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 global platform for publishing high-quality research and intellectual contributions in the field of information and communication technology (ICT). The journal aims to disseminate scientific knowledge and technological innovation to a global audience, supporting the advancement of ICT for the benefit of society and human development. IJ-ICT covers a wide range of topics, including computer science, software engineering, data science, artificial intelligence and machine learning, networking and communication systems, cybersecurity, information systems, cloud and edge computing, Internet of Things (IoT), embedded systems, multimedia technologies, and human–computer interaction. The journal emphasizes the integration of hardware and software for data storage, retrieval, sharing, processing, analysis, visualization, and interpretation, as well as their applications in domains such as education, healthcare, industry, government, and smart environments. In line with current trends, IJ-ICT also welcomes research on emerging topics such as generative AI, explainable and responsible AI, big data analytics, blockchain, 5G/6G communication, smart systems, cybersecurity and data privacy, extended reality (AR/VR), and sustainable or green ICT. The journal publishes peer-reviewed original research articles and technical notes intended for researchers, academics, practitioners, and students interested in state-of-the-art developments 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.
Institute of Advanced Engineering and Science
Smart accommodation solution: innovative boarding house locator in Bayombong municipality
The search for affordable and conveniently located student accommodation is a common challenge, especially for students unfamiliar with their surroundings. This study presented the development and evaluation of a geographical information system (GIS)-enabled boarding house locator developed for Nueva Vizcaya State University (NVSU) students. The platform simplified the accommodation search process by providing a digital solution that integrates spatial data, real-time updates, and filtering options. The platform significantly reduced the time and cost of traditional housing searches. It helped students save 181.25 minutes per search and an average of 35 PHP in transportation costs compared to conventional methods like physical visits and word-of-mouth. Usability testing with 175 participants revealed high satisfaction, with the platform receiving an average rating of 4.83 for usability and 4.75 for performance. Key features such as interactive maps, location-based searches, and real-time updates enhanced the user experience by providing accurate, and up-to-date listings. The GIS-based platform outperformed traditional search methods in terms of efficiency and user satisfaction and offered a digital solution to common housing challenges faced by students. The results suggested the platform had strong potential for wider application at other universities. Overall, this system provides a scalable, cost-effective solution to improve student accommodation search and management.
Copyright (c) 2026 Carmelo Alejo D. Bisquera, Vilchor G. Perdido, Napoleon Anthony M. Mendoza https://creativecommons.org/licenses/by/4.0/
A comparative study and design investigation: scalable magnitude comparators across technology nodes
In recent times, the convergence of innovative design technologies such as very large-scale integration (VLSI), cadence design systems, and fieldprogrammable gate array (FPGA) has become crucial to address the growing demand for enhanced efficiency, scalability, and reduced power consumption in electronic designs. This paper introduces a novel approach to designing non-pipelined and pipelined scalable magnitude comparators (MCs), which integrates 4-bit MCs. The frontend implementation of the MCs is achieved using quartus prime, an FPGA board. The backend implementation is done using cadence design system, evaluated across the three distinct CMOS technology nodes. The literature review highlights the influence of technology scaling on area, power consumption, and propagation delay, analyzing various comparator designs and their associated trade-offs. The results provide valuable insights into the design and optimization of MCs for future applications in image processing and nano computing.
Copyright (c) 2026 Anitha Juliette Albert, Umamaheswari Ramalingam, Ashlin Leon A. S., Sinthia Panneer Selvam, Sripriya Thiagarajan, Arunkumar Kuppusamy https://creativecommons.org/licenses/by/4.0/
DFIG integration with ReLIFT converter for grid-connected systems: ANFIS MPPT control
Although dispersed generation and non-linear loads provide difficulties for contemporary power systems that depend on power electronics, renewable energy sources (RES) are essential for meeting the world’s energy demands. This paper provides a unique method for maximum power point tracking (MPPT) in doubly fed induction generators (DFIG) system using an Adaptive network based fuzzy inference system (ANFIS) inference system. The suggested ANFIS MPPT controller adaptively modifies discontinuous control gain to reduce chattering phenomena in the excitation system while preserving the resilience of the closed-loop system. Prior to using a DQ control theory controller for rotor magnitude adjustment to accomplish vector control of active and reactive power, the turbine and DFIG must be modeled. The converter maximizes output current while striving for unity power factor and allowable harmonic content.
Copyright (c) 2026 Aravindhan Karunanithy, Chidambararaj Natarajan, Sanjay Selvan Arul Manimaran Malathy, Siva Malavan Elantherayan Sharmila https://creativecommons.org/licenses/by/4.0/
Digital platforms and cloud computing for smart cities: a review
The rapid urbanization of the modern world initiated the emergence of digital cities, where advanced technologies converge to optimize urban living and address the limitations of a rapidly growing population. Central to this transformation are digital platforms and cloud computing. These interconnected technologies aid in shaping the future of urban landscapes, fostering sustainability, efficiency, and improved quality of life. Digital platforms serve as the backbone of smart cities, enabling seamless integration and management of various urban services and systems. One significant application of digital platforms in smart cities is the implementation of intelligent transportation systems (ITS). By integrating real-time traffic data, public transit information, and ride-sharing services, these platforms facilitate efficient transportation management, reduce congestion, and decrease carbon emissions. Cloud computing serves as a key enabler for managing the massive data flows generated by smart city infrastructures. The scalability and flexibility offered by cloud-based solutions allow cities to manage their resources efficiently and access computing power on demand without the need for extensive physical infrastructure. Cloud computing enhances smart city development by enabling collaborative data access and interaction among diverse stakeholders, from government agencies to private firms and residents.
Copyright (c) 2026 William Christopher Immanuel, Anitha Juliette Albert, Limsa Joshi Jerald Jobitham, Roselene Rebecca Selvaraj, Benita Sharon Ruban, Bennet Vini Robin, Andria Morais Allen https://creativecommons.org/licenses/by/4.0/
Integrating smart technologies for sustainable crop management in hydroponics
Hydroponics has become a game-changing technique in agriculture's constantly changing terrain, upending traditional soil-based farming. The smart hydroponics management system, a cutting-edge method intended to maximize plant development and resource use, is presented in this study. The approach aims to push the limits of conventional farming, drawing inspiration from sustainable horticultural concepts as well as the principles described in Howard M. Resh's book on hydroponic production. This abstract integrates cuttingedge sensor technology and automation methodologies to capture the core of the smart hydroponics management system. It presents the system as a complete answer to the problems facing modern agriculture, rather than just a technique of cultivation. By drawing comparisons with seminal works in computer vision, the unique character of the system is highlighted, demonstrating a dedication to advanced and flexible agricultural techniques.
Copyright (c) 2026 Jeyaprakash N., Jayachandran M., Poornavikash T. http://creativecommons.org/licenses/by-sa/4.0
Exploratory data analysis and forecasting of dengue outbreaks in Pangasinan using the ARIMA model
Dengue fever remains a critical public health concern in tropical countries like the Philippines, with Pangasinan frequently experiencing outbreaks due to favorable environmental conditions for mosquito breeding. Despite ongoing efforts to control the disease, the absence of a reliable forecasting tool limits the ability of health authorities to implement proactive measures. This study developed a forecasting model using the autoregressive integrated moving average (ARIMA) technique, following an initial exploratory data analysis (EDA) to identify trends and patterns in historical dengue case data from 2019 to 2024. The ARIMA model was trained and validated using historical data, capturing seasonal variations and projecting future dengue outbreaks. The evaluation metrics, including mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE), indicated that the model achieved an accuracy of approximately 78.3%, suggesting reasonable predictive capability. Forecasts for the year 2025 indicate a potential rise in dengue cases, particularly during peak seasons, aligning with observed historical trends. These predictions offer valuable insights for local health authorities, enabling them to plan targeted interventions, allocate resources efficiently, and mitigate the impact of future outbreaks. The study demonstrates the practical application of time series analysis in public health forecasting and provides a proactive tool tailored for the needs of Pangasinan.
Copyright (c) 2026 Patrick Mole, Thelma Palaoag https://creativecommons.org/licenses/by/4.0/
Analysis of congestion management using generation rescheduling with augmented Mountain Gazelle optimizer
This study presents an original blockage of the executive’s approach utilizing age rescheduling with the augmented mountain gazelle optimizer (AMGO). Enlivened by the versatility of mountain gazelles, AMGO is applied to enhance age plans for a reasonable power framework situation. The strategy successfully mitigates clogs, taking into account functional imperatives, market elements, and vulnerabilities. Recreation results show AMGO’s heartiness, seriousness, and proficiency in contrast with existing strategies. Notwithstanding its heartiness in blockage the board, the AMGO presents a state-of-the-art versatile element, enlivened by the spryness of mountain gazelles, empowering constant changes in accordance with developing power framework conditions and contrasted and genetic algorithms and PSO. The review adds to propelling streamlining methods for clogging the executives, offering a promising device for improving power framework, unwavering quality and productivity.
Copyright (c) 2026 Chidambararaj Natarajan, Aravindhan Karunanithy, S. Jothika, R. P. Linda Joice https://creativecommons.org/licenses/by/4.0/
Mapping academic outcomes to student routines using machine learning: a data-driven approach
In today’s environment, students often struggle with time management and dealing with emotions like frustration and anxiety, which may have an adverse impact on their academic achievement. This research aims to enhance time management and educational support for college students by leveraging demographic characteristics and performance in specific assignments to develop a predictive model for academic performance. The study evaluates various regression algorithms to identify the most accurate method for predicting students’ semester grade point average (SGPA) based on their activities. This predictive model aims to optimize students’ learning experiences and mitigate challenges such as frustration and anxiety. The findings highlight the potential of personalized educational assistance in improving student learning outcomes. Various machine learning algorithms, including decision trees, support vector regression (SVR), ridge regression, lasso regression, XGBoost, and gradient boosting, were implemented in Python for this study. Results show that XGBoost achieved the lowest root mean square error (RMSE) of 9.39 with a 60:40 data split ratio, outperforming other algorithms, while decision trees exhibited the highest RMSE. The findings emphasize the potential of personalized educational assistance to improve learning outcomes by helping students adjust study habits to address weaknesses and reduce anxiety. Future studies can explore integrating real-time data and additional features such as emotional wellbeing and extracurricular activities to further improve the model’s predictive capabilities.
Copyright (c) 2026 Selvakumar Venkatachalam, Pillalamarri Lavanya, Shreesh V. Deshpande, R. J. Akshaya Shree, S. V. Thejaswini https://creativecommons.org/licenses/by/4.0/
GSM based load monitoring system with ADL classification and smart meter design
This paper introduces a method for the classification of activities of daily living (ADL) by utilizing smart meter and smart switch data in a synergistic approach. Through the integration of these internet of things (IoT) devices, the paper aims to enhance the application of ADL classification. Guided by recent advancements in load monitoring and energy management systems, the methodology incorporates machine learning techniques to analyze data streams from both the smart meter and smart switch. Drawing inspiration from prepaid smart meter monitoring systems, IoT-based smart energy meters for optimizing energy usage, and energy metering chips with adaptable computing engines, our design incorporates diverse perspectives. Additionally, we consider the utilization of mobile communication for prepaid meters, remote detection of malfunctioning smart meters, and an empirical investigation into the acceptance of IoT-based smart meters. We substantiate our proposed approach through experimental results, showcasing its effectiveness in accurately classifying diverse ADL scenarios. This research contributes to the field of smart home technology by offering an advanced method for ADL classification. The integration of smart meter and smart switch data provides a comprehensive understanding of energy consumption patterns, opening avenues for improved energy management and informed decision-making within smart homes.
Copyright (c) 2026 Debani Prasad Mishra, Rudranarayan Senapati, Rohit Kumar Swain, Subhankar Dash, Raj Alpha Swain, Surender Reddy Salkuti https://creativecommons.org/licenses/by/4.0/
Fuzzy logic-based driver fatigue prediction system for safe and eco-friendly driving
The advancement of intelligent car systems in recent years has been significantly influenced by developments in information technology. Driver fatigue is a dominant problem in car accidents. The goal of advanced driving assistance is to develop an advanced driving assistance system (ADAS) a eco-friendly model which focuses on the detection of drowsy driver, to notify drivers of their fatigued condition to prevent accidents on the roads. With relation to driving, the driver mustn’t be distracted by alarms when they are not tired. The answer to this unanswered question is provided by 60- second photograph sequences that were taken when the subject’s face was visible. To reduce false positives, two alternative solutions for determining whether the driver is drowsy have been developed. To extract numerical data from photos and feed it into a fuzzy logic-based system, convolutional network is applied initially; later deep learning technique is followed. The fuzzy logic-based solution avoids the false alarm of the system.
Copyright (c) 2026 Raghavan Sheeja, Chidambaranathan Bibin, Selvaraj Vanaja, Shakeela Joy Arul Dhas, Alex Arockia Abins, Padmavathi Balasubramaniam https://creativecommons.org/licenses/by/4.0/
Plant disease sensing using image processing (with CNN)
Plant disease is a significant challenge for agriculture, leading to reduced yield, economic loss, and environmental impact. Leveraging digital photos of plant leaves, convolutional neural networks (CNNs) have emerged as promising tools for disease detection. The methodology involves several steps, including image pre-processing, segmentation, feature extraction using CNNs. Crucially, a diverse dataset comprising images of both healthy and diseased leaves under varying conditions is necessary for training accurate models. Transfer learning, particularly with pre-trained models like ImageNet, can further enhance accuracy, allowing for better performance with fewer training samples. The proposed method demonstrates impressive results, achieving over 95% accuracy, outperforming existing state-of-the-art techniques. This system could serve as a valuable tool for farmers, facilitating timely disease identification and treatment, ultimately leading to increased agricultural yields, reduced financial losses, and the adoption of more sustainable farming practices. Additionally, beyond its practical applications, the proposed system holds promise for advancing sustainable agriculture by promoting environmentally friendly farming methods and contributing to the overall resilience and productivity of agricultural systems.
Copyright (c) 2026 Haresh Rajkumar, Harry Jakin S., Sudhakar Thirumalaivasal Devanathan, Booapthy Kannan https://creativecommons.org/licenses/by/4.0/
Electrifying the roads using wireless charging solutions for next-gen electric vehicles
This paper outlines a solar charging device designed for electric vehicles (Evs), mitigating the drawbacks of conventional fuel-based transportation and environmental pollution. Because EVs are becoming more and more popular throughout the world, there are more of them on the road. Beyond environmental benefits, EVs offer cost savings by substituting expensive fuel with more economical electricity. The study introduces innovative solutions in EV charging, enabling separate charging stations, continuous motion charging, and wireless charging without external power sources. The communication and system operations are controlled by an ESP8266 controller. This advanced approach eliminates the need for intermittent charging stops, representing a solar-powered wireless charging solution for plug-in EVs in transit. This work underscores the critical importance of addressing energy and environmental sustainability.
Copyright (c) 2026 Venkatesh Kumar Chandrasekaran, Ramesh Babu Muthu, Lekha Shri S, Malathi M https://creativecommons.org/licenses/by/4.0/
Enhancing intellectual property rights management through blockchain integration
The generational improvement has significantly converted several industries, and the area of intellectual property rights (IPR) isn’t any exception. IPRs, being as important as they are, need to be securely managed in some way. Blockchain, with its decentralized and immutable nature, gives a promising answer for enhancing the management of intellectual property (IP). This paper explores the strategic integration of blockchain generation for the control of IPR. The proposed system consists of a complete system, from registration and validation to predictive evaluation and royalty distribution, all facilitated through clever contracts. The use of zero-knowledge proofs guarantees the safety and confidentiality of sensitive information. The paper discusses the advantages and future implications of implementing this type of device.
Copyright (c) 2026 Raghavan Sheeja, Sherwin Richard R., Shreenidhi Kovai Sivabalan, Srinivas Madhavan https://creativecommons.org/licenses/by/4.0/
A comparative analysis of PoS tagging tools for Hindi and Marathi
Many tools exist for performing parts of speech (PoS) data tagging in Hindi and Marathi. Still, no standard benchmark or performance evaluation data exists for these tools to help researchers choose the best according to their needs. This paper presents a performance comparison of different PoS taggers and widely available trained models for these two languages. We used different granularity data sets to compare the performance and precision of these tools with the Stanford PoS tagger. Since the tag sets used by these PoS taggers differ, we propose a mapping between different PoS tagsets to address this inherent challenge in tagger comparison. We tested our proposed PoS tag mappings on newly created Hindi and Marathi movie scripts and subtitle datasets since movie scripts are different in how they are formatted and structured. We shall be surveying and comparing five parts of speech taggers viz. IMLT Hindi rules-based PoS tagger, LTRC IIIT Hindi PoS tagger, CDAC Hindi PoS tagger, LTRC Marathi PoS tagger, CDAC Marathi PoS tagger. It would also help us evaluate how the Bureau of Indian Standards’s (BIS) tag set of Indian languages compares to the Universal Dependency (UD) PoS tag set, as no studies have been conducted before to evaluate this aspect.
Copyright (c) 2026 Pratik Narayanrao Kalamkar, Prasadu Peddi, Yogesh Kumar Sharma https://creativecommons.org/licenses/by/4.0/
A decision support system for mushroom classification using Naïve Bayesian algorithm
Mushrooms are rich in vitamins and proteins, a well-known superfood, however, cases of harmful mushroom consumption worldwide result in hallucinations, illness, or death. A significant challenge is that some poisonous mushrooms closely resemble edible varieties, making it difficult for mushroom foragers to distinguish between them. This study introduced KabuTeach, a decision support system (DSS) designed to classify mushrooms based on their morphological characteristics using the Naïve Bayes (NB) algorithm. The classification model was applied to a real-world dataset of 8,124 instances from Kaggle, containing 23 attributes. Evaluation metrics, including accuracy, recall, precision, specificity, and F1-score, were used to assess the classifier’s performance. Results indicated that the NB classification algorithm integrated into KabuTeach achieved a high accuracy level of 89.13%, using a 70:30 data split and 5-fold cross-validation approaches. The 0.98 AUC (area under the curve) value further concluded that the model was excellent in classifying between edible and poisonous mushrooms. These findings showed that KabuTeach is a reliable classification tool that aids mushroom foragers in differentiating mushrooms and promoting safer consumption practices. This innovation in agricultural technology could potentially reduce health risks by minimizing accidental ingestion of toxic mushrooms, ultimately contributing to public health safety.
Copyright (c) 2026 Vilchor G. Perdido, Thelma D. Palaoag https://creativecommons.org/licenses/by/4.0/
DeepRetina: a multimodal framework for early diabetic retinopathy detection and progression prediction
Diabetic retinopathy (DR) remains one of the top causes of vision loss globally, and early detection and accurate progression prediction are critical in its management. This paper introduces DeepRetina, a deep learning framework that integrates state-of-the-art multimodal retinal imaging techniques with patient-specific clinical data for the improved diagnosis and prognosis of DR. DeepRetina harnesses cutting-edge convolutional neural networks (CNNs) and attention mechanisms to jointly analyze optical coherence tomography (OCT) scans and fundus photographs. The architecture further includes a temporal module that investigates the longitudinal changes in the retina. DeepRetina fuses these heterogeneous data sources with patient clinical information in pursuit of early detection of DR and provides personalized predictions for the progression of the disease. We use a specially designed CNN architecture to process high-resolution retinal images, coupled with a self-attention mechanism that focuses on the most relevant features. This recurrent neural network (RNN) module empowers it to integrate time-series data that captures the evolution of retinal abnormalities. Another neural network branch considering patientspecific clinical data, such as demographic information, medical history, and laboratory test results, was taken into account and concatenated with the imaging features for a holistic analysis. DeepRetina achieved 95% sensitivity, 98% specificity for early DR detection, and a 0.92 area under the curve (AUC) for 5-year progression prediction, outperforming existing methods.
Copyright (c) 2026 Sunder Ramasamy, Brindha Mohanraj, Sridhar Pushpanathan, Thenmozhi Elumalai, Prabu Kaliyaperumal, Rajakumar Perumal https://creativecommons.org/licenses/by/4.0/
Video-based physical violence detection model for efficient public space surveillance
This study aims to develop an effective real-time model for detecting violence in public spaces, focusing on achieving a balance between accuracy and computational efficiency. We evaluate various model architectures, with the main comparison between the ConvLSTM2D and Conv3D models commonly used in video analysis to capture spatial and temporal features. The ConvLSTM2D model, combined with preprocessing layers such as change detection and motion blur, showed optimal performance, achieving 86% accuracy after Bayesian optimization. With a low parameter count of 25,137, this model enables fast inference in just 0.010 seconds, making it suitable for real-time applications that require efficient computation. In contrast, the Conv3D model, which is also combined with preprocessing layers such as change detection and motion blur and has more than nine million parameters, shows a lower accuracy of 77.5% as well as a slower inference time of 0.025 seconds, making it unsuitable for real-time applications. The results of this study show that the ConvLSTM2D model is promising for real-time violence detection systems in public spaces, where a fast and accurate response is essential to prevent further acts of violence.
Copyright (c) 2026 Erick Erick, Benfano Soewito https://creativecommons.org/licenses/by/4.0/
Practice-based teaching using an AI platform to strengthen faculty competency
This research aimed to i) analyze faculty members’ knowledge, understanding, and skills in using AI for practice-based teaching enhancement, ii) evaluate factors affecting faculty readiness in integrating AI into teaching processes, and iii) design and develop an AI platform to enhance faculty competency in practice-based teaching. The questionnaire, validated by five experts, was administered to 200 respondents divided into two groups: 100 faculty members from public universities and 100 from private universities. Comparative analysis revealed that public university faculty and private university faculty statistically significant differences in challenges and concerns at the 05 level, with public university faculty expressing higher concerns. Significant differences were found in AI experience and skills, attitudes toward AI use, and challenges and concerns. However, no significant differences were observed in three other areas: AI knowledge and understanding, AI readiness, and belief in AI’s effectiveness for practice-based learning enhancement. Data from both groups were utilized in designing and developing the AI platform to enhance practicebased teaching competency in higher education. Expert evaluation of the platform’s suitability showed high levels of demand for the AI platform and high appropriateness of the technology used in platform development.
Copyright (c) 2026 Angsana Phonsuk, Phakharach Plirdpring https://creativecommons.org/licenses/by/4.0/
NLP-based fraudulent biomedical news identification using LSTM-SGD deep learning algorithm
Concern over bio medical fake news is rising, particularly as false information about illnesses, medical procedures, and public health regulations becomes more prevalent. It is essential to recognize such false information, and deep learning (DL) algorithms can offer a potent remedy, especially when paired with sophisticated natural language processing (NLP) methods. This technique improves the model's capacity to ignore frequently used but uninformative terms and concentrate on important terminology. The model's capacity to concentrate on the most pertinent phrases for fake news identification is enhanced by the use of chi-squared, a statistical test that ascertains the dependency between various variables and aids in the removal of unnecessary data. By reducing less significant characteristics to zero, the Lasso approach, a kind of regression, is used for feature selection, guaranteeing that the model only utilizes the most predictive features for classification. A crucial step in getting the data ready for DL models is feature extraction, which turns unprocessed text into numerical data. After the structured data has been analyzed, algorithms like as stochastic gradient descent (SGD), long short-term memory (LSTM) may determine whether or not an article is accurate. The authenticity and dependability of medical information provided across platforms may be ensured by effectively identifying biomedical fake news by fusing DL with sophisticated NLP techniques.
Copyright (c) 2026 Siva Dhievaraj, Agusthiyar Ramu https://creativecommons.org/licenses/by/4.0/
An integration clustering and multi-target classification approach to explore employability and career linearity
This study analyzes job placement waiting times and job linearity among female science, technology, engineering, and mathematics (STEM) graduates using clustering and multi-target classification (MTC) models. The K-means least trimmed square (LTS) algorithm, known for its robustness against outliers, was employed for clustering. With k = 2 and a trimming percentage of 30%, the model achieved a silhouette score of 77%, resulting in two distinct clusters: ideal and non-ideal. To enhance the dataset for classification, synthetic data was generated using the adaptive synthetic (ADASYN)-gaussian method. Principal component analysis (PCA) was used for visualization purposes, along with overlapping histograms, to illustrate that the synthetic data distribution closely resembled the original. For classification, a random forest (RF) model was used to predict both jobs waiting time and job linearity. Hyperparameter tuning produced an optimal model with a classification accuracy of 92%. Cross-validation (CV) confirmed the model’s robustness, with F1-micro and F1-macro scores of 94% and 93%, respectively. Results show that although women in STEM are underrepresented, 73% of the female alumni analyzed belonged to the short job waiting group. Furthermore, a strong negative correlation between GPA and job waiting time suggests that higher-GPA graduates tend to secure employment more quickly.
Copyright (c) 2026 Nadzla Andrita Intan Ghayatrie, Devi Fitrianah https://creativecommons.org/licenses/by/4.0/
Leveraging distillation token and weaker teacher model to improve DeiT transfer learning capability
Recently, distilling knowledge from convolutional neural networks (CNN) has positively impacted the data-efficient image transformer (DeiT) model. Due to the distillation token, this method is capable of boosting DeiT performance and helping DeiT to learn faster. Unfortunately, a distillation procedure with that token has not yet been implemented in the DeiT for transfer learning to the downstream dataset. This study proposes implementing a distillation procedure based on a distillation token for transfer learning. It boosts DeiT performance on downstream datasets. For example, our proposed method improves the DeiT B 16 model performance by 1.75% on the OxfordIIIT-Pets dataset. Furthermore, we present using a weaker model as a teacher of the DeiT. It could reduce the transfer learning process of the teacher model without reducing the DeiT performance too much. For example, DeiT B 16 model performance decreased by only 0.42% on Oxford 102 Flowers with EfficientNet V2S compared to RegNet Y 16GF. In contrast, in several cases, the DeiT B 16 model performance could improve with a weaker teacher model. For example, DeiT B 16 model performance improved by 1.06% on the OxfordIIIT-Pets dataset with EfficientNet V2S compared to RegNet Y 16GF as a teacher model.
Copyright (c) 2026 Christopher Gavra Reswara, Gede Putra Kusuma https://creativecommons.org/licenses/by/4.0/
Classification and regression tree model for diabetes prediction
Diabetes mellitus is characterized by excessive blood glucose that occurs when the pancreas malfunctions while producing insulin. High blood glucose levels can cause chronic damage to organs, particularly the eyes and kidneys. Diabetes prediction models traditionally use a variety of machine learning (ML) algorithms by combining data from the glucose levels, patient health parameters, and other biomarkers. Prior research on diabetes prediction using various algorithms, such as support vector machine (SVM) and decision tree (DT) models, demonstrates an accuracy rate of approximately 70%, which is relatively modest. Therefore, in this study, a classification and regression tree (CART) multiclassifier model has been proposed to improve the accuracy of diabetes prediction, which is based on three classes: non-diabetic, pre-diabetic, and diabetic. The study involved data preprocessing steps, hyperparameter tuning, and evaluation of performance metrics. The model achieved 97% accuracy while utilizing the value of 5 for the number of leaves per node, the value of 10 for the maximum number of splits, and deviance as the split criterion, which also resulted in a precision of 98%, recall of 97%, and F1-score of 98%, showing that the proposed multiclassifier model can accurately predict diabetes. In conclusion, the proposed CART model with the best hyperparameter setting can enable the highest accuracy in predicting diabetes classes.
Copyright (c) 2026 Farah Najidah Noorizan, Nur Anida Jumadi, Li Mun Ng https://creativecommons.org/licenses/by/4.0/
Fetal electrocardiogram extraction and signal quality assessment using statistical method
Abdominal electrocardiogram (aECG) can be used to monitor fetal heart rate (fHR), providing critical insights into fetal health during pregnancy. However, separating the mixed signals of fetal ECG (fECG) and maternal ECG (mECG) within the aECG remains a critical challenge. This paper investigates the integration of statistical metrics, including signal-to-noise ratio (SNR), skewness, kurtosis, standard deviation, and variance to assess fECG signal quality during extraction using three adaptive filtering metods ((Least mean square (LMS), normalized LMS (NLMS), and recursive least square (RLS)) and independent component analysis (ICA). The findings reveal that RLS achieves the best performance among the three AF methods, with the highest SNR of 5.6 dB at the step size, µ of 0.9. For ICA with a bandpass Chebyshev filter (low-cut frequency = 1 Hz, high-cut frequency = 50 Hz) produces an SNR of 0.86 dB. Additionally, both RLS and ICA yield similar fHR values of 133 bpm with a PE measurement of 0.9%. In conclusion, integrating statistical metrics with ICA and RLS effectively extracts fECG with good signal quality. Future research could explore other ECG datasets and incorporate machine learning to further improve fECG extraction and signal quality assessment.
Copyright (c) 2026 Li Mun Ng, Nur Anida Jumadi, Farah Najidah Noorizan https://creativecommons.org/licenses/by/4.0/
Design and development of machine learning-based web application for oil palm yield prediction
The prediction of crop yields is influenced by various factors such as weather conditions, agronomic practices, and management strategies. Accurately predicting oil palm yield is crucial for sustainable production, as it plays a significant role in global food security. Challenges such as climate change and nutrient deficiencies have adversely affected yields, highlighting the necessity for a specialized web application tailored to the oil palm industry. This study presents a machine-learning-based web application that utilizes a deep learning model to estimate oil palm yields by integrating key parameters, including weather, agronomy, and satellite data. The application features a user-friendly interface and a dashboard for comparing predicted and actual yields, enhancing user engagement and facilitating collaboration among stakeholders. By deploying this tool on the cloud, plantation managers can make informed decisions early in the yield prediction process, ultimately improving plantation management and profitability. This web application is designed to provide valuable insights to stakeholders, contributing to effective decision-making in the oil palm sector.
Copyright (c) 2026 Yuhao Ang, Helmi Zulhaidi Mohd Shafri, Yang Ping Lee, Shahrul Azman Bakar, Hwee San Lim, Rosni Abdullah, Yusri Yusup, Mohammed Mustafa Al-Habshi https://creativecommons.org/licenses/by/4.0/
Exploring diverse perspectives: enhancing black box testing through machine learning techniques
Black box testing plays a crucial role in software development, ensuring system reliability and functionality. However, its effectiveness is often hindered by the sheer volume and complexity of big data, making it difficult to prioritize critical test cases efficiently. Traditional testing methods struggle with scalability, leading to excessive resource consumption and prolonged testing cycles. This study presents an AI-driven test case prioritization (TCP) approach, integrating decision trees and genetic algorithms (GA) to optimize selection, eliminate redundancy, and enhance computational efficiency. Experimental results demonstrate a 96% accuracy rate and a 90% success rate in identifying relevant test cases, significantly improving testing efficiency. These findings contribute to advancing automated software testing methodologies, offering a scalable and efficient solution for handling large-scale, data-intensive testing environments.
Copyright (c) 2026 Heba Nafez Jalal, Aysh Alhroob, Ameen Shaheen, Wael Alzyadat https://creativecommons.org/licenses/by/4.0/
Adaptive intrusion detection system with DBSCAN to enhance banking cybersecurity
The accelerating pace of digital transformation in the banking sector has highlighted the critical need for comprehensive cybersecurity strategies capable of countering evolving cyber threats. This study introduces an innovative intrusion detection framework tailored for banking environments, leveraging the CICIDS2017 and CSECICIDS2018 datasets for evaluation and validation. The proposed framework integrates data preprocessing, feature reduction, and advanced attack detection methods to enhance detection accuracy. A basic autoencoder is utilized for dimensionality reduction, streamlining input data while preserving essential attributes. The density-based spatial clustering of applications with noise (DBSCAN) algorithm is then applied for attack detection, enabling the detection of intricate attack patterns and their classification into specific attack groups. The proposed adaptive intrusion detection system (IDS) framework demonstrates outstanding performance, achieving precision, recall, F1-score, and accuracy rates exceeding 98%. Comparative evaluations against conventional techniques, such as support vector machines (SVM), long short-term memory (LSTM), and K-means, highlight its superiority in terms accuracy and computational efficiency. This research address key challenges, including high-dimensional datasets, class imbalance, and dynamic threat landscapes, offering a scalable and efficient solution to enhance the security of banking operations and enable proactive threat mitigation in the sector.
Copyright (c) 2026 Sathiyaseelan Periyasamy, Anubhav Kumar, Karupusamy Muthulakshmi, Thenmozhi Elumalai, Prabu Kaliyaperumal, Rajakumar Perumal https://creativecommons.org/licenses/by/4.0/
Enhanced smart farming security with class-aware intrusion detection in fog environment
The adoption of the internet of things (IoT) in smart farming has enabled real-time data collection and analysis, leading to significant improvements in productivity and quality. However, incorporating diverse sensors across large-scale IoT systems creates notable security challenges, particularly in dynamic environments like Fog-to-Things architectures. Threat actors may exploit these weaknesses to disrupt communication systems and undermine their integrity. Tackling these issues necessitates an intrusion detection system (IDS) that achieves a balance between accuracy, resource optimization, compatibility, and affordability. This study introduces an innovative deep learning-driven IDS tailored for fog-assisted smart farming environments. The proposed system utilizes a class-aware autoencoder for detecting anomalies and performing initial binary classification, with a SoftMax layer subsequently employed for multi-class attack categorization. The model effectively identifies various threats, such as distributed denial of service (DDoS), ransomware, and password attacks, while enhancing security performance in environments with limited resources. By utilizing the Fog-to-Things architecture, the proposed IDS guarantees reliable and low-latency performance under extreme environmental conditions. Experimental results on the TON_IoT dataset reveal excellent performance, surpassing 98% accuracy in both binary and multi-class classification tasks. The proposed model outperforms conventional models (convolutional neural network (CNN), recurrent neural network (RNN), deep neural network (DNN), and gated recurrent unit (GRU)), highlighting its superior accuracy and effectiveness in securing smart farming networks.
Copyright (c) 2026 Selvaraj Palanisamy, Radhakrishnan Rajamani, Prabakaran Pramasivam, Mani Sumithra, Prabu Kaliyaperumal, Rajakumar Perumal https://creativecommons.org/licenses/by/4.0/
A meta-learning framework for leaf disease detection using vision transformer-based feature extraction, PCA, and tuned SVM classifier
A hybrid meta-learning approach is proposed for effective leaf disease detection by integrating vision transformer (ViT), principal component analysis (PCA), and support vector classifier (SVC). The objective of this study is to accurately classify plant leaf conditions into three categories: healthy, angular leaf spot, and bean rust. The dataset consists of 1,167 labeled leaf images, divided into training (974 images), validation (133 images), and testing (60 images) sets. A pretrained ViT model is employed for feature extraction, producing a feature vector of shape (974, 64) for the training data. To mitigate the curse of dimensionality and improve classification performance, PCA is applied, reducing the features to 41 principal components while retaining 98% of the original variance and accuracy 97.85%. For the classification task, an SVC is used and fine-tuned using the Optuna hyperparameter optimization framework to enhance accuracy and generalization. A distributed deep learning strategy is incorporated to accelerate training and scale computation, while the tf.data API is utilized to construct an efficient and scalable data input pipeline. The hybrid model demonstrates strong classification performance on the test set, indicating that combining deep transformer-based feature extraction with dimensionality reduction and optimized classical machine learning classifiers is effective for plant disease identification. This approach offers a robust and computationally efficient solution for precision agriculture, enabling automated and accurate leaf disease diagnosis and supporting early intervention strategies in crop management.
Copyright (c) 2026 Jayalakshmi Venkataraman, Bhargavi Devi Potluri, Vignesh Arumugam, Shoba Balasubramaniyam, Banumathi Subramani https://creativecommons.org/licenses/by/4.0/
Neurophysiological impact of Vedic chanting on human brainwaves: a spectral electroencephalogram analysis using Gabor transform
Electroencephalogram (EEG) analysis explores brainwave changes resulting from Vedic chanting (VC) in this experimental study. In this study participants received Vedic recitations from the Rig Veda (RV), Yajur Veda (YV), Sama Veda (SV), and Atharva Veda (AV) which were evaluated through alpha wave (8-12 Hz) measurement to evaluate relaxation response effects known to cause cognitive relaxation and mindfulness. The research captured EEG signals from twenty participants who belonged to four age categories between twenty and fifty years using a fourteen-channel EEG recording system. The signals underwent wavelet-based denoising procedures and Gabor transform (GT) enabled their spectral analysis. Scientists calculated the relaxation factor (RF) for understanding Vedic chant effects on human beings. Vedic Sama provided maximum relaxation effects leading to a 25% RF enhancement whereas YV produced a 20% increase and RV generated 15% enhancement and AV yielded 10% relaxation. The participants between 30 and 45 years old experienced the largest relaxation effects yet their left-brain hemisphere enhanced alpha waves stronger than their right brain region. The statistical methods supported that these results showed meaningful variations. Neural relaxation results from VC practice according to research evidence which shows SV provides the most powerful relaxation effects.
Copyright (c) 2026 Veera Raghava Swamy Nalluri, V. J. K. Kishor Sonti https://creativecommons.org/licenses/by/4.0/
Development of machine learning techniques for automatic modulation classification and performance analysis under AWGN and fading channels
Automatic modulation classification (AMC) is essential in modern wireless communication for optimizing spectrum usage and adaptive signal processing. This study explores the use of various machine learning (ML) methods for AMC, focusing on their performance in additive white Gaussian noise (AWGN) and fading channels. This study evaluates of ML classifiers such as support vector machines (SVM), K-nearest neighbors (KNN), decision trees (DT), and ensemble methods with a dataset spanning signalto-noise ratios (SNRs) from -30 dB to +30 dB. Higher-order statistical features including moments and cumulants are used to train the classifiers for AMC. Performance is measured in terms of classification accuracy and computational efficiency across different SNR levels. The findings show that linear SVM, fine KNN, and fine trees consistently achieved high classification accuracy, even at low SNRs. From the analysis, it is observed that linear SVM and fine KNN achieve over 96% accuracy at 0 dB SNR. These classifiers demonstrate significant robustness, maintaining performance in challenging noise conditions. The research highlights the promise of ML techniques in improving AMC, providing a detailed comparison of classifiers and their strengths.
Copyright (c) 2026 P. G. Varna Kumar Reddy, M. Meena https://creativecommons.org/licenses/by/4.0/
A survey on fronthaul signaling of user-centric cell-free massive MIMO networks
The mandate for high data rates in mobile communication is increasing and will continue to do so in the future. Although the latest network technologies can meet this demand, they result in more-dense networks. Networks like ultra-dense networks and massive multiple-input multiple-output provide very high data rates, but they cannot meet the future demand. The main issue with existing networks is inter-cell interference and variations in quality of service esp. at the cell edges, leading to research on new network architectures that offer intelligent coordination and collaboration capabilities are being researched, like user-centric cell-free (UC-CF) massive-multipleinput-multiple-output (mMIMO). This network combines the best of ultradense networks and mMIMO and eliminates cell edge problems. It is served by access points that cooperate and coordinate with each other. This paper reviews the challenges and opportunities in physical layer parameterfronthaul signaling for UC-CF mMIMO. We discuss the basics of the network, the importance of fronthaul signaling, and propose various approaches in the literature to address challenges and identify research gaps and provide future directions. Our aims to provide a comprehensive overview of the current state of fronthaul signaling and highlight the key issues that need to be addressed to realize its full potential.
Copyright (c) 2026 Syed Tariq Ali, Anamika Singh https://creativecommons.org/licenses/by/4.0/
Dynamic monitoring for enhancing QoS and security in distributed systems
Distributed systems are integral to modern digital infrastructure, supporting communication and data exchange across various sectors. Ensuring security while maintaining quality of service (QoS) in such environments presents a significant challenge. This study introduces a dynamic network monitoring system (DNMS) that incorporates adaptive monitoring mechanisms and dynamic security metrics to safeguard distributed systems. The proposed architecture utilizes an event analyzer (EA) to evaluate and classify system events based on criticality, enabling secure transmission decisions and efficient threat detection. Experimental evaluations demonstrate the DNMS achieves a low processing overhead of 12%, supports a high data handling capacity of 5,000 requests per second, and maintains a latency of just 150 milliseconds. Additionally, it ensures strong compliance with regulatory standards-achieving 95% alignment with GDPR and 97% with ISO 27001- and high threat detection accuracy, with 98% for phishing, 94% for malware, and 96% for insider threats. These results confirm the framework’s effectiveness in enhancing adaptive security, offering scalable and regulation-compliant solutions for complex distributed environments.
Copyright (c) 2026 Sudhakar Periyasamy, Vijayalakshmi Alagarsamy, Palani Latha, Karuppiah Tamilarasi, Thenmozhi Elumalai, Prabu Kaliyaperumal https://creativecommons.org/licenses/by/4.0/
A high linearity low noise amplifier with modified differential inductor for bluetooth profiles
In today’s rapidly evolving communication landscape, electronic devices rely heavily on high-performance components to ensure seamless connectivity. A low-noise amplifier (LNA) is a critical front-end element in any receiver chain, where its performance significantly influences the overall system efficiency. As integrated circuits continue to shrink with advancements in technology, challenges such as linearity degradation have become increasingly prominent. This work presents a modified derivative (MD) narrowband common source low-noise amplifier (CSLNA) designed using 0.13 µm CMOS technology, offering improved linearity and frequency characteristics. The proposed design adopts a hybrid architecture, combining a folded cascode gain stage with a common-gate configuration. An optimized modified differential inductor is employed at the input for effective impedance matching and reduced noise figure (NF). The implemented LNA achieves a gain of 25.81 dB, an input return loss of –24.86 dB, and maintains a low NF of 0.3 dB at an operating frequency of 2.4 GHz. Furthermore, the linearity metrics-third-order input intercept point (IIP3) and 1 dB compression pointare significantly improved to –16.70 dBm and –21.89 dBm, respectively. These results highlight the LNA's suitability for Bluetooth and other shortrange wireless communication applications.
Copyright (c) 2026 Ghattamaneni Usharani, Sourirajan Varadarajan https://creativecommons.org/licenses/by/4.0/
Towards efficient fog computing in smart cities: balancing energy consumption and delay
In this work, we propose fog-based energy-delay optimization (F-EDO) approach and benchmark its performance against the cloud-based energydelay optimization (C-EDO) method, focusing on energy consumption and delay. Unlike previous studies that optimize energy or delay separately, FEDO minimizes both metrics simultaneously, achieving up to 52.2% energy savings with near-zero delay. Additionally, increasing the number of users also leads to energy savings. This is due to the optimized placement of fog servers at the access layer which reduces network energy consumption compared to C-EDO. F-EDO also significantly reduces delay, with negligible delay compared to C-EDO due to fog servers are placed closer to the users which minimized the transmission distances. Besides, the results also show that the energy saving in F-EDO compared to the C-EDO increased as the processing capacity of the processing server increased while maintaining its minimal delay. Overall, F-EDO proves to be a more energyefficient and lower-delay solution for IoT networks, offering a better alternative to cloud-based offloading.
Copyright (c) 2026 Ida Syafiza Md Isa, Nur latif Azyze Mohd Shaari Azyze, Haslinah Mohd Nasir, Vigneswara Rao Gannapathy, Ashwini Jayadevan Naidu https://creativecommons.org/licenses/by/4.0/
High gain multi-layered microstrip patch antenna for x- band applications
This research investigates the development of a multi-stacked microstrip antenna featuring two patch elements positioned in a layered configuration. The antenna design incorporates three substrates with different dielectric constants, separated by an air gap, to evaluate their impact on improving bandwidth and gain. The primary objective of this research is to enhance the efficiency of a microstrip patch antenna by utilising a multilayer substrate structure. Simulation results indicate that stacking substrates with varying dielectric properties significantly enhances antenna performance. The bandwidth increases considerably, from 1.38 GHz to 2.37 GHz, while the peak gain improves from 6.6 dBi to 7.9 dBi. These advancements highlight the antenna's effectiveness in operating within the X-band frequency range, making it suitable for wireless and satellite communication systems. The design and its performance were analysed using high-frequency structure simulator (HFSS) simulation software, which validated its practical feasibility. This innovative configuration addresses the bandwidth limitations typically associated with conventional microstrip antennas, ensuring improved operational efficiency for modern communication technologies. The findings highlight the benefits of utilising a multi-stacked structure to achieve superior antenna performance, particularly in advanced communication applications.
Copyright (c) 2026 Jada Nageswara Rao, Ragipindi Ramana Reddy https://creativecommons.org/licenses/by/4.0/
Applying fuzzy Tsukamoto method to improve production efficiency in manufacturing industry
Manufacturing can increase competitiveness and reduce costs by improving production efficiency. The study’s goal is to develop a production prediction system using the fuzzy Tsukamoto technique. This method is used to model the uncertainty that occurs during the production process. Thus, production planning based on demand and inventory availability can be more accurate. After being tested on production data from a manufacturing company, the fuzzy Tsukamoto method showed the ability to make more efficient decisions than conventional methods. This system not only significantly reduces production costs but also improves overall operational efficiency, including resource management, waste reduction, and cycle time optimization. The adoption of this method provides added value to companies in facing increasing market competition while keeping production costs low without compromising quality.
Copyright (c) 2026 Moch Taufik, Andi Riansyah, Muhammad Qomaruddin, Muhammad Sholahuddin https://creativecommons.org/licenses/by/4.0/
Renewable energy optimization for sustainable power generation
To improve sustainability in power generation, this study presents a thorough data-driven method for maximizing renewable energy sources. It employs measures like capacity utilization factor (CUF) and efficiency to evaluate the performance of solar and wind energy using historical weather and energy-generating data. The study offers practical suggestions for improving renewable energy systems, such as weather-energy correlation analysis and machine learning-based forecasting models. In addition, a comparative analysis is carried out to ascertain which energy source is better, and useful real-world data is provided, including a summary of all India’s total renewable energy generation (excluding large hydro) for June 2023 and a performance comparison year over year. A useful, data-driven approach for enhancing renewable energy is provided by this work, which advances the topic of sustainable energy.
Copyright (c) 2026 Debani Prasad Mishra, Sarita Samal, Rohit Kumar, Arun Kumar Sahoo, Surender Reddy Salkuti https://creativecommons.org/licenses/by/4.0/
Self-adaptive firefly algorithm-based capacitor banks and distributed generation allocation in hybrid networks
Power system deregulation has made significant changes to the power grid through various technologies, privatization of entities, and improved efficiency and reliability. This work mainly focuses on different combinations of distributed generation (DG) and capacitor banks (CBs) integration to cater to multiple technical, economic, environmental, and reliable concerns. A new optimal planning framework is proposed for optimally allocating the DG units and CBs to achieve multiple objectives. In this work, an augmented objective function is formulated by considering active power losses, voltage deviation, and voltage stability index objectives. This objective function is solved considering various equality and inequality constraints. This work proposes a novel approach for allocation of DGs and CBs in the radial distribution systems (RDSs) using an evolutionary-based self-adaptive firefly algorithm (SAFA). The effectiveness of the developed planning approach is demonstrated on IEEE 33 bus RDS in MATLAB software. The obtained results indicate that proposed planning approach resulted in reduced power losses, voltage deviations, and improved voltage stability.
Copyright (c) 2026 Seong-Cheol Kim, Sravanthi Pagidipala, Surender Reddy Salkuti https://creativecommons.org/licenses/by/4.0/
Optimizing solar energy forecasting and site adjustment with machine learning techniques
Estimation of solar radiation is a key task in optimizing the operation of power systems incorporating high levels of photovoltaic (PV) generation. This paper discusses the application of machine learning techniques, namely extreme gradient boosting (XGBT) and random forest (RF), to improve accuracy in the forecasting of solar radiation while adapting for different sites. Utilizing datasets such as meteorological and solar radiation data, the suggested models demonstrate the enhancement of forecasting accuracy by 39% from traditionally applied statistical practices. Along with this, this study also encompasses how endogenous and exogenous factors could be involved in better predictions of solar energy availability. From our findings, XGBT, as well as other machine learning techniques, do enjoy superior performance levels when it comes to the forecasting of solar radiation, which in turn promotes efficient management and potential adaptation of solar energy systems. This study demonstrates how this last generation of algorithms could be applied to noticeably improve the efficiency of solar power forecasting and thereby contribute to more sustainable and reliable energy systems as a byproduct of that.
Copyright (c) 2026 Debani Prasad Mishra, Jayanta Kumar Sahu, Soubhagya Ranjan Nayak, Anurag Panda, Priyanshu Paramjit Dash, Surender Reddy Salkuti https://creativecommons.org/licenses/by/4.0/
Privacy-preserving fitness recommendation system using modified seagull monarch butterfly optimized deep learning model
This paper presents a novel modified seagull monarch butterfly optimization (MSMBO) algorithm, with a multi-objective focus on privacy and personalization in the fitness recommender system using a refined three-tier deep learning structure. The method is divided into three phases. In the first phase, fitness data from wearable devices undergoes preprocessing to eliminate noise and standardize features. The second phase incorporates improved elliptic curve cryptography (IECC) alongside the MSMBO to encrypt user data securely, ensuring privacy in cloud storage. This phase also enhances neural network performance by optimizing weights and hyperparameters through feature selection, effectively reducing data complexity while boosting accuracy. In the third phase, ConvCaps extracts spatial data features, while Bi-LSTM identifies temporal dependencies. The proposed system balances multiple objectives like novelty, accuracy, and precision, while safeguarding user data through robust encryption. With the experimental findings, our suggested method performs better than current existing models, especially in heart rate prediction and fitness pattern identification. The overall outcome makes the system ideal for privacyconscious, personalized fitness recommendations. The model’s shows significant improvement in mean squared error (MSE), normalized mean squared error (NMSE), and mean absolute percentage error (MAPE), thus verifying its effectiveness in secure, real-time fitness tracking.
Copyright (c) 2026 Esmita Gupta, Shilpa Shinde http://creativecommons.org/licenses/by-sa/4.0
Detection model for pulmonary tuberculosis and performance evaluation on histogram enhanced augmented X-rays
Tuberculosis is one of the biggest threats that has been remaining a contagious disease since its discovery, posing a significant risk to millions of lives. Many people yield to TB because of incomplete treatments or the lack of preventive measures. An effective pulmonary TB diagnostic system has remained a big challenge. As it is a contagious disease, it mainly affects the lungs and other vital organs of the human body. We find DL as a subset of ML that runs an incurable disease diagnostic system with multi-neural architectures. In recent ages, a neural model can detect more accurately and quickly resulting in classified labels as normal and positive TB cases. It helps medical practitioners to identify bacterial infections in the early stage. It has also enabled proper diagnosis and treatment for pulmonary tuberculosis. Through this paper, an enhanced detection model to classify TB and non-TB cases using clinical X-ray images has been proposed. The augmented histogram equalized X-rays were applied to top state-of-the-art classifiers. The evaluation matrics have been compared with and without histogram equalization and a comparative study is done to find the best CNN classifiers. The Resnet 50 and ResNet169 have shown the higest accuracy on preprocessed chest X-rays with 99.6% and 99.48% respectively.
Copyright (c) 2026 Abdul Karim Siddiqui, Vijay Kumar Garg https://creativecommons.org/licenses/by/4.0/
Lightweight deep learning approach for retinal OCT image classification: A CNN with hybrid pooling and optimized learning
Optical coherence tomography (OCT) is a non-invasive technique through which a retina specialist can see the structure behind the eye. This technol ogy offers a key role to identify various abnormalities in the retina: Drusen, diabetic macular edema (DME) and choroidal neovascularization (CNV). However, manual analysis of OCT scans can be time-consuming and prone to variability among clinicians. To address this challenge, we present a lightweight and explainable deep learning-based approach for automatic classification of retinal OCT images. The primary goal of this research is a model that delivers high diagnostic accuracy. A computer-aided suggestive method can help retinal doctors automatically classify the anomalies with more confidence and precision. In this paper, we proposed a novel approach based on deep learning: a six-layer convolutional neural network (CNN) integrated with hybrid pooling for effective feature extraction. Data augmentation and exponential learning rate is implemented to handle data imbalance between classes and for stabilized learning consecutively. Our proposed approach achieved 98.75% of accuracy while testing on the dataset. To further enhance the interpretability of the model, we also integrate explainable AI (XAI) using class activation mapping (CAM) to visualize the critical regions in the retina that contribute to the classification decisions.
Copyright (c) 2026 Parth R. Dave, Nikunj H. Domadiya https://creativecommons.org/licenses/by/4.0/
Reputation-enhanced two-way hybrid algorithm for detecting attacks in WSN
Wireless sensor networks (WSNs) are susceptible to a variety of attacks, such as data tampering attacks, blackhole attacks, and grayhole attacks, that can affect the reliability of communication. We proposed a reputationenhanced two-way hybrid algorithm (RCHA) that uses cryptographic hash functions and reputation-based trust management to detect and de-escalate attacks accurately. The RCHA algorithm implements two hash functions RACE integrity primitives’ evaluation message digest (RIPEMD) and secure hash algorithm (SHA-3), to initiate the integrity check for the entire packet sent across the network. Every node in the WSN tracks a reputation score for each neighbor the node is connected to, and this score is dynamically updated based on the behavior of each neighbor. If a neighboring node’s reputation drops below a threshold, the node is sent a maliciousness designation. At that time, the node will broadcast an alert message to its neighboring nodes and begin to reroute its data through one of its trusted neighbors to ensure the reliability of the communication. The simulation results reported that the RCHA algorithm improved the accuracy of the attack detection rate and the number of packets delivered compared to traditional attack detection methods. The RCHA algorithm was able to maintain low computational and energy overhead for the WSN, making it an attractive option for a resource-constrained application in a WSN. Given the trends towards more collaborative networks, the reputation mechanism in the RCHA algorithm improves the overall reliability and capabilities of the WSN, regardless of adversaries.
Copyright (c) 2026 Divya Bharathi Selvaraj, Veni Sundaram https://creativecommons.org/licenses/by/4.0/
Securing Defi: a comprehensive review of ML approaches for detecting smart contract vulnerabilities and threats
The rapid evolution of decentralized finance (DeFi) has brought revolutionary innovations to global financial systems; however, it has also revealed some major security vulnerabilities, especially of smart contracts. Traditional auditing methods and static analysis tools are prone to fail in identifying sophisticated threats, including reentrancy attacks, front-running, oracle manipulation, and honeypots. This review discusses the growing role of machine learning (ML) in enhancing the security of DeFi systems. It provides a comprehensive overview of modern ML-based methods related to the detection of smart contract vulnerabilities, transaction-level fraud detection, and oracle trust assessment. The paper also provides publicly available datasets, necessary toolkits, and architectural designs used for developing and testing these models. Additionally, it provides future directions like federated learning, explainable AI, real-time mempool inspection, and cross-chain intelligence sharing. While it is full of promise, the application of ML in DeFi security is plagued by issues like data scarcity, interoperability, and explainability. This paper concludes by highlighting the need for standardised benchmarks, shared data initiatives, and the integration of ML into development pipelines to deliver secure, scalable, and reliable DeFi ecosystems.
Copyright (c) 2026 Dhivyalakshmi Venkatraman, Manikandan Kuppusamy https://creativecommons.org/licenses/by/4.0/