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28,593 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

Hyperparameter optimization of convolutional neural network using grey wolf optimization for facial emotion recognition

10.11591/ijeecs.v40.i2.pp898-906
Muhammad Munsarif , Muhammad Saman , Ernawati Ernawati , Budi Santosa
Facial emotion recognition (FER) is a challenging task in computer vision with wide applications in areas such as human-computer interaction, security, and healthcare. To improve the performance of convolutional neural networks (CNN) in FER, a novel approach combining CNN with grey wolf optimization (GWO) was proposed to optimize key hyperparameters. The CNN-GWO model was fine-tuned by adjusting hyperparameters such as the number of convolutional layers, kernel size, number of filters, and learning rate. This model was evaluated using the CK+ dataset and achieved an accuracy of 90.97%, demonstrating its competitive performance compared to existing methods. The optimized hyperparameters included three convolutional layers, 35 filters, a kernel size of 5, a learning rate of 0.045990, a dropout rate of 0.4988, and a max pooling size of 3. These results confirm that GWO is effective in optimizing CNN for FER tasks, providing an efficient solution to enhance model accuracy. This approach shows promising potential for future FER applications, highlighting GWO as a valuable optimization technique for CNN architectures.
Volume: 40
Issue: 2
Page: 898-906
Publish at: 2025-11-01

Image recognition using deep learning: a review

10.11591/ijeecs.v40.i2.pp953-967
Osama M. Hassan , Ashraf A. Gouda , Mohammed Abdel Razek
This paper presents a comprehensive review of recent advancements in image recognition, with a focus on deep learning (DL) techniques. Convolutional neural networks (CNNs), in particular, have significantly transformed this domain, enabling substantial improvements in both accuracy and efficiency across diverse applications. The review explores state-of-the-art methods, highlighting their practical implementations and the progress achieved. It also addresses key challenges such as data scarcity and model interpretability, offering perspectives on emerging opportunities and future directions. By synthesizing current trends with forward-looking insights, the paper aims to serve as a valuable resource for researchers and practitioners seeking to navigate and contribute to the evolving landscape of image recognition. Moreover, the paper examines critical challenges that persist in the field, such as transfer learning, data augmentation, and explainable artificial intelligence (AI) approaches. By synthesizing current trends with emerging innovations, the review not only maps the trajectory of progress but also highlights future directions and research opportunities. This synthesis aims to provide researchers, developers, and industry practitioners with a solid understanding of the dynamic and rapidly evolving environment surrounding image recognition technologies.
Volume: 40
Issue: 2
Page: 953-967
Publish at: 2025-11-01

Adoption of virtual tours for tourism promotion in Tegal Regency: a technology acceptance model analysis

10.11591/ijeecs.v40.i2.pp1109-1120
Dairoh Dairoh , Sharfina Febbi Handayani , Dwi Intan Af'idah
Tegal Regency has various tourist attractions that have the potential to be increased as a stimulus for the district's economy. So that this potential can have an optimal positive impact, the tourist destination should be promoted to the general public to increase tourism visits. This effort can be carried out by utilizing existing technological developments through virtual tour (VT), but their implementation requires careful consideration. This study explored how perceived usefulness (PU), perceived ease of use (PEU), attitude, behavioral intention (BI), and tourism promotion (TP) relate to each other within the context of virtual tourism. Data were collected from 126 participants via an an online survey developed using the technology acceptance model (TAM) framework. The partial least squares structural equation modeling (PLS-SEM) method was employed for analyzing the data. The result revealed significant relationships between PU and ease of use, user attitudes (AT), and BIs. Furthermore, BI, PU, and PEU were all considerable predictors of TP. However, no significant relationship was found between user AT and BIs.
Volume: 40
Issue: 2
Page: 1109-1120
Publish at: 2025-11-01

Design and implementation of heterogeneous IoT wearables for multi-disease monitoring with OFDM-based spectrum allocation

10.11591/ijeecs.v40.i2.pp667-677
Shittu Moshood Boladale , Omotayo Olabowale Oshiga , Opeyemi Ayokunle Osanaiye , Abdulrasaq Olanrewaju Amuda , Abigail Chidimma Odigbo , Timothy Oluwaseun Araoye
This research proposes a comprehensive and scalable architecture for intelligent healthcare monitoring, integrating heterogeneous wearable biosensors, edge computing, and bio-inspired optimization techniques employing an orthogonal frequency division multiplexing (OFDM)-based spectrum allocation strategy. The system continuously monitors key physiological parameters, including heart rate, electrocardiogram (ECG), blood glucose levels, body temperature, blood pressure, and respiratory rate, using low-power, biocompatible sensors with wireless communication capabilities. An edge computing layer performs real-time signal preprocessing (noise filtering, normalization, compression), significantly reducing latency and bandwidth demands. To optimize system performance, the walrus optimization algorithm (WOA), a novel metaheuristic inspired by walrus social and hunting behaviors, is employed. WOA is utilized to dynamically adjust critical parameters, including transmission power, modulation index, bandwidth allocation, and routing efficiency. Experimental results demonstrate notable improvements: signal-to-noise ratio (SNR) increased from 5 dB to over 31 dB, latency reduced from 10 ms to under 4 ms, and bit error rate (BER) was minimized to 8×10⁻⁶. Hybrid models incorporating WOA with machine learning (WOA-ANN, WOA-SVM) achieved spectral efficiencies up to 3.7 bits/s/Hz and energy efficiencies up to 22 bits/Joule. The proposed system supports reliable, real-time health data acquisition and transmission in both urban and remote healthcare environments. Its modular, power-efficient, and adaptive architecture demonstrates high potential for deployment in telemedicine, chronic disease management, and emergency response systems, establishing a robust foundation for next-generation smart healthcare infrastructure.
Volume: 40
Issue: 2
Page: 667-677
Publish at: 2025-11-01

Federated learning in edge AI: a systematic review of applications, privacy challenges, and preservation techniques

10.11591/ijeecs.v40.i2.pp926-940
Christina Thankam Sajan , Helanmary M. Sunny , Anju Pratap
Edge artificial intelligence (Edge AI) involves the implementation of AI algorithms and models directly on local edge devices, such as sensors or internet of things (IoT) devices. This allows for immediate processing and analysis of data without the need for continuous dependence on cloud infrastructure. Concerns about privacy have grown importance in recent years for businesses looking to uphold end-user expectations and safeguard business models. Federated learning (FL) has emerged as a novel approach to enhance privacy. To improve generalization qualities, FL trains local models on local data. These models then collaborate to update a global model. Each edge device (like smartphones, IoT sensors, or autonomous vehicles) trains a local model on its own data. This local training helps in capturing data patterns specific to each device or node. Poisoning, backdoors, and generative adversarial network (GAN)-based attacks are currently the main security risk. Nevertheless, the biggest threat to FL’s privacy is from inference-based assaults such as model inversion attacks, differential privacy shortcomings and FL utilizes blockchain and cryptography technologies to improve privacy on edge devices. This paper presents a thorough examination of the current literature on this subject. In more detail, we study the background of FL and its different existing applications, types, privacy threats and its techniques for privacy preservation.
Volume: 40
Issue: 2
Page: 926-940
Publish at: 2025-11-01

Predictive model for high-risk healthcare clients and claims frequency

10.11591/csit.v6i3.p346-354
Lenias Zhou , Mainford Mutandavari , Lucia Matondora
Global healthcare spending surged to approximately USD 9.8 trillion in the aftermath of the COVID-19 pandemic, intensifying the need for effective risk management strategies in healthcare insurance. This study proposes a predictive model designed to identify high-risk clients for timely targeted interventions and to forecast claims frequency for optimized resource allocation. A real-world claims dataset from a healthcare insurance provider was utilized. Bayesian optimization was employed to enhance data labelling. A deep learning (DL) model with sigmoid activation was used to classify high-risk clients, while a regression model forecasted claims frequency. The model was trained and validated, and gave an accuracy of 97%, a precision of 95.2%, a recall of 98.1% and an F1-score of 96.6%. The results confirmed the model’s accuracy in identifying high-risk clients and its ability to provide reliable forecasting of future claims frequency. Importantly, the model also provided the reason behind its classification decision, enhancing transparency and trust. This research provides valuable data-driven insights to both the healthcare insurers and clients, giving them the power to stay ahead in managing key risks, which ultimately reduces the cost of healthcare insurance. This work contributed a scalable and interpretable solution for risk prediction in healthcare insurance.
Volume: 6
Issue: 3
Page: 346-354
Publish at: 2025-11-01

Monocular vision-based visual control for SCARA-type robotic arms: a depth mapping approach

10.11591/ijeecs.v40.i2.pp801-813
Diego Chambi Tula , Bryan Challco , Jonathan Catari , Walker Aguilar , Lizardo Pari
The accelerated growth of an increasingly automated industry requires the use of autonomous robotic systems. However, the use of these systems commonly requires an enormous amount of sensors. In this paper we evaluate the performance of a new system for visual control of a selective compliance assembly robot arm (SCARA) robotic arm using a monocular depth map that only requires one monocular camera. This system aims to be an efficient alternative to reduce the number of sensors in the robotic arm area while maintaining the effectiveness of traditional vision algorithms that use stereoscopic architectures of cameras. For this purpose, this system is compared with representative state-of the-art vision algorithms focused on the control of robotic arms. The results are statistically analyzed, indicating that the algorithm proposed in this research has competitive performance compared to state-of-the-art robotic arm visual control algorithms only using a single monocular camera.
Volume: 40
Issue: 2
Page: 801-813
Publish at: 2025-11-01

Cloud computing needs to explore into sky computing

10.11591/csit.v6i3.p294-306
Arif Ullah , Hassnae Remmach , Hanane Aznaoui , Canan Batur Şahin , Amine Mrhari
This paper evaluates key issues in cloud computing and introduces a novel model, known as sky computing, to address these challenges. Cloud computing, a transformative technology, has played a critical role in reshaping modern operations—especially following the COVID-19 pandemic, when many human activities shifted to technology-driven platforms. It offers multiple service models, including Software as a Service, Hardware as a Service, Desktop as a Service, Backup as a Service, and Network as a Service, each tailored to user requirements. However, the rapid expansion of cloud-based technologies and interconnected systems has intensified infrastructure and scalability challenges. Sky computing, or the “cloud of clouds,” emerges as an advanced layer above traditional cloud models, enabling dynamically provisioned, distributed domains built over multiple serial clouds. Its core capability lies in offering variable computing capacity and storage resources with dynamic, real-time support, providing a robust and unified platform by integrating diverse cloud resources. This paper reviews related technologies, summarizes prior research on sky computing, and discusses its structural design. Furthermore, it examines the limitations of current cloud computing frameworks and highlights how sky computing could overcome these barriers, positioning it as a pivotal architecture for the future of distributed computing.
Volume: 6
Issue: 3
Page: 294-306
Publish at: 2025-11-01

Characteristics ransomware stop/djvu remk and erqw variants with static-dinamic analysis

10.11591/csit.v6i3.p283-293
Dodon Turianto Nugrahadi , Friska Abadi , Rudy Herteno , Muliadi Muliadi , Muhammad Alkaff , Muhammad Alvin Alfando
Ransomware has developed into various new variants every year. One type of ransomware is STOP/DJVU, containing more than 240+ variants. This research to determine changes in differences characteristics and impact between ransomware variants STOP/DJVU remk, which is a variant from 2020, and the erqw variant from 2023, through a mixed-method research approach. Observation, simulation using mixing static and dynamic malware analysis methods. Both variants are from the Malware Bazaar site. The total characteristics based on dynamic analysis, the remk variant has 177, and the erqw variant has 190, which increased by 1.8%. The total characteristics based on static analysis, the remk variants have 586, and the erqw variants have 736, which increased by 5.7%. All characteristics from remk to erqw increasing in dynamic analysis, except the number of payloads that decreased about 20%. In static analysis, all characteristics from remk to erqw increase except the number of sections decreased about 1.5%. It can be the affected CPU performance, because the remk variant affects performance by increasing CPU work by 3.74%, while the erqw variant affects performance by reducing CPU work by 1.18%, both compared with normal CPU. which will affect the ransomware's destructive work and require changes in its handling.
Volume: 6
Issue: 3
Page: 283-293
Publish at: 2025-11-01

Optimizing diplomatic indexing: full-parameter vs low-rank adaptation for multi-label classification of diplomatic cables

10.11591/csit.v6i3.p274-282
Dela Nurlaila , Abba Suganda Girsang
Accurate classification of diplomatic cables is crucial for Mission’s evaluation and policy formulation. However, these documents often cover multiple topics, hence a multi-label classification approach is necessary. This research explores the application of pre-trained language models (CahyaBERT, IndoBERT, and MBERT) for multi-label classification of diplomatic cable executive summaries, which align with the diplomatic representation index. The study compares full-parameter fine-tuning and low-rank adaptation (LoRA) techniques using cables from 2022-2023. Results demonstrate that Indonesian-specific models, particularly the IndoBERT, outperform multilingual models in classification accuracy. While LoRA showed slightly lower performance than full fine-tuning, it significantly reduced GPU memory usage by 48% and training time by 69.7%. These findings highlight LoRA’s potential for resource-constrained diplomatic institutions, advancing natural language processing in diplomacy and offering pathways for efficient, real-time multi-label classification to enhance diplomatic mission evaluation.
Volume: 6
Issue: 3
Page: 274-282
Publish at: 2025-11-01

Implementation of IoT-based water quality monitoring instruments in cantang grouper cultivation ponds

10.11591/csit.v6i3.p235-244
Hollanda Arief Kusuma , M Hasbi sidqi Alajuri , Anggarudin Anggarudin , Dwi Eny Djoko Setyono , Henky Irawan
Grouper fish farming in Indonesia has great potential, but water quality management remains a challenge. Manual monitoring at hatchery D-Marine aquaculture struggles to detect sudden changes, risking mass mortality. This study developed an IoT-based water quality monitoring system using an ESP32 microcontroller, DS18B20 temperature sensors, pH sensors, dissolved oxygen (DO) sensors, a micro-SD card, an organic light emitting diode (OLED) display, and the Ubidots platform. The methodology involved device design, sensor calibration, and field testing. Calibration showed sensor accuracy above 90%. Field tests recorded water temperatures of 26.84 °C (tank 1) and 27.74 °C (tank 2), with pH values of 6.73 and 6.87, which did not meet Indonesian national standard (SNI) standards. Data transmission to Ubidots had a 95% packet delivery ratio (PDR) for device 1 and 97% for device 2. The system successfully provided real-time water quality data, supporting effective farm management. However, improvements to the dissolved oxygen sensor and an automatic control system are needed for better stability and efficiency.
Volume: 6
Issue: 3
Page: 235-244
Publish at: 2025-11-01

Mediterranean and northern european archaeology: a computational comparison

10.11591/csit.v6i3.p326-334
Hamza Kchan , Saira Noor
Despite the proliferation of computational tools in archaeology, few studies systematically compare their regional adaptations or explore the epistemological assumptions guiding their application. This paper addresses four critical research gaps: (i) the lack of comparative regional analysis between the Mediterranean and Northern Europe in computational archaeology, (ii) the insufficient integration of philosophical and epistemological frameworks in predictive modeling, (iii) the underexplored application of artificial intelligence (AI) and network theory in spatial analysis, and (iv) the limited interdisciplinary synthesis of biological, geospatial, and digital data. By examining representative case studies from both regions, we highlight the methodological innovations, theoretical orientations, and institutional dynamics that shape regional practices. The study underscores the necessity of integrating computational methods with interpretive depth and interdisciplinary collaboration to foster a more reflective and inclusive digital archaeology. 
Volume: 6
Issue: 3
Page: 326-334
Publish at: 2025-11-01

Javanese and Sundanese speech recognition using Whisper

10.11591/csit.v6i3.p253-261
Alim Raharjo , Amalia Zahra
Automatic speech recognition (ASR) technology is essential for advancing human-computer interaction, particularly in a linguistically diverse country like Indonesia, where approximately 700 native languages are spoken, including widely used languages like Javanese and Sundanese. This study leverages the pre-trained Whisper Small model an end‑to‑end transformer pretrained on 680,000 hours of multilingual speech, fine tuning it specifically to improve ASR performance for these low resource languages. The primary goal is to increase transcription accuracy and reliability for Javanese and Sundanese, which have historically had limited ASR resources. Approximately 100 hours of speech from OpenSLR were selected, covering both reading and conversational prompts, the data exhibited dialectal variation, ambient noise, and incomplete demographic metadata, necessitating normalization and fixed‑length padding. with model evaluation based on the word error rate (WER) metric. Unlike approaches that combine separate acoustic encoders with external language models, Whisper unified architecture streamlines adaptation for low‑resource settings. Evaluated on held‑out test sets, the fine‑tuned models achieved Word Error Rates of 14.97% for Javanese and 2.03% for Sundanese, substantially outperforming baseline systems. These results demonstrate Whisper effectiveness in low‑resource ASR and highlight its potential to enhance transcription accuracy, support language preservation, and broaden digital access for underrepresented speech communities. 
Volume: 6
Issue: 3
Page: 253-261
Publish at: 2025-11-01

Intrusion detection system using hybrid CNN-LSTM model in cloud computing

10.11591/ijeecs.v40.i2.pp840-849
Maha Mohammad Alshehri , Shoog Abdullah Alshehri , Samah Hazzaa Alajmani
Cloud computing has revolutionized online service delivery with its flexibility and cost efficiency. Nevertheless, the growing importance of stored data makes it a target for cyberattacks, posing security and privacy risks. This calls for effective solutions to safeguard data and infrastructure, particularly with regard to intrusion attacks and distributed attacks such as distributed denial of service (DDoS). Therefore, there is a need to develop an effective intrusion detection system (IDS) using deep learning to ensure the protection of cloud data and infrastructure. In this paper, a hybrid model aims to leverage the power of convolutional neural networks (CNNs) to analyze spatial features and extract complex patterns, while long short-term memory LSTMs are used to understand temporal data sequences and detect attacks that evolve over time to detect intrusions in cloud computing environments on the CSE-CIC-IDS2018 dataset. The model was trained and tested on DDoS attacks, and the results demonstrated high performance in detecting attacks with high accuracy and efficiency. This hybrid model achieved an accuracy of 99.88%, a precision of 99.83%, a recall of 99.94%, and an F1-score of 99.88%.
Volume: 40
Issue: 2
Page: 840-849
Publish at: 2025-11-01
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