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28,188 Article Results

Estimating broiler heat stress using computer vision and machine learning

10.11591/ijai.v14.i4.pp2922-2934
Muhammad Iqbal Anggoro Agung , Eko Mursito Budi , Miranti Indar Mandasari
To optimize and enhance the efficiency of broiler chicken farming, it is essential to maintain the chicken’s welfare, as heat stress can decrease growth efficiency. The temperature-humidity index (THI) is a key indicator used to determine if chickens are experiencing heat stress. Precision livestock farming (PLF) based on computer vision is one method that can assist farmers in continuously and automatically monitoring the condition of their chickens. This research developed a computer vision-based PLF system to observe chickens with CP 707 strain in a commercial farm using the Mask region-based convolutional neural network (Mask R-CNN) method and object tracking algorithms to analyze features such as the cluster index, unrest index, and the distance traveled by broilers. The results indicated that all features tend to inversely correlate with the THI value, with the cluster index showing the most noticeable tendency. Additionally, it was found that external factors, such as the presence of farmers around the observation area, can affect the chickens' behavior, although the cluster index feature is relatively resilient to disturbances if the operator is not captured by the camera. It was concluded that there is a relationship between the features and the THI value; however, these features are not yet sufficient to distinguish the condition of chickens under high and low THI conditions in real-time.
Volume: 14
Issue: 4
Page: 2922-2934
Publish at: 2025-08-01

Power of blockchain technology for enhancing efficiency transparency and data provenance in supply chain management

10.11591/ijai.v14.i4.pp3452-3461
Kanimozhi Thirunavaukkarasu , Inbavalli Mani
Global supply chains face increasing challenges in improving efficiency, transparency, and compliance with regulatory requirements. Traditional supply chain systems often suffer from inefficiencies due to fragmented data and manual processes, which result in delays and higher costs. Blockchain technology has emerged as a potential solution by offering decentralization, data immutability, and automation through smart contracts. However, existing blockchain implementations struggle with issues like scalability and transaction speed, which limits their effectiveness in supply chain management. This study introduces a new framework based on distributed ledger technology (DLT) with enhanced smart contract functions and data provenance tracking. The framework aims to improve transaction throughput, reduce latency, and provide better data integrity, enabling more efficient and transparent supply chain operations. By incorporating mechanisms to track the origin and movement of goods, the framework ensures that stakeholders have real-time access to accurate information, improving decision-making and trust across the supply chain. We evaluate the performance of this framework using the AnyLogic simulation platform, comparing it to traditional blockchain systems. Metrics such as transaction throughput, latency, and efficiency are analyzed to demonstrate the improvements achieved by the proposed system. The results show significant enhancements in transaction speed and operational efficiency, offering a practical solution for optimizing supply chains in various industries.
Volume: 14
Issue: 4
Page: 3452-3461
Publish at: 2025-08-01

Comparing bidirectional encoder representations from transformers and sentence-BERT for automated resume screening

10.11591/ijai.v14.i4.pp3404-3411
Asmita Deshmukh , Anjali Raut Dahake
In today’s digital age, organizations face the daunting challenge of efficiently screening an overwhelming number of resumes for job openings. This study investigates the potential of two state-of-the-art natural language processing models, bidirectional encoder representations from transformers (BERT) and sentence-BERT (S-BERT), to automate and optimize the resume screening process. The research addresses the need for accurate, efficient, and unbiased candidate evaluation by leveraging the power of these transformer-based language models. A comprehensive comparison between BERT and S-BERT is performed, evaluating their performance across multiple metrics, including accuracy, screening time, correlation with job descriptions, and ranking quality. The findings reveal that S-BERT outperforms BERT, achieving higher accuracy (90% vs. 86%), faster screening time (0.061 seconds vs. 1 second per resume), and stronger correlation with job descriptions (0.383855 vs. 0.1249). S-BERT though has a smaller vector size of 384 enables capturing richer semantic information compared to BERT’s vector size of 768, contributing to its superior performance. The study provides insights into the strengths and limitations of each model, offering valuable guidance for organizations seeking to streamline their talent acquisition processes and enhance candidate selection through automated systems.
Volume: 14
Issue: 4
Page: 3404-3411
Publish at: 2025-08-01

Generative Indonesian chatbot for university major selection using transformers embedding

10.11591/ijai.v14.i4.pp3474-3482
Mutiara Auliya Khadija , Bambang Harjito , Morteza Saberi , Astrid Noviana Paradhita , Wahyu Nurharjadmo
Selecting a university major is a crucial decision that impacts students' future career paths and personal fulfillment. Traditional guidance methods often lack the personalization and timeliness needed to support students effectively. This study explores the use of Indonesian generative artificial intelligence (AI) chatbots and transformer embeddings to enhance decision-making for university major selection. By leveraging advanced AI techniques, such as bidirectional encoder representations from transformers (BERT) and Gemini embeddings, the research aims to provide personalized, interactive, and contextually relevant guidance. Experiments showed that BERT embeddings achieved the highest accuracy, with recurrent neural network (RNN) and long short-term memory (LSTM) models also performing well but facing issues with overfitting. Gemini embeddings provided strong performance but slightly less effective than BERT. The findings suggest that BERT-based models with RNN are superior for developing decision-support systems in 92% accuracy. Future work should focus on further optimization and integration of user feedback to ensure the relevance and effectiveness of these AI tools in educational settings.
Volume: 14
Issue: 4
Page: 3474-3482
Publish at: 2025-08-01

Synthesizing strategies and innovations in combating land degradation: a global perspective on sustainability and resilience

10.11591/ijai.v14.i4.pp3133-3142
Gangamma Hediyalad , Ashoka Kukkuvada , Govardhan Hegde Kota
This paper presents a comprehensive examination of land degradation, a critical environmental challenge with far-reaching implications for agricultural productivity, ecosystem sustainability, and socio-economic stability worldwide. With the backdrop of escalating human population pressures and the exacerbating impact of climate change. It delves into the causes and consequences of soil erosion, desertification, salinization, and biodiversity loss, highlighting the interplay between natural processes and anthropogenic activities. Through a detailed review of literature spanning various remediation technologies, conservation practices, and policy frameworks, the paper critically assesses the effectiveness of current land management approaches, including the utilization of biosurfactants, remote sensing technologies, and agroforestry systems. Furthermore, it identifies significant research gaps and future directions, emphasizing the need for quantitative assessments, exploration of socio-economic impacts, and evaluation of restoration techniques. By offering evidence-based recommendations for policymakers and practitioners, this paper contributes to the global dialogue on sustainable land management and aims to catalyze action towards halting the advance of land degradation, ensuring food security, and preserving biodiversity for future generations. This work not only advances our understanding of land degradation challenges but also outlines a path forward for research, policy, and practice in the pursuit of environmental sustainability and resilience.
Volume: 14
Issue: 4
Page: 3133-3142
Publish at: 2025-08-01

Comparative evaluation of left ventricle segmentation using improved pyramid scene parsing network in echocardiography

10.11591/ijai.v14.i4.pp3214-3227
Jin Wang , Sharifah Aliman , Shafaf Ibrahim
Automatic segmentation of the left ventricle is a challenging task due to the presence of artifacts and speckle noise in echocardiography. This paper studies the ability of a fully supervised network based on pyramid scene parsing network (PSPNet) to implement echocardiographic left ventricular segmentation. First, the lightweight MobileNetv2 was selected to replace ResNet to adjust the coding structure of the neural network, reduce the computational complexity, and integrate the pyramid scene analysis module to construct the PSPNet; secondly, introduce dilated convolution and feature fusion to propose an improved PSPNet model, and study the impact of pre-training and transfer learning on model segmentation performance; finally, the public data set challenge on endocardial three-dimensional ultrasound segmentation (CETUS) was used to train and test different backbone and initialized PSPNet models. The results demonstrate that the improved PSPNet model has strong segmentation advantages in terms of accuracy and running speed. Compared with the two classic algorithms VGG and Unet, the dice similarity coefficient (DSC) index is increased by an average of 7.6%, Hausdorff distance (HD) is reduced by 2.9%, and the mean intersection over union (mIoU) is improved by 8.8%. Additionally, the running time is greatly shortened, indicating good clinical application potential.
Volume: 14
Issue: 4
Page: 3214-3227
Publish at: 2025-08-01

Contract-based federated learning framework for intrusion detection system in internet of things networks

10.11591/ijai.v14.i4.pp3324-3333
Yuris Mulya Saputra , Divi Galih Prasetyo Putri , Jimmy Trio Putra , Budi Bayu Murti , Wahyono Wahyono
A plethora of national vital infrastructures connected to internet of things (IoT) networks may trigger serious data security vulnerabilities. To address the issue, intrusion detection systems (IDS) were investigated where the behavior and traffic of IoT networks are monitored to determine whether malicious attacks or not occur through centralized learning on a cloud. Nonetheless, such a method requires IoT devices to transmit their local network traffic data to the cloud, thereby leading to data breaches. This paper proposes a federated learning (FL)-based IDS on IoT networks aiming at improving the intrusion detection accuracy without privacy leakage from the IoT devices. Specifically, an IoT service provider can first motivate IoT devices to participate in the FL process via a contract-based incentive mechanism according to their local data. Then, the FL process is executed to predict IoT network traffic types without sending IoT devices’ local data to the cloud. Here, each IoT device performs the learning process locally and only sends the trained model to the cloud for the model update. The proposed FL-based system achieves a higher utility (up to 44%) than that of a non-contract-based incentive mechanism and a higher prediction accuracy (up to 3%) than that of the local learning method using a real-world IoT network traffic dataset.
Volume: 14
Issue: 4
Page: 3324-3333
Publish at: 2025-08-01

Urban incident detection based on hybrid convolutional neural networks and bidirectional long short-term memory

10.11591/ijai.v14.i4.pp3153-3159
Meryem Ayou , Jaouad Boumhidi
Real-time incident detection is a major challenge in urban roads. This paper proposes an innovative hybrid method for incident detection, combining convolutional neural networks (CNN) and bidirectional-long short-term memory (Bi-LSTM). CNN extracts complex spatial features from raw data, while Bi-LSTMs are used for incident detection by capturing long-term temporal dependencies present in data. The proposed algorithm is evaluated using simulated data from the open-source software simulation of urban mobility (SUMO). This combination improves incident detection's accuracy and robustness by exploiting spatial and temporal information. Experimental results show that our hybrid approach outperforms the support vector machine (SVM), random forest (RF), and Bi-LSTM algorithms, with a substantial decrease in false positives and the speed of detecting urgent situations.
Volume: 14
Issue: 4
Page: 3153-3159
Publish at: 2025-08-01

Integrating random forest and genetic algorithms for improved kidney disease prediction

10.11591/ijai.v14.i4.pp2797-2804
Bommanahalli Venkatagiriyappa Raghavendr , Anandkumar Ramappa Annigeri , Jogipalya Shivananjappa Srikantamurthy , Gururaj Raghavendrarao Sattigeri
This work offers a novel method for predicting chronic kidney disease (CKD) by combining random forest (RF) classification with genetic algorithm (GA) to optimize important parameters. The dataset comprises 1,659 patients with 51 clinical parameters. The suggested method emphasizes the optimization of random state values, test size, and essential hyperparameters, such as the number of trees in the forest, the least number of samples needed at a leaf node, and the smallest number of samples necessary to split an internal node. The optimization process is conducted in two stages: the first stage optimizes the random state and test size, while the second stage focuses on hyperparameters. Through extensive simulations over 50 runs, the study demonstrates that the optimized model achieves an accuracy ranging from 0.9451 to 0.9738. The results indicate a maximum increase in accuracy of 2.09%, showcasing the effectiveness of the GA-RF integrated approach in enhancing model performance. This work provides valuable insights into the impact of parameter optimization on machine learning (ML) models, particularly in medical diagnostics, and offers a robust framework for developing highly accurate predictive models.
Volume: 14
Issue: 4
Page: 2797-2804
Publish at: 2025-08-01

Wirelength estimation for VLSI cell placement using hybrid statistical learning

10.11591/ijeecs.v39.i2.pp840-849
Joyce Ng Ting Ming , Ab Al-Hadi Ab Rahman , Nuzhat Khan , Muhammed Paend Bakht , Shahidatul Sadiah , Mohd Shahrizal Rusli , Muhammad Nadzir Marsono
Optimizing wirelength involves predicting the total length of wires needed to connect different components within a chip during cell placement. It is a fundamental challenge in very-large-scale integration (VLSI) of integrated circuit (IC) design, as it directly impacts the overall performance and manufacturability of chips. Accurate wire-length estimation in the early stages of the design process is critical for guiding subsequent optimization tasks. This paper proposes a novel hybrid linear regression wirelength (hybrid-LRWL) method that combines the strengths of existing methods rectilinear Steiner minimal tree (RSMT) for low-degree nets and a statistical learning-based approach for high-degree nets. Additionally, it compares the performance of three well-established wirelength estimation techniques: half-perimeter wirelength (HPWL), rectilinear minimum spanning tree (RMST), and RSMT. The methods were evaluated using the International Symposium on Physical Design (ISPD) 2011 benchmark suite, considering accuracy and computational efficiency. The experimental results demonstrated that the proposed hybrid method achieves superior accuracy, with a mean error of less than 0.05% in total wirelength, closely approximating RSMT results. The proposed method reduces computational time up to 3.6 times faster than traditional RSM-based methods. The results establish a strong framework for accurate and efficient wirelength estimation in VLSI design for modern, high-performance ICs.
Volume: 39
Issue: 2
Page: 840-849
Publish at: 2025-08-01

Binary white shark optimization algorithm with Z-shaped transfer function for feature selection problems

10.11591/ijeecs.v39.i2.pp1269-1279
Avinash Nagaraja Rao , Sitesh Kumar Sinha , Shivamurthaiah Mallaiah
Feature selection is critical for improving model performance and managing high-dimensional data, yet existing methods often face limitations such as inefficiency and suboptimal results. This study addresses these challenges by introducing a novel approach using the white shark optimization (WSO) algorithm and its binary variants to enhance feature selection. The proposed methods are evaluated on various datasets, including “Dorothea,” “Breast Cancer,” and “Arrhythmia,” focusing on classification accuracy, the number of features selected, and fitness values. Results demonstrate that the WSO algorithms significantly outperform traditional methods, offering notable improvements in accuracy and efficiency. Specifically, the WSO variants consistently achieve higher accuracy and better fitness values while effectively reducing the number of selected features. This research contributes to the field by providing a more effective optimization approach for feature selection, addressing existing inefficiencies, and suggesting future directions for further refinement and broader application. The findings highlight the potential of advanced optimization techniques in enhancing data analysis and model performance, offering valuable insights for practitioners and researchers.
Volume: 39
Issue: 2
Page: 1269-1279
Publish at: 2025-08-01

Date fruit classification using CNN and stacking model

10.11591/ijeecs.v39.i2.pp1373-1383
Ikram kourtiche , Mostefa M. O. Bendjima , Mohammed El Amin Kourtiche
In North Africa and the Middle East, the date is the most popular fruit, with millions of tons harvested annually. They are a crucial component of the diet due to their exceptional content of essential vitamins and minerals, which confer a high nutritional value. The ability to accurately identify and differentiate between date varieties is therefore of paramount importance in agriculture. It is crucial for improving agricultural practices, ensuring harvest quality, and contributing to the economic development of date-producing regions. In this paper, we propose a hybrid method for classifying date fruit varieties based on two stages. In the first stage, we select the two best-performing pre-trained models from six experimented deep learning models, and we concatenate the feature maps extracted from these two models. In the second stage, we apply different classification methods, including artificial neural networks (ANN), support vector machines (SVM), and logistic regression (LR). The performance achieved by these methods is 97.22%, 98.46%, and 99.07%, respectively. Then, with the stacking model, we combined these methods, and the performance result was increased to 99.38%. This result demonstrates the effectiveness of the hybrid model for identifying date fruit varieties.
Volume: 39
Issue: 2
Page: 1373-1383
Publish at: 2025-08-01

A simulation-based investigation into the bidirectional charge and discharge dynamics in lead-acid batteries

10.11591/ijeecs.v39.i2.pp783-796
Muhammad Aiman Noor Zelan , Muhammad Nabil Hidayat , Nik Hakimi Nik Ali , Muhammad Umair , Muhammad Izzul Mohd Mawardi , Ahmad Sukri Ahmad , Ezmin Abdullah
This paper presents a comprehensive simulation-based investigation into the bidirectional charge and discharge dynamics of lead-acid batteries within electric vehicles (EVs) and energy storage systems (ESS). Utilizing a bidirectional DC-DC converter (BDC) integrated with a lead-acid battery, the study explores the performance of these batteries through various charging and discharging scenarios. The simulation model, implemented using MATLAB, assesses the impact of charging strategies on battery behavior, focusing on key metrics such as state of charge (SOC), energy performance, and charging rates. The results reveal that lead-acid batteries, when paired with appropriate charging infrastructure and strategies, demonstrate enhanced performance and reliability in both EV and ESS applications. The study highlights the significant role of BDC topology in facilitating efficient energy transfer and optimizing battery usage. The findings underscore the potential for improved performance and widespread adoption of bidirectional converters in sustainable energy solution.
Volume: 39
Issue: 2
Page: 783-796
Publish at: 2025-08-01

Devising the m-learning framework for enhancing students' confidence through expert consensus

10.11591/ijeecs.v39.i2.pp1035-1052
Teik Heng Sun , Muhammad Modi Lakulu , Noor Anida Zaria Mohd Noor
Past research has shown the relationship between self-regulated learning (SRL) and academic success. Self-regulated learners will monitor their learning, reflect on what they have learnt, adjust their learning strategies accordingly, and repeat this entire process throughout their learning. The ability to perform SRL will require the individual to have the belief and confidence in his/her capacity to succeed and accomplish the tasks. Therefore, this study aims to devise a mobile learning (m-learning) framework for enhancing the students’ confidence. To achieve this, the Fuzzy Delphi method was used to validate the proposed framework where the survey questionnaire was distributed to 21 experts who are the experts in their respective fields for their consensus to be obtained. Consensus showed that “assessment data” can indicate the students’ confidence when they attempt the assessment. Experts opined that “goal expectation,” and “viewed lessons, chapters, or syllabus” exert the most influence on the students’ confidence when they attempt their assessment. There was strong consensus from experts that “data security” is the most important element in the system infrastructure, and the “text mining technique” element can be used to evaluate the students’ confidence.
Volume: 39
Issue: 2
Page: 1035-1052
Publish at: 2025-08-01

Systematic literature review of learning model using augmented reality for generation Z in higher education

10.11591/ijeecs.v39.i2.pp1109-1120
Zulfachmi Zulfachmi , Normala Rahim , Wan Rizhan , Puji Rahayu , Aggry Saputra
Higher education is evolving with innovations aimed at enhancing the quality of learning, and one prominent innovation is the integration of augmented reality (AR) technology into the learning process. AR merges real-world and virtual elements in real-time, creating interactive and immersive educational experiences. This technology supports the display and interaction with virtual objects, enhancing engagement and comprehension among students. However, effective integration of AR in higher education faces challenges such as limited technological infrastructure, the need for skilled lecturers, and the adaptation of teaching methods to suit generation Z's learning preferences. Despite their technological proficiency, many educational institutions struggle to optimally implement innovations like AR. This systematic literature review aims to explore and identify an AR-based learning model suitable for generation Z in higher education. Findings suggest that AR technology can significantly enhance learning by offering engaging visualizations and interactive experiences, aligning well with generation Z's characteristics and learning styles. Effective AR implementation requires suitable platforms, such as mobile, desktop, wearable, and projection platforms, each offering unique benefits. By designing AR learning models that cater to generation Z, educational institutions can improve learning outcomes and experiences.
Volume: 39
Issue: 2
Page: 1109-1120
Publish at: 2025-08-01
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