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

31,456 Article Results

Arabic calligraphy style recognition with edge enhancement and attention

10.11591/ijict.v15i4.pp1510-1522
Bilal Abdulrahman Tuama , Farhan Mohamed
Arabic calligraphy style recognition is a challenging task because visually similar styles often share curved strokes, connected structures, and subtle ornamental variations. This study proposes a convolutional neural network (CNN) architecture that combines learnable edge enhancement, squeeze-and excitation (SE) attention, and optional BiLSTM-based sequential modeling for Arabic calligraphy style classification. The edge module is designed to preserve fine-grained stroke boundaries, while the attention mechanism emphasizes informative channel responses and the BiLSTM models width wise contextual dependencies. Experiments were conducted on a public Arabic calligraphy style dataset using a stratified 70/15/15 train-validation test split. The best configuration achieved 98.22% accuracy and 98.20% F1-score on the test subset. These findings indicate that adaptive edge-aware feature learning can improve the discrimination of visually similar Arabic calligraphy styles. Nevertheless, broader validation on additional datasets and repeated runs is still needed to confirm generalizability.
Volume: 15
Issue: 4
Page: 1510-1522
Publish at: 2026-12-01

Smart integrated system for optimal resource utilisation

10.11591/ijict.v15i4.pp1921-1928
Rama Krishna Peddarapu , Vaishnavi Chillara , Sudha Manasa Jagilanka , Maneesh Kanumolu , Manvitha Choutymetla
One of the main obstacles to agriculture development and sustainability is the improper use of such natural resources as water, fertilizer, and energy. In this paper, the concept of smart integrated system for optimal resource utilization (SISORU) will be presented. The idea of the project consists of the development of the web application based on ICT, machine learning (ML), and visualizing technology on behalf of the executive process in agriculture. SISORU differs from existing agricultural solutions by combining the recommendations on crop choice, cost estimation, and resource shortages detection in one solution.
Volume: 15
Issue: 4
Page: 1921-1928
Publish at: 2026-12-01

Deep transformer architectures for real-time e-payment fraud detection under class imbalance

10.11591/ijict.v15i4.pp1768-1776
Riad Loukili , Fayçal Messaoudi , Raouya El Youbi , Manal Loukili
E-commerce has become a rapidly expanding channel, leading to more e-commerce payment transactions and an increased threat for advanced fraudulent activities. New frauds include cross-channel attacks, adversarial behavior and synthetic identities, which are challenging to rule-based detection. In this paper, a deep learning framework for detecting e-payment fraud in real-time is proposed. The model integrates self-attention, explainable AI, class balancing using GANs, and a transformer-based architecture to enable transparent decision-making. The solution tackles major issues in fraud detection, such as class imbalance, evolving transaction trends, and the requirement for interpretability in compliance with regulations like the EU AI Act. Experiments on benchmark datasets demonstrate good results, with an accuracy of 99.82%, and an AUC-ROC of 99.56%. Based on the results, the use of transformer-based models with the help of synthetic data generation and explainability can enhance the reliability and transparency of e-payment fraud detection systems.
Volume: 15
Issue: 4
Page: 1768-1776
Publish at: 2026-12-01

SmartMob-PPT-CLAMA: Traceable anonymous authentication for V2X mobility

10.11591/ijict.v15i4.pp1954-1963
Haewon Byeon
Smart mobility ecosystems connect autonomous vehicles, shared mobility platforms, electric-vehicle charging infrastructure, pedestrians, and roadside units through real-time V2X messages. This paper redesigns PPT-CLAMA, a privacy-preserving and traceable certificateless anonymous mutual authentication scheme, for high-speed internet of vehicles (IoV) and smart mobility environments. The proposed SmartMob-PPT-CLAMA introduces mobility-adaptive pseudonym pools, RSU-assisted handover caching, lightweight V2X authentication headers, hierarchical traceability, and Sybil aware regional trust filtering. The protocol preserves the original security objectives of anonymity, unlinkability, perfect forward secrecy, known session-specific temporary information security, and lawful traceability, while addressing mobility-specific weaknesses such as long-lived pseudonym tracking, handover re-authentication delay, centralized trace authority failure, high-speed Sybil attacks, and broadcast replay. In the evaluated Raspberry Pi 4B and SUMO/NS-3 workload, the proposed design reduces authentication delay from 1.10 ms to 0.82 ms, reduces communication overhead from 470 bits to 312 bits, and achieves 99.1% handover re-authentication success. The design is mapped to ETSI ITS security, IEEE 1609.2, ISO/SAE 21434, and GDPR Article 32 requirements.
Volume: 15
Issue: 4
Page: 1954-1963
Publish at: 2026-12-01

A blockchain-enabled edge–cloud framework for secure, scalable IoT data sharing

10.11591/ijict.v15i4.pp1777-1790
Peda Narayana Bathula , Sadhu Radha Krishna , Meruva Sreenivasulu
Hybrid edge exchange chain (HybrEx-chain) is a blockchain-enabled hybrid edge–cloud framework for secure and scalable data sharing in internet of things (IoT) networks. The framework combines edge computing for fast local pro cessing with cloud computing for resource-intensive tasks and global coordina tion. Proxy re-encryption (PRE) based on elliptic curve cryptography (ECC) protects data end-to-end and enables confidential data sharing without exposure of plaintext at intermediate nodes. A hierarchical consensus mechanism inte grates delegated practical byzantine fault tolerance (d-PBFT) at edge clusters with delegated proof of stake (DPoS) at the cloud layer to improve scalability and reduce consensus delay. Smart contracts on the blockchain manage time based re-encryption key rotation and enforce adaptive access policies to support automated security management. The framework is evaluated in a simulation environment based on Urban Deployment, Industrial IoT, and Smart Healthcare scenarios. The results show lower latency (140.5–155 ms), higher throughput (1150–1255 transactions per second), high scalability (up to 99%), and reduced consensus time compared with existing PRE-based frameworks FE-PRE and PREA. By combining edge responsiveness, cloud-level scalability, and cryptographic key management, HybrEx-chain provides an effective approach for secure and efficient IoT data sharing.
Volume: 15
Issue: 4
Page: 1777-1790
Publish at: 2026-12-01

SSDHR classifier network for brain tumor detection using MRI images

10.11591/ijict.v15i4.pp1459-1468
Kalpana Telkar , K. Anusudha
Brain tumors are the chaotic growth of abnormal brain cells, which is the main cause of cancer worldwide. Symptoms vary depending on tumor size and location, and these tumors can be cancerous or non-cancerous. Early detection and characterization are crucial but challenging due to the brain's complex structure. This study proposes a self-supervised classification deep hierarchical reconstruction network (SSDHR), a combination of generative and discriminative models. This network uses convolutional Kernels of different sizes for processing of input data on separate branches to extract effective discriminative features. A parallel-structured symmetric fusion attention (SFA) module is introduced, combining attention mechanisms for coordinates, time, and frequency to enhance the model's comprehension and feature selection. The SSDHR classifier network achieves the highest accuracy (91.9%) among 10 state-of-the-art classifiers, classifying tumors into 4 classes: pituitary, meningioma, glioma, and no tumor. The model's performance is evaluated using various metrics that demonstrate its effectiveness in brain tumor classification.
Volume: 15
Issue: 4
Page: 1459-1468
Publish at: 2026-12-01

5G network performance analysis and planning for the development of 5G services in Padang City

10.11591/ijict.v15i4.pp1791-1799
Afrizal Yuhanef , Zurnawita Zurnawita , Farhan Sya’banul Alza
The rapid deployment of fifth-generation (5G) mobile networks requires accurate performance assessment to support effective network planning. This study presents a combined field measurement and simulation approach, integrating drive test data with Atoll-based coverage planning to evaluate the early deployment of 5G non-standalone (NSA) in Padang City, Indonesia. Radio performance was assessed using reference signal received power (RSRP) and signal-to-interference-plus-noise ratio (SINR) obtained from drive tests using a Samsung Galaxy A26 5G and G-NetTrack Pro, while Atoll was used to analyze planned coverage. The results show that 64% of measurement points achieved very good RSRP (≥ −85 dBm) and 56% recorded very good SINR (≥ 10 dB), although several locations exhibited poor SINR despite strong RSRP, indicating interference associated with the NSA architecture. Atoll simulation showed that only 36.42% of the planned coverage area reached excellent RSRP, revealing gaps between measured and predicted performance. These findings demonstrate that integrating drive test measurements with propagation simulation provides a practical framework for identifying coverage deficiencies and supporting 5G network planning through interference mitigation, antenna parameter adjustment, and migration toward SA architecture.
Volume: 15
Issue: 4
Page: 1791-1799
Publish at: 2026-12-01

Accent-aware arabic speech processing using CNN–BiLSTM for dialect classification

10.11591/ijict.v15i4.pp1673-1681
Tarik Abdel-Kareem AbuAin , Waleed Abdel Karim Abuain
The Arabic language presents considerable dialectal diversity, which can pose challenges for speech technologies, particularly automatic speech recognition (ASR). Traditional models trained primarily on modern standard Arabic (MSA) do not account for regional accents, leading to poor performance on underrepresented dialects. However, this paper addresses this gap by introducing a hybrid CNN-BiLSTM model designed for automatic Arabic dialect classification. The dataset was divided into five categories gathered from various YouTube videos: Levantine (Jordan, Lebanon, Syria, Palestine), Gulf (Saudi Arabia, Yemen, Qatar, UAE), Nile Valley (Egypt, Sudan), Mesopotamian (Iraq, Kuwait), and North African (Algeria, Libya, Mauritania, Morocco). Therefore, a systematic augmentation was applied to address this imbalance, including pitch shifting, time stretching, and amplitude scaling, leading to 4,000 balanced utterances standardized to 16 kHz and 8 seconds. Convolutional neural network (CNN) layers captured localized spectral features, while bidirectional long short-term memory (BiLSTM) units modeled long-range temporal dependencies. Evaluation metrics, including classification report, confusion matrix, and learning curves, showed effective performance, with an overall accuracy of 88%.
Volume: 15
Issue: 4
Page: 1673-1681
Publish at: 2026-12-01

DDoS attack detection on portmap using a bidirectional recurrent neural network method

10.11591/ijict.v15i4.pp1601-1613
Wisnu Adi Putra , Deris Stiawan , Qurhanul Rizqie , Nurul Afifah , Septiani Kusuma Ningrum , Rahmat Budiarto
Portmap is a protocol used in Unix and Linux operating systems to map port numbers to their associated program numbers. In a distributed denial of service (DDoS) attack on portmap, attackers will exploit a weakness in this protocol to send many malicious or manipulative server-running portmap requests Due to the connectionless and unauthenticated nature of UDP, the portmap server will attempt to respond to every request it receives. However, attackers often disguise this so that their IP address is hidden or spoofed. As a result, the server will attempt to reply to requests to a spoofed or invalid IP address, using server resources and causing a denial of service for valid users. A DDoS attack on portmap can be quite damaging. Services associated with portmap may become unresponsive, causing significant downtime for applications or services that rely on the protocol. This study uses the CSE-CIC-IDS 2019 dataset, especially the DDoS attack dataset on portmap. This paper also employs the correlation-based feature selection (CSF) approach to extract significant characteristics from the dataset Bidirectional RNN is used to detect DDoS assaults. Utilizing 30% testing data and 70% training data, the Bidirectional RNN approach can effectively identify DDoS attacks on portmap. Accuracy is 99.9917%, recall is 99.9917%, specificity is 99.9942%, precision is 99.994%, F1-Score is 99.88%, balanced accuracy (BACC) is 99.92%, and matthews correlation coefficient (MCC) is 99.98%. By merging the CFS feature selection technique with the Bidirectional RNN architecture, this work mainly contributes to creating a more accurate and efficient DDoS attack detection method. With this method, the detection process can be carried out faster and more precise, reducing the computational burden without sacrificing detection performance.
Volume: 15
Issue: 4
Page: 1601-1613
Publish at: 2026-12-01

Developing a machine learning-based algorithm for secure routing in autonomous vehicles

10.11591/ijict.v15i4.pp1746-1758
Shwetansh Priyadarshi , Alok Srivastava
Over the last several years, we have noticed that the administration and car companies have been refraining from investing capital in connected autonomous cars. A chain of interconnected sensors, actuators, and devices—combining lidar, GPS, radar, in-system machines, cameras, and more—allows autonomous cars to learn how to operate in real-time scenarios. However, data from recent cyberattacks and mishaps suggests that malicious attackers can access this sensor data or even the main control system through hacking, leading to a variety of losses. As time passes by, people will be less interested in connected autonomous vehicles (CAVs), and various surveys say that it will be mainly because of the absence of a proper security infrastructure. A study of cyberattacks on CAVs and strategies to eliminate them is discussed in this study. In this study, we have proposed a hybrid model solution that uses convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU), and we are optimizing the Hyperparameters using the dwarf mongoose optimization algorithm (DMOA). The proposed model is also tested in Comparison with other existing models, and we are able to detect DoS, fuzzy, spoofing the driving gear, and spoofing the RPM gauge attacks with an accuracy of more than 98%.
Volume: 15
Issue: 4
Page: 1746-1758
Publish at: 2026-12-01

Development of KabuTeach: a character-based decision support system for mushroom classification

10.11591/ijict.v15i4.pp1561-1576
Vilchor G. Perdido , Melinda A. Beninsig , Daniel Marvin B. Ramel
Mushroom misidentification remains a persistent public health risk in rural communities, causing poisoning and fatalities worldwide. This study presents KabuTeach, a web-based decision support system (DSS) for classifying mushrooms as edible or poisonous. The system is trained on the UCI mushroom dataset (8,124 instances, 22 categorical morphological attributes) using the categorical Naïve Bayes algorithm with Laplace smoothing (α=1.0) through the Rubix ML framework. The system employed a hybrid interactive structure and event-driven architecture with the model view–controller pattern, enabling real-time classification, human-in-the-loop expert validation, and SMS-based result delivery for low-connectivity rural environments. Model evaluation using a 70:30 holdout method achieved 99.63% accuracy, while 5-fold cross-validation yielded a mean accuracy of 99.66% (±0.1118%). Performance metrics, including a confusion matrix, per-fold results with standard deviations, precision, recall, F1-score, and specificity, confirm model robustness, attributed to the clean and discriminative categorical features of the dataset and the use of Laplace smoothing. Usability testing with 102 respondents using the USE questionnaire achieved a grand mean of 5.93 (agree). Reliance on the North American UCI dataset limits direct Philippine applicability; future work will focus on locally sourced, expert-labeled datasets. KabuTeach is a reliable, interpretable, and user-centered DSS that bridges traditional knowledge and modern ICT for safer rural mushroom foraging.
Volume: 15
Issue: 4
Page: 1561-1576
Publish at: 2026-12-01

Exploring effective feature selection methods in logistic regression for credit scoring models in XYZ bank

10.11591/ijict.v15i4.pp1614-1623
Brigitta Permadhi , Devi Fitrianah
This research explores optimized variable selection in credit scoring models, comparing wrapper, embedded, none, and hybrid approaches. Logistic regression is applied to model the dependence of explanatory variables on the target variable while mitigating underfitting, overfitting, and computational challenges. Feature selection methods help mitigate these issues by reducing dimensionality and improving model generalization. The Hybrid model achieved the best overall performance with accuracy = 0.9840 on the test set and accuracy = 0.9839 on the training set (test-train difference = -0.0001), as well as the best recall = 0.9993, F1-score = 0.9842, and the least number of false positives (FP = 47) and false negatives (FN = 1), which suggests good predictive ability and stability with the fewest misclassifications. The wrapper and embedded models have lower accuracies of 0.9793 and 0.9830, respectively, and mild overfitting. The baseline model with no feature selection performed the worst in terms of both accuracy and F1-score (accuracy = 0.5000, F1-score = 0.6667). In conclusion, the results indicate that the combination of wrapper and embedded approaches is the most accurate, stable, and generalisable model for credit scoring tasks.
Volume: 15
Issue: 4
Page: 1614-1623
Publish at: 2026-12-01

A hybrid AI framework for hyper-personalized recommendations and customer engagement prediction in e-commerce

10.11591/ijict.v15i4.pp1871-1880
Parashiva Murthy B. M. , Scaria Alex , Isaivani Mariyappan , Kalyan Devappa Bamane , Kavitha R. , S. Athinarayanan , Sunder R
The explosion of e-commerce has led to an increase in the demand for smart systems that can provide hyper-personalized experiences and enhance customer engagement. This study proposes a hyper personalized e-commerce framework by incorporating the deep learning (DL) based hybrid recommendation models along with the ensemble classifiers for engagement prediction. Real world public e-commerce datasets were used in a two-stage evaluation that measured recommendation effectiveness on Precision@10, Recall@10 and NDCG@10, and engagement prediction on Accuracy, F1-Score, and ROC-AUC. The results of the proposed framework were compared with the baseline approaches in terms of Precision@10, Recall@10, and NDCG@10, which gave Precision@10 = 0.382, Recall@10 = 0.441, and NDCG@10 = 0.469, Accuracy = 91.2%, F1-Score = 0.886, and ROC-AUC = 0.937. These results show that an ensemble of DL architectures and gradient boosting techniques strongly improves the quality of personalization and behaviour prediction. This study highlights the game changing power of recommender and predictive AI systems in driving customer experience and data-driven digital commerce approaches.
Volume: 15
Issue: 4
Page: 1871-1880
Publish at: 2026-12-01

Evaluating browser-based immersive technology for cultural heritage

10.11591/ijict.v15i4.pp1664-1672
Muhammad Dedi Irawan , Yusuf Ramadhan Nasution , Yustria Handika Siregar , Muhammad Eka , Nurmaliana Pohan
Digital cultural heritage requires accessible information and communication technology (ICT) channels that can present cultural content without requiring dedicated software installation. This study evaluates a browser based WebAR–WebVR platform for the interactive exploration of Malay hand-drawn batik. The evaluation comprised expert readiness review, functional verification, task-based interaction involving 60 participants aged 15–30 years, a five-point user-perception questionnaire, and qualitative technical observation. All eight predefined WebAR and WebVR functions produced the expected outcomes on Google Chrome using a Vivo Y36 5G smartphone and a VR Box. Mean scores were 4.60 for ease of interaction, 4.40 for increased interest in batik, 4.20 for attractiveness, and 4.60 for cultural-promotion usefulness. Triangulation of functional, perceptual, and operational evidence showed that successful task completion corresponded with positive ease ratings, while complex multimedia assets increased loading and rendering burden. Lighting, marker visibility, camera permission, device capability, and multimedia complexity were the main operating constraints. The findings provide practical evidence for browser based cultural ICT applications in museums, tourism, and digital education.
Volume: 15
Issue: 4
Page: 1664-1672
Publish at: 2026-12-01

Energy aware clustering and routing for wireless sensor networks using metaheuristics optimization algorithms

10.11591/ijict.v15i4.pp1736-1745
Sagar Mekala , Nagaraju Karinagappa , Mohan Dholvan
Wireless sensor networks (WSNs) and their applications reduce energy consumption to enhance the lifetime of the network. WSN applications require energy-efficient communication, but the network includes resource constraint tiny sensors across monitoring area. Data processing, aggregation, and clustering are energy-intensive, and deployment and discovery of nodes are complex. In order to overcome certain issues in WSN application, optimal selection of fine cluster heads (FCHs) and cluster heads (CHs) and optimal routes to the base station (BS) are proposed, still lot of optimizations are essential to enhance lifetime of the network. This research paper employs bacterial foraging and ant colony optimization (BFACO) to identify optimal gateways (FCHs), CHs, and energy-efficient paths from sensor modes to BS while optimizing node energy consumption. The algorithm dynamically creates routing paths with lower network overhead, enabling long-term network utilization and enhanced lifetime. Performance is evaluated against parallel research algorithms using metrics such as energy-usage, network lifespan, and packet delivery ratio (PDR). An energy-usage (52.8% of energy reduction over best case), packet delivery ratio (8.7% of enhancement over best case), and network lifetime (4.3% of rounds over best case) are WSN performance outcomes for 300 sensors. The BFACO using a clustering and routing algorithm outperforms over parallel research.
Volume: 15
Issue: 4
Page: 1736-1745
Publish at: 2026-12-01
1 2 3
...
2097 2098
Show 1 of 2098

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