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30,376 Article Results

A novel approach for detection of cracks in painting and concrete surface images using CNN models

10.11591/ijeecs.v40.i2.pp988-1000
Deepti Vadicherla , Poonam Gupta
Discovering the beginnings of historical artworks takes one on an amazing voyage across space and time. People all around the world have been captivated by India's rich cultural heritage throughout its history, and ancient paintings have always been a very important part of it. Over the period of time, these ancient paintings can get cracks on it due to many factors. This research introduces an automated image classification system where the cracks on the paintings as well as the concrete surface will get detected. Detecting cracks on the concrete surface is important because the longevity and upkeep of concrete structures rely on the prompt identification and treatment of cracks, which can weaken the structure and necessitate expensive repairs. In this study, we focus on image classification using general convolution neural network (CNN), Inception V3, VGG-16, and ResNet-50 models of CNN. These models are trained and validated separately on two different datasets of paintings and concrete surfaces. Inception V3 and VGG-16 models achieve high accuracy, respectively in painting and concrete datasets in comparison with general CNN and ResNet-50 models.
Volume: 40
Issue: 2
Page: 988-1000
Publish at: 2025-11-01

Interpretable federated deep learning models for predicting gait dynamics in biomechanics

10.11591/ijeecs.v40.i2.pp1087-1099
Shaik Sayeed Ahamed , Akram Pasha , Syed Ziaur Rahman , D. N. Puneeth Kumar
Accurate prediction of human joint angle dynamics and reliable gait classifica tion are essential for applications in rehabilitation, biomechanics, and clinical monitoring. Traditional machine learning (ML) models trained on centralized data raise concerns about privacy, scalability, and transparency. This study proposes a federated deep learning (DL) framework that integrates privacy preserving model training with interpretable predictions. Specifically, a gated recurrent unit- deep neural network (GRU-DNN) hybrid model is developed for regression of joint angles, while a Long short-term memory- convolutional neural network (LSTM-CNN) hybrid model is designed for binary and multi class gait classification. The framework is deployed using the federated av eraging (FedAvg) algorithm across simulated clients, with each client training locally on its data. To enhance interpretability, the local interpretable model agnostic explanations (LIME) algorithm is integrated at the client level to gener ate human-understandable explanations for model predictions. The experimen tal results demonstrate significant improvements, including a reduction in global mean squared error (GMSE) from 56.16 to 3.31 and an increase in R-squared score from 0.80 to 0.99 for regression, along with classification accuracies of 0.97 (binary) and 0.94 (multi-class). This scalable, privacy-preserving frame work bridges the gap between accuracy and transparency, offering impactful applications in biomechanics, healthcare, and personalized medicine.
Volume: 40
Issue: 2
Page: 1087-1099
Publish at: 2025-11-01

Generalized domain tutoring framework for AI agents with integrated explainable AI techniques

10.11591/ijeecs.v40.i2.pp860-870
László Csépányi-Fürjes , László Kovács
This paper proposes a novel approach to integrate tutoring functionality into AI systems to counteract the potential decline of human intelligence caused by AI-driven over-automation. Existing explainable AI methods primarily emphasize transparency while lacking inherent educational functionality. Consequently, users are essentially left as passive recipients of AI-driven decisions without any structured learning mechanism in place. To address this, this paper introduces the knowledge-sharing-bridge (KSB), a component designed to transform AI into an active tutor. Unlike traditional intelligent tutoring systems (ITS), which operate separately from AI decision-making processes, the KSB is embedded within AI frameworks, ensuring continuous and context-aware learning opportunities. The proposed framework uses structured knowledge representation tools, such as category maps and word-clouds, to improve the user’s understanding of the decisions made by the AI systems. Prototype implementation demonstrates how these elements work together to provide real-time, interactive learning experiences. The results indicate that integrating KSB into AI enhances both explainability and user learning. This approach promotes a more in-depth interaction with AI insights and enables AI systems to become lifelong learning companions, closing the gap between automation and education.
Volume: 40
Issue: 2
Page: 860-870
Publish at: 2025-11-01

Development and evaluation of a generalized ontology framework for software requirement specification

10.11591/ijeecs.v40.i2.pp1050-1064
Sourav Kundu , Soumay Kanti Das , Abu Rafe Md Jamil , Md Kamrul Islam , SK. Shalauddin Kabir , Mostafijur Rahman Akhond
This paper presents an ontology developed to address challenges such as com munication gaps, risks of errors, and inconsistencies during the manual process of creating software requirement specifications (SRS). The proposed ontology offers a systematic and formal depiction of the requirements, enhancing consis tency and communication among stakeholders. The ontology has been devel oped from the software requirements documents to facilitate the development process. This paper discusses the process of creating the ontology and demon strates using Pellet Reasoner for inference and Prot´eg´e for ontology construction to save and reuse information. The ontology seems to be efficient in manag ing complex software projects, enabling accurate requirement retrieval through SPARQL queries. This study emphasizes how incorporating ontologies into re quirement engineering can significantly enhance the quality and reliability of SRS.
Volume: 40
Issue: 2
Page: 1050-1064
Publish at: 2025-11-01

Automatic wildlife species identification on camera trap images using deep learning approaches: a systematic review

10.11591/ijeecs.v40.i2.pp968-977
Siyabonga Mamapule , Bukohwo Michael Esiefarienrhe , Ibidun Christiana Obagbuwa
The foundation of systematic research depends on precise species identification, functioning as a critical component in the processes of biological research. Wildlife biologists are prompting for more effective techniques to fulfill the expanding need for species identification. The rise in open source image data showing animal species, captured by digital cameras and other digital methods of collecting data, has been monumental. This rapid expansion of animal image data, integrated with state-of-the-art machine learning techniques such as deep learning which has shown significant capabilities for automating species identification. This paper focuses on the role of deep neural network architectures in furthering technological advancements in automating species identification in recent years. To advocate further investigation in this field, an examination of machine learning architectures for species identification was presented in this work. This examination focuses primarily on image analyses and discusses their significance in wildlife conservation. Fundamentally, the aim of this article is to offer insights into the present advancements in automating species identification and to act as a reference for scholars who are keen to integrate machine learning techniques into ecological studies. Systems designed through Artificial Intelligence are extensive in providing toolkits for systematic identification of species in the upcoming years.
Volume: 40
Issue: 2
Page: 968-977
Publish at: 2025-11-01

Interactive multimedia e-collaboration for innovative linguistics education

10.11591/ijeecs.v40.i2.pp1149-1157
Syarifa Rafiqa , Nofvia De Vega , Arifin Arifin
This study aims to investigate the needs of students and lecturers regarding interactive multimedia resources in linguistics at the Faculty of Teacher Training and Education, Universitas Borneo Tarakan, to facilitate further development. The findings reveal a significant gap between current instructional provisions and the specific needs of students and faculty, highlighting the necessity for pedagogical innovation to enhance interaction and understanding in linguistics. Utilizing a mixed-methods approach, the research included surveys and interviews with participants in linguistics courses. Results indicated that 86% of students sought in-depth knowledge of linguistics, and 73% felt that existing support was inadequate. It underscores a high demand for a focus on selected topics, simplified explanations, and multimedia interactivity. The findings demonstrate that instructional materials are poorly aligned with teaching needs, negatively impacting educational methodologies and failing to effectively address students' relevant needs. The implications of this study extend to practice and further research, urging faculty members to increasingly integrate multimedia elements into their teaching and develop tailored resources based on identified needs. Newly created materials should undergo practical evaluation to enhance student satisfaction and performance in linguistics studies.
Volume: 40
Issue: 2
Page: 1149-1157
Publish at: 2025-11-01

A comparative study of solar photovoltaic array configurations to optimize power harvesting in a real-world system under various partial shading conditions

10.11591/ijeecs.v40.i2.pp558-566
Karthick Balakrishnan , Sudhakaran Mahalingam
Partial shading (PS) significantly reduces power generation and efficiency in solar photovoltaic (PV) systems. This research presents a novel totalcross-tied (TCT) methodology designed to mitigate shading effects by optimizing array layout while preserving electrical connectivity. The TCT method is compared to three established configurations: series-parallel (S-P), bridge-linked (B-L) and honey-comb (H-C). MATLAB simulations on a (9×9) PV array under variousshading conditions demonstrate TCT’s superior performance in achieving the global maximum power point (GMPP). Key findings indicate that TCT surpasses the other configurations, reaching a maximum power output of 16,650W at GMPP, with a mismatch power loss of 2,600W, a power loss of 13.32%, a fill factor (FF) of 38.27, and an execution ratio (ER) of 0.866.
Volume: 40
Issue: 2
Page: 558-566
Publish at: 2025-11-01

AlGaN/GaN MSM UV photodetector without and with BGaN back-barrier layer comparison study by SILVACO-TCAD

10.11591/ijeecs.v40.i2.pp590-600
Aicha Benyettou , Abedelkader Hamdoune , Belkacem Benadda , Djamal Lachachi
Using DevEDIT and atlas under SILVCAO-TCAD, we were able to achieve high photodetector metal-semiconductor-metal (MSM) AlGaN/GaN/BGaN performance with high electronic mobility. Our device demonstrated a sensitivity of 286 (I illumination/I dark) at Vanode 20V with an illumination current of 26 mA, a photocurrent of 1.56e-7 A at a wavelength of 0.350 µm, and an appropriate efficiency value of 87% without BGaN, and we also studied the influence of the boron B0.03Ga0.97N back-barrier layer. As a result, we obtain a sensitivity of 293,4 at Vanode 20V with an illumination current of 27 mA, a photocurrent of 1,85e-7 A at a wavelength of 0.350 µm, and an appropriate efficiency value of 90%. Additionally, this type of photodetector has been effectively created to detect UV light in the 100–450 nm range, and it may find value in both medical and military settings. Astronomical, medical diagnostics, environmental sensing, remote sensing, thermal imaging, optical signal detection, night vision cameras, missiles, and target tracking.
Volume: 40
Issue: 2
Page: 590-600
Publish at: 2025-11-01

Improving recommendations with implicit trust propagation from ratings and check-ins

10.11591/ijeecs.v40.i2.pp814-828
Sara Medjroud , Nassim Dennouni , Mourad Loukam
This paper investigates how the propagation of implicit trust between users affects the quality of point-of-interest (POI) recommendations in location-based social networks (LBSNs). Through the analysis of user interactions via ratings and check-ins, this work proposes a recommendation model known as propagation of rating/check-in for implicit trust (PRCT). This model relies on two primary approaches: Similarity trust rating (STR), which utilizes user ratings, and similarity trust check-in (STC), which focuses on check-ins data. Both approaches employ trust propagation to enhance their similarity matrices between users. An evaluation of the PRCT model using the Yelp dataset shows that the STR approach surpasses other variants in terms of PRECISION and RECALL, while the STC approach demonstrates superior performance in terms of RMSE. Furthermore, while trust propagation in the PRCT model increases the density of its similarity matrices, it does not consistently enhance its PRECISION parameter. Only the similarity Jaccard check-in (SJC) and similarity cosine check-in (SCC) approaches show a significant improvement of this parameter. 
Volume: 40
Issue: 2
Page: 814-828
Publish at: 2025-11-01

Panic detection through facial recognition paradigm using deep learning tools

10.11591/ijeecs.v40.i2.pp1001-1010
Sameerah Faris Khlebus , Mohammed Salih Mahdi , Monji Kherallah , Ali Douik
Recently, panic detection has become essential in security, healthcare, and human-computer interaction. Automatic panic detection (APD) systems are designed to monitor physiological signals and behavioral patterns in real-time to detect stress responses. APD is increasingly adopted across many sectors, including disaster preparedness, COVID-19, and terror attacks. Their integration with various applications reduces human efforts and saves costs. However, most studies rely on existing models with fewer new ones or techniques. This study proposes a vision-based panic detection model using MobileNet, ResNet, and convolutional neural network (CNN). The FER2013 dataset is used for the model training and testing. The results indicate that MobileNet is the most effective model for image-based panic detection across ten folds with an accuracy of 90%, recall of 96.9%, and mean accuracy of 0.032. MobileNet also showed a mean absolute error (MAE) between 0.02 and 0.04. This study has been to confirm MobileNet's suitability for image-based panic detection. The findings contribute to developing more reliable and accurate image-based panic detection systems in real-world applications. It offers valuable insights and lays the groundwork for future deep-leaning-based panic detection studies.
Volume: 40
Issue: 2
Page: 1001-1010
Publish at: 2025-11-01

End-to-end system for translating bahasa isyarat Indonesia sign language gestures into Indonesian text

10.11591/ijeecs.v40.i2.pp719-734
Satria Putra , Erdefi Rakun
This study addresses critical challenges in developing an end-to-end bahasa isyarat Indonesia (BISINDO) SLT by integrating advanced deep learning techniques to overcome complex background interference, transitional gesture recognition, and limitations in dataset availability. While existing SLT systems struggle with isolated word recognition and manual preprocessing, our work introduces three key innovations: (1) implementation of YOLOv8 for optimized object detection, achieving 88% mAP and reducing WER to 11.40%, outperforming YOLOv5/v7 in handling complex backgrounds; (2) automated removal of transitional gestures using Threshold conditional random fields (TCRF), which attained 95.68% accuracy, significantly improving upon MobileNetV2’s performance (WER: 6.89% vs. 93.53%); and (3) end-to-end BISINDO SLT by expansion of the BISINDO dataset to 435 word labels, enabling comprehensive sentencelevel translation. Experimental results demonstrate the system’s robustness, with 8.31% of WER, 84.13% of SAcc, and 87.08% of SacreBLEU after dataset expansion and redundancy elimination through grouping methods. The proposed framework operates without manual intervention, marking a substantial advancement toward real-world applicability.
Volume: 40
Issue: 2
Page: 719-734
Publish at: 2025-11-01

Room energy management utilizing internet of things technology for decreasing electricity consumption

10.11591/ijres.v14.i3.pp734-744
Winasis Winasis , Suroso Suroso , Azis Wisnu Widhi Nugraha , Priswanto Priswanto
This paper proposes a novel internet of things (IoT)-based control system for energy management to reduce electricity consumption from the two most dominant loads in buildings: air conditioners (AC) and lighting. The proposed system provides a comprehensive operational control strategy that integrates scheduling, human detection, ambient temperature, and light intensity for optimal room-level energy management employed. The proposed system employs wireless fidelity (WiFi)-enabled temperature, presence, and light sensors for comprehensive room conditions monitoring. Additionally, a WiFi-connected infrared module serves as an actuator to regulate the AC unit. Testing results demonstrate compelling energy savings, achieving up to 36% for the AC and 72% for the lighting while maintaining a comfortable indoor environment. These results were obtained from an experimental test in a private room within a residence over an 8-hour daytime period with 50% occupancy time. The proposed IoT system offers a highly effective and easily deployable solution for sustainable energy reduction in residential settings.
Volume: 14
Issue: 3
Page: 734-744
Publish at: 2025-11-01

Enhancing cross-cutting concerns in the internet of things with applying aspect oriented programming

10.11591/ijres.v14.i3.pp745-753
Khalifa Fatiha , Guelta Bouchiba
Aspect oriented programming (AOP) is a new programming model that provides new concepts to handle cross-cutting concerns about code. The idea of introducing AOP in the internet of things (IoT) is inherited from the complexity of sensor operations involving data acquisition, processing, and communication, the need to support multiple simultaneous services for users particularly security services such as authentication, authorization, data traceability, and transaction management, and the challenges posed by the IoT deployments, the treatment of these data volumes lead to problematic code redundancy and cross-cutting concerns that compromise system maintainability. In this context, AOP enables the separation of core functionalities, data management, and cross-cutting concerns, allowing them to be developed and reused independently within the same codebase. To address these issues, this paper proposes an AOP model for IoT systems based on the Petri net representations. The model strategically integrates the core AOP advantages of modularity, reusability, and extensibility, microservices based architectural decomposition and specialized handling of sensor-specific requirements in IoT environments.
Volume: 14
Issue: 3
Page: 745-753
Publish at: 2025-11-01

The impacts of optical display BaF2-Ce materials on solid-state lighting

10.11591/ijres.v14.i3.pp717-724
Luu Hong Quan , Nguyen Thi Phuong Loan
Transparent ceramic doped with barium fluorid cerium (BaF2-Ce) was created via a sintering method and its brightness and scintillation characteristics were examined. The luminescence is associated with the 5d-4f transitions in the Ce3+ ion and exhibits emitting maxima at 310 and 323 nm. For Na-22 radioisotopes, photo-maximum at 511 keV and 1274 keV were achieved using translucent ceramic BaF2-Ce. The translucent ceramic BaF2-Ce has been determined to have a power resolution of 13.5% at 662 keV. A luminescent production rate was measured for the BaF2-Ce (0.2%) ceramic, which is similar to sole crystal. Calculations of the scintillation degradation period beneath 662 keV gamma stimulation reveal a quick part of 58 ns and a somewhat sluggish part of 434 ns. The more gradual part in BaF2-Ce(0.2%) ceramic is linked to the dipole-dipole power transmission from the host structure to the Ce3+ luminous core and is quicker comparing to self-trapped excitons (STE) emitting in BaF2 host. BaF2-Ce offer various qualities, including significant illumination output, rapid degradation duration, and rapid scintillating reaction, which are desirable for many global fields such as medicine, radiation detection, industrial systems and nuclear safety.
Volume: 14
Issue: 3
Page: 717-724
Publish at: 2025-11-01

Design of mobile application for communication and user interface of ESP32 potentiostat system

10.11591/ijres.v14.i3.pp725-733
Retno Supriyanti , Wahyu Widanarto , Putra Dwi Susanto , Madya Ardi Wicaksono , Syafrudin Rais Akhdan , Muhammad Alqaaf
The potentiostat utilizing the ESP32 has a 12-bit analog-to-digital converter (ADC), meaning the maximum value for ADC voltage readings on the ESP32 is 4095. These ADC readings are then converted into actual voltage units, ensuring more accurate measurements on the potentiostat. To facilitate the use of the ESP32 potentiostat, a mobile application must be designed as a user interface for data communication. The application will be developed on a mobile platform using a Bluetooth low energy (BLE) communication channel for easier access. The development process will utilize visual studio code as the code editor and programming languages like Dart and Flutter. The resulting application will feature a user-friendly dashboard, display data in a cyclic voltammetry graph, and store data in comma-separated values (CSV) files or images in the phone’s memory. This stored data will simplify observing results obtained from the ESP32 potentiostat.
Volume: 14
Issue: 3
Page: 725-733
Publish at: 2025-11-01
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