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29,922 Article Results

Adaptive control of ball and beam system using SNA-PID combined with recurrent fuzzy neural network identifier

10.11591/ijai.v15.i2.pp1202-1210
Minh-Thanh Le , Chi-Ngon Nguyen
The ball and beam system is a nonlinear and inherently unstable single input, multiple-output (SIMO) system, which poses significant challenges for control design. Intelligent control algorithms are often applied to autonomously control complex systems when there are changes in parameters or the control environment. Therefore, in this paper, we research and develop two methods: proportional integral derivative (PID) and single neuron adaptive (SNA)-PID-recurrent fuzzy neural network identifier (RFNNI) to control the ball and beam system. Simulation results on MATLAB/Simulink show that the SNA-PID-RFNNI controller provides a more stable output signal than the traditional PID controller, with minimal overshoot and a settling time of about 15 seconds. Next, we will conduct real-time experiments on the object using the proposed algorithm through the MEGA2560 control board with an ultrasonic positioning mechanism.
Volume: 15
Issue: 2
Page: 1202-1210
Publish at: 2026-04-01

Genetic algorithm-based chicken manure weight prediction system development

10.11591/ijai.v15.i2.pp1247-1260
Rida Hudaya , Septriandi Wirayoga , Moechammad Sarosa , Muhammad Yusuf , Armanda Dwi Prayugo
This research presents design and implementation of internet of things (IoT) based monitoring and predictive system for evaluating chicken manure weight and environmental conditions in poultry housing. The proposed system integrates MQ-137 sensor for ammonia detection, DHT22 sensor for temperature and humidity measurement, and load cell modules for manure weight monitoring. All sensor data are transmitted in real time to cloud platform, enabling continuous environmental assessment. A 30-day experimental study was conducted using two controlled chicken drum models, each containing 15 broiler chickens and provided with different feed types to observe variations in manure production and air quality. Sensor calibration results indicate high accuracy, with average error of 0.31% for ammonia readings and 0.10% for manure weight measurement. Experimental findings show that feed type A generates lower manure weight, reduced ammonia concentration, and more stable temperature conditions compared to feed type B, suggesting improved feed efficiency and better overall chicken health. A genetic algorithm (GA) was employed to optimize regression model predicting manure weight using ammonia concentration and temperature as input features. The GA-optimized model achieved strong predictive performance, with root mean square error (RMSE) of 0.358 g and coefficient of determination (R2) value of 0.992. The results demonstrate that proposed system provides reliable, scalable, and data-driven solution for smart poultry monitoring and early health detection.
Volume: 15
Issue: 2
Page: 1247-1260
Publish at: 2026-04-01

Deep learning ensembles for lung cancer detection in thoracic CT scans leveraging generative adversarial network technology

10.11591/ijai.v15.i2.pp1605-1612
Bineesh Moozhippurath , Jayapandian Natarajan
Effective treatment of lung cancer depends on early and accurate detection, which continues to be a major cause of cancer-related fatalities globally. Conventional diagnostic techniques are useful, but their efficacy in handling large amounts of thoracic computed tomography (CT) scan data is limited by their time-consuming nature and susceptibility to human error. The research here suggests a new deep learning model that integrates generative adversarial networks (GANs) for data improvement with a sophisticated ensemble approach to classification. GANs are employed to generate realistic synthetic CT images, addressing the challenges of limited datasets. The backbone of the proposed approach is a consensus-guided adaptive blending (CGAB) ensemble model that learns to dynamically combine the predictions of three top-performing convolutional neural networks (CNNs): ResNet-152, DenseNet-169, and EfficientNet-B7. The CGAB model improves prediction accuracy through model contribution weighting based on confidence scores and inter-model consensus, while a conflict-resolving auxiliary decision model is used. The approach was tested using the lung image database consortium and the image database resource initiative (LIDC-IDRI) dataset with a detection rate of 97.35%, surpassing single model and traditional ensemble methods. The current work provides a solid and scalable approach to lung cancer detection with better generalization, increased diagnostic consistency, and applicability for clinical use.
Volume: 15
Issue: 2
Page: 1605-1612
Publish at: 2026-04-01

Explainable social media disaster image classification using a lightweight attention-based deep learning approach

10.11591/ijai.v15.i2.pp1464-1472
Rashmi Kangokar Taranath , Geeta Chidanandappa Mara
In recent years, the rapid dissemination of social media content during natural and man-made disasters has created a need for automated and accurate disaster image classification systems. This paper proposes lightweight explainable attention-based disaster network (LEAD-Net), a deep learning (DL) model designed for classifying disaster-related images with high accuracy and interpretability. The system integrates an EfficientNet-B0 backbone enhanced with squeeze-and-excitation (SE) attention modules and a lightweight neural architecture search (NAS-lite) strategy for tuning the classifier head and training hyperparameters. The model was evaluated on two benchmark datasets comprehensive disaster dataset (CDD) and damage multimodal dataset (DMD) achieving 96% and 87% accuracy, respectively, outperforming several established convolutional neural network (CNN) baselines. To ensure transparency, gradient-weighted class activation mapping (Grad-CAM) was employed to generate visual explanations of the model’s decisions, confirming its focus on semantically relevant image regions.
Volume: 15
Issue: 2
Page: 1464-1472
Publish at: 2026-04-01

Comparative deep learning study for downy mildew detection in vegetables

10.11591/ijai.v15.i2.pp1719-1732
Supreetha Shivaraj , Manjula Sunkadakatte Haladappa
Several vegetable crops are affected by downy mildew, a major foliar disease resulting in notable reductions in yield. For sustainable agriculture and disease prevention, early and precise detection is crucial. To be able to detect downy mildew in five varied vegetables—bitter gourd, bottle gourd, cauliflower, cucumber (Rashid), and cucumber (Sultana)—this study evaluates three deep learning architectures: VGG19, DenseNet201, and MobileNetV2. This work focuses on imbalanced datasets collected from several sources, in opposition to prior work that depended on balanced laboratory datasets. Accuracy, precision, recall, and F1-score metrics were used to evaluate the models shortly after they were trained using transfer learning, data augmentation, and 5-fold cross-validation. Model focus regions were assessed by using gradient-weighted class activation mapping (Grad-CAM) visualizations, and statistical reliability was assessed based on paired t-tests and Wilcoxon signed-rank tests. By achieving mean accuracies above 98% and statistically significant results (p <0.05) on cucumber datasets, DenseNet201 accomplished superior performance. Despite attaining slightly lower accuracy (89.6–100%), MobileNetV2 offered the smallest model size (12.9 MB) and minimum inference time (85 ms). The proposed approach demonstrated a transparent, generalizable, and computationally efficient deep learning pipeline for precision agriculture’s real-time downy mildew detection.
Volume: 15
Issue: 2
Page: 1719-1732
Publish at: 2026-04-01

Performance assessment of an adaptive model predictive control with torque braking for lane changes

10.12928/telkomnika.v24i2.27167
Zulkarnain; Universitas Sriwijaya Zulkarnain , Irwin; Universitas Sriwijaya Bizzy , Armin; Universitas Sriwijaya Sofijan , Mohd Hatta Mohammed; Universiti Teknologi Malaysia Ariff
The growing demand for autonomous vehicles requires robust control systems that can maintain safety during complex maneuvers like lane changes. However, a significant research gap exists in developing controllers that effectively manage the combined challenges of steering and braking across diverse and unpredictable driving conditions, such as varying speeds and low-friction road surfaces. This research addresses this gap by proposing and evaluating an adaptive model predictive control (MPC) system integrated with a torque braking distribution strategy. The key advantage of our adaptive method is its ability to continuously update its internal model in real-time, allowing it to anticipate and respond to changing road friction and vehicle dynamics more effectively than a static controller. In simulations of a lane change maneuver across speeds of 10-25 m/s and road friction levels from 0.3 (icy) to 1 (dry asphalt), the proposed system demonstrated a substantial performance improvement. The proposed framework demonstrated a 52.8% average reduction in lateral tracking error and enhanced stability by reducing the yaw rate by up to 41.8% on low-friction surfaces, compared to a non-adaptive MPC baseline. These results quantitatively confirm that our framework’s synergistic coordination of steering and braking significantly enhances the safety, precision, and reliability of autonomous lane change maneuvers.
Volume: 24
Issue: 2
Page: 696-706
Publish at: 2026-04-01

Overvoltage assessment of wind energy integration in low voltage distributed grids

10.11591/ijeecs.v41.i3.pp859-872
Farid Merahi , Badoud Abd Essalam
Large-scale integration of renewable energy (RE) resources into the electrical grid has increased significantly over the last decade, affecting the network at various nodes even at considerable distances from the common connection point. This paper presents an overvoltage assessment caused by the integration of two wind generators (WGs) into a low voltage distribution grid, which is structured into three zones. Two scenarios are studied, the first one considers the low voltage grid without WGs, representing its natural operating condition. In the second scenario, two WGs are connected in zone 3, inducing voltage rises at different nodes within the same zone, by reaching 7.9%, and affecting nodes located in other zones (Zone 1 and Zone 2). The simulation is performed using MATLAB/Simulink (R2025a), and the results obtained are compared to the standards test feeder IEEE 33-bus network, showing the overvoltage caused by WGs integration at nodes close to the connection point while improving voltage quality at distant nodes.
Volume: 41
Issue: 3
Page: 859-872
Publish at: 2026-03-10

A multimodal framework for explainable chest X-ray report generation

10.11591/ijeecs.v41.i3.pp1060-1069
Hamza Chehili , Nourhene Bougourzi , raida malak Makhlouf , hadjer Taib , Mustapha Bensaada
Chest X-ray (CXR) interpretation remains a challenging task due to overlapping anatomical structures, variability in disease presentation, and increasing clinical workload. Existing automated report-generation models provide promising results but often lack explicit interpretability, limited clinical alignment, and insufficient comparative evaluation with established baselines. This study proposes an explainable multimodal framework that combines a dual CNN encoder (ResNet-50 and EfficientNet-B0) with the Gemma-3 1B language model fine-tuned using low-rank adaptation (LoRA). Visual explanations are produced through Gradient-weighted Class Activation Mapping (Grad-CAM) to enhance transparency in the decision process. Unlike prior image-to-text pipelines, our approach follows a findings-guided paradigm and integrates both visual and textual cues during generation. Experiments conducted on public datasets demonstrate consistent improvements over representative vision-language baselines reported in recent literature, with notable gains in BLEU, ROUGE, METEOR, and BERTScore. Generated reports show improved factual completeness and clinically relevant region-level attention. Limitations include the absence of evaluation against emerging foundation models and the need for anatomical- level explainability metrics. Future work will extend benchmarking to models such as M2-Transformer, MedCLIP-GPT, and R2Gen, and will explore clinical validation in real-world workflows.
Volume: 41
Issue: 3
Page: 1060-1069
Publish at: 2026-03-10

Intelligent artificial neural network-based control for solar electric vehicle charger

10.11591/ijeecs.v41.i3.pp885-893
Rajeshkumar Damodharan , Pradeep Kumar S
The performance of electric vehicle (EV) charging systems in response to sudden changes in solar irradiation and dynamic battery load variations. EV chargers must have effective power conversion and flexibility as the use of renewable energy sources increases. This paper suggests a charging system based on resonant converters that minimizes heat and losses in EV charging stations by enabling high-efficiency, soft-switching power transfer. For modern EV applications, the ability to manage large voltage fluctuations ensures reliable, quick, and portable charging. The artificial neural networks (ANN) controller overcomes the drawbacks of conventional Perturb and Observe (P&O) for solar DC-DC converters and PI control for resonant converter approaches. MATLAB simulation results demonstrate that the proposed system outperforms traditional techniques in terms of an ANN based controller, which enhances maximum power point tracking (MPPT) efficiency to 98.6%, reduces oscillations near the maximum power point by approximately 80%, and increases total EV charging efficiency by 3%. The ANN-based control to EV charging infrastructure greatly enhances overall system dependability and real-time responsiveness, making it a good fit for subsequent smart grid and renewable energy applications.
Volume: 41
Issue: 3
Page: 885-893
Publish at: 2026-03-10

Satellite-based assisted-offloading for energy-constrained edge networks

10.11591/ijeecs.v41.i3.pp935-945
Thembelihle Dlamini , Mengistu A. Mulatu , Sifiso Vilakati
As the need for global broadband internet connectivity increases, there is a need to consider the use of non-terrestrial networks (NTNs) to extend the network coverage to protected areas (e.g., national parks). Usually, protected areas are prohibited from having power lines thus lacking wireless connectivity. To over come this challenge, energy can be provided through the use of green energy from a solar photovoltaic (PV) system. Then, a green energy-based base station (BS) can be deployed within the area in order to provide mobile connectivity to visitors, as well as also using the NTNs to handle excess traffic or take over the traffic in the event the BS does not have sufficient green energy from stor age. In this paper, a hybrid wireless communication system is proposed to in clude BS sites located in a protected area and satellites in the low earth orbits (LEO), coupled with new offloading strategies, with the main goal of optimizing the trade-off between energy consumption and end-to-end delay for the green energy-based BS sites. For accuracy of our simulations, we consider real data from a solar photovoltaics system, traffic workloads, visitor’s location data, and satellite orbits from Starlink constellations. Our results demonstrate that the co existence of the BS and satellite achieve energy savings from 59% to 34%, with an average system delay of 0.83 seconds and a packet drop rate that ranges from 8.3% to 2.7%, when compared with our benchmark.
Volume: 41
Issue: 3
Page: 935-945
Publish at: 2026-03-10

Synthetic inertia controller of a wind power plant as a means of increasing the stability of electric power systems

10.11591/ijeecs.v41.i3.pp1117-1123
Makhmudov Tokhir Farkhadovich , Ramatov Adxam Nasiriddin o’gli
The article discusses the use of wind power plants as sources of synthetic inertia to enhance power system stability and reduce frequency fluctuations. This research explores the feasibility of implementing a synthetic inertia controller in wind power plants to decrease the magnitude of frequency oscillations during transient operating conditions. The growing integration of wind farms into modern power grids leads to a reduction in the overall kinetic energy, or inertia, available in the system. As a result, the grid may become more vulnerable to disturbances. When the system inertia is too low, frequency stability can be affected, especially when large generating units suddenly fail or disconnect from the grid. In general, a lower level of inertia in the system causes larger frequency deviations following an imbalance in active power. To overcome this issue, a synthetic inertia regulator for wind power plants has been developed, enabling wind turbines to support the grid and reduce the depth of frequency drops during transient events.
Volume: 41
Issue: 3
Page: 1117-1123
Publish at: 2026-03-10

A hybrid approach for measuring semantic similarity in lexically identical but ambiguous sentences

10.11591/ijeecs.v41.i3.pp954-965
Btissam El Janati , Adil Enaanai , Fadoua Ghanimi
This study addresses the critical challenge of semantic similarity and lexical disambiguation in natural language processing, focusing on sentences with structural and lexical ambiguities. We introduce an innovative hybrid approach that synergistically combines symbolic and neural methods to better align with human judgment. Our methodology dynamically integrates fuzzy Jaccard’s lexical precision with SBERT embeddings’ contextual sensitivity, enabling adaptive semantic ambiguity resolution. Experimental evaluation on 33 ambiguous sentences demonstrates that our approach significantly outperforms conventional artificial intelligence (AI) systems, achieving an 11.7% reduction in mean absolute error compared to reference models, with statistical analysis confirming robust results (d = -0.80, p < 0.001). This represents a 65% improvement in human evaluation alignment over existing methods. Our research contributes to advancing the field by showing that architectural intelligence can surpass mere parameter scaling, offering an effective solution for applications requiring both precision and interpretability, with promising directions for multilingual extension and explainable AI integration.
Volume: 41
Issue: 3
Page: 954-965
Publish at: 2026-03-10

Exploring word embeddings and clustering algorithms for user reviews

10.11591/ijeecs.v41.i3.pp1017-1024
Zuleaizal Sidek , Sharifah Sakinah Syed Ahmad
The rapid advancement of information technology has led to a significant surge in the volume of unstructured textual data. This has posed a major problem in terms of analyzing, organizing, and automatically clustering text for research purposes, which is crucial for extracting valuable insights. The process of manually clustering the unstructured data, such as customer reviews on the Internet, which capture the opinions of customers regarding products, services, and social events, requires significant financial resources, manpower, and time. Most of the studies are directed towards the analysis of sentiment in user reviews. In order to address the issues effectively, automated text clustering could assist in categorizing reviews into various themes, thereby simplifying the analysis process. Therefore, in this paper, we present and compare the result of experiment the combination of five text clustering techniques, namely K-means, fuzzy C-mean (FCM), non-negative matrix factorization (NMF), latent dirichlet allocation (LDA), and latent semantic analysis (LSA) with different embedding techniques, namely term frequency–inverse document frequency (TF-IDF), Word2Vec, and global vectors (GloVe). The experiments revealed that LDA is a reliable algorithm as it consistently produces good results across three-word embeddings. The highest Silhouette score recorded in the experiments was 0.66 using LDA and Word2Vec as word embedding. Simultaneously, the application of LSA in conjunction with Word2Vec yields superior outcomes, as evidenced by a Silhouette score of 0.65.
Volume: 41
Issue: 3
Page: 1017-1024
Publish at: 2026-03-10

ARX based cipher with S-box augmentation: statistical and differential evaluation

10.11591/ijeecs.v41.i3.pp946-953
Manita Rajput , Pranali Chaudhari
With the growth of internet of medical things (IoMT), the continuous transfer of vital biomedical data requires lightweight encryption with strong resistance to statistical and differential attacks. The Speck cipher is a suitable candidate because of its low memory and execution time. However, its vulnerability to differential cryptanalysis limits wider use in healthcare environments. In this work, a hybrid lightweight algorithm is proposed by integrating the PRESENT substitution box within the Speck64/96 round structure. The substitution layer was evaluated at three different positions in the round function. Statistical and differential analyses were performed on four sets of plaintext data, each containing 1,000 test pairs. Index of coincidence (IoC), entropy, and avalanche effect were used as the primary statistical metrics. Differential trail strength was assessed using ciphertext differences and round-wise differential probability (DP). The experimental results show that the proposed version, named Speckpres_S, achieves a 6.02% reduction in IoC, a 3.8% improvement in entropy, and a 1.7% rise in avalanche effect when compared with Speck64/96. The differential trail becomes weaker, with a 46% reduction in trail probability and a 12–15% increase in trail weight across all datasets. The execution time remained within IoMT limits. This indicates stronger resistance to differential attacks with predictable diffusion. The study demonstrates that Speckpres_S improves security while maintaining practical latency and throughput for IoMT applications. Although execution time increases marginally, the gain in differential resistance and statistical performance makes the proposed algorithm a more robust option for transmitting sensitive biomedical parameters.
Volume: 41
Issue: 3
Page: 946-953
Publish at: 2026-03-10

Design and implementation of novel encryption architecture using mix column with novel adder

10.11591/ijeecs.v41.i3.pp1134-1140
Radha Appisetty , Munuswamy Siva Kumar
Digital information is extremely simple to process these days, but it can be accessed by unauthorized people. Cryptography is one of the most effective and widely used methods for data security, to protect this information. The cryptography techniques are becoming popular and widely adopted due to the security threats during data transmission. An essential part of a cryptographic system, cryptography algorithms are developed and implemented to increase data security. The developers of these cryptographic algorithms took into consideration additional parameters, including speed, resource consumption, reliability, usage type, and flexibility, even if their primary goals are confidentiality, integrity, and authenticity. It’s important to understand that each component affects the way that a cryptographic technique is designed. Hence, this analysis presents the design and implementation of a novel encryption architecture using mix column with a novel adder. The novel encryption algorithm is designed for an encryption architecture (EA) with mix column using novel adder. This novel encryption algorithm will attain better security and performance.
Volume: 41
Issue: 3
Page: 1134-1140
Publish at: 2026-03-10
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