Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

29,922 Article Results

A comprehensive survey of cyberbullying on social media: challenges, detection, and AI-based prevention

10.11591/ijai.v15.i1.pp86-96
Ammar Odeh , Osama Alhaj Hassan , Anas Abu Taleb , Abobakr Aboshgifa , Nabil Belhaj
Cyberbullying is a pervasive issue in the digital landscape, particularly on social media platforms, where individuals engage in online harassment, intimidation, and abuse. Unlike traditional bullying, cyberbullying has a broader reach, anonymity, and persistence, making it a growing concern for mental health, social well-being, and online safety. This paper provides a comprehensive survey of cyberbullying trends, its psychological and social impacts, and the role of social media in amplifying the problem. It explores existing detection and prevention strategies, including artificial intelligence (AI)-driven approaches, policy frameworks, and platform-based moderation techniques. Furthermore, it discusses challenges in enforcement, the limitations of automated detection systems, and the need for improved legal measures. This paper uniquely contributes an integrated perspective on cyberbullying detection and prevention by synthesizing current research across psychological, sociocultural, and technical dimensions. It emphasizes underexplored gaps such as multilingual detection, real-time moderation, and cross-platform enforcement, and proposes a layered framework to guide future research and policy.
Volume: 15
Issue: 1
Page: 86-96
Publish at: 2026-02-01

Adaptive feature fusion network for fetal head segmentation in ultrasound images

10.11591/ijai.v15.i1.pp841-851
Vimala Nagabotu , Pavan Kumar Reddy Sana , B. Lakshmi Bhavani , Donapati Srikanth
The measurement of fetal biometrics from ultrasound images plays a vital role in assessing potential development during pregnancy. However, existing fetal segmentation methods failed to accurately segment and asses the head circumference that gives inaccurate segmentation results. To overcome this limitation, a feature feedback and global feature with adaptive feature fusion network (FGA–Net) model is proposed to enhance fetal head segmentation (FHS). It involves four key components for feature extraction, fusion, and correction, respectively. The adaptive feature fusion module (AFFM) and correction map integrate the local features and global features and refine the features to enhance accurate FHS from the ultrasound images efficiently. Initially, ultrasound images are obtained from the two publicly available datasets and preprocessed using normalization and data augmentation techniques. Finally, preprocessed images are fed to FHS by proposed FGA Net utilizing EfficientNet-B0 as the backbone network for efficient feature extraction. Experimental results of proposed FGA-Net are evaluated using the dice coefficient (DC) of 95.78% and 98.95% for FH-PS-AoP and HC-18 datasets, which shows better results than the existing segmentation approaches like inverted bottleneck patch expanding (IBPE) method.
Volume: 15
Issue: 1
Page: 841-851
Publish at: 2026-02-01

Adaptive transformer architecture for scalable earth observation via hyperspectral imaging

10.11591/ijai.v15.i1.pp824-830
Devendra Kumar Saragoor Madanayaka , Devanathan Muthukrishnan
Hyperspectral Image (HSI) classification is one of the critical processes involved in remote sensing application that plays a crucial role towards earth observation. Owing to complex spatial-spectral relationship and high dimensionality, it is quite a challenging task to subject HSI content to conventional data analytics or existing methods. Hence, the proposed study introduces a novel computational model known as Adaptive Spectra-Spatial Transformer (ASST) to address these ongoing challenges and shortcoming of existing Artificial Intelligence (AI) based modelling. The proposed model contributes towards a novel transformer-based architecture where a distinct spectral-spatial attention method has been used with transformer encoder. This novel combination facilitates highly adaptive and contextually enriched feature extraction. Tested on universally standard HSI dataset of Pavia University, the proposed ASST model has been benchmarked with notice 97.26% of overall accuracy and faster processing duration computed via training and response time in contrast to frequently adopted ML and DL models. The accomplished study outcomes truly exhibited highly improved feature representation as well as robust performance against class imbalance problems towards scalable data analysis of HSI contents for earth observation.
Volume: 15
Issue: 1
Page: 824-830
Publish at: 2026-02-01

Single hidden layer feedforward neural networks for indoor air quality prediction

10.11591/ijai.v15.i1.pp322-328
Dwi Marisa Midyanti , Syamsul Bahri , Ilhamsyah Ilhamsyah , Zalikhah Khairunnisa , Hafizhah Insani Midyanti
Indoor air quality (IAQ) has become a problem because it affects human health, comfort, and productivity. Predicting air quality is a complex task due to the dynamic nature of IAQ variable values simultaneously. In this study, the single hidden layer feedforward neural networks model is used, namely radial basis function (RBF), self-organizing maps (SOM)-RBF, and extreme learning machine (ELM) to classify IAQ. This study also observed the effect of the number of neurons in the hidden layer on the model accuracy and overfitting of each network. The experimental results show that the number of neurons in the hidden layer can affect the accuracy of the RBF and SOM-RBF models. Among the three models used, RBF produces very good training data accuracy but also the most significant overfitting value. The largest overall accuracy was obtained using SOM-RBF, with a value of 86.37%.
Volume: 15
Issue: 1
Page: 322-328
Publish at: 2026-02-01

Integrating contrastive and generative AI with RAG for responsible and fair CV classification

10.11591/ijeecs.v41.i2.pp710-719
Soumia Chafi , Mustapha Kabil , Abdessamad Kamouss
The automation of curriculum vitae (CV) classification raises major challenges related to accuracy, fairness, and the heterogeneity of candidate documents. Existing approaches often address these dimensions separately and struggle to reduce demographic bias while maintaining high predictive performance. This study addresses this gap by proposing a hybrid pipeline that combines contrastive learning for representation with a lightweight generative model within a retrieval-augmented generation (RAG) framework. The method is evaluated on a large dataset of 50,000 CVs, using standard classification metrics as well as fairness indicators based on reductions in demographic disparities and equality of opportunity. Experiments show that our approach achieves an accuracy of 95.6% and a fairness index of 0.94, reducing gender-related disparities from 4.8% to 0.3%. These results demonstrate that it is possible to simultaneously improve predictive performance and fairness through a multi-level fairness strategy. The proposed system thus represents a practical and responsible solution for integrating AI into recruitment processes.
Volume: 41
Issue: 2
Page: 710-719
Publish at: 2026-02-01

Joint angle prediction and joint-type classification in human gait analysis using explainable deep reinforcement learning

10.11591/ijeecs.v41.i2.pp564-578
Deepak N. R. , Soumya Naik P. T. , Ambika P. R. , Shaik Sayeed Ahamed
Human gait analysis is a key component of rehabilitation, prosthetics, and sports science, especially for clinical evaluation and the development of adaptive assistive technologies. Accurate joint-angle estimation and dependable joint-type classification remain difficult because of the complex temporal behavior of gait signals and the limited interpretability of many deep learning (DL) approaches. While recent techniques have enhanced predictive accuracy, their clinical applicability is often limited by insufficient transparency and adaptability in learning mechanisms. To overcome these limitations, this work proposes an integrated framework that unifies DL, reinforcement learning (RL), and explainable artificial intelligence (XAI). Stochastic depth neural networks (SDNN) are applied for joint-angle regression, whereas deep feature factorization networks (DFFN) are used for multi-class joint-type classification. Optimization is achieved using Q-learning (QL) and mutual information maximization (MIM), ensuring stable convergence and improved learning efficiency. To improve interpretability, the framework incorporates counterfactual and contrastive explanations, feature ablation studies, and prediction probability analysis. Experimental findings show that the SDNN MIM model attains an R2 score of 0.9881, with RL rewards increasing from 0.997 to 0.999 during regression training. For joint-type classification, the DFFN MIM model achieves an accuracy of 0.95, with reward values improving from 0.90 to 0.98. These results demonstrate the effectiveness of the proposed framework in delivering accurate and interpretable gait predictions, supporting its relevance to biomechanics, healthcare, personalized rehabilitation, and intelligent assistive systems.
Volume: 41
Issue: 2
Page: 564-578
Publish at: 2026-02-01

A hybrid edge–cloud computing framework for low-latency, energy-efficient, and sustainable smart city applications

10.11591/ijeecs.v41.i2.pp791-799
Kamal Saluja , Tanya Khaneja , Sunil Gupta , Reema Goyal , Wai Yie Leong
Smart-city applications demand ultra-low latency, high reliability, and sustainable operation, which are difficult to achieve using cloud-only or edge-only computing paradigms. This study suggests a carbon-conscious architecture for managing smart cities’ intelligent job offloading between the edge and the cloud. This is made possible by the Internet of Things and driven by reinforcement learning (RL). A deep Q-network (DQN) is used to dynamically assign tasks to cloud servers and edge nodes based on how much energy they use, how long it takes to send data over the network, and how much bandwidth they have. A lightweight permissioned blockchain layer makes sure that data is correct across all of its parts, and carbon-aware scheduling puts low-carbon resources first. EdgeCloudSim is used to test the system with real-world smart city workloads. When compared to systems that simply use the cloud, the proposed solution showed a 64.6% drop in average latency, a 24.2% drop in energy use, and a 15% drop in carbon emissions. Combining artificial intelligence (AI)-driven orchestration with scheduling that takes sustainability into account in a hybrid edge-cloud environment yields positive outcomes.
Volume: 41
Issue: 2
Page: 791-799
Publish at: 2026-02-01

Enhancing industrial cybersecurity via IoT device-trusted remote attestation framework with zero trust architecture in brewery operations

10.11591/ijeecs.v41.i2.pp720-730
Muhammad Salman , Alan Budiyanto
The rapid expansion of industrial internet of things (IIoT) adoption in Industry 4.0 has improved automation and real-time control yet simultaneously increased security risks in operational technology (OT) environments, where device integrity and system reliability are critical. Existing attestation approaches such as SAFEHIVE, SEDA, CRA, and ERASMUS provide scalable verification capabilities but still lack continuous hardware-rooted validation and adaptive access control required for real-time industrial systems. To address this gap, this study proposes a hybrid cybersecurity framework that integrates IoT device-trusted remote attestation (ID-TRA) based on trusted platform module (TPM) with zero trust architecture (ZTA) to ensure continuous device trustworthiness in brewery operations. The framework was implemented on an industrial testbed with programmable logic controllers (PLCs), edge devices, and industrial switches, and it was evaluated through measurements of attestation latency, false positive rate, communication overhead, and TPM resource utilization. Experimental results show that the framework achieves an average attestation latency of 250 ms, a false positive rate below 2%, and a communication overhead of only 1.1%, while TPM resource usage remains within acceptable bounds (62% CPU and 48 MB RAM). These outcomes demonstrate that the proposed solution can reliably detect unauthorized firmware modifications, prevent compromised devices from accessing critical network zones, and maintain compatibility with real-time control processes. Overall, the integration of ID-TRA and ZTA enhances device-level assurance and strengthens industrial cybersecurity resilience against firmware tampering, replay attacks, and unauthorized lateral movement.
Volume: 41
Issue: 2
Page: 720-730
Publish at: 2026-02-01

A new hybrid model based on machine learning and fuzzy logic for QoS enhancing in IoT

10.11591/ijeecs.v41.i2.pp624-632
Oussama Lagnfdi , Marouane Myyara , Anouar Darif
The fast expansion of internet of things (IoT) devices presents a more complicated scenario for maintaining a stable quality of service (QoS), which would guarantee the network’s dependable operation. The emergence of increasingly complex applications that call for additional devices makes this even more crucial. Adaptive intelligence solutions that guarantee optimal network behavior are therefore required. This paper presents a hybrid optimized solution for a three-layer IoT network that models the application, network, and perception layers of an IoT network using machine learning and fuzzy logic (FL). This method guarantees optimal QoS prediction with improved network adaptability by using fuzzy membership parameters. When the number of devices increases from 100 to 1,500, FLGA maintains an average QoS of 95% to 87%, while FL maintains 84% and RANDOM maintains 79%. At the application level, genetic algorithm (GA) continues to outperform RANDOM by 15.57% and FL by 6.32%. The goal of this paper is to provide a solid network solution that could enhance the consistency of QoS performance in order to combat the increasingly complex scenario of an IoT network.
Volume: 41
Issue: 2
Page: 624-632
Publish at: 2026-02-01

Enhanced soil moisture sensing using graphene-coated copper electrodes

10.11591/ijeecs.v41.i2.pp470-477
Nuralam Nuralam , Rizdam Firly Muzakki , Sri Lestari Kusumastuti
Soil moisture monitoring is essential for precision agriculture to optimize irrigation and increase crop productivity. Traditional conductivity-based sensors often face limitations such as low sensitivity, slow response, and measurement instability. This study presents a simple and effective enhancement method by applying a graphene coating on copper electrodes using the drop casting technique. Experimental evaluations were conducted on natural soil samples at varying moisture levels. The graphene-coated sensor exhibited a significantly higher sensitivity of 23.0 Ω/% compared to 12.0 Ω/% for the uncoated sensor, a faster response time of approximately 5 seconds, and improved measurement consistency with a reduced standard deviation of ±15 Ω. Graphene's superior electrical conductivity and strong water affinity are key factors contributing to this performance improvement. These findings indicate that graphene-coated sensors offer a promising solution for reliable, cost-effective soil moisture monitoring in smart farming systems.
Volume: 41
Issue: 2
Page: 470-477
Publish at: 2026-02-01

Intelligent cybersecurity framework for real-time threat detection and data protection

10.11591/ijeecs.v41.i2.pp504-514
Gunti Viswanath , Kurapati Srinivasa Rao
Organizations operating across cloud, mobile, and enterprise environments are increasingly exposed to sophisticated cyberattacks that traditional rule-based security systems struggle to detect in real time. These legacy approaches lack adaptability, making it difficult to continuously monitor distributed networks, identify anomalies, and prevent zero-day threats before sensitive data is compromised. To address these challenges, this paper proposes an intelligent cybersecurity framework that integrates real-time network monitoring with AI/ML-based anomaly detection models. The framework utilizes structured preprocessing, feature engineering, and supervised learning on the UNSW-NB15 dataset (version 2015, Cyber Range Lab) to enhance detection accuracy and reduce response time. The experimental setup evaluates multiple ML classifiers using stratified train- test splitting and 5-fold cross-validation, ensuring robust performance validation. Experimental results show that the random forest (RF) model achieves 94.28% accuracy, a 2.93% false-positive rate, and an average detection time of 0.41 seconds, outperforming other baseline models. In addition to the detection layer, the framework incorporates mobile device management (MDM) controls and cloud-storage policy enforcement to strengthen organizational security posture. The main contributions of this work include: i) a unified AI/ML-driven anomaly detection model, ii) integration of MDM and cloud policy enforcement for end-to-end protection, and iii) improved empirical performance validated using a benchmark cybersecurity dataset. This combined architecture significantly enhances real-time threat identification and reduces alert latency, supporting a more security-aware and resilient enterprise environment.
Volume: 41
Issue: 2
Page: 504-514
Publish at: 2026-02-01

Robust palmprint biometric solution for secure mobile authentication

10.11591/ijeecs.v41.i2.pp680-689
Son Nguyen , Arthorn Luangsodsai , Pattarasinee Bhattarakosol
Smartphones increasingly rely on biometric authentication for access to financial and personal services, creating a need for palmprint recognition that is accurate, fast, and deployable on device. This paper proposes an end-to-end smartphone palmprint authentication framework that integrates guided mobile image capture, landmark-based region-of-interest (ROI) extraction, and compact embedding inference. A ResNet-18 teacher is first trained with self-supervised contrastive learning to reduce dependence on labeled biometric data, then distilled into a lightweight MobileNetV3 student for efficient mobile deployment. The learned embeddings support both on device verification and large-scale identification using an approximate nearest neighbor index (FAISS). Experiments on a public Kaggle palm dataset achieve 99.2% accuracy with a 0.15% equal error rate (EER). On an iPhone 13, the end-to-end pipeline runs in 87.0 ms with a 12.4 MB student model. For a 1 million-entry gallery, FAISS provides 32 ms query latency while maintaining 99.5% Recall@1. Limitations include evaluation under mostly controlled capture conditions and the absence of an explicit liveness or presentation attack detection (PAD) module; future work will address unconstrained testing and anti-spoofing integration.
Volume: 41
Issue: 2
Page: 680-689
Publish at: 2026-02-01

ETV: efficient text vision for text localization in natural scene images

10.11591/ijeecs.v41.i2.pp812-822
Suman Suman , Champa H. N.
In the current digital era, the extraction and comprehension of textual information from images have emerged as pivotal tasks. With the exponential growth of text documents, efficient processing and analysis have become imperative. However, text localization in images remains challenging due to complex backgrounds, uneven illumination, diverse text styles, and perspective distortions, rendering traditional optical character recognition (OCR) techniques inadequate. To address these challenges, this paper proposes an integrated method named efficient text vision (ETV). ETV combines the OCR capabilities of Tesseract with the efficient and accurate scene text detector (EAST) algorithm, supplemented by nonmaximum suppression (NMS). The Tesseract OCR component facilitates the extraction and identification of individual characters, while EAST excels in the efficient detection and localization of complete text sections. The incorporation of NMS enhances localization accuracy by eliminating redundant or overlapping bounding boxes.
Volume: 41
Issue: 2
Page: 812-822
Publish at: 2026-02-01

Control of multi-level NPC inverters in PV/grid systems using ADRC and MADRC

10.11591/ijeecs.v41.i2.pp456-469
Gherici Dinar , Ahmed Tahour
Grid-connected photovoltaic (PV) systems consist of solar panels that convert sunlight into electrical energy, interconnected directly with the utility grid. These systems comprise several key components: PV, multilevel, controllers, and grid interface equipment. In this context, fivelevel inverters are increasingly favoured over three-level inverters due to their ability to reduce total harmonic distortion (THD), improve efficiency, and ensure better power quality in grid-connected applications. This research presents a three-level enhanced control scheme aimed at optimizing the performance of a grid-connected photovoltaic system with a five-level inverter. A fractional-order proportional-integral (FOPI) controller is utilized for maximum power point tracking (MPPT) to ensure precise tracking under variable irradiance conditions. At the grid-interface stage, a modified active disturbance rejection controller (MADRC) is developed for grid-interface, featuring an inner loop for DC-link voltage regulation based on Lyapunov theory, leading to improved dynamic performance with lower THD of the grid current and enhanced efficiency. Simulation results highlight the effectiveness of the proposed system. Compared with the FOPI-ADRC, a three-level configuration (0.38% THD), the proposed FOPI-MADRC with a five-level inverter achieves superior performance, with only (0.22% THD). These results confirm the advantages of combining advanced control strategies with multilevel inverter technology in improving both power quality and system efficiency.
Volume: 41
Issue: 2
Page: 456-469
Publish at: 2026-02-01

Engineering intelligence for sustainable and secure digital futures

10.11591/ijeecs.v41.i2.pp453-455
Tole Sutikno
This editorial introduces Volume 41, Number 2 (February 2026) of the Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), which presents a diverse collection of peer-reviewed articles reflecting recent advances in electrical engineering, electronics, and computer science. The issue highlights the convergence of power and energy systems, artificial intelligence, cybersecurity, the Internet of Things (IoT), and datadriven engineering methodologies in addressing contemporary technological and societal challenges, with key contributions focusing on renewable energy integration, intelligent control strategies, secure and trusted digital infrastructures, smart IoT-based systems, and AI-driven applications in healthcare, finance, industrial automation, and human-centered computing. Particular emphasis is placed on energy efficiency, system resilience, explainable and trustworthy artificial intelligence, and sustainable engineering practices. Collectively, the published works demonstrate how interdisciplinary research can bridge theory and real-world implementation while supporting the United Nations Sustainable Development Goals, including affordable and clean energy, good health and well-being, sustainable cities, responsible consumption, and strong digital institutions. By fostering innovation, cross-domain collaboration, and responsible technology development, this issue of IJEECS aims to advance secure, intelligent, and sustainable engineering solutions that respond to both current demands and future global challenges. This issue further reinforces the journal’s commitment to advancing engineering intelligence that is ethically grounded, environmentally responsible, and resilient by design.
Volume: 41
Issue: 2
Page: 453-455
Publish at: 2026-02-01
1 2 3
...
1994 1995
Show 49 of 1995

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration