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 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

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

Smart home automation using internet of things

10.11591/ijeecs.v41.i2.pp579-588
Roopa R. , Pallavi B. , Lakshmi Neelima , Parikshith J. , Kashish Agarwal
This research paper delves into the development and implementation of an advanced home automation system utilizing internet of things (IoT) technology to bolster safety and comfort within residential environments. The proposed system architecture revolves around an ESP8266 microcontroller board interfaced with a diverse array of sensors, including motion detectors, temperature and humidity sensors, and air quality sensors specifically designed to detect gas leaks. Additionally, the system incorporates a servo motor for stove control and relays for fan activation. The described system adds novel safety-focused features, including servo-controlled stoves and fan-gas leak integration, making it applicable for critical home safety scenarios. However, it shares common weaknesses with existing systems, such as inadequate attention to security, energy efficiency, and scalability. By addressing these gaps, this system could set itself apart as a comprehensive IoT solution for home automation.
Volume: 41
Issue: 2
Page: 579-588
Publish at: 2026-02-01

A new approach for distance vector-Hop localization algorithm improvement in wireless sensor networks

10.11591/ijeecs.v41.i2.pp515-531
Omar Arroub , Anouar Darif , Rachid Saadane , My Driss Rahmani , Zineb Aarab
This article shows a new range-free localization technique based on a metaheuristic algorithm (MA) dedicated to wireless sensor network (WSN), named sequential online-grey wolf optimization-distance vector-Hop (SOGWO-DVHOP). Indeed, we use the improved GWO based on selective opposite learning to improve GWO in order to enhance the traditional DVHOP localization algorithm. In reality, we choose GWO due to its better outcomes compared to other meta-heuristics, which leads us to improve this algorithm further. In the literature, the improvement works of GWO try to reconstruct the hierarchy of GWO or improve specifically the role of omega individuals. In our contribution, we opt for opposition-based learning (OBL) to ameliorate GWO, aiming to further enhance the quality of localization made by DVHOP. On the other hand, we make an empirical comparison of DVHOP and its improved versions in terms of accuracy. The results of the simulation demonstrate that SO-GWO-DVHOP gives the best performance when we vary the anchor ratio and the density of nodes.
Volume: 41
Issue: 2
Page: 515-531
Publish at: 2026-02-01

Energy-efficient AI-enhanced secure routing for protecting IoT networks from advanced attacks

10.11591/ijeecs.v41.i2.pp%p
Leelavathi R. , Vidya A.
This paper proposes artificial intelligence-enhanced secure routing (AIRS), a lightweight AI-enhanced secure routing protocol for internet of things (IoT) networks operating under advanced routing attacks. Unlike existing approaches that treat intrusion detection and routing separately, AIRS tightly integrates anomaly scoring into trust-aware routing decisions using a compact random forest model designed for constrained nodes. The anomaly detector is trained offline on simulated IoT traffic features and deployed for real-time inference during routing. Extensive Cooja simulations demonstrate that AIRS improves intrusion detection accuracy and packet delivery while reducing energy consumption compared to secure-RPL and trust-LEACH. The current validation is limited to simulation environments, and real-world testbed evaluation is left for future work.
Volume: 41
Issue: 2
Page: 731-739
Publish at: 2026-02-01

IoT-enabled connected incubator with redundant communication for real-time neonatal monitoring

10.11591/ijeecs.v41.i2.pp633-644
Naçima Mellal , Soumia Hadj Maatallah , Ammar Merazga , Rachida Bouchouareb , Souad Nacer
Premature birth remains a major challenge in neonatal care, especially in resource-constrained settings, where continuous monitoring and timely intervention are limited. Most existing neonatal incubators offer limited real-time monitoring, unreliable alerting, and lack communication redundancy, potentially delaying critical responses. This paper presents a comprehensive internet of thing (IoT) enabled connected incubator with redundant communication (Wi-Fi and GSM) for real-time monitoring of physiological and environmental parameters. The system integrates sensing, processing, cloud connectivity, a mobile application, and multi-channel alerts (App notifications, SMS, voice calls, and local alarms). It was experimentally evaluated under controlled laboratory conditions. Quantitative evaluation shows a cloud transmission success rate of 99.1%, end-to-end communication latency below 1 second via Wi-Fi and 2.2 seconds via GSM, with 98% of alerts successfully delivered within 6 seconds. The proposed system provides a low-cost, reliable platform that enhances neonatal safety, supports timely clinical decisions, and is scalable for resource-constrained healthcare environments.
Volume: 41
Issue: 2
Page: 633-644
Publish at: 2026-02-01

Teacher preparedness for competency-based curriculum in Kenyan schools: training and perceptions prior to implementation

10.11591/ijere.v15i1.35742
Nathan Maina Mwangi , Simon Karuku , Elizabeth Obura
Pre-implementation training and teachers’ perceptions are critical factors influencing successful implementation of a new curriculum. This paper reports on some of the findings from a study that assessed the preparedness of primary school educators to implement the newly introduced competency-based curriculum (CBC) in Kenya. The study participants were primary school teachers and school heads drawn from 37 public elementary schools in Embu, Kenya. Using a mixed-methods approach, both qualitative and quantitative data were gathered through surveys, interviews, and observations. Qualitative data were analyzed using thematic analysis, whereas quantitative data were examined using descriptive and inferential statistics. The study revealed that 95% of teachers had been trained to implement CBC, and 65% of the teachers held negative perceptions toward the new curriculum. The study also established a weak but significant correlation (Spearman’s rho=0.268, p<0.05) between pre-implementation training and teachers’ perceptions on the CBC implementation. The findings suggest that continuous, structured in-service training is critical for CBC success, particularly in building competencies and improving teachers’ perceptions.
Volume: 15
Issue: 1
Page: 714-724
Publish at: 2026-02-01

Intelligent plant disease detection using twin attention optimal convolutional neural network

10.11591/ijai.v15.i1.pp756-765
Prameetha Pai , Namitha S. J. , Sowmya T. , Amutha S. , Nisarga Gondi
Farming is one of the most important ways for people in India to make a living. Rice is a staple food, and when farmers successfully harvest rice crops, pests often attack them, which costs agriculture a lot of money. There are now a lot of new AI-based ways to help with this problem in rice plants. But those ways don’t work very well because they take a long time and make mistakes when sorting things. This article talks about a new hybrid deep learning (DL) method for finding leaf diseases in rice plants. This process has four main steps: pre-processing, segmentation, feature extraction, and classification. A hybrid DL-based twin attention convolutional neural network (CNN) model classifies segmented images into healthy and unhealthy leaves. But this method has the problem of overfitting. An optimization method based on chaotic slime mould (CSM) solves this problem. The proposed method is compared with bidirectional long short-term memory (Bi-LSTM), recurrent neural network (RNN), deep neural network (DNN), and deep belief network (DBN). The suggested method has an overall accuracy of 99.56%, an F-measure of 99.21%, a sensitivity of 99.16%, a specificity of 98.56%, a precision of 99.26%, a mean absolute error (MAE) of 0.004, a mean squared error (MSE) of 0.004, and a root mean square error (RMSE) of 0.06.
Volume: 15
Issue: 1
Page: 756-765
Publish at: 2026-02-01

Comparison of image enhancement methods for pratima theft detection using artificial intelligence

10.11591/ijai.v15.i1.pp213-228
Made Sudarma , Ni Wayan Sri Ariyani , I Putu Agus Eka Darma Udayana , Ida Bagus Gde Pranatayana , Lie Jasa
The theft of pratima in Balinese temples threatens the spiritual and cultural balance of the community. These sacred objects, regarded as manifestations of God in Hinduism, hold profound religious significance, and their loss represents both material and spiritual desecration. To address this issue, this study investigates a security system that leverages image enhancement for low-light detection. Four techniques—contrast limited adaptive histogram equalization (CLAHE), adaptive histogram equalization (AHE), histogram equalization (HE), and gamma correction—were evaluated to improve image quality. CLAHE yielded the lowest mean squared error (MSE) of 21.16 and the highest peak signal-to-noise ratio (PSNR) of 38.13 dB. For object detection, VGG-19 and AlexNet were assessed. The best configuration, VGG-19 with HE, reached 83.33% accuracy and 93.75% recall, and achieved a receiver operating characteristic area under the curve (ROC AUC) of 0.90±0.02 across five runs. Thresholds derived from the ROC analysis were selected using the Youden J statistic to balance sensitivity and specificity. The approach outperformed lightweight and classical baselines in AUC, indicating superior discrimination under low illumination. These findings show that superior image quality does not always align with higher detection accuracy, and they highlight the importance of pairing effective enhancement with robust detectors for temple security. The study contributes practical insights for preserving Balinese cultural and spiritual heritage by strengthening efforts to protect pratima against theft.
Volume: 15
Issue: 1
Page: 213-228
Publish at: 2026-02-01

Automated ergonomic sitting postures detection for office workstation using XGBoost method

10.11591/ijai.v15.i1.pp506-514
Theresia Amelia Pawitra , Farida Djumiati Sitania , Anindita Septiarini , Hamdani Hamdani
Sedentary office work increases musculoskeletal risk, underscoring the need for non-intrusive, real-time posture monitoring. This study presents a computer vision approach that classifies ergonomic versus non-ergonomic sitting postures using upper body key points extracted by MoveNet thunder. Images from 30 participants were captured from frontal and side views, and labeled according to SNI 9011:2021 criteria. Seventeen key points were detected, with head-to-hip landmarks retained, then normalized and centered. Three classifiers—adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and a multi-layer perceptron (MLP)—were trained and evaluated with 10-fold stratified cross-validation. XGBoost achieved the best performance, with accuracy 93.0%±1.9%, precision 94.6%, recall 91.4%, F1-score 92.9%, and area under the receiver operating characteristic curve (ROC-AUC) 0.974±0.010, outperforming MLP and AdaBoost. The method supports privacy-preserving, on-device inference and is suitable for integration into smart office systems to reduce exposure to high-risk postures. Limitations include controlled capture conditions and an upper body focus; future work will expand posture taxonomy and real-world deployment.
Volume: 15
Issue: 1
Page: 506-514
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

Parkinson's disease diagnosis using voice biomarkers: a machine learning approach

10.11591/ijeecs.v41.i2.pp800-811
Amit Kumar , Neha Sharma , Shubham Mahajan , Seifedine Kadry
Parkinson's disease (PD) is a degenerative neurological disease, and at present there are no reliable laboratory tests for it. So how does this happen when people go to identify PD? vocal biomarkers, combined with machine learning (ML), seem to be an option for noninvasive diagnostics. In our work, we used a voice recording dataset which consisted of 26 different feature sets mined by various techniques. When using the extreme gradient boosting (XGBoost) method, out of all these models tested, an accuracy of 91.79% was achieved. As can be seen from its high precision, recall and F1- score, XGBoost performed very well in differentiating PD cases from non-cases. The study concludes that the application of ML, particularly XGBoost, to the diagnostic process can establish a valuable tool for early screening of PD, which will facilitate more speedy and correspondingly cost-effective clinical evaluations. This paper represents an important contribution to the rapidly developing fields of artificial intelligence-based on diagnosis of neurological diseases and digital health.
Volume: 41
Issue: 2
Page: 800-811
Publish at: 2026-02-01

Quantitative evaluation of a virtual tour navigation system using satisfaction modeling: a case study in Thai cultural tourism

10.11591/ijeecs.v41.i2.pp690-699
Ekapong Nopawong , Rawinan Praditsangthong
This research aims to develop and evaluate the Lak Hok virtual tour navigation system to promote sustainable cultural tourism by showcasing Thai wisdom through immersive digital experiences. The system utilized 360-degree panoramic images hosted on a web server and supported accessibility via laptops, smartphones, and virtual reality (VR) headsets. Both subjective evaluations and objective performance metrics were employed to assess the system’s usability, aesthetic appeal, and content quality (CQ). User satisfaction, measured through a survey of 87 participants, demonstrated consistently high ratings (mean scores: 3.59-3.77 for ease of use (EU), 3.32-3.95 for design aesthetics, and 3.62-3.70 for content knowledge). Objective tests revealed an average system response time of 1.45 seconds, a false interaction rate of 4.2%, and a navigation accuracy of 98.5%. Statistical analysis showed no significant differences in user satisfaction across gender, age, or region, highlighting the system’s broad accessibility and usability. Unlike prior systems, this study formalizes satisfaction modeling via equation-based analysis. This virtual tour system provides a scalable and engaging platform for preserving and promoting cultural heritage, offering a sustainable solution for modern tourism development.
Volume: 41
Issue: 2
Page: 690-699
Publish at: 2026-02-01

Parameter-efficient fine-tuning of small language models for code generation: a comparative study of Gemma, Qwen 2.5 and Llama 3.2

10.11591/ijece.v16i1.pp278-287
Van-Viet Nguyen , The-Vinh Nguyen , Huu-Khanh Nguyen , Duc-Quang Vu
Large language models (LLMs) have demonstrated impressive capabilities in code generation; however, their high computational demands, privacy limitations, and challenges in edge deployment restrict their practical use in domain-specific applications. This study explores the effectiveness of parameter efficient fine-tuning for small language models (SLMs) with fewer than 3 billion parameters. We adopt a hybrid approach that combines low-rank adaptation (LoRA) and 4-bit quantization (QLoRA) to reduce fine-tuning costs while preserving semantic consistency. Experiments on the CodeAlpaca-20k dataset reveal that SLMs fine-tuned with this method outperform larger baseline models, including Phi-3 Mini 4K base, in ROUGE-L. Notably, applying our approach to the LLaMA 3 3B and Qwen2.5 3B models yielded performance improvements of 54% and 55%, respectively, over untuned counterparts. We evaluate models developed by major artificial intelligence (AI) providers Google (Gemma 2B), Meta (LLaMA 3 1B/3B), and Alibaba (Qwen2.5 1.5B/3B) and show that parameter-efficient fine-tuning enables them to serve as cost-effective, high-performing alternatives to larger LLMs. These findings highlight the potential of SLMs as scalable solutions for domain-specific software engineering tasks, supporting broader adoption and democratization of neural code synthesis.
Volume: 16
Issue: 1
Page: 278-287
Publish at: 2026-02-01

A comparative MRI-based study of ResNet-152 and novel deep learning approaches for early Alzheimer’s disease classification

10.12928/telkomnika.v24i2.27576
Kelvin Leonardi; STMIK TIME Kohsasih , Octara; STMIK TIME Pribadi , Andy; STMIK TIME Andy , Daniel; STMIK TIME Smith Sunario
Alzheimer’s disease (AD) is the leading cause of dementia, making early-stage detection essential for timely intervention. Most prior studies have focused on binary AD classification, which limits sensitivity to disease progression. This study addressed this gap by evaluating whether tailored convolutional neural network (CNN) architectures could improve stage-aware classification using a publicly available magnetic resonance imaging (MRI) dataset containing 35,984 images across four diagnostic categories. The dataset underwent grayscale conversion, resizing, contrast enhancement, normalization, and class balancing prior to model development. Four models were trained and compared: ResNet-152, a custom multiclass CNN, a one-vs-one (OvO) model, and a one-vs-rest (OvR) model. Performance was measured using accuracy, precision, recall, F1 score, and confusion-matrix–based metrics. The custom multiclass CNN achieved the strongest performance, yielding the highest accuracy and balanced results across all evaluation metrics. These findings demonstrate the value of systematically comparing decomposition strategies for multi-stage Alzheimer’s detection and highlight the potential of the proposed approach to enhance early diagnostic support. Future work may incorporate multimodal inputs or hybrid architectures to improve sensitivity to subtle structural changes and further strengthen clinical applicability.
Volume: 24
Issue: 2
Page: 536-548
Publish at: 2026-01-30
1 2 3
...
1994 1995
Show 56 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