest X-rays is time-consuming and often subjective, especially when images are of low quality. This study presents the development of a web-based system utilizing the DenseNet201 architecture to address these challenges. A series of experiments were conducted to evaluate three optimizers Adam, Adamax, and Adadelta over fifty epochs. Among them, Adamax yielded the best performance, achieving a training accuracy of 93.67% and a validation accuracy of 94.20%. When tested on new data, the system consistently delivered high performance, with accuracy, precision, recall, and F1 score all reaching 96%. These results suggest that the proposed system has the potential to significantly enhance the accuracy and efficiency of pneumonia diagnosis based on chest X-rays.
Copyright (c) 2025 Muhammad Qomaruddin http://creativecommons.org/licenses/by-sa/4.0
Quantifying the severity of cyber attack patterns using complex networks
This work quantifies the severity and likelihood of cyberattacks using complex network modelling. A dataset from common attack pattern enumerations and classifications (CAPEC) is collected and formalized as nodes and edges aiming at creating a network model. In this model, each attack pattern is represented as a node, and an edge is created between two nodes when there is a relation between them. The dataset includes 559 attack patterns and 1921 relations among them. Network metrics are used to perform the analysis on the network level and node level. Moreover, a rank of the CAPECs based on a complex network perspective is generated. This rank is compared with the CAPEC ranking system and deeply discussed based on cybersecurity perspective. The findings show interesting facts about the likelihood and severity of attacks. It is found that the network perspective should be given attention by the CAPEC ranking system. Finally, the results of this work can be of high interest to security architects.
Copyright (c) 2025 Ahmed Salih Hasan, Yasir Faraj Mohammed, Basim Mahmood http://creativecommons.org/licenses/by-sa/4.0
Efficient design of approximate carry-based sum calculating full adders for error-tolerant applications
Approximate computing is an innovative circuit design approach which can be applied in error-tolerant applications. This strategy introduces errors in computation to reduce an area and delay. The major power-consuming elements of full adder are XOR, AND, and OR operations. The sum computation in a conventional full adder is modified to produce an approximate sum which is calculated based on carry term. The major advantage of a proposed adder is the approximation error does not propagate to the next stages due to the error only in the sum term. The proposed adder was coded in verilog HDL and verified for different bit sizes. Results show that the proposed adder reduces hardware complexity with delay requirements.
Copyright (c) 2025 Shiva Kumar B, Umamaheswara Reddy Galiveeti http://creativecommons.org/licenses/by-sa/4.0