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 Intelektual Pustaka Media Utama (IPMU). Proudly now indexed in SCOPUS! The journal is now officially listed on the Scopus source page with coverage starting from 2023, ensuring high visibility and global recognition for your research. In addition, the journal has also been indexed in Scimago Journal Country Rank (ScimagoJR) with an SJR of 0.153.
Institute of Advanced Engineering and Science
Mitigating gender bias in STEM study field classification using GRU and LSTM with augmented dataset technique
This study examines gender bias in artificial intelligence (AI), focusing on the classification of high school students into science, technology, engineering, and mathematics (STEM) and non-STEM fields. Using Indonesian student Computer Science Department, BINUS Graduate Program – Master of Computer Science, Bina Nusantara University, Jakarta, 11480 data, conditional variational autoencoder (CVAE) and multilabel synthetic minority over-sampling technique (MLSMOTE) were employed for data augmentation to mitigate bias before training gated recurrent unit (GRU) and long short-term memory (LSTM) models for prediction. The combination of MLSMOTE and GRU demonstrated superior performance, achieving accuracies of 93% for female students and 94% for males. These results indicate that MLSMOTE and GRU effectively predict fields of study while addressing gender bias. The findings contribute to advancing fairness in AI systems for education and beyond, ensuring equitable opportunities across diverse applications.
Copyright (c) 2026 Devi Fitrianah, Sarah Safitri, Nadzla Andrita Intan Ghayatrie https://creativecommons.org/licenses/by/4.0/
A critical review of information retrieval techniques: current trends and challenges
The realm of information retrieval is witnessing transformative advancements, driven by the integration of deep learning techniques, specialized algorithms, and domain-specific applications. Information retrieval systems play an important role in many applications including in the Artificial Intelligence powered systems that can be seen in many applications. Information Retrieval, generally, acts an important task in the knowledge discovery phase of any query based intelligent system. This paper presents a comprehensive review by conducting a detailed analysis of the technological nuances, dataset specifications, and pivotal findings. This detailed review has been done with the special emphasis on the kind of technology used to achieve accurate information retrieval, domain of the study, and the system’s ability to retain or work with tables and figures, among other parameters. Navigating through the rich tapestry of methodologies, the paper underscores the pivotal role of deep learning frameworks in revolutionizing traditional retrieval paradigms. Furthermore, it sheds light on the innovative integration of textual information, algorithmic advancements, and specialized datasets to enhance the efficacy and granularity of information retrieval mechanisms.
Copyright (c) 2026 Sanket D. Patil, Zahir Aalam https://creativecommons.org/licenses/by/4.0/
Machine learning centered energy optimization in mobile edge computing: a review
Current literature reviews on machine learning-based approaches for mobile edge computing (MEC) energy optimization often lack in-depth gap analysis and fail to identify trends or offer actionable insights. Most focus narrowly on comparing MEC frameworks without critically evaluating or benchmarking prior research. This review contributes by addressings these gaps via analysis of existing reviews and related studies, with a focus on ML models, research objectives, evaluation metrics, datasets, tools, and gap identification. The review method follows a systematic literature review (SLR) using the PRISMA framework for transparency and reproducibility. Key findings reveal persistent challenges in energy consumption, computational overhead, cost, and poor performance in accuracy, QoS, latency, scalability, and carbon footprint. Deep reinforcement learning (DRL) emerges as the most commonly used model (55%), while TensorFlow (35%) is the most adopted tool, valued for its flexibility and robust community support. The AudioSet dataset is frequently used (28%) due to its compatibility. However, methodology limitations include dependency on study quality and exclusion of grey literature, context sensitivity. The review concludes by recommending advanced solutions such as serverless computing, liquid cooling, containerization, software-defined power, quantum computing, and blockchain to drive future MEC energy optimization.
Copyright (c) 2026 Chandapiwa Mokgethi, Tshiamo Sigwele, Kabo Clifford Bhende, Aone Maenge, Selvaraj Rajalakshmi https://creativecommons.org/licenses/by/4.0/
Stacking of machine learning classifiers for bot detection using account level data
Social media is a platform for individuals to connect, share, and create information. Social bots produce automated content and interact with humans; in the process, they learn and mimic humans’ behaviour. This research study addresses the challenge of identifying social media bots (SMB) that can rapidly disseminate information or misinformation on platforms like Twitter. It contributes to the field by reviewing literature to define bot behaviours and exploring advanced machine learning classifiers for effective bot detection using account-level data. The study employed Spearman's rank correlation coefficient to select relevant features for SMB classification, then trained six different machine learning models: decision tree (DT), random forest (RF), logistic regression (LR), support vector machine (SVM), and k-nearest neighbour (KNN). To further improve accuracy, a classifier stacking technique was applied. Key findings revealed that while individual classifiers performed variably, with RF leading at 89% accuracy, the stacked classifier approach outperformed all single-classifier methods with an impressive 90% accuracy rate. The results underscore the potential of combining multiple classifiers to enhance the precision of social media bot detection efforts.
Copyright (c) 2026 Jwala Sharma, Samarjeet Borah https://creativecommons.org/licenses/by/4.0/
A review of sensemaking design elements: towards an affordances typology
This study explores the intersection of interaction design and sensemaking within digital systems, aiming to identify and categorize key affordances that enhance user sensemaking. Starting with a focused literature review, key design elements such as tagging and annotation are identified, important for effective sensemaking in interaction design. Drawing on Maier's construct of affordances, the behaviours of these design elements are analyzed to derive specific affordances integral to enhancing user experience. The primary objective is to develop a generalized affordance typology that supports sensemaking across various digital systems. This typology organizes the derived affordances into broad themes such as effortless discovery, expressive freedom, collaborative engagement, cognitive support, insight enhancement, and user empowerment. This typology serves as a tool for interaction designers, facilitating the application of these themes in various design scenarios to create more intuitive and effective digital environment for sensemaking.
Copyright (c) 2026 Fadzlin Ahmadon, Murni Mahmud, Muna Azuddin https://creativecommons.org/licenses/by/4.0/
Enhanced transfer learning framework for brain tumor detection from MRI scans using attention-based feature fusion
Due to the complexity of the different tumor types in medical imaging detection of brain tumor is still as prominent challenge. This paper present the innovative technique enhanced transfer learning framework (ETLF) which integrating the advanced pre-processing with hybrid fine-tuned method for accurate brain tumor detection from magnetic resonance imaging (MRI) scans. The proposed model combine the strength of pre-trained convolutional neural networks (CNNs) such as EfficientNetB0 through domain specific transfer learning and attention based fine tuning. A novel feature fusion layer and adaptive learning rate scheduler are key indicators for model performance and prevent overfitting. The methodology is assessed on the benchmark dataset BraTS and Kaggle brain tumor datasets. The main contribution of work lies in development of domain- adaptive transfer learning with different datasets. The ETLF shows the high accuracy of 98.76% which able outperforms effectively in diagnosing tumor suitable of clinical purpose.
Copyright (c) 2026 Smita Bharne, Ekta Sarda, Shamal Salunkhe https://creativecommons.org/licenses/by/4.0/
Arobust outlier detection based filtering for noise removal in grayscale images
Salt-and-pepper noise severely degrades the visual quality of digital images, par ticularly at high noise densities where conventional denoising techniques often fail. Median- and mean-based filters tend to oversmooth images and blur fine structures when the majority of pixels within a local window are corrupted. This paper proposes a robust dual-layer denoising framework for grayscale images that integrates rank-based prescreening, interquartile range (IQR)-based statis tical outlier detection using Tukey fences, and a lightweight post-processing sharpening stage. In the first layer, a rank-4 trimmed estimator suppresses ex treme impulse values and stabilizes local statistics. In the second layer, adap tive IQR thresholds are employed to detect and replace residual outliers, even in heavily corrupted neighborhoods. A final step involving selective sharpen ing combined with mild smoothing enhances edge details without amplifying residual noise. Extensive experiments on standard grayscale images (Lenna, Barbara, lake, boat, and living room) across salt-and-pepper noise levels from 10% to 90% demonstrate that the proposed approach consistently outperforms conventional methods, including mean, median, Gaussian, modified decision based unsymmetrical trimmed median filter (MDBUTMF), and pixel density based filter (BPDF). Quantitative evaluation indicates peak signal-to-noise ratio (PSNR) values reaching 38.23dB, structural similarity index (SSIM) values up to 0.99, and significant reductions in mean squared error (MSE), particularly at higher noise densities. These results confirm that the proposed framework ef fectively suppresses noise while preserving edges and textures, making it well suited for practical applications such as medical imaging, remote sensing, and surveillance.
Copyright (c) 2026 Ali Salem Al Rawash, Farah Aini Abdullah, Ahmad Kadri Junoh, Abdallah Alshbeel, Mohammed Banikhalid https://creativecommons.org/licenses/by/4.0/
Enhancing support vector machine performance using particle swarm optimization for sentiment analysis
Recently, social media has established itself as a leading platform in various sectors. Meanwhile, text extraction and sentiment analysis classification have attracted significant attention in research. Regrettably, traditional sentiment analysis often falls short of accurately capturing sentiment nuances. At the same time, machine learning has enabled more effective sentiment analysis, data mining, and classification, as well as the development of models that incorporate artificial intelligence. Therefore, the purpose of this study is to optimize sentiment analysis of public opinion in social media regarding Grand Prix motorcycle racing (MotoGP) and World Superbike (WSBK) events using machine learning and an optimized machine learning method. This study applies the support vector machine (SVM) machine learning method and enhances its performance through optimization by integrating it with the particle swarm optimization (PSO) algorithm. This study found that the SVM method achieved 80.15% accuracy, 75.63% recall, and 76.89% F1-score. In contrast, the SVM method combined with PSO achieves accuracies of 81.82%, 79.9%, and 79.62% for recall, precision, and F1-score, respectively, in classifying the sentiment of sporting events. The implications suggest that applying Hybrid SVM with PSO significantly enhances classification accuracy in sentiment analysis.
Copyright (c) 2026 Christofer Satria, Anthony Anggrawan, Peter Wijaya Sugijanto, Husain Husain, I Nyoman Yoga Sumadewa, Victoria Cynthia Rebecca https://creativecommons.org/licenses/by/4.0/
Enhancing Bitcoin price forecasting: a comparative analysis of advanced time series models with hyperparameter optimization
This paper evaluates state-of-the-art time series forecasting to predict next day Bitcoin prices via distinct architectures and methodologies in a real-time setting. We study six advanced models, KAN, TimesNet, NBEATS, NHITS, PatchTST and BiTCN, applied to a Jan 1, 2023, to Dec 1, 2024. We simulate real world applications via a rolling forecast strategy, in which we predict daily prices from the most recent data. The dataset consists of daily Bitcoin closing prices and data preprocessing and integrity checks for its constituent data. Additionally, rigorous accuracy and reliability were investigated using performance metrics such as the MAE, RMSE, MAPE, and R². NBEATS and NHITS were the top performers, achieving an R² score of 0.967, explaining complex patterns in volatile cryptocurrency data. The specific importance of model architecture and further hyperparameter optimization in achieving higher forecasting accuracy is highlighted in this study. The practical implications of these findings for the advancement of time series forecasting in financial markets are leveraged here, where timely and accurate forecasts are critical.
Copyright (c) 2026 Amine Batsi, Mohamed Biniz, Rachid El Ayachi https://creativecommons.org/licenses/by/4.0/
Intelligent home automation framework using sensor fusion and machine learning for energy efficiency and thermal comfort
This paper presents an innovative, intelligent home automation framework integrating sensor fusion and machine learning to promote energy efficiency and thermal comfort in residential settings. Utilising low-cost hardware such as the Arduino Uno R3, passive Infrared (PIR) sensors, KY-018 photoresistors, and KY-028 temperature sensors, the system achieves a human presence detection accuracy of 95.3% via a random forest classifier. Over a three-month period, testing in several homes showed that the system is 99.7% reliable, responds in 1.2 seconds, and costs 85% less than commercial options. This research lays the groundwork for sustainable smart homes by providing a mathematical model for optimizing energy use and a unified modeling language (UML) model of the system architecture. These results show how important it is to have open-source technology that is cheap and could help smart building systems spread around the world. The study utilized a controlled experimental design featuring five families, with sensor data gathered at 10-second intervals over a three-month period. A random forest classifier trained on 10,000 labeled data points could correctly guess whether or not a person was present 94.8% of the time and 95.7% of the time. The framework is useful because it combines cheap sensors with a lightweight machine-learning pipeline that can work on small microcontrollers. This solves the long-standing problem of the cost performance gap seen in prior smart-home deployments.
Copyright (c) 2026 Franklin Ovuolelolo Okorodudu, Gracious Chukwuweike Omede, Etinosa Eugene Osawe https://creativecommons.org/licenses/by/4.0/
A mHealth adoption model for diabetes self-management: patient-centered insights from UNRWA clinics
This study develops and validates a mobile health (mHealth) adoption model to enhance diabetes self-management among type 2 diabetes mellitus (T2DM) patients in UNRWA primary healthcare clinics across Palestinian refugee camps. This study fills a gap in research on mHealth adoption in low-resource settings by combining the technology acceptance model (TAM), task-technology fit (TTF), and self-efficacy theory (SET). A descriptive, cross-sectional design was employed using a structured, validated questionnaire administered to 503 T2DM patients. Reliability analysis yielded high internal consistency (Cronbach’s α = 0.808–0.966). Structural equation modeling (SEM) using SPSS and AMOS validated the model fit, evidenced by a comparative fit index (CFI) of 0.941 and a root mean square error of approximation (RMSEA) of 0.048. Out of the eleven factors that were examined, Perceived Usefulness had the most positive impact on self-care management (β = 0.67, p < 0.001), while Task Requirement had the least. Notably, Perceived Self-Efficacy showed no significant effect on behavior (p > 0.05). These findings highlight usability, usefulness, and tool functionality as central to promoting mHealth use. The validated model can be modified for other chronic disease settings in comparable healthcare environments and provides practical advice for creating patient-centered mHealth interventions.
Copyright (c) 2026 Saleem Mohammad Faraj, Haw Yuan Kang, Raja Rina Raja Ikram, Lizawati Salahuddin https://creativecommons.org/licenses/by/4.0/
Ensemble windows intrusion detection system using XGBoost and deep learning
Intrusion detection systems (IDS) are critical for preserving the Windows environment from an ever-changing collection of cyber threats. Current IDS uses deep learning (DL), which are heavy models if used for detection, while others use machine learning (ML) techniques, which require external feature extraction. To resolve this challenge, this paper introduces XGBNN, a new ensemble model that combines the benefits of ML and DL to identify and mitigate attacks against Windows machines effectively. The various ML methods are trained on the publicly available dataset to classify eight types of attacks in a Windows environment. Additionally, deep neural networks (DNNs) are proposed by optimizing the layers and hyperparameters to achieve the best accuracy. Then, the DNN model and XGBoost model are integrated to detect intrusions by utilizing the feature extraction ability of DNN and providing the intermediate features extracted from the last second layer of the DNN to the XGB for classification. The Ensemble model XGBNN optimizes features and offers better decisions. The proposed model achieves an exceptional accuracy of 100%, as demonstrated by the empirical results, and outperforms the benchmark models with an improvement of 0.004%. The purpose of this study is to highlight the effectiveness of hybrid architectures in intrusion detection. These architectures offer a more robust, scalable, and effective method to improve the security of the Windows system against more sophisticated attacks.
Copyright (c) 2026 Pranitha Kedambady Shiva, Pushparaj D. Shetty https://creativecommons.org/licenses/by/4.0/
Multiclass classification using variational quantum circuit on benchmark dataset
Classification is a major task in data science. Data classification is required in many industries such as healthcare, transport, and finance. Noisy intermediate-scale quantum (NISQ) era. Quantum computers are capable of solving complex data challenges and can be used for the classification of the data with minimum features. In this regard, quantum neural networks are being used extensively for data classification. In this paper, we employ variational quantum circuits for the task of multiclass classification. A hybrid approach is used for building the neural network. In which quantum circuits are used for the feedforward architecture, while in back-propagation, parameters are updated using a classical optimizer on classical computers. We have successfully demonstrated multiclass classification using the proposed approach on benchmark data sets. Our results show that variational quantum circuit (VQC) are a promising candidate for classification problems with fewer features. We have performed experiments on International Business Machines Corporation (IBM) quantum hardware and simulators.
Copyright (c) 2026 Muhammad Hamid, Bashir Alam, Om Pal https://creativecommons.org/licenses/by/4.0/
Coastline segmentation on Landsat 8 OLI images using majority voting with deep learning models
Coastlines are highly dynamic due to both natural processes and anthropogenic factors, including global warming and sea level rise. Accurate coastline segmentation is essential for effective monitoring and management. Although previous studies have applied deep learning for coastline detection, many existing models still suffer from instability across scenes, blurred boundaries, and segmentation artifacts, indicating that model generalization remains a challenge. This study aims to develop a more robust coastline segmentation approach by introducing an automated majority voting strategy that integrates three deep learning models: ResNet50, ResNet18, and MobileNet-V2. Landsat 8 OLI imagery is used for training and testing. The Jaccard index results show that ResNet18, ResNet50, and MobileNet-V2 achieved scores of 0.96, 0.98, and 0.95 respectively, while the proposed majority voting method also achieved 0.98. Despite the producing a similar numerical score to the best individual model (ResNet50), the ensemble method improves segmentation consistency by reducing artifacts such as unwanted peripheral shapes and cracks within land areas. These findings demonstrate that combining multiple segmentation outputs yields more stable and reliable coastline detection than using single models. Future work will apply this approach to broader Indonesian coastal regions to further assess its generalizability across diverse shoreline conditions.
Copyright (c) 2026 Nur Nafiiyah, Salwa Nabilah, Nur Azizah Affandy, Rifky Aisyatul Faroh, Esa Prakasa https://creativecommons.org/licenses/by/4.0/
A systematic mapping study: exploring islamic inheritance in computing research
Islamic inheritance, a fundamental component of Islamic jurisprudence governing asset allocation among heirs, presents challenges due to its complexity. Accessible resources are crucial to address these challenges, with computational technologies offering promising solutions. This systematic mapping study provides a comprehensive overview of research at the intersection of computing and Islamic inheritance, comprising 20 studies identified primarily through snowballing. It analyses publication trends, identifies primary application domains, explores computational technologies utilized, assesses empirical evaluation methods, and uncovers gaps, challenges, and limitations in the existing literature, ultimately determining areas necessitating further research. The findings suggest a significant presence of researchers from Southeast Asia, predominantly with backgrounds in computing. The studies focused on the computation of wealth distribution, employing various computational technologies. Furthermore, the findings emphasise the importance of interdisciplinary collaboration and empirical evaluation to enhance technological solutions in this domain.
Copyright (c) 2026 Ghader Reda Kurdi https://creativecommons.org/licenses/by/4.0/
A novel Lucas-based adaptive sampling optimization for enhancing network lifetime
This paper introduced to enhance network lifetime using a novel Lucas based adaptive sampling methodology by sampling network condition to dynamically modifying sampling intervals using the Lucas sequence, this sequence not only used for sampling but also used to modify data collection, optimizing accuracy and energy efficiency. This technique aims to reduce superfluous data transmissions and conserve network resources by monitoring network utilization and adjusting sample with low medium and high rates. We enhance the network performance and longevity using Lucas based technique via simulation and demonstrating its potential. This may effectively approach novel address to challenges associated with constrained networks, particularly in the domain of IoT and wireless sensor networks (WSNs).
Copyright (c) 2026 Kanaka Raju Rajana, Shanmuk Srinivas Amiripalli https://creativecommons.org/licenses/by/4.0/
Enhancing road damage detection performance using the YOLOv9 model
Roads are essential infrastructure that support community mobility, and their condition significantly impacts road user safety. However, manual road damage detection remains inefficient, time-consuming, costly, and prone to human error. To address this issue, this study proposed the YOLOv9 model for automated road damage detection and explored parameter combinations to optimize its performance. The proposed solution leverages the YOLOv9 model, which offers enhanced detection speed and accuracy compared to previous YOLO versions, due to its improved backbone and dynamic label assignment techniques. The method uses pre-trained weights and performs parameter tuning to adapt the model for identifying common road defects, including potholes, longitudinal, lateral, and alligator cracks. A publicly available dataset of road condition images was used for training and evaluation. Experimental results demonstrated that the optimized YOLOv9 model achieved a mean average precision (mAP) of 62.8%, indicating a promising ability to detect multiple types of road damage accurately. This study highlights the potential of YOLOv9 as an effective tool for road monitoring systems, contributing to proactive maintenance strategies and more efficient infrastructure management.
Copyright (c) 2026 Muhammad Farkhan Adhitama, Sutikno, Rismiyati https://creativecommons.org/licenses/by/4.0/
Deep learning-based optimization techniques for network lifetime enhancement in wireless sensor networks
Wireless sensor networks (WSNs) are integral to applications like environmental monitoring, healthcare, and surveillance, yet they face the critical challenge of limited energy resources, which shortens the network's operational lifespan. Addressing this issue, this paper explores deep learning-based optimization techniques as a solution to enhance network lifetime by efficiently managing energy consumption. We present a detailed review of the existing challenges in WSNs and examine various deep learning methods, including neural networks, deep reinforcement learning (DRL), and generative adversarial networks, specifically tailored for WSN optimization. In this study, we introduce a new reinforcement learning (RL) based optimization algorithm to prolong the network lifetime. The proposed technique is intended to smartly distribute the energy consumption among the network elements, leading to desirable performance with regard to the battery lifetime. The paper ends with a summary of design aspects and future research directions to improve the WSN performance further based on deep learning.
Copyright (c) 2026 Abhay Raghunath Gaidhani, Amol D. Potgantwar https://creativecommons.org/licenses/by/4.0/
Evaluating user experience of a mobile website and redesigning its user interface using goal-directed design method
This study evaluated the usability of the user interface (UI) of a mobile website using its user experience (UX) perspectives. The website serves as an information portal intended for access via smartphones and other handheld devices. The objective of the study was to assess the usability of its current interface, redesign it using the goal-directed design (GDD) method, and compare the usability performance before and after the redesign. The study was conducted in five main steps using the cognitive walkthrough, think-aloud, post-study system usability questionnaire (PSSUQ), and interview techniques with five representative participants and 50 respondents. The most important findings of the study were that the redesigned mobile website showed improved usability of the website, as indicated by increased effectiveness and efficiency values, enhanced PSSUQ satisfaction scores, and more positive user feedback.
Copyright (c) 2026 Aang Subiyakto, Muhammad R. Alghifari, Nuryasin N., Muhammad Q. Huda, Nashrul Hakiem, Viva Arifin, Dwi Yuniarto, Hadi Rahman, Thosporn Sangsawang, Naeem Atanda Balogun https://creativecommons.org/licenses/by/4.0/
Exploring player interaction and team cooperation in MMOG playability enhancement
The massively multiplayer online games (MMOGs) continue to grow in popularity, and it has become particularly important to understand the key factors that influence team playability. While existing research has focused primarily on system functionality and individual player experience, insufficient attention has been paid to the role of team dynamics in player satisfaction. This study focuses on the core variables that influence team playability, including teamwork, task dependency, team loyalty (TLO), and team relationships (TR), and explores how these variables work together to influence player experience. This study used a combination of exploratory research (multi-variates) and a questionnaire survey (N=1064) to initially construct a team playability model, which was validated by structural equation modeling (SEM). The results show that TR have a significant positive effect on teamwork efficiency, and captains with transformational leadership (TL) styles not only enhance TR but also further improve overall team effectiveness (TE) and player satisfaction. This study provides MMOG developers with theoretical support for designing game mechanics centered on team interaction to enhance overall playability and player stickiness.
Copyright (c) 2026 Gong Xiaoxue, Lili Nurliyana Abdullah, Azrul Hazri Jantan, Noris Mohd Norowi, Fatimah Sidi, Gulmira Abildinova https://creativecommons.org/licenses/by/4.0/
Energy-efficient lightweight blockchain framework for scalable and secure sensor networks
Wireless sensor networks (WSNs) integrated with the internet of things (IoT) are hybrid technologies of interconnected systems. The IoT connects various devices, from sensors to smart gadget networks, and leverages a framework to provide secure solutions. This paper presents a lightweight adaptive proof-of-stake (APoS) blockchain framework design specifically for IoT-WSN. It focuses on efficient energy, scalability, and robust security. The proposed model integrates a hybrid APoS-delegated PoS (DPoS) consensus mechanism, trust-based routing, and a random forest (RF)-driven intrusion detection system (IDS). Extensive simulations of 100 to 10,000 nodes display energy usage of 0.018–0.019 mJ/node, breach of privacy rates of 0.02%, and throughput up to 9.92 tx/round for 1,000 nodes and 3.40 tx/round for GreenOrbs validation. The IDS achieves 94.21% accuracy for 1,000 nodes and 88.89% for GreenOrbs against distributed denial-of-service (DDoS), Sybil, and Jamming attacks. Validated using the GreenOrbs dataset, the framework ensures real-world applicability in resource-constrained WSNs. Future research has validated and verified the use of APoS and PoS hybrid models for broader decentralised IoT–WSN deployments.
Copyright (c) 2026 Surendran Swapna Kumar, Kalli Satyanarayan Reddy https://creativecommons.org/licenses/by/4.0/
Efficient email classification technique: a comparative study of header-only and full-content approaches
The purpose of this research is to explore efficient techniques and sufficient features for organizational email classification, with a focus on identifying emails that are not beneficial for work to reduce the burden of email management. This study proposes a novel approach by comparing the performance of using email header features (Header-Only) versus full email data (Header + Body), aiming to evaluate the accuracy and processing time of widely used machine learning algorithms, including Random Forest, SVM, KNN, XGBoost, and ANN. The experiment was conducted using the Enron dataset, with key features extracted from email headers such as sender and recipient addresses and from the body content. The results show that using only header information provides classification performance comparable to using full email content. In particular, models such as Random Forest, XGBoost, and LightGBM achieved accuracy exceeding 95%, while reducing processing time by up to 21.66% in the Random Forest model. It is evident that classifying emails using header-only features is both highly accurate and resource-efficient. This research offers practical guidance for organizations in developing effective email filtering systems without compromising classification quality.
Copyright (c) 2026 Worawit Kitikusoun, Nawaporn Wisitpongphan https://creativecommons.org/licenses/by/4.0/
Harnessing NLP and AI to decode political discourse: speech patterns, sentiment analysis, and public perception
Using natural language processing (NLP) and artificial intelligence (AI), this study analyzes the frequencies of words and phrases in political leaders’ speeches to track patterns in political discourse. The objective is to identify language patterns, sentiments, and topics of political addresses using state of-the-art methods like automatic transcription (Whisper), Bidirectional gated recurrent unit (GRU) for sentiment analysis, and BERTopic. Through the use of Whisper’s state-of-the-art transcription service, we were able to transcribe the political speeches into machine-readable text, which in turn provides for other types of analysis. Bidirectional GRU classifies sentiment as positive, negative, or neutral with the aim to study how politicians use sentiment to manipulate their listeners. Furthermore, we use BERTopic for tracking the evolution of rhetoric, key trend summarisation, and topic mining and analysis. It illustrates how politicians employ discursive strategies and epilinguistic elements to manage the public mind and reality. Achievements and objectives are framed with positive and defensive emotions aimed at threats or criticisms. The emotional grab of it all is still important. It locates in these the thematic coherence and shifting sentiment that lie at the heart of political storytelling. It shows how political communication is evolving to stay relevant in the digital media age and delivers language – even real-time language pattern tracking – via the use of AI and big data. Further study is needed of multimodal and flexible techniques for analysing political discourse across languages and time periods.
Copyright (c) 2026 Malayaj Kumar, Anuj Kumar Singh, Soumitra Das https://creativecommons.org/licenses/by/4.0/
Android mobile 3D augmented reality engineering devices design using marker-based technique
Engineering teaching and learning utilizing using augmented reality (AR) technologies is crucial with new technology adaptation. This study has developed an Android mobile based augmented reality of engineering device (ARED) with description using marker-based technique. Unity 3D, Vuforia, and Blender Animation were used to design 3D models of engineering devices on AR platforms. ARED is used to scan a marker and display an AR 3D model of engineering devices with its information. Ten engineering devices models were created using Blender Animation Tools and exported to Unity 3D which are Ups Power, Infrared Thermometer, Cisco Router, Multi meter, Poe Switch, Clamp Meter, Power Supply, Arduino Uno, Raspberry Pi and Oscilloscope. ARED mobile app is successfully tested which presents users can interact with the 3D model using touch input to enhance their learning experience. Result presents user’s evaluation analysis at 86.2% of ARED’s effectiveness and impact for future education. The technical analysis shows that ARED can handle the optimum distance range between 35 to 100 cm, operation angle is best between 45 and 135 degrees and occlusion average maximum of 55%. The significance of the research is to improve the quality and process of engineering education by using AR and promotes the learning society’s transition to digital learning with mixed reality in engineering, which creates a borderless learning environment.
Copyright (c) 2026 Mohamad Azim Ibrahim, Murizah Kassim, Jasni Mohammad Zain, Suhaili Beeran Kutty, Marina Mohd Yusoff, Barokah Isdaryanti, Farid Ahmadi, Nor Syazwani Mohd Pakhrudin https://creativecommons.org/licenses/by/4.0/
An machine learning-enhanced reconfigurable software defined radio architecture for adaptive 5G wireless systems
This paper presents a machine learning (ML)-enhanced software defined radio (SDR) architecture optimized for adaptive 5G wireless communication. The system incorporates predictive ML algorithms to enable real-time modulation selection, finite impulse response (FIR) filter reconfiguration, and spectrum adaptation based on dynamic channel parameters such as bit error rate (BER), received signal strength indicator (RSSI) and signal-to-noise ratio (SNR). A decision tree classifier and a deep Q-learning agent dynamically determine optimal modulation schemes (BPSK, QPSK, 16-QAM, OQAM) and filter tap configurations (4/8/16 taps), ensuring efficient communication under varying network conditions. Implemented on a Xilinx Zynq SoC using Verilog for datapath design and Python for ML control via AXI4-Lite, the architecture achieves a maximum operating frequency of 182.4 MHz, 40.7% logic utilization, and only 122.3 mW power consumption. Compared to existing SDR implementations, the system demonstrates a 17% frequency improvement, 28% power reduction, and 21% area savings. Real-time electrocardiogram (ECG) transmission confirms the system’s adaptability, achieving BER < 10⁻³ at 22 dB SNR and < 10⁻⁵ at 26 dB. These results affirm the viability of the proposed ML-SDR for edge-based biomedical and ultra-reliable low-latency communications (URLLC) applications in 5G networks.
Copyright (c) 2026 Vijaya Bhaskar Chalampalem, Sancarapu Nagaraju, Venkata Vara Prasad, R. Kiran Kumar, Shanmugham Balasundaram https://creativecommons.org/licenses/by/4.0/
Designing a flutter-based community recipe mobile application
This study focuses on developing a cross-platform mobile application for community-based recipe sharing, addressing the increasing role of mobile technology in daily life. Although recipe apps are globally popular, their adoption in Malaysia remains limited. The proposed application aims to fill this gap by providing users an interactive platform to explore, share, and try new recipes within a cooking-focused community. Key features include personalized recipe suggestions, and an intuitive, easy-to-use interface designed for all devices, enhancing user engagement and promoting community interaction. A background study is conducted to understand the existing landscape and user needs. It is followed by a design phase, which will lay the groundwork for addressing the identified challenges. Based on the insights gained from the background study and design outline, a mobile application is developed, aligning with the analyzed requirements and system design. This paper reports on the design and usability evaluation of this study. Based on the design guidelines, it has been found that this application could provide an intuitive and seamless user experience. Future works include the integration of smart kitchen features and personalized machine learning for better user experience.
Copyright (c) 2026 Nik Ahmad Uzair, Zarina Che Embi https://creativecommons.org/licenses/by/4.0/
Utilizing the machine learning-driven techniques used to ECG dataset for predicting coronary heart disease
The worldwide cause of mortality is cardiovascular heart disease. The automatic prediction of heart disease can be made to possible for accurate detection in initial stage. In recent year, the artificial intelligence approaches giving promising outcomes in predicting various types of cardiovascular conditions. The main focous of this work is to implementation of various machine learning techniques used to predict cardiovascular heart disease (CHD) using electrocardiogram (ECG) datasets. ECG provide the electrical Signal from the heart that identify the presence of disease or not. The preprocessing method are used for improving the quality of ECG signals and extract the features from ECG of patients. There are several well-established machine learning techniques, including support vector machine (SVM) and K-nearest neighbour (KNN)., logistic regression and decision tree classifier used for prediction of the disease. So, our finding of this paper will provide the new understanding regarding CHD prediction using different machine learning techniques. The Decision Tree-based machine learning model demonstrated excellent performance, achieving 98% accuracy, 96% precision, 100% recall, and an F1-score of 97%, which is better than rest of other comparative machine learning models. Finaly expermental results shows that decision tree approach providing better outcome amongs all the algorithms with respect to all above mensioned parameter.
Copyright (c) 2026 Mohd Osama, Rajesh Kumar, Chandrakant Kumar Singh https://creativecommons.org/licenses/by/4.0/
When web apps heal themselves: a MAPE-K based approach to fault tolerance and adaptive recovery
Ensuring the reliability and resilience of modern web applications remains a critical challenge due to increasing system complexity and dynamic runtime environments. This study proposes a modular self-healing framework based on the monitor–analyze–plan–execute over a shared knowledge base (MAPE-K) model, integrated with an AutoFix-inspired mechanism for adaptive fault recovery. Using a design and development research (DDR) approach, the system was implemented and evaluated through controlled fault injection experiments across twenty runtime failure scenarios, including service crashes, memory leaks, and database disconnections. Experimental results demonstrate that the proposed framework achieved a mean fault detection F1-score of 90.7% and a recovery success rate of 93.2%. The AutoFix module reduced the average time-to-recovery (TTR) by 56.2%, achieving an average recovery time of 3.92 seconds. System throughput was maintained between 88% and 95% during fault conditions, with only a 3.1% increase in response time. Additionally, iterative feedback mechanisms improved recovery efficiency by 18.6% over multiple cycles. These findings indicate that the proposed framework provides a practical and extensible approach to enhancing fault tolerance in web applications through feedback-driven adaptation. While the current implementation relies on predefined recovery strategies, the integration of learning-oriented feedback establishes a foundation for future development of more autonomous self healing systems.
Copyright (c) 2026 Sales G. Aribe Jr., Rov Japheth G. Oracion https://creativecommons.org/licenses/by/4.0/
Business intelligence and its impact on organizational decision-making: a systematic review
This research examines in detail how business intelligence (BI) supports and guides organizations in decision-making for their plans. The paper warns that the BI tool must be adapted to users' real needs. It's super crucial to keep all the important info in one spot. This optimizes resources and boosts the system's capabilities. The study used a set approach to tackle its main question. This included much searching through big science lists. Scopus and Web of Science were on the list. The search term was a particular word used to pinpoint documents. The review looked at studies from 2019 to 2025. Initially, we found 77 papers. Rules were then applied to include or exclude papers. These descartes criteria take into account the kind of paper, the language used, and how relevant it is to the subject. In the end, 24 papers went through the peer review process. These were reviewed following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The findings indicate that the application of BI considerably improves the group’s ability to attain superior goals. Some research showed a 93% boost in productivity. Profits went up by 65%, too. These results come only from articles written in English, Spanish, and Portuguese. They mainly focus on explaining the functioning in wealthier nations. The results really show off the main perks of BI. It facilitates informed decision-making more easily for all organisations.
Copyright (c) 2026 Cesar Patricio-Peralta, Hernan Peña Carnero, Jesús Zamora Mondragon, Adan Eugenio Contreras Angeles, Marina Vargas Vega, Walter Patricio Peralta, Marco Mayor Ravines, Juan Mayor Gamero, Cesar Paccha Rufasto https://creativecommons.org/licenses/by/4.0/
Diabetic retinopathy detection using SWIN transformer
Diabetic retinopathy (DR) is a diabetes related eye disorder that damages the retina. DR is among the most specific complications of diabetes. A vital challenge for automated detection systems in medical image diagnosis is to minimize the false negative rate for patients’ timely treatment. This paper presents a novel strategy employing the shifted window (SWIN) Transformer for efficiently modeling local and global visual information to address this challenge. We have proposed our work to maximize the true positive ratio and minimize the false negative ratio for the automated process of diagnosing the level of DR, so that patients with positive signs of DR can be predicted most accurately and can save vision. The results suggest that SWIN Transformer architecture, along with the contrast-limited adaptive histogram equalization (CLAHE) technique, provides a robust option for developing a reliable DR detection system. The results indicate that the proposed approach achieves 96% weighted recall across all the levels of DR detection and 97.45% validation accuracy for the eyePACS DR detection dataset, as well as 99% weighted recall across all the levels of DR detection, along with 99.26% validation accuracy for APTOS 2019 Blindness Detection dataset. Thus, this study aimed to develop a DR detection system focused on minimizing false negatives using the SWIN transformer.
Copyright (c) 2026 Sheetal J. Nagar, Nikhil Gondaliya https://creativecommons.org/licenses/by/4.0/
Advanced machine learning for enhanced abdominal organ segmentation
This research evaluates the ResUnet model’s performance in using computed tomography (CT) images to segment various abdominal organs. Weak boundaries, computing efficiency, and anatomical diversity are the current obstacles in abdominal multi-organ segmentation. By merging residual networks with U-Net, ResUnet overcomes obstacles by increasing precision and effectiveness, which qualifies it for use in medicine. The model’s effectiveness was assessed on a number of organs, and the segmentation accuracy was measured using the dice similarity coefficient (DSC). The ResUnet model’s ability to precisely segment organs with distinct borders was proved by its exceptional accuracy in segmenting important organs, such as the liver (mean DSC: 0.880) and spleen (mean DSC: 0.830). However, the model struggled to separate the esophagus correctly (mean DSC: 0.000) and struggled with smaller and more complex organs like the pancreas (mean DSC: 0.429) and gallbladder (mean DSC: 0.143). These results highlight the method’s limitations when handling organs with asymmetrical shapes or hazy borders.
Copyright (c) 2026 Rohini Pawar, Rohini Jadhav, Rohit Jadhav https://creativecommons.org/licenses/by/4.0/
Hybrid deep neural network model for aspect and opinion extraction with multi-head attention-driven sentiment analysis
Finding and extracting significant features from review sentences is known as aspect triplet extraction, and it provides succinct information on the elements that users have addressed. This method makes sentiment analysis and opinion mining easier, which helps to provide an adequate understanding of user opinions in reviews. This research presents a novel approach to achieve aspect-sentiment triplet-extraction (ASTE) using a deep neural network and transformer-based multi-head attention model. The proposed hybrid model adopts a pipeline methodology, concurrently extracting opinions and aspects while performing sentiment classification. The study addresses the intricate challenge of identifying triplets that capture nuanced relationships between terms and sentences, employing a deep neural network for joint extraction of aspects and opinions using a sequential tagging method. Sentiment classification is seamlessly integrated into the pipeline, treating sentiment recognition as a classification task, and aspect and opinion extraction as text-extraction challenges. Evaluations was out experimentally on the SemEval 2016 restaurant dataset demonstrate the effectiveness of the model, despite issues with unequal distribution of data.
Copyright (c) 2026 Abhinandan Shirahatti, Ramesh Medar, Vijay Rajpurohit, Sanjeev Kaulgud, Mrutyunjaya Mathad Shivamurthaiah https://creativecommons.org/licenses/by/4.0/
MLP-DT: a deep learning model for early prediction of diabetes and thyroid disorders
In this paper we present an intelligent and automated system for controlling diabetes and thyroid disorders. This system is designed to self-diagnose autoim mune diseases as early as possible in order to treat them quickly and thus slow down or stop their progression and thus provide a tool for self-control of dis eases. Our system is based on deep neural networks (DNNs), it contains several layers and it is classified as multi-layer perceptron (MLP). The proposed model called MLP model for early prediction of diabetes and thyroid disorders (MLP DT)uses a set of biomedical variables, allowing the system to formulate person alized treatment recommendations. To improve diagnostic accuracy and facili tate early screening, the system also incorporates machine learning techniques. The optimization in MLP-DT is provided by the adam optimizer algorithm, it is always applied to adjust the weights of the three hidden layers and the output layer (Sigmoid or Softmax). Experimental results demonstrate that the proposed MLP-DT model achieves reliable predictive performance and supports effective early screening of diabetes and thyroid disorders. These findings highlight the potential of the proposed approach as an intelligent decision-support tool for personalized healthcare and preventive medicine.
Copyright (c) 2026 Aouatef Chaib, Ouahiba Djama, Sabar Messaoudi https://creativecommons.org/licenses/by/4.0/
A new modified characteristic equation for optimal coordination of directional overcurrent relays
The integration of distributed generation (DG) into power systems is increasing to meet the requirements of the utility system. Renewable energy sources are given priority due to their clean energy and high consistency advantages. Integration of DG into the system makes the bi-directional flow of current. Directional type overcurrent relays are usually used for protection of lines associated with bidirectional power flows. The installation of DGs, (especially, inverter-based) invites challenges to the existing protection schemes. A new modified characteristic equation-based approach is proposed in this paper to obtain the faster operational time of relays. The relay coordination scheme proposed in this paper is applied to an 8-bus test system integrated with the solar-based photovoltaic integrated distributed generator (PVIDG). The comparative analysis between the conventional and proposed approaches is done.
Copyright (c) 2026 Neelakanteshwar Rao Battu, Surender Reddy Salkuti https://creativecommons.org/licenses/by/4.0/
Performance improvement of DC microgrids via adaptive neuro-fuzzy inference system -optimized AI-tuned fractional order proportional-integral-derivative controllers
This paper presents a novel approach to enhance the dynamic performance of direct current (DC) microgrids using an artificial intelligence (AI)-tuned fractional order proportional-integral-derivative (FO-PID) controller, further optimized through an adaptive neuro-fuzzy inference system (ANFIS). Conventional PID controllers tend to fail when it comes to dealing with microgrid environment-related non-linearities and uncertainties, particularly under changing load and generation situations. To remedy this, the suggested approach combines AI-tuned tuning algorithms for selecting initial parameters, and then ANFIS optimization to fine-tune the FOPID gains adaptively for better control precision. The performance of the hybrid control approach is tested through MATLAB simulations on a generic DC microgrid model that includes distributed energy resources, power electronic converters, and dynamic loads. Comparative evaluation against standard PID and independent FOPID controllers verifies remarkable advantages in terms of voltage regulation, stability, and transient response in various operating conditions. Amongst the achieved outcomes, it highlights the strength of the proposed ANFIS-optimized AI-tuned FOPID controller as a smart and robust strategy for real-time control of DC microgrids.
Copyright (c) 2026 Debani Prasad Mishra, Sarita Samal, Manas Ranjan Sahu, Sonna Murari, Piyuskant Das, Surender Reddy Salkuti https://creativecommons.org/licenses/by/4.0/
Predicting battery life performance using artificial intelligence techniques in electric vehicles
Electric vehicles’ (EVs’ performance and sustainability are significantly influenced by the efficiency and lifespan of their lithium-ion batteries. This paper explores the critical factors affecting battery degradation, focusing on parameters such as charge cycles, thermal management, and voltage dynamics. Utilizing a dataset of 14 batteries, the study employs data-driven machine learning (ML) to predict the remaining useful life (RUL) of batteries. The ensemble-based regression model demonstrated superior predictive accuracy through comprehensive analysis, achieving R² values of 97.89% for training and 94.69% for testing. Feature importance analysis identified cycle index (CI) as the most critical determinant of battery health, followed by discharge time and voltage stability. Visualizations, including correlation heatmaps and residual plots, validate the robustness of the selected model. Additionally, sustainable charging strategies, such as steady current-steady voltage (also known as CC-CV), are highlighted for their role in enhancing battery longevity. This research offers actionable insights into battery management systems, providing a robust foundation for predictive maintenance and the development of sustainable electric mobility solutions.
Copyright (c) 2026 Debani Prasad Mishra, Munavath Pavan Kalyan, Shivam Tyagi, Piyushjeet, Shiv Grover, Surender Reddy Salkuti https://creativecommons.org/licenses/by/4.0/
Early prediction of myocardial infarction using proposed score tree algorithm
Early detection and diagnosis of a diseases will have a big impact on the medical field and help to prevent loss of life. This study begins by gathering information on myocardial infraction patients from hospitals and focuses on earlier diagnostics. In fact, the pre-processed, confirmed data from a qualified doctor is used for this research. Early prediction of myocardial infarction (MI) is proposed by many researchers. They have used Kaggle datasets that is not recent, and they work on post MI. We have proposed early myocardial infraction detection works on unsupervised datasets. To identify myocardial infraction, numerous machines learning supervised algorithms, including decision tree (DT), random forest (RF), are employed in the literature. In this study, we use the score tree algorithm (STA), which operates on an unsupervised dataset, to present a unique early MI prediction method.
Copyright (c) 2026 Nusrat Parveen, Utkarsha Pacharaney, Gayatri Hegde, Mohammad Rafique, Sana Firoj Nalband, Shamim Akhtar, Satish Devane https://creativecommons.org/licenses/by/4.0/
Can machines imagine? Critical thinking and cultural reasoning in multimodal-multilingual AI
Effective communication across languages and cultures is essential in today’s interconnected world. Multimodal-multilingual language models (MMMLMs) aim to advance this goal by integrating text, speech, and visual understanding across diverse linguistic contexts. This study evaluates four leading MMMLMs-GIT, mPLUG, CLIP, and Whisper + GPT-4V-on cross lingual and cross-modal tasks, including image captioning, visual question answering, speech-to-image generation, and idiomatic translation. Performance was assessed in high-resource (English, Arabic), medium resource (Malay), and low-resource (Macedonian) settings. Results show strong performance in structured tasks but notable limitations in cultural reasoning, figurative language interpretation, and semantic grounding in low-resource environments. GIT delivered the most consistent multilingual results, while Whisper + GPT-4V excelled in fluency yet lacked cultural sensitivity. To address these gaps, the study proposes culturally informed evaluation protocols that integrate quantitative metrics such as BLEU, CIDEr, and F1 with qualitative, community-centered approaches. These include cross-cultural annotation panels, inter-rater reliability validation using Cohen’s kappa, and a novel “cultural fidelity” metric to measure alignment with culturally specific norms. The findings emphasize the need for inclusive datasets, ethical development, and interdisciplinary collaboration to ensure MMMLMs support equitable and culturally aware global communication.
Copyright (c) 2026 Mohammad Awad AlAfnan, Siti Fatimah MohdZuki, Shefa Mohammad AlAfnan https://creativecommons.org/licenses/by/4.0/
Trust-based secure routing in IoT networks using machine learning for enhanced anomaly detection and risk mitigation
The rapid growth of the internet of things (IoT) has led to the development of new challenges in ensuring secure and reliable data transmission. This paper proposes a trust-based secure routing protocol (TBSRP) designed to mitigate security threats such as routing attacks in IoT networks. The core innovation lies in the dual-layer trust evaluation mechanism, which combines reputation-based trust and behavioral analysis to dynamically adjust routing decisions based on real-time performance and historical behavior of network nodes. To enhance security, the protocol incorporates an adaptive threshold mechanism that adjusts trust criteria based on observed network conditions and an anomaly detection system utilizing machine learning (ML) algorithms for real-time monitoring of node behavior. Experimental evaluation demonstrates that TBSRP outperforms existing protocols (such as Ad hoc on-demand distance vector (AODV), trust-based AODV (TB-AODV), energy-efficient secure routing (ESR), and Secure AODV (SEC-AODV)) in key performance metrics, including packet delivery ratio (PDR), end-to-end delay, throughput, and routing overhead. The proposed protocol exhibits strong resilience to the increasing number of malicious nodes and varying network conditions, making it highly effective for securing IoT networks. This work contributes to the development of adaptive, scalable, and secure routing protocols for IoT environments, with the potential for further optimization through advanced ML techniques and real-world implementation.
Copyright (c) 2026 Sangeetha Krishnaswamy, Arulanandam Karalagan https://creativecommons.org/licenses/by/4.0/
Mobile device application design for ThingSpeak interface using flutter
The rapid development of internet of things (IoT) is prompting many people to design applications, particularly for monitoring applications based on mobile apps. This includes research designs to monitor electrical parameters from PV and the development of health monitoring applications. Previous research required a separate application to scan each IoT device. In this research, a mobile app-based IoT monitoring system was built using flutter. With this, people no longer need to design separate mobile apps for various IoT devices. This application utilizes the flutter framework, while the cloud component uses ThingSpeak. These research results show that data from multiple IoT devices can be transferred to the user’s mobile app. This application enables the monitoring of various IoT devices through a single mobile app, thereby enhancing the efficiency of IoT device design and management.
Copyright (c) 2026 Moehammad Sauqy Ihza Zuliandra, Tigor Hamonangan Nasution, Ainul Hizriadi https://creativecommons.org/licenses/by/4.0/
Advanced IoT-integrated real-time fire detection and automated mitigation system
In the field of industry and commerce safety, tackling the most challenging and ongoing fire threats requires the advance internet of things (IoT) integrated real-time fire detection and automated mitigation system. Leveraging IoT and multi-modal sensing in fire safety, the system combines flame, gas, and humidity sensors and cameras to provide continuous real time monitoring and appropriate management of the threats. Real-time automated hazard interventions, such as sprinkler system engagement and geocoded alerts to fire departments, significantly improve life safety outcomes of the system. Active damage mitigation IoT devices provide integrated damage mitigation safety and individual IoT device remote monitoring. In the scope of industry and commerce, this system is a demonstration of the impact of IoT on improving fire safety.
Copyright (c) 2026 Rama Krishna Peddarapu, Ajimera Abhinav, Gnana Sathwika V. N. V., Poosa Brijesh, Amrutha Varshini Ravula https://creativecommons.org/licenses/by/4.0/
Real-time emotion prediction system using big data analytics
Emotions are an inseparable part of human existence. Emotions have a big impact on the success and failure of the human race. Comprehending human emotions could prove beneficial in creating improved systems for education, security, market sales, production, healthcare and other areas. Big data analytics applied to streamlined real time emotion sensor’s data can give new insights to anticipate stress before it arises and help in making significant choices that improve people's quality of life. This work proposes a framework for big data management and analysis of GSR sensor’s data in real-time for predicting emotions in human participants. Supervised learning techniques, ensemble boosted tree, neural network, Naïve Bayes, support vector machine, decision tree, K-nearest neighbor, and quadratic discriminant analysis are applied to the collected data. Two distinct criteria have been utilized for testing on real-time data one is trained on all participant data, resulting in a generalized system, while the other is trained on participant-specific data, resulting in a personalized system. Hence, the personalized system achieves an accuracy of up to 80.64% across all classes and 100% for binary classes as compare to generalized system achieves 78.12% accuracy. It is concluded that for the purpose of predicting emotions, the personalized model performs better than the generalized model.
Copyright (c) 2026 Manpreet Kaur Dhaliwal, Rohini Sharma, Rajbinder Kaur https://creativecommons.org/licenses/by/4.0/
Manufacturing mycelium moulds under controlled conditions using IoT
In the process of making plastics, potentially dangerous substances like colourants or stabilisers are added. One example is phthalates, which are used to make PVC. The ecology is significantly impacted by the way plastic products are disposed of as well. The majority of plastics can take a long time to biodegrade lengthy time to break down if disposed of in a landfill. The issue of plastic trash is getting worse. Plastic is incredibly valuable due to its cheap availability and low cost of production; however, its recyclability has been oversold. Mycelium mould is a fantastic substitute for plastic. Mycelium is more efficient in terms of biodegradability and sustainability compared to plastic. The properties of Mycelium include heat insulation, fire resistance, water resistant, acoustic insulation, low weight, vegan meat, beauty products, and mainly bio-degradable. All these features make mycelium our only last chance to win the war against the plastic with greater potential than the other alternatives for plastics available in the market currently. Here, we have shown how mycelium can be grown in the most efficient way ever without any contamination and faster growth cycle. The primary goal is to lower the cost of mycelium mould, lessen mycelium spoiling, and accelerate its growth cycle by offering an ideal growing.
Copyright (c) 2026 Subbulakshmi V., Jeevaa Katiravan, Parvathy M., Sridevi S. https://creativecommons.org/licenses/by/4.0/
Integrating IoT for advancing agriculture: innovations and implications for future surveys
The internet of things (IoT) is revolutionizing agriculture, offering a paradigm shift in how we cultivate crops and manage livestock. By integrating IoT devices such as sensors, drones, and smart machinery into farming practices, agricultural operations gain unprecedented levels of data driven insights and control. This abstract emphasizes the pivotal role of IoT in agriculture and its far-reaching implications for the future. IoT empowers farmers with real-time information on essential factors like moisture of soil, nutrient levels, weather patterns, and health of crops, helping make accurate decisions while optimizing resources. Through IoT-enabled monitoring and automation, farmers can remotely manage irrigation, pest control, and livestock health, reducing manual labor and minimizing environmental impact. The implications of IoT in agriculture extend beyond individual farms, shaping the future of food production on a global scale. With a burgeoning world population and climate change threatening traditional farming methods, IoT offers solutions for enhancing productivity, sustainability, and resilience in the face of emerging challenges. From precision agriculture to smart supply chains, the revolutionary prospect of IoT in agriculture promises to ensure food security, economic viability, and environmental stewardship for generations to come.
Copyright (c) 2026 Debani Prasad Mishra, Rakesh Kumar Lenka, Aditya Kumar, Aditya Jasrotia, Surender Reddy Salkuti https://creativecommons.org/licenses/by/4.0/
IoT-enabled smart hydroponic system using nutrient film technique for precision agriculture
The study aims to develop an internet of things (IoT)-enabled automated hydroponic system using the nutrient film technique (NFT) to optimize plant growth with minimal human intervention. The system integrates sensors, microcontrollers, and cloud-based monitoring to maintain optimal conditions for crops. The system utilizes Arduino Uno, ESP8266 Wi-Fi module, and sensors including pH, TDS, DHT11 and water level sensors. Data collected from these sensors is processed in real time, allowing automated adjustments through relay-controlled water and nutrient pumps. The system transmits data to the ThingSpeak IoT platform, enabling remote monitoring and predictive analytics. The proposed hydroponic system ensures stable environmental conditions, improving plant growth efficiency. Key parameters such as pH, TDS levels and humidity are maintained within optimal ranges. The automated system reduces manual intervention, enhances water and nutrient efficiency, and increases yield consistency compared to traditional farming methods. The IoT-based NFT hydroponic system demonstrates significant potential in urban agriculture and controlled environment farming. By leveraging automation, AI-driven analytics, and cloud-based monitoring, it provides a scalable and sustainable solution for precision farming. Future advancements may include AI-based predictive analytics, solar-powered energy solutions, and robotic automation for further optimization.
Copyright (c) 2026 Varuna Kumara, Akshatha Naik, Fatima Tahsir, Sinchana Bommayya Devadiga, Vinitha Ramesh Naik https://creativecommons.org/licenses/by/4.0/
Semantic interoperability in IoT for Industry 4.0: Review, taxonomy, challenges, and future research
Semantic interoperability is a critical enabler for achieving the Industry 4.0 vi sion, ensuring that heterogeneous IoT devices, systems, and applications can ex change and interpret data consistently. Despite its importance, achieving seman tic interoperability continues to pose significant challenges due to the diversity of data formats, standards, and ontologies used across industrial IoT environ ments. This paper presents a comprehensive review and taxonomy of semantic interoperability within Industry 4.0, analyzing existing frameworks, protocols, and ontological models. We classify current approaches based on their architec tural layers, semantic technologies, and application domains. Additionally, this study identifies the limitations of prevailing solutions, highlights open research challenges, and proposes future directions for enhancing semantic interoperabil ity in industrial IoT systems. The insights provided aim to support researchers and practitioners in developing scalable, secure, and semantically aligned IoT ecosystems for Industry 4.0.
Copyright (c) 2026 Devamekalai Nagasundaram, Erum Ashraf, Selvakumar Manickam, Shams Ul Arfeen Laghari, Shankar Karuppayah https://creativecommons.org/licenses/by/4.0/