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 publishes peer-reviewed original research articles, review articles and technical notes for researchers, academics, practitioners, developers, and students interested in state-of-the-art developments and practical applications in ICT, computing, electronics, intelligent systems, and related technologies. IJ-ICT is indexed in Scopus, with coverage starting from 2023. The journal is currently classified in Scopus under three principal subject areas: General Computer Science (ASJC 1700), Control and Systems Engineering (ASJC 2207), and Electrical and Electronic Engineering (ASJC 2208). IJ-ICT is also indexed in SCImago Journal & Country Rank (SCImagoJR), with an SJR of 0.153. IJ-ICT is published by the Intelektual Pustaka Media Utama (IPMU). The IJ-ICT is an open access international journal providing a platform for high-quality research and scholarly contributions at the intersection of informatics, computing, electrical and electronic engineering, control and systems engineering, and information and communication technology (ICT). The journal aims to disseminate scientific knowledge, technological innovation, and interdisciplinary research to a global audience, supporting the advancement of ICT and intelligent technological systems for the benefit of society and human development. To provide a clear and sustainable disciplinary identity, the scope of IJ-ICT is organized around three principal subject areas aligned with its current Scopus classification: 1700 – General Computer Science Computing & Informatics Core — This area covers research in computing, informatics, and intelligent information technologies, including artificial intelligence and machine learning, data science and analytics, software engineering, information systems, computer networks, cybersecurity, computer vision, image and signal processing, natural language processing, multimedia computing, cloud and edge computing, distributed and high-performance computing, computational intelligence, human–computer interaction, knowledge engineering, and digital transformation. 2207 – Control and Systems Engineering Intelligent Systems & Automation — This area covers research in the analysis, design, optimization, and application of intelligent and automated systems, including control systems, intelligent and adaptive control, optimization, automation, robotics, autonomous systems, system modeling and simulation, system identification, cyber-physical systems, networked control systems, AI-based and data-driven control, fault diagnosis, intelligent monitoring, industrial automation, and smart systems. 2208 – Electrical and Electronic Engineering Technology & Hardware Foundation — This area covers research involving the technological and hardware foundations of ICT, including electronic circuits and systems, embedded systems, sensors and instrumentation, communication technologies, Internet of Things (IoT) devices, VLSI and reconfigurable systems, microcontrollers and microprocessors, hardware–software co-design, communication electronics, wireless and mobile technologies, energy-efficient electronic systems, edge computing hardware, intelligent devices, and wearable technologies. Communication Technology as a Cross-Cutting Theme Communication Technology serves as a cross-cutting and integrative theme connecting the three principal subject areas of IJ-ICT. The journal welcomes interdisciplinary research that combines computing, electronics, communication, control, and intelligent systems to develop connected, intelligent, secure, autonomous, and sustainable ICT solutions. This includes research in Internet of Things, edge and cloud technologies, wireless and next-generation communications, cyber-physical systems, intelligent transportation, smart healthcare, smart agriculture, smart industry, smart cities, digital twins, secure connected systems, and other emerging ICT applications. Through this Three-ASJC-Aligned Editorial Architecture, IJ-ICT welcomes theoretical, methodological, experimental, system-oriented, and application-driven research that contributes to one or more of its principal subject areas. Interdisciplinary contributions integrating two or more areas are particularly encouraged when they demonstrate clear scientific novelty, technological innovation, rigorous evaluation, and relevance to the advancement of informatics and communication technology.
Institute of Advanced Engineering and Science
Machine learning models for predicting daily profitability in Mauritanian digital banking operations: a case study
This article presents a case study on predicting daily profitability in Mauritanian digital banking operations using machine learning models. We utilized a dataset containing detailed information on daily operations to assess predictive models and predict profitability. The study assesses various machine learning models like logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron classifier (MLPClassifier), in order to identify the most precise model for predicting daily profitability. A systematic approach guides the analysis of banking transactions by performing detailed preprocessing operations which include type conversions, feature selection and missing value management. The dataset receives systematic partitioning into three parts for training, validation and testing to establish model reliability. LR had a recall rate of 99% and an F1-score of 99%, SVM had a recall rate of 53% and an F1-score of 54%, KNN had a recall rate of 96% and an F1-score of 95%, and MLPClassifier had a recall rate of 94% and an F1-score of 97%. The findings show that the LR model performed better than the other models in terms of both recall and F1-score. Future research will investigate both deep learning approaches and hybrid models to enhance prediction accuracy.
Copyright (c) 2026 Mohamed Lemine Sidibba, Mohamedou Cheikh Tourad, Ahmad Outfarouin, Nema Sidi Mohamed Mawloud, Mohamedade Farouk Nanne https://creativecommons.org/licenses/by/4.0/
Cross-modal attention fusion using vision transformers for robust student attentiveness estimation
Automated student attentiveness estimation is a fundamental component of intelligent e-learning systems and adaptive classroom analytics. Traditional convolutional and recurrent architectures often struggle to model long-range temporal dependencies and complex inter-modal relationships inherent in engagement behavior. To address these limitations, this paper proposes a cross-modal attention fusion framework built upon a vision transformer (ViT) backbone for robust student attentiveness estimation. The proposed architecture leverages patch-based visual encoding through a ViT to capture global spatial dependencies, while behavioral cues such as gaze direction, head pose, and blink dynamics are embedded into a shared latent representation space. A cross-modal multi-head attention mechanism is introduced to dynamically learn interactions between visual and behavioral modalities, replacing static weighted fusion strategies. Temporal dynamics are modeled using a Transformer encoder, enabling effective long-range sequence modeling without recurrent dependencies. Experimental evaluation on a benchmark attentiveness dataset demonstrates superior performance compared to CNN–LSTM-based models, achieving improved accuracy, F1 score, and robustness under challenging lighting and occlusion conditions. Ablation studies validate the contribution of cross-modal attention and transformer-based temporal modeling. The proposed framework maintains real-time feasibility while significantly enhancing discriminative capability.
Copyright (c) 2026 Rajasekaran Mariswamy, Praveen Sundar https://creativecommons.org/licenses/by/4.0/
Lightweight parallel feedback network based on CRL with policy transfer and enhancement for image super-resolution
Image super-resolution (SR) is essential in applications such as surveillance, medical imaging, and remote sensing, but existing deep learning (DL) models often require high computational resources and struggle to recover fine details in lightweight architectures. Although feedback and attention based methods have shown improvements, they still lack an effective combination of efficient feature refinement, edge enhancement, and low parameter complexity. To address this gap, we propose a lightweight parallel feedback network (LPFN) that combines three key components: a feedback block for repeated feature refinement, a dispersion-aware attention residual block (DARB) for highlighting important spatial and channel details, and EdgeNet for edge sharpening for sharper boundaries. These components are supported by curriculum reinforcement learning (CRL), an adaptive training strategy that gradually improves the model’s learning behavior. Instead of relying on a fixed loss function, LPFN uses a dynamically learned global feedback loss to refine reconstruction quality at each stage. Experiments on DIV2K and Flickr2K show that LPFN achieves higher PSNR and SSIMscores while keeping the model lightweight and efficient. This study emphasizes an effective lightweight feedback framework, an enhanced attention and edge-refinement mechanism, and an adaptive learning strategy that improves both accuracy and stability under different degradation conditions.
Copyright (c) 2026 S V R Manimala, T Kavitha https://creativecommons.org/licenses/by/4.0/
Conversational AI in museums: a systematic literature review using the people–process–technology framework
The adoption of conversational artificial intelligence (AI) in museums has opened a new opportunity to create a richer visiting experience to tell stories for the preservation of culture. This paper contributes a systematic literature review (SLR) of 33 peer-reviewed papers covering the period from 2020 to mid-2025, by applying the people–process–technology (PPT) model to examine the social technological aspects of AI implementation. This combination is novel in a museum context, as previous research has largely treated these separately. Findings indicate a pronounced shift from text based chatbots (21% or 7 of 33 papers) to more immersive and interactive platforms (30% or 10 of 33 papers), reflecting the transition from the pandemic (2020 – 2023) to the post-pandemic period (2024 – 2025). Besides the evolution of these technologies, the technology component highlights the importance of data governance, digital preparedness, and value alignment at and across different levels. The people component includes the relevance of hedonic and utilitarian values. Meanwhile, the process component emphasizes both strategic and technical aspects of AI design and implementation, such as knowledge structuring, media selection, and narrative representation. In line with this agenda, this study addresses the trends in literature and provides a path for sustainable appropriation by museums.
Copyright (c) 2026 Diana Utomo, Siti Elda Hiererra https://creativecommons.org/licenses/by/4.0/
Improving the performance of leaf disease detection and classification using beetle swarm optimization technique
The timely identification and diagnosis of leaf diseases is crucial for crop productivity and health. This study proposes a robust approach to this issue by combining beetle swarm optimization (BSO) with other ML models. Four different datasets were used to train our model: apple leaf, grape leaf, plant village leaf, and tomato leaf for disease detection. The process begins with preparing the leaf images, involving contrast enhancement and noise reduction. Through color-based segmentation, we can distinguish healthy regions from diseased ones, aiding in the classification process. Our research demonstrates the effectiveness of the BSO-convolutional neural networks (CNN) method in recognizing and categorizing plant diseases with high accuracy rates. Leveraging the power of BSO to adjust the model’s parameters and incorporating color-based segmentation enhances the model’s robustness and accuracy. The results of this study highlight the potential of automated disease management systems for agriculture, providing agronomists and farmers with the necessary tools to address and monitor emerging threats to their crops effectively.
Copyright (c) 2026 Penugonda Seetha Rama Krishna, S. Nagarajan https://creativecommons.org/licenses/by/4.0/
QSUMeMarket: a decision support system framework for smes to process customer orders
With the progression of big data analytics (BDA), point of sale (PoS) could be amalgamated with an inventory management (IM) system. The problem of SMEs is to generate a financial report only using simple calculations based on income and expenses. To develop a web-based computerized system to solve the problems encountered and ease the marketing office’s operations. The used of a descriptive research design and software development life cycle (SDLC) methodology. The ISO/EIC 25010:2011 software quality standard was chosen as one of the most comprehensive software-quality evaluation models. The software performs exceptionally well in several key areas, including performance efficiency (4.00), usability (3.94), portability (3.89), compatibility (3.83), security (3.80), maintainability (3.70), and reliability (3.67). These ratings highlight its strengths in speed, security, and overall user experience. However, with the lowest score being 3.44, there is still potential for improvement to better align the software with user needs and business objectives. While it meets industry standards and is suitable for practical use, ongoing enhancements are essential to sustain and elevate its quality.
Copyright (c) 2026 Winston G. Domingo, Jennifer A. Gamay, Virdi C. Gonzales, Selino S. Malunao https://creativecommons.org/licenses/by/4.0/
Prototype of real-time Mexican sign language classifier
Mexican sign language (MSL) is the language used by the deaf community in Mexico. Like Spanish, it has its own distinct grammar, syntax, and vocabulary. However, instead of relying on sounds, MSL conveys meaning through gestures, facial expressions, and body movement. This research proposes the creation of an image dataset of the MSL alphabet for real-time sign detection. A neural network model was developed to recognize these signs, achieving an accuracy of approximately 60%. Although this result is modest, the study establishes a foundation for future work that could facilitate communication for MSL users or lead to the development of educational applications for language learning.
Copyright (c) 2026 Alan Ramírez-Noriega, Yobani Martínez-Ramírez, Samantha Jiménez, Marcos Murillo-Corrales https://creativecommons.org/licenses/by/4.0/
Digital forensics for cultural preservation: multi-device image classification of the historic Surabaya City Hall
Cultural heritage preservation increasingly relies on digital forensics to ensure authenticity and consistency in heritage documentation. This study presents a digital forensic framework based on machine learning for classifying multi-device images of the historic Surabaya City Hall. The dataset was collected from nine smartphone devices and preprocessed through standardization, 360° rotational augmentation, and three filtering methods: gaussian, median, and laplacian. Three supervised algorithms (support vector machine (SVM), K-nearest neighbor (KNN), and logistic regression (LR)) were evaluated using accuracy, macro average, and weighted average of precision, recall, and F1-score. The results indicate that image preprocessing substantially affects model performance, with the gaussian-filtered KNN achieving the best result, reaching 92% accuracy, and balanced macro and weighted F1-scores of 0.92-0.93. Confusion-matrix analysis revealed minor misclassifications among iPhone models with similar sensor characteristics, while other devices were accurately identified. The findings confirm that gaussian filtering improves feature consistency and that KNN’s distance-based classification exhibits robustness across heterogeneous image sources. However, the study is limited to a single heritage object and a restricted number of devices, which may affect generalizability. The proposed framework provides a reproducible and interpretable method that supports digital authenticity verification and aligns with UNESCO’s vision for open, transparent cultural heritage preservation.
Copyright (c) 2026 Ulfa Meilinda Putri, Imam Yuadi https://creativecommons.org/licenses/by/4.0/
Development of highway vehicle detection using background subtraction and Haar cascade methods
Vehicle recognition is a critical component of traffic analysis and the progress of advanced transportation systems, underscoring the importance of automated, real-time methods that reduce the need for manual observation. While the field has seen notable innovations in deep learning-centric detection technologies, many of these approaches require considerable computational strength and are not well-suited for real-time application in resource-constrained environments. In response to this limitation, the present study introduces a streamlined vehicle detection framework that combines background subtraction for motion-oriented foreground extraction with a Haar cascade classifier for object identification in traffic video sequences. The system is evaluated using real-world highway traffic recordings under different illumination conditions, including both day and night scenarios. The experiment's findings show that the system achieves an overall accuracy of 82.08%, with a precision of 85.33%, a recall of 66.67%, and an F1-score of 74.86%. The system also demonstrates consistent performance across different lighting conditions. These findings indicate a trade-off between detection accuracy and computational efficiency, where the proposed approach prioritizes practical deployment feasibility. Overall, the results suggest that classical computer vision techniques remain viable alternatives for real-time traffic monitoring in environments with limited computational resources.
Copyright (c) 2026 Ni Gusti Ayu Dasriani, Anthony Anggrawan, Khasnur Hidjah, Christofer Satria, I Nyoman Yoga Sumadewa https://creativecommons.org/licenses/by/4.0/
AI-powered cardiovascular risk prediction using deep learning images in IoT-blockchain systems
The increasing prevalence of cardiovascular diseases (CVDs) necessitates the development of intelligent, secure, and scalable diagnostic systems capable of accurate and early disease prediction. The importance of this research is to develop a robust and secure system for predicting CVD risk from echocardiogram imaging integrated within an internet of things (IoT) framework and enhanced by blockchain technology. Due to non-invasive feature identification problems and dimensionality, prediction accuracy is degraded due to higher false positives, which leads to lower precision and recall rates. To resolve this problem, implement AI-powered CVD risk prediction based on smart-featured deep learning in Echocardiogram images for an IoT-blockchain environment. The first phase contains data analysis echocardio-dataset vision transformation technique is functional to find the risk level of the disease. The adaptive gaussian filter is applied for normalization process and design a spread-spectral canny edge morphological segmentation and SURF-scaled invariant feature selection for dimensionality scaling. Then, visual geometry network (VGNet) convolutional neural network (CNN) is applied for disease classification. In second phase, the advanced blockchain technology will provide a decentralized and immutable record of patient data, thereby ensuring data integrity and security. The proposed system produces higher performance by analysing the sensitivity specificity as well by ensuring the disease detection level. The blockchain provides higher security to safe in repository for image data for carrying sensitive data with a platform for sharing and validating predictive insights among healthcare providers, researchers, and patients, thus fostering collaborative healthcare efforts.
Copyright (c) 2026 Deepika Prabhakar, Agusthiyar Ramu https://creativecommons.org/licenses/by/4.0/
Probabilistic inventory modeling for chlorine gas using minitab and python: a comparative study of demand distributions
The availability of chlorine gas (Cl2) is a critical component in the drinking water disinfection process at the regional drinking water company (PDAM), as it plays a vital role in ensuring microbiological safety. Disruptions in the chlorine gas supply may lead to interruptions in water distribution and pose significant public health risks. This study investigates the application of a probabilistic (Q, r) inventory model for managing chlorine gas stock, incorporating several probability distributions that satisfy the underlying model assumptions. The resulting optimal inventory policies derived from each distribution are then compared. Chlorine gas demand forecasting is also performed using the seasonal autoregressive integrated moving average (SARIMA) model. The objective of this research is to generate an optimal inventory policy and accurate demand forecasts, with the entire implementation carried out in Python software. The results show that the best model obtainis the SARIMA (0,1,0)(0,1,1)12 model, with a MAPE value of 5.48%, and that the chlorine gas demand data follow normal, gamma, exponential, and erlang probability distributions. The comparison results show that the optimal policy of the gamma probabilistic model provides the best results, as well as being better than Normal and exponential policies in previous studies.
Copyright (c) 2026 Oki Dwipurwani, Fitri Maya Puspita, Siti Suzlin Supadi, Evi Yuliza https://creativecommons.org/licenses/by/4.0/
Car parking lot availability detection using the faster R-CNN method
Parking demand continues to rise as private vehicle use increases, making timely information about available spaces essential for efficient parking management. Many existing monitoring approaches still rely on fixed slot sensors or visual detectors that report accuracy without examining how confidence settings affect the final availability decision. This work investigates Faster region-based convolutional network (Faster R-CNN) with a ResNet-50 backbone for image-based parking availability detection using a public parking-lot dataset annotated in Pascal visual object classes (VOC) format. The experiment evaluates several confidence thresholds to determine how each setting changes the balance among accuracy, precision, recall, and F1-score. The most balanced setting was obtained at a threshold of 0.5, where the model achieved 95% accuracy and 97.3% for precision, recall, and F1-score. These results show that threshold configuration is an important factor in reducing missed detections and false alarms, although validation using real campus CCTV data and direct comparison with lightweight detectors remain necessary before practical deployment.
Copyright (c) 2026 Andi Riansyah, Alif Hakim Al Faruq, Badieah https://creativecommons.org/licenses/by/4.0/
Design a Gaussian mixture-based clustering model for enhancing accuracy and robustness in smart homes
Nowadays, smart homes have become quite complicated systems. Thus, an appropriate technique for controlling all those devices is necessary, especially considering that certain nodes are likely to be broken. In that connection, we have proposed two algorithms related to Gaussian mixture models (GMM): GMM equal and GMM unequal. They were compared with graph neural network (GNN) equal, GNN unequal, and the LucasWheel algorithms. The peculiarity of the GMM equal algorithm consists in the fact that all clusters should have similar sizes and shapes, which is quite useful for routing and balancing purposes, while the clusters in the GMM unequal algorithm can have various sizes and shapes depending on the data distribution. All five models were analyzed using 843 nodes, where failure rates ranged from zero to fifty percent. The surprising outcome of this analysis is that GMM equal performed better than the other four models in every aspect. Efficiency was steady and steadily increased in accordance with the rising failure rate. The Wiener index gradually fell from its initial value to nearly zero, suggesting a dense connection among the nodes and an evenly spread-out network. Furthermore, GMM equal attained the highest modularity among the five models at every failure level. In combination, these results indicate that GMM equal is the most balanced topology, with the best balance between reliability, efficient communication, and scalability when applied to the internet of things and smart homes.
Copyright (c) 2026 Kanaka Raju Rajana, Shanmuk Srinivas Amiripalli https://creativecommons.org/licenses/by/4.0/
Classroom behavior mining in adolescents: a cognitive and data-driven approach using BEHAVE_Apriori
Adolescence is a critical developmental stage that leads to essential changes in social, emotional, and cognitive domains that affect conduct in the classroom. Students' perceptions, processing, and reactions to their learning environment are better-understood thanks to cognitive psychology. However, contemporary data mining techniques frequently ignore the environmental, emotional, and cognitive elements influencing teenage behavior in learning environments. This research presents a comprehensive approach to analyzing teenage college students' classroom behavior by integrating cognitive psychology with data-driven methods to identify key behavioral traits shaped by both external and internal factors. A brand-new algorithm called the behavioral evaluation via hybrid attributes and valuable extraction using the Apriori (BEHAVE_Apriori) approach is presented. Also, a variety of feature selection (FS) strategies, including information gain (IG), chi-squared (CS), and tree-based approaches, are used for FS. Then, using the Apriori algorithm, association rules are found that relate behavior patterns to elements like family history, academic involvement, and peer influence. The IG-based FS combined with the Apriori algorithm delivered the best performance, generating 95 rules in 0.0241 seconds, outperforming CS (154 rules, 0.0629s) and tree-based FS (251 rules, 0.1394s), while the unfiltered dataset produced 514 rules in 0.2853 seconds.
Copyright (c) 2026 Suresh Govindarajalu, Muthukumaran Subramaniyan, Kamatchy Balakrishnan, Kalaichelvi Nagarajan, Nandhini Krishnamoorthy https://creativecommons.org/licenses/by/4.0/
Prognosis of vector borne dengue disease outbreak in urban areas using multivariate analysis
Vector borne disease like dengue continues to pose a significant climate-sensitive public health challenge in tropical regions such as Brazil, Peru, and India. This study examines the feasibility of predicting dengue outbreaks using weekly multivariate time-series data from San Juan (SJ), Puerto Rico and Iquitos (IQ), Peru. Dengue incidence was analyzed alongside meteorological, environmental, and vegetation-based variables to capture key climatic influences. Several machine learning and deep learning approaches were evaluated, including LightGBM. Model performance was assessed using root mean square error (RMSE) and mean absolute error (MAE). The results show that LightGBM achieved the low est RMSE/MAE, indicating strong short-term predictive accuracy and excellent interpretability. Feature importance analysis and principal component analysis (PCA) identified precipitation, dew point temperature, and humidity as the most influential predictors of dengue incidence. The study demonstrates that advanced machine learning models can serve as reliable early warning systems for vector-borne diseases. While this research focuses on dengue, the methodology is adaptable to other vector-bone datasets and diseases, offering a flexible tool for public health authorities to predict and mitigate outbreaks in diverse urban contexts.
Copyright (c) 2026 Pratik S. Machchar, Purvi N. Ramanuj, Rajan Patel, Jitendra Bhatia, Kuntesh Jani https://creativecommons.org/licenses/by/4.0/
Integrating F-filter and K-Means SMOTE to enhance LGWUM-based turnover prediction
High employee turnover poses a significant challenge for organizations. While uplift modeling offers a prescriptive analytics approach by estimating differential treatment effects to optimize retention programs, its performance is often hindered by irrelevant features and imbalanced class distributions. To address these issues, this study proposes an employee turnover model utilizing lai’s generalized weighted uplift method (LGWUM), enhanced with F-Filter feature selection and K-Means SMOTE for a refined feature space and balanced treatment representation. Evaluated across three HR datasets with four engineered uplift classes (CN, CR, TN, TR), the integrated framework significantly improves uplift performance, yielding increased Qini coefficients of 0.0755 and 0.0870 on Datasets 2 and 3, respectively. Furthermore, top-decile probability distribution analysis confirms a clearer separation between positive and negative responders, with XGBoost demonstrating the most robust and reliable uplift discrimination across the models.
Copyright (c) 2026 Risyda Miftahur Rahmah, Sigit Priyanta https://creativecommons.org/licenses/by/4.0/
Early detection of vascular streak dieback (VSD) disease in cocoa plants using deep learning
Vascular streak dieback (VSD), caused by Ceratobasidium theobromae, poses a significant threat to cocoa (Theobroma cacao) production, leading to substantial yield losses and plant mortality. Early detection is critical to mitigate disease spread and reduce economic impact. While convolutional neural network (CNN) architectures like VGG-16 and ResNet-50 excel in leaf disease detection, no prior studies address stem-based VSD symptomatology where the disease originates in vascular tissues. This study presents the first CNN specifically developed for cocoa stem VSD detection, achieving 99.16% test accuracy with a lightweight architecture that outperforms VGG16/ResNet50 in both accuracy and mobile inference speed. A novel dataset of 215 cocoa stem images was curated and augmented for robustness. Additional evaluation metrics, including precision, recall, specificity, and F1-score, further confirm the reliability of the model. The model was successfully converted into TensorFlow Lite (TFLite) format, enabling deployment on mobile devices for real-time disease detection. This study highlights the potential of integrating deep learning into mobile and drone-based agricultural systems to support precision farming and early intervention strategies.
Copyright (c) 2026 Dewi Marini Umi Atmaja, Arif Rahman Hakim, Fadhil Rozi Hendrawan, Komang Diah Devi Pramesty, Naufal Fadhilah Fitrah, Faiz Rochmatullah Widhaputra https://creativecommons.org/licenses/by/4.0/
Exploring multi-answer visual question answering with object detection: a systematic review
Visual question answering (VQA) is a challenging research area that enables machines to answer natural language questions based on visual content by jointly understanding images and text. Conventional VQA systems typically produce a single answer for each image–question pair. However, many real world visual questions are ambiguous or complex, allowing multiple valid answers to exist. This systematic literature review (SLR) focuses on multi answer VQA systems and the use of object detection, following the PRISMA 2020 guidelines. We analyzed 58 peer-reviewed journal articles retrieved from the Scopus database published between 2020 and 2025. Ten of these studies clearly stated that generating multiple answers was their main goal. Forty-eight others indirectly supported answer variability by using object-based or multi-instance reasoning. Through this review, we examine the current methodologies for supporting multi-answer generation, including model architecture, datasets, and evaluation metrics. Most multi answer generation approaches utilize attention mechanisms, graph neural networks, and transformer-based models. Additionally, we propose a taxonomy of multi-answer VQA organized along four dimensions. Limitations are identified in datasets and evaluation metrics (i.e., answer ambiguity/subjectivity). Future research should focus on improving model interpretability and designing an evaluation framework that incorporates subjective and context-sensitive responses.
Copyright (c) 2026 Nidaul Hasanati, Taufik Djatna, Imas Sukaesih Sitanggang, Arif Imam Suroso https://creativecommons.org/licenses/by/4.0/
Insight invest: sentiment-aware stock prediction using LSTM and conversational interface
The volatile nature of financial markets requires sophisticated tools that integrate advanced analytics with accessible interfaces to facilitate informed investment decisions. This research introduces Insight Invest, an intelligent investment assistant that combines sentiment analysis with time-series forecasting to deliver comprehensive stock market insights. The platform introduces the emotional quotient (EQ), a novel metric derived from the sentiment analysis of financial news, to quantify market sentiment and align it with historical stock price data. Leveraging long short-term memory (LSTM) models, the system provides precise predictions of future stock trends. Automated data collection and processing are achieved through a Flask-based backend, while an OpenAI-powered chatbot delivers intuitive interpretations of predictions and EQ values. The user-centric design, implemented using Next.js, ensures a seamless and responsive experience. By integrating state-of-the-art machine learning techniques with intuitive interfaces, Insight Invest bridges the gap between complex predictive analytics and practical usability, offering a robust framework for informed investment strategies.
Copyright (c) 2026 Ankit Pande, Aakhyan Jeyush, Abhishek K. Lakhote, Saket A. Rathi, Manoj B. Chandak https://creativecommons.org/licenses/by/4.0/
Wavelet-based spectrum sensing with improved thresholding for enhanced detection in cognitive radio networks
Cognitive radio (CR) technology is an adaptive, intelligent radio and network technology that can automatically detect available channels in a wireless spectrum. Spectrum sensing is the most important component in CR due to its ability to sense and recognize parameters related to the radio channel characteristics. However, there are some spectrums that are not used known as spectrum holes. It is challenging to accurately identify these spectrum holes, especially when employing traditional energy detection techniques, which suffer from incorrect threshold selection at low signal-to noise ratio (SNR) levels. This work suggests a wavelet-based spectrum sensing technique in conjunction with an enhanced thresholding method to improve detection accuracy and decrease noise to overcome this constraint. MATLAB simulations are used for evaluating three threshold functions: hard, soft, and improved. The results indicate that the improved threshold achieves superior denoising performance and a higher detection probability compared to the traditional energy detection method. In this study, the energy detection technique was also implemented for comparison with the wavelet-based approach. The findings reveal that wavelet-based sensing consistently provides a higher detection probability (𝑃𝐷𝐸𝑇), demonstrating its effectiveness and reliability for cognitive radio (CR) application.
Copyright (c) 2026 Nur Hanis Abdul Rani, Mas Haslinda Mohamad, Nurusolihah Zamri, Nor Khairiah Ibrahim https://creativecommons.org/licenses/by/4.0/
Design of a pyramidal slotted wide-band patch antenna for directive wireless communications over Ka-Band
A wide Band for directive wireless communications over Ka-Band was presented. The proposed antenna printed on Rogers RT/Duroid 5880 substrate and achieved a moderate gain of 6.80 dB at resonating freqenc of 28 GHz. The pyramidal antenna achieved a wide operating bandwidth of 11.55 GHz. The proposed antenna achieved high size reduction percentage with a compact overall dimension of (4.8×6.9×1.5) mm3. The antenna achieved the wide operating bandwidth based on two factors; the first is selection of low loss dielectric material to ensure etter antennas performance. The other is etching pyramidal with 2×2 squared – shapes slots array etched from patch’s surface to reduce stored reactive energy which decreases overall ualit factor of and improves the -10 dB bandwidth obviously.
Copyright (c) 2026 Safa Nassr Nafea https://creativecommons.org/licenses/by/4.0/
Advanced materials for crosstalk and power optimization in TSV-enabled 3D ICs
The continued scaling of semiconductor devices has exposed the limitations of traditional two-dimensional (2D) integrated circuit architectures. To address performance bottlenecks and interconnect constraints, the industry is increasingly adopting three-dimensional (3D) integration technologies. through-silicon vias (TSVs) are a fundamental enabler of this advancement, facilitating vertical signal transmission between stacked silicon layers. Despite their benefits, TSVs face critical challenges related to crosstalk, power dissipation, and signal delay issues that are especially pronounced in dense via arrays. This research explores the use of multi-walled carbon nanotube (MWCNT) based TSVs insulated with different dielectric liners, including silicon dioxide (SiO₂), PPC, polyimide, and benzocyclobutene (BCB). HSPICE simulations are used to evaluate crosstalk noise, power dissipation, power delay product (PDP), and energy delay product (EDP) across varying TSV pitches. Among the materials studied, BCB demonstrates the most promising results. Specifically, MWCNT TSVs with BCB at a 10,000 μm pitch achieve up to 58% reduction in functional crosstalk, 75% in dynamic crosstalk, 78% in power dissipation, and a 52% improvement in PDP compared to single-walled CNT (SWCNT) based TSVs. These findings confirm the suitability of combining MWCNT cores with low-k BCB liners for enhancing performance, energy efficiency, and signal reliability in advanced 3D integrated circuits.
Copyright (c) 2026 Tappeta Chinna Sanjeeva Rayudu, Merrin Prasanna Nagadasari https://creativecommons.org/licenses/by/4.0/
Enhanced anomaly detection in IoT networks via feature fusion and learning-based echo state networks
The fast development of internet of things (IoT) networks has led to an increased probability of cyberattacks. Intrusion detection systems (IDS) are needed for identifying unauthorised access and malicious activities in such dynamic environments. However, existing machine learning (ML) models failed to handle the complexity and variability of modern cyber threats. In this work, a hybrid deep learning (DL)-based anomaly detection model is presented for IoT cybersecurity. The model combines three types of features: (i) supervised feature extraction using linear discriminant analysis (LDA) to extract the most discriminative features, (ii) unsupervised feature learning through autoencoders to capture latent representations of the input data, and (iii) statistical features such as mean, variance, skewness, and kurtosis to learn input characteristics. The fused feature matrix is fed into a learning based echo state network (LBESN) for final detection. The parameters of the LBESN model are tuned using black eagle optimizer (BEO). Experimental results on standard intrusion detection datasets such as UNSW-NB15, KDD99, and InSDN show that the proposed model achieves superior performance in terms of accuracy, precision, recall, and F1-score compared to conventional DL techniques.
Copyright (c) 2026 P. Palpandi, B. Sakthivel, M. Ponnrajakumari, M. Indirani, S Govindaraju, S. Deivasigamani https://creativecommons.org/licenses/by/4.0/
A pilot review of the Google cluster workload trace 2019, methodology and its alternatives: analysis of workload in large scale data centres
Cloud data centres require shared services that are highly available, elastic capacity, managed operations, and robust recovery capabilities to facilitate the next generation of efficient, reliable, and diverse connected computing environments. Nevertheless, large-scale cloud infrastructures continue to fail regularly despite their availability, scalability, and cost efficiency, primarily due to low resource utilisation and inadequate early-stage failure management. The key to effective resource management and minimizing failures in such settings is understanding the nature of the workload and its failure modes. The current review considers the Google cluster workload trace 2019 to investigate workload and failure patterns and to generalise the results of 24 articles. The analysis is also compared with other major datasets, such as Microsoft Azure Trace, Tencent Trace, and Alibaba Trace. The paper establishes the relevance of Google cluster traces, describes the key contents of the 2019 dataset, and contrasts prior literature with respect to research objectives, trace datasets, significant results, and limitations. Moreover, it briefly describes methods for analyzing and modeling cluster traces and identifies gaps in the research that should be addressed to advance the study of cluster traces.
Copyright (c) 2026 Akash Patel, Amit Nayak, Khushi Patel, Anand Patel https://creativecommons.org/licenses/by/4.0/
Smart drones for human detection in disaster response
Natural disasters require swift, coordinated responses to minimise human casualties and infrastructure damage. This paper presents a novel AI-assisted drone system designed to enhance disaster relief efforts through advanced human detection and a distributed emergency Wi-Fi network. This drone system, equipped with state-of-the-art machine learning algorithms and thermal imaging, excels at locating and identifying individuals even in challenging conditions, such as smoke, debris, or low visibility. The drone fleet operates autonomously, dynamically forming an ad-hoc network that adapts to the evolving needs of the disaster zone. By integrating real-time data processing with efficient network management, our system provides a critical lifeline for communication and a powerful tool for rescuers to navigate and respond effectively. The detection and communication times are observed to be within the range of 5 to 8 seconds for this proposed system, which is widely used in disaster response.
Copyright (c) 2026 Menakadevi Nanjundan, Y. L. Ajay Kumar, V. R. Seshagiri Rao, Nagarjuna Telagam, Seetha Chaithanya, Manikadan S. https://creativecommons.org/licenses/by/4.0/
Design and analysis of low-k dielectric TSV liners for noise mitigation in high-frequency 3D ICs
Moore’s Law has driven the development of very large-scale integration (VLSI) technology, allowing continuous transistor scaling to increase speed, density, and performance. However, as two-dimensional (2D) integrated circuits (ICs) near their physical and performance boundaries, and 2.5D ICs still face interconnect delay and power issues, three-dimensional (3D) integration has become a practical solution. In 3D ICs, multiple active layers are vertically stacked and connected via through-silicon vias (TSVs), providing short, high-bandwidth interconnects between layers. Electrical TSVs are essential for signal transmission, but also cause noise coupling between adjacent TSVs, where an aggressive TSV can induce interference in a nearby TSV. This coupling can impair signal integrity, increasing delay and power consumption. To mitigate this, low-dielectric-constant (low-k) materials are used to reduce capacitive coupling. In this study, materials such as benzocyclobutene (BCB), Perylene-N, and Teflon AF 1600 are compared with conventional SiO₂. Generally, TSVs are two structures — single-liner and stacked-liner — which are analysed at 10 GHz and 1 THz frequencies. At 10 GHz, the single-liner structure incorporating SiO₂ exhibits a noise reduction of about 6.56 dB, whereas the stacked-liner configuration using Teflon AF 1600 provides a noticeably greater reduction of 8.40 dB. As the operating frequency increases to 1 THz, the advantage of the low-k dielectric becomes more evident, yielding 9.63 dB noise reduction for the single-liner and 12.04 dB for the stacked-liner structure. These results indicate that low-k materials effectively suppress capacitive coupling and mitigate high-frequency interference in 3D ICs. The stacked-liner design contributes additional isolation by creating a secondary dielectric barrier, which further minimizes electric field interaction between neighboring interconnects. Thus, the integration of low-k dielectrics with optimized liner architectures significantly enhances signal integrity and overall electromagnetic performance in advanced high-frequency 3D IC systems.
Copyright (c) 2026 Guru Prathap Reddy Pathakunta, Sravan Abhilash K http://creativecommons.org/licenses/by-sa/4.0
An AI-powered knowledge graph-based question answering system for Charak Samhita: integrating sanskrit NLP and graph data science
Originating in India, Ayurveda is an ancient medical system focused on holistic healing that considers the mind, body, and spirit. This study utilizes knowledge graph (KG) technology to develop a KG model for an Ayurveda question-and-answer system. The system includes modules for knowledge extraction from चरकसंहिता, कायहचहकत्सा, भैषज्यरत्नावली and द्रव्यगुण संग्रि, construction of KG from this extracted knowledge and construction of AI supported Question answer system. In the methodology, domain-specific KG is constructed in Neo4j. Entities such as diseases (Vyadhi व्याधी), symptoms (Lakshana लक्षण), doshas (दोष), herbs, and treatments are incorporated. Advanced Sanskrit natural language processing (NLP) pipelines using ByT5-Sanskrit, SanskritBERT, and fine-tuned BioBERT facilitate named entity recognition (NER) and relation extraction. Graph-based reasoning models such as graph attention networks (GAT) and graph reasoning enhanced language models (GREASELM) enhance multi-hop reasoning across Ayurvedic concepts. Evaluation was conducted using a gold-standard annotated dataset of Charak Samhita verses mapped to disease–symptom–treatment relationships. Performance metrics included precision, recall, F1-score, mean reciprocal rank (MRR), and overlap coefficient. Superior accuracy can be seen in the proposed model as compared to baseline BERT-QA and subgraph QA approaches. This research has integrated Sanskrit computational linguistics and KG science. The approach mentioned in this paper has mentioned a framework that is scalable, interpretable and culturally significant. With the focus on Ayurveda, the methodology also mentions the potential for developing cross-cultural medical questions–answering systems, thereby bridging ancient wisdom with modern technological approaches.
Copyright (c) 2026 Sharayu Mirasdar, Mangesh Bedekar http://creativecommons.org/licenses/by-sa/4.0
Enhanced thermal management in 3D integrated circuits coupling
3D IC integration, which comprises vertically stacking several IC layers, is one of the new technologies that works well with complementary metal-oxide-semiconductor (CMOS) implementations. The layers of a three-dimensional integrated circuit (3D IC) are physically and electrically connected via copper-silicon bonding and through silicon vias (TSVs). Limitations in 3D IC designs, such as layer-to-layer thermal difficulties and TSV-to-substrate and TSV-to-TSV noise coupling, significantly impact system performance as a whole. Integrating 3D ICs relies heavily on heat spreaders and thermal through silicon vias (TTSVs). Overheating is a common cause of IC failure; however, heat spreaders and FIN to TTSV have been suggested as potential remedies for this problem in the last few years. A 3D IC might melt under the stress of an applied voltage because it becomes hotter inside. Engineers have added fins to the TTSV in a number of ways, each of which maximizes heat dissipation in a different way, in order to reduce this danger. The exceptional thermal cooling characteristics of graphene and carbon nanotubes (CNTs) have led to their widespread dissemination. This research shows that a FIN may efficiently transport thermal energy to a heat sink by using heat spreaders and optimum orientations to distribute heat in all directions. Additionally, we demonstrated the many scenarios in which the IC's potential distribution is impacted by various thermal cooling effects. We found that when it comes to transferring heat away from heat sources and TSVs, CNTs outperform Graphene. We included Al2o3, Si3N4, and SiO2 as examples to examine the consequences of modifying the model's dielectric characteristics.
Copyright (c) 2026 Institute of Advanced Engineering and Science http://creativecommons.org/licenses/by-sa/4.0
A recent hybrid of IoT with adaptive extended Kalman filter fuzzy logic for children’s health dietary
The increasing prevalence of childhood obesity highlights the critical need for intelligent dietary monitoring systems that are tailored to individual nutritional requirements. This work describes the creation and testing of an internet of things (IoT) hybrid using an adaptive extended Kalman filter and fuzzy logic (AEKFFL-IoT) model aimed at providing personalised food calorie prediction for children. The system uses IoT devices to collect real-time sensor data, such as height, weight, and BMI, and then employs extended Kalman filter (EKF) algorithms to denoise signals and anticipate trends. Fuzzy logic inference is then utilised to adaptively calculate caloric requirements based on biometric data. Experimental results reveal that the proposed AEKFFL model has a training root mean square error (RMSE) of 21.43 kcal and a testing RMSE of 22.35 kcal, exceeding existing rule-based, wearable, and ANN-driven models in terms of accuracy and generalisation. Furthermore, the system achieves high classification accuracy (94.5%) for BMI categorisation and fuzzy rule application. Comparative examination confirms the model’s adaptability, real-time integration, and mobile deployment capability. This study presents a scalable and intelligent approach for child-centered dietary monitoring, paving the path for personalised digital health interventions.
Copyright (c) 2026 Noorrezam Yusop, Massila Kamalrudin, Mohd Nazrien Zaraini, Siti Fairuz Nurr Sardikan http://creativecommons.org/licenses/by-sa/4.0
Neural network-based diagnosis of type 2 diabetes using an iridology approach
The growing global occurrence of type 2 diabetes requires the development of non-invasive and effective diagnostic methods. This work proposes a novel approach to detecting type 2 diabetes using iridology and machine learning (ML) techniques. By analyzing the iris of the right eye, a single region of interest (ROI) corresponding to the head of the pancreas is recognized for feature extraction. A total of 112 statistical and texture features are extracted using gray-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) algorithms. Five neural network (NN) models, narrow, medium, wide, bi-layered, and tri-layered are deployed to classify healthy and diabetic people. The models are trained and assessed using a range of k-fold values (2 to 20) to optimize performance. The highest classification accuracy of 83.2% was reached using the narrow neural network (NNN) model at 7-fold cross-validation. This work exhibts the potential of iridology-based ML approaches for non-invasive diabetes diagnosis, providing a promising substitute to traditional blood tests.
Classification of encryption attacks and strategies for mitigation
Cryptography is essential for securing digital communications, yet it remains vulnerable to various malicious attacks. These attacks can be classified based on the type of cryptography they target symmetric or asymmetric. This paper presents a comprehensive classification of encryption attacks, examining the specific vulnerabilities associated with each cryptographic approach. By analyzing these attack vectors, the study shows the importance of understanding weaknesses in cryptographic systems. Furthermore, it proposes several mitigation strategies to strengthen defenses and enhance protection of sensitive information in the digital domain.
Design and implementation of an AI, IoT, and blockchain-based system for circular economy transition in landfill management: a case study of Quilmaná, Peru
This study presents the design and implementation of an integrated system based on artificial intelligence (AI), internet of things (IoT), and blockchain to support circular economy practices in landfill management. The system addresses the lack of integrated and validated digital solutions for environmental monitoring, resource optimization, and social inclusion in resource-constrained contexts. Developed under a design science research (DSR) approach, the system combines IoT sensors for real-time monitoring, machine learning models (LSTM for methane prediction, CNN for waste classification, and reinforcement learning (RL) for biogas optimization), and a blockchain-based platform for transparent transactions and recycler formalization. The system was implemented and evaluated over 12 months using operational data. The LSTM model achieved 95% prediction accuracy, while the CNN model demonstrated high classification performance. Results indicate a 35% reduction in landfill waste, a 40% decrease in CH₄ emissions, and a 30% increase in recycler income, with 60% of informal workers formalized. These findings demonstrate that integrating AI, IoT, and blockchain enables the transformation of landfill systems into scalable circular economy platforms for sustainable waste management.
Copyright (c) 2026 CESAR HERNAN PATRICIO PERALTA http://creativecommons.org/licenses/by-sa/4.0
Advanced encryption standard with asymmetric key exchange for text encryption
When creating communication systems, securing data is crucial and improved randomization in creating secret keys contributes to more secure systems. Unfortunately, the symmetric ciphers used for data encryption, such as the advanced encryption standard (AES), may be subject to attacks that exploit timing measurements to deduce the secret key used for encryption, resulting in a significant lack of research on the security of hybrid implementations (usually defined as incorporating AES and asymmetric ciphers) for AES encryption. We introduce a new hybrid encryption method that combines the AES encryption standard with elliptic curve cryptography (ECC). In this hybrid form of encryption, ECC is utilized to facilitate secure encryption and transmission of the AES key and cycle through 16 rounds of AES to encrypt the majority of the data. We provide a performance comparison between the new algorithm and the AES-128 encryption standard. Our findings indicate that the new hybrid encryption method produced an average encryption time of 0.0002 seconds for 10MB files, significantly faster than the AES-128 encryption standard, which takes an average of 0.0016 seconds. The new hybrid method exhibits significant resistance to cryptanalytical attempts, experiencing an average avalanche effect of 49.84%, while maintaining the AES nonlinearity value at 112. As such, we conclusively state that the new hybrid encryption method utilizing ECC and AES will provide effective security for user data against timing side-channel attacks while remaining an efficient method for performing encryption.
Copyright (c) 2026 Ravindra Reddy K http://creativecommons.org/licenses/by-sa/4.0
Bridging the linguistic divide: recent developments in machine translation for Indian languages
Significant advances have been achieved in machine translation (MT) in recent times, particularly state of the art (SOTA) models for languages like English and Indian having distinct grammatical structures and limited monolingual training data. This paper analyses various recent state-of-the-art variants of large language models (LLMs) and neural machine translation (NMT) for Indian languages in comparison to statistical machine translation (SMT). It tackles key questions, such as idiomatic expressions, morphologically complex grammar or the scarceness of parallel corpora. Furthermore, it studies bytewise BPE, compares translation models in terms of BLEU scores using separate and shared-vocabulary representation with copy actions between the BPE translations, and analyses how multitask learning (Caruana (1997)) and attention mechanisms can contribute to the quality of translation. In summary, it provides directions for future work by suggesting new avenues of research including better curated datasets, more efficient approaches for lowresource languages and culturally aware translations.
Intelligent engineering framework for managing hospital cardiac arrest resources
In-hospital cardiac arrest in intensive care remains frequent (often cited incidence roughly 0.5%-7.8% of admissions), while causes differ in what staff and equipment must be ready. We ask whether vital-sign trajectories from a standard EHR can classify which of three cardiac-related mechanisms is most salient arrhythmia, acute myocardial infarction (AMI), or respiratory failure or hypoxia so ICU resources can be aligned with risk. Using MIMIC-IV, we extracted diagnoses and charted vitals in the 12 hours before the index event, applied cleaning, aggregation, label encoding, sequence padding, and class balancing (3,000 cases per class), then trained and compared eXtreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and logistic regression (LR) with 5-fold cross-validation on an 80/20 split. XGBoost performed best (about 93% accuracy; sensitivity 89.15%; specificity 90.43%; AUC-ROC 0.94). Feature importance highlighted heart rate, oxygen saturation, and blood pressure patterns consistent with bedside monitoring practice. The study supports mechanism-oriented triage labels derived from widely recorded vitals, as a complement to generic early warning scores, for prioritizing telemetry, respiratory support, and cardiology pathways. External validation and prospective evaluation are needed before deployment.
Attention-enhanced VGG-16 architecture for precision weed detection in wheat fields
This study delineates five advanced convolutional neural network designs that employ deep learning for the classification of wheat weeds. The dataset consists of greyscale photos taken in agricultural fields, enhanced with RGB lighting and cropped to 256×256×3 pixels. Normalization, batch-wise augmentation, contrast stretching, or histogram equalization were all part of the preprocessing that improved picture quality and model learning efficiency. These enhancements developed feature extraction by increasing picture contrast and homogeneity. A VGG16-based model with further Conv2D layers and spatial attention is one of the five models recommended. An additional design, inspired by ResNet50 that applies residual blocks and worldwide average pooling. A hybrid RNN integrated ICNA-CNN or LSTM for spatial-temporal content learning is another. Lastly, InceptionResNetV2 is enhanced with CNN layers and a query-key attention mechanism. We used the Adam optimizer and categorical cross-entropy to estimate the loss throughout the training for the round. At the end, all the models had ReLU activation, batch normalization, MaxPooling2D, and a dropout charge of 0.5 to keep them from overfitting. Upon evaluating their performance based on accuracy, recall, reliability, and training loss, VGG16 emerged as the standout model, achieving an impressive 99.07% precision and a remarkably low training loss of just 0.0079. The study determined that VGG16 is the optimal choice for precise wheat–weed categorization because to its superior generalizability and accuracy.
Copyright (c) 2026 Akanksha Bodhale http://creativecommons.org/licenses/by-sa/4.0
An effective blockchain-based system for tracking viral content origins in social media
Tracking the origins of viral content in social media is crucial for identifying misinformation, ensuring accountability, and protecting intellectual property. By tracing content back to its source, platforms can curb the spread of false narratives, hold malicious actors responsible, and safeguard creators’ rights. In this work, “Effective blockchain based system for tracking viral content origins in social media-(BCTVCO)” is proposed. The BCTVCO is a blockchain-based content authentication platform that leverages interplanetary file system (IPFS), smart contracts, and cryptographic hashing to verify digital assets and detect unauthorized reuse. This decentralized application addresses the challenges of content authenticity, ownership verification, and intellectual property protection in the digital space. The system integrates Ethereum smart contracts (Solidity) to store immutable content records and uses SHA-256 hashing for secure file integrity verification. Content is uploaded to IPFS via Web3. Storage, ensuring distributed and tamper-resistant storage. The React-based frontend with MetaMask authentication allows users to seamlessly register, upload, and track their content. In implementation, BCTVCO outperformed existing methods and proved to be a scalable, transparent, and secure blockchain-based content verification system. Future enhancements of BCTVCO involve multi-chain support and AI-powered content analysis to strengthen security and usability.
Predicting student academic success using entry test, language, and spiritual formation data with ensemble learning
Student academic success is influenced by various factors, both academic and non-academic. This study aims to examine the correlation between key student attributes and final grade point average (GPA), as well as to develop a machine learning model to predict academic success. The correlation analysis involved academic variables such as admission scores (Mathematics, English, Indonesian, and academic aptitude test/TPA), English ability test (EAT), spiritual formation (SF), and first-year GPA (GPA_1). The results indicate that GPA_1 has the highest correlation with final GPA (0.63), followed by SF (0.44), while other variables exhibit lower correlations. To enhance prediction accuracy, a machine learning approach using three primary models was employed: Naïve Bayes, support vector machine (SVM), and an ensemble learning method based on a stacking classifier that combines SVM and Naïve Bayes. The evaluation used five train-test split ratios and performance metrics, including accuracy, precision, recall, and F1-score. Experimental results reveal that the SVM model achieves the highest accuracy at 88.40%, followed by the ensemble model combining SVM and Naïve Bayes (88.00%) and the Naïve Bayes model (87.10%). These findings confirm that the machine learning approaches, could effectively predict student academic success, providing a foundation for academic decision-making and educational intervention strategies.
Smart road maintenance: real-time surface damage detection and mapping with YOLOv8
The degradation of road surfaces presents considerable obstacles for the management of urban infrastructure. This study presents a deep learning model based on YOLOv8 that can find many types of road faults, like potholes, longitudinal cracks, transverse cracks, and alligator cracks, using pictures, video streams, and live webcam feeds. The suggested system can find things with an accuracy of 91.2%, and the confidence levels range from 60% to 95%. Streamlit has been utilized to develop a web interface that makes it easier to use in real life. It makes it easy for users to choose inputs and provides outputs with notes and boundary boxes. GPS makes it possible to find problems with great accuracy, and a graphical dashboard presents damage categories and confidence levels in real time. Road maintenance is considerably more efficient with automated detection, location mapping, and easy-to-understand visualization. It is also easier to keep a check on smart municipal infrastructure. The results demonstrate that using computer vision and geospatial analytics together could make it easier to automatically check road conditions and make better decisions about how to run a city.
Evaluation of text correction using a combination of Levenshtein distance and Trie algorithm
Nowadays, technology is advancing with various applications, especially in text processing, such as news recommendations, sentiment analysis, automatic scoring, and language translation. In some cases, spelling errors often occur when inputting text for the translation process, necessitating text correction methods to display suggestions as a result. Therefore, the problem statement raised is about how to improve the accuracy of text correction and evaluate the translation quality at the word level after correcting input text in the context of translation from Indonesian to English. This research aims to develop and evaluate the combination of Levenshtein distance algorithm and Trie to correct input text and evaluate the translation quality at the word level after correcting text. There are various text correction methods, such as Hamming distance, Levenshtein distance, Damerau-Levenshtein distance, and N-Gram. Among several text correction methods. Levenshtein distance algorithm is commonly used to calculate the distance between texts and can be enhanced by using a Trie for more efficient computation in evaluating text correction. This research method resulted in an accuracy of 82.25% with an F1 score of 84.39%, where the developed text correction model produced a good translation using BLEU score with an increase of 1.55% after text correction.
Copyright (c) 2026 Cynthia Natalie, Abba Suganda Girsang http://creativecommons.org/licenses/by-sa/4.0
Deep reinforcement learning inspired optimization framework using Optuna for brain tumor detection
Accurate brain tumor detection is essential for effective clinical diagnosis; however, the performance of deep learning models is highly sensitive to manually selected architectures and hyperparameters. To address this challenge, this paper presents a reinforcement learning–inspired automated optimization framework for brain tumor detection that eliminates manual trial-and-error tuning of hyperparameters. The proposed approach integrates EfficientNetB0 as a fixed feature extractor (base model) with an Optuna-based reinforcement learning strategy to jointly optimize the classifier architecture and key training hyperparameters, including learning rate, batch size, dropout rate, and network depth. Unlike existing studies that rely on static or heuristically tuned models, the proposed framework dynamically adapts model configurations based on validation feedback. Experiments conducted on the BraTS 2020 MRI dataset demonstrate that the optimized model achieves an accuracy of 92%, an F1-score of 92%, and a ROC–AUC of 0.96. Additional evaluations on imbalanced and cross-dataset settings show stable minority-class performance and good generalization. The results confirm that the proposed automated optimization framework offers a robust, scalable, and clinically relevant solution for brain tumor detection, representing a significant advancement over manually tuned deep learning approaches.
Copyright (c) 2026 Aashutosh Kharb Kharb, Prachi Chaudhary http://creativecommons.org/licenses/by-sa/4.0
Explainable AI-based hybrid GNN-MLP model for strawberry fruit disease detection using hyperspectral imaging
Strawberries are severely affected by the main fungal diseases such as anthracnose fruit rot, grey mould, and powdery mildew, directly reducing commercial yield and post-harvest quality. In this paper, we propose an enhanced hybrid deep-learning method by combining graph neural networks (GNNs) and multi-layer perceptrons (MLPs) for effective strawberry disease detection in real environments of fields. This dataset contains images acquired from both public domain repositories as well as farm operational settings, encompassing three classes of diseases with varying lighting conditions, background noise, and occlusion. A structured pre-processing workflow comprising contrast enhancement, denoising, and synthetic hyperspectral simulation is used to enhance subtle lesion features and stabilize the subsequent feature extraction. The hybrid GNN–MLP framework possesses the merits of spatially local lesion topology and two-point contextual information, which can improve disease classification compared with common CNN-based structures. 5-fold cross-validation shows that the model holds 93.59% accuracy and a macro F1-score of 90.39%, showing good generalization even with heterogeneous input regimes. Transparent models use local interpretable model-agnostic explanations (LIME) and gradient-weighted class activation mapping (Grad-CAM) in combination, highlighting disease-relevant areas that give an interpretable rationale for each prediction. To conclude, the developed system offers an accurate and explainable solution that has a computationally efficient commitment for real-time monitoring of disease in smart agriculture settings, particularly on low-cost hardware assets.
A multi-expert approach to content-based image retrieval using feature fusion and late re-ranking
As digital data rapidly grows, content-based image retrieval (CBIR) has become important for optimizing collections of visual data. This work proposes a retrieval framework which operates in two stages and improves accuracy by using systematic fusion of features. In the first stage, first-stage wide-scope descriptors called bag-of-visual-words (BoVW), scattering wavelet transform (SWT), discrete cosine transform (DCT), and principal component analysis (PCA) retrieve initial candidate images. The second stage undertakes detailed re-ordering of candidate images by implementing the local binary pattern (LBP), histogram of oriented gradients (HOG), and singular value decomposition (SVD) descriptors to re-evaluate similarity scores. Each individual descriptor returned results for mean average precision for the top 10 retrieved images (mAP, top-10) of between 0.63 and 0.79 and the fused framework achieved 0.88, which is evidence of the viability of complementary feature integration. These findings support the hypothesis that while multiple descriptors performed well and delivered high retrieval accuracy, hierarchical fusion of multiple handcrafted descriptors does not involve the computational costs associated with deep learning methods.
Copyright (c) 2026 Yousif Samer Mudhafar, Ali Abdulazeez Qazzaz http://creativecommons.org/licenses/by-sa/4.0
Performance evaluation of a solar-driven IoT water quality monitoring system using descriptive and ANOVA analysis
Water quality monitoring is vital for protecting aquatic ecosystems and ensuring sustainable water resource management. Traditional manual sampling methods are often costly, time-consuming, and unsuitable for real-time assessment. This study presents a newly designed solar-powered IoT-based water quality monitoring system for remote and continuous data collection. The system utilizes an ESP32 microcontroller integrated with pH, temperature, and total dissolved solids (TDS) sensors, powered by a 10W solar panel. Data is transmitted to a cloud-based platform wirelessly, enabling remote access and visualization via a mobile app. Performance evaluation included descriptive statistics and one-way ANOVA across four sampling sites. ANOVA results showed statistically significant differences (p < 0.05) in water quality parameters among locations, confirming the system’s sensitivity. Sensor accuracy was validated against standard meters, revealing mean relative errors below 5% for pH and TDS. The system reliably provides real-time, accurate data, supporting proactive water quality management. Integrating IoT with renewable energy offers a cost-effective, scalable, and energy-efficient solution for environmental monitoring in remote or resource-limited areas.
Social media interaction of halal fashion brand in Indonesia: a netnographic study of image management
Research on halal fashion has largely focused on consumer purchase decisions, with limited attention to how halal fashion brands interact with consumers and manage their brand image on social media platforms such as Instagram. This study addresses this gap by examining brand interaction patterns and image management strategies among leading halal fashion brands in Indonesia. Using a netnographic approach, it analyzed 1,321 Instagram posts from six halal fashion brands over six months (July–December 2022), applying content and image‑management codes to classify post types (photos and videos) and representation strategies (personalized, contextual, and celebrity use). The findings show a slightly higher proportion of photo posts (51%, 674 posts) than video posts (49%, 647 posts), with hijab fashion brands more active than Muslim and sports fashion brands in producing content. Across all brands, image management relied predominantly on personal context and non‑celebrity representation, while professional context and celebrity‑based posts were used less frequently. These results suggest that halal fashion brands strategically emphasize relatable, personalized, and non‑celebrity content to build brand image and engagement on Instagram, offering practical guidance for brand managers in designing effective social media strategies and contributing novel empirical evidence on brand interaction and image management in the halal fashion sector.
Metaverse based immersive learning prototype for satellite communication using silvercoms model
Advancements in immersive technologies and the metaverse are transforming engineering education. Satellite communication, as a complex and abstract domain, requires innovative approaches to enhance conceptual understanding and learner engagement beyond traditional methods. This study proposes the Silvercoms model, an immersive learning framework tailored for satellite communication systems education, integrating metaverse technology with pedagogical design. The model combines 3D interactive simulations, collaborative virtual environments, and systems-level content delivery, structured using the 6E instructional model and supported by the motivated strategies for learning questionnaire (MSLQ) to address both cognitive and motivational aspects of learning. The system is developed using a systems engineering approach and implemented with Unity3D in a virtual reality (VR) based metaverse environment. The prototype includes modules such as satellite orbit simulation, satellite history, and interactive satellite systems, enabling experiential and concept-driven learning. This study contributes by integrating immersive technology with structured pedagogical frameworks, offering a novel approach to improving learning effectiveness in satellite communication systems education.
Multi-criteria optimization of emergency unit allocation using COPRAS and SMART: a case study in Palembang
Increasing living standards and instant eating patterns have improved people's demands for quality health services. Hospitals as health service facilities are actually real-time networks that expected to be able to provide effective and efficient services. This research uses the complex proportional assessment (COPRAS) and simple multi-attribute rating technique (SMART) methods to determine the hospital with the most optimal emergency unit (EU) services in each subdistrict based on predetermined criteria. The research results show that the COPRAS method is produces performance index values ranging from 0.0195 to 0.1317, while the SMART method yields scores between 0.054 and 0.122, both demonstrating consistent ranking outcomes. The three hospitals, with the most optimal EU performance are Dr. Mohammad Hoesin, RSU Pertamina, and RSJ Ernaldi Bahar, with Dr. Mohammad Hoesin achieving the highest utility value (0.1317). The novelty of this study lies in the integration of real-time spatial and operational data from Google Maps and RS Online into a hybrid set covering problem (SCP) framework, combining the strengths of COPRAS and SMART.
Navigating digital parenting: a bibliometric exploration of trends on children’s digital soothing practices
The digital age has transformed parenting practices, with an increasing reliance on digital devices for managing children’s behavior, particularly as calming tools. This study addresses the growing phenomenon of digital parenting, highlighting its implications on child development and family dynamics. Despite the benefits of digital media, concerns persist regarding its overuse for emotional regulation, which may impede children’s self-regulation skills and parent-child interactions. This study aims to explore the evolution of research on digital parenting using bibliometric analysis. A comprehensive dataset was extracted from the Scopus database, focusing on publications from 2020 to 2024 within the Social Sciences domain. The inclusion criteria included peer-reviewed, open-access articles written in English. A systematic methodology ensured the analysis of performance metrics, trends, and co-authorship patterns. Results indicate a significant increase in scholarly attention to digital parenting, with 837 articles meeting the inclusion criteria. Leading contributions emerged from journals such as Sustainability Switzerland and Education Sciences, with prolific authors and institutions from the United Kingdom and the United States dominating the field. The analysis underscores the interdisciplinary nature of the topic, reflecting contributions from education, media studies, and child development. This study offers valuable theoretical insights and practical recommendations, emphasizing balanced digital media use and informed parenting strategies to foster healthier family dynamics.
Copyright (c) 2026 Rita Wong Mee Mee, Noor Hanim Harun, Lim Seong Pek, Suzulaikha Mohamed, Tengku Shahrom Tengku Shahdan, Nurul Asyiqin Jalil, Anisa Ahmad http://creativecommons.org/licenses/by-sa/4.0
A multi-cancer detection framework using deep learning and hybrid machine learning approaches
The diagnostic solutions offered by the present artificial intelligence (AI) solutions suffer from non-generalizability and heavy reliance on complex models. In an attempt to solve these issues, we propose a lightweight yet versatile method consisting of a combination of ResNet50 transfer learning and hybrid machine learning. Image features are extracted using dermoscopy, magnetic resonance imaging (MRI), and histopathological images. These are subjected to principal component analysis (PCA) dimensionality reduction followed by classification using support vector machine (SVM), random forest (RF), logistic regression (LR), and XGBoost algorithms. This segregation of the two processes improves efficiency. The hybrid approach using ResNet50 + LR yielded an accuracy of 91.01% in the case of breast cancer detection compared to 86.26% of a baseline convolutional neural network (CNN). Also, ResNet50 gave an accuracy of 96.61% in diagnosing skin cancer. Custom CNN provided an accuracy of 99.42% for lung cancer and 96.33% for brain tumor detection.