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29,939 Article Results

Highly sensitive microwave sensor for metallic mine detection

10.11591/ijece.v15i3.pp2631-2641
Maged A. Aldhaeebi , Thamer S. Almoneef
This study introduces an innovative microwave system for detecting buried metallic landmines, providing an alternative to conventional imaging approaches. The system consists of two highly sensitive sensors, each configured with identical antennas arranged in a triangular formation to enhance sensitivity. The proposed microwave sensors exhibit exceptional sensitivity in detecting metallic landmines buried at various depths within sand and at different distances. Simulation and experimental studies were conducted using a foam box filled with sand and a metallic cube to simulate a landmine. The sensor’s sensitivity is evidenced by shifts in both the magnitude and phase of insertion loss (𝑆21) between scenarios with and without a metallic mine, attributed to differences in dielectric properties between the sand and the mine in the microwave spectrum. The results from both simulations and experiments confirm the sensor’s capability to detect metallic mines at varying depths within the sand medium. The proposed system offers significant advantages over imaging technologies for mine detection, including cost-effectiveness, simplicity, and ease of data processing without the need for complex imaging algorithms.
Volume: 15
Issue: 3
Page: 2631-2641
Publish at: 2025-06-01

Leukemia detection using SegNet and faster region-based convolutional neural network

10.11591/ijece.v15i3.pp3028-3038
Della Reasa Valiaveetil , T. Kanimozhi
Prevention of cancer is mostly attained by surveillance of the transformation zones. White blood cells (WBCs) are established in the bone marrow and intemperate growth of WBC leads to leukemia. Hematologists examine the microscopic images in manual method for predicting leukemia, but it is very complex process and without any guaranteed for accurate. In this proposed study, deep learning techniques involved to segment and classify the three types of leukemia like acute lymphocytic leukemia (ALL), acute myeloid leukemia (AML) and chronic lymphocytic leukemia (CLL) using the BioGps dataset. The purpose of deep learning in medical science enhances the accuracy and precision of determining leukemia in early stages. In this study, introducing a sigmoid stretching (SS) in pixel enhancement for preprocessing; SegNet (St) is comfort to extract the structural features of the leukocytes and to segment the normal and blast cells for a clear classification; faster region-based convolutional neural network (faster R- CNN) carried under the process of classification and optimization done by dragon fly algorithm. The result of this work achieves best accuracy related to the existing techniques of convolutional neural network (CNN) such as support vector machine (SVM), k-nearest neighbors (kNN) and Bayesian model. This study achieves the accuracy rate of 97%, precision rate of 94% and sensitivity rate of 90% respectively with low complexity.
Volume: 15
Issue: 3
Page: 3028-3038
Publish at: 2025-06-01

Comparative study on fine-tuning deep learning models for fruit and vegetable classification

10.11591/ijaas.v14.i2.pp384-393
Abd Rasid Mamat , Mohamad Afendee Mohamed , Mohd Fadzil Abd Kadir , Norkhairani Abdul Rawi , Azim Zaliha Abd Aziz , Wan Suryani Wan Awang
Fruit and vegetable recognition and classification can be a challenging task due to their diverse nature and have become a focal point in the agricultural sector. In addition to that, the classification of fruits and vegetables increases the cost of labor and time. In recent years, deep learning applications have surged to the forefront, offering promising solutions. Particularly, the classification of fruits using image features has garnered significant attention from researchers, reflecting the growing importance of this area in the agricultural domain. In this work, the focus was on fine-tuning hyperparameters and the evaluation of a state-of-the-art deep convolutional neural network (CNN) for the classification of fruits and vegetables. Among the hyperparameters studied are the number of batch size, number of epochs, type of optimizer, rectified unit, and dropout. The dataset used is the fruit_vegetable dataset which consists of 36 classes and each class contains 1,000 images. The results show that the proposed model based on the batch size=64 and the number of epochs=25, produces the most optimal model with an accuracy value (training) of 99.02%, while the validation is 95.73% and the loss is 6.06% (minimum).
Volume: 14
Issue: 2
Page: 384-393
Publish at: 2025-06-01

Impact of natural-white and red-blue light-emitting diode lighting on hydroponic basil growth and energy efficiency

10.11591/ijaas.v14.i2.pp406-415
Chaiyant Boonmee , Warunee Srisongkram , Wipada Wongsuriya , Patcharanan Sritanauthaikorn , Paiboon Kiatsookkanatorn , Napat Watjanatepin
Advanced phosphor-converted white light-emitting diodes (pc-WLEDs) have been developed to mimic the natural sunlight spectrum, potentially enhancing plant growth compared to traditional red-blue (R-B) LEDs. This study aimed to compare the effects of natural-white pc-WLED (nsW-pcLED) and conventional R-B LED (R:B 3.24) on the growth, yield, and energy efficiency of hydroponically grown sweet basil. It was cultivated in a deep-water culture system under identical conditions with a photosynthetic photon flux density (PPFD) of 200±10 µmol·m⁻²·s⁻¹ and a 16/8 light/dark photoperiod over 28 days. Key growth parameters, including plant height, stem diameter, leaf number, and plant fresh weight (PFW), were measured, while energy consumption was recorded to assess efficiency. Results indicated that nsW-pcLED significantly enhanced growth, with plants achieving an average height of 44.30±1.51 cm, stem diameter of 6.68±0.21 mm, and a PFW of 34.20±6.12 g, compared to 35.88±4.05 cm, 4.66±0.88 mm, and 23.02±5.26 g under R-B LED (p <0.05), respectively. The nsW-pcLED treatment produced an average net growth of 1,221 g·m⁻² versus 536.43 g·m⁻² for R-B LED and delivered 33.05 g·m⁻²·kW·h⁻¹ compared to 11.17 g·m⁻²·kW·h⁻¹, while consuming 23% less energy. These findings highlight nsW-pcLED’s superior performance for indoor hydroponic cultivation. Future studies should explore its application in large-scale systems and across diverse crop species.
Volume: 14
Issue: 2
Page: 406-415
Publish at: 2025-06-01

Optimization of cashew apple extract as a tomato sauce substitute in chicken steak marinades

10.11591/ijaas.v14.i2.pp590-597
Siti Susanti , Fatma Puji Lestari , Agus Setiadi , Budi Hartoyo , Ahmad Ni'matullah Al-Baarri
This study aims to optimize the use of cashew apple extract (CAE) as a substitute for tomato sauce in marinades and evaluate its effects on the chemical and sensory qualities of chicken steak. Four different marinade formulations containing varying concentrations of CAE (0, 5, 10, and 15%) were applied to chicken steak samples. Chemical analyses measured protein, fat content, and polycyclic aromatic hydrocarbon (PAH) levels, while sensory evaluations were conducted to assess tenderness, juiciness, aroma, and overall preference using a semi-trained panel. The results demonstrated that increasing CAE concentrations significantly elevated protein content (p<0.05) and reduced fat levels. PAH levels were below detectable limits in all samples, suggesting the marinade’s potential in reducing PAH formation. Sensory analysis revealed that the 5% CAE marinade was the most preferred, particularly for tenderness and juiciness. These findings suggest that CAE is a viable alternative to tomato sauce in marinades, offering both nutritional benefits and consumer acceptability.
Volume: 14
Issue: 2
Page: 590-597
Publish at: 2025-06-01

A hybrid convolutional neural network-recurrent neural network approach for breast cancer detection through Mask R-CNN and ARI-TFMOA optimization

10.11591/ijece.v15i3.pp3084-3094
Keshetti Sreekala , Srilatha Yalamati , Annemneedi Lakshmanarao , Gubbala Kumari , Tanapaneni Muni Kumari , Venkata Subbaiah Desanamukula
This paper presents a novel hybrid deep learning-based approach for breast cancer detection, addressing critical challenges such as overfitting and performance degradation in varying data conditions. Unlike traditional methods that struggle with detection accuracy, this work integrates a unique combination of advanced segmentation and classification techniques. The segmentation phase leverages Mask region-based convolutional neural network (R-CNN), enhanced by the adaptive random increment-based tomtit flock metaheuristic optimization algorithm (ARI-TFMOA), a novel algorithm inspired by natural flocking behavior. ARI-TFMOA fine-tunes Mask R-CNN parameters, achieving improved feature extraction and segmentation precision while ensuring adaptability to diverse datasets. For classification, a hybrid convolutional neural network-recurrent neural network (CNN-RNN) model is introduced, combining spatial feature extraction by CNNs with temporal pattern recognition by RNNs, resulting in a more nuanced and comprehensive analysis of breast cancer images. The proposed framework achieved significant advancements over existing methods, demonstrating improved performance. This hybrid integration of ARI-TFMOA and Hybrid CNN-RNN models represents a unique contribution, enabling robust, accurate, and efficient breast cancer detection.
Volume: 15
Issue: 3
Page: 3084-3094
Publish at: 2025-06-01

Enhancing malware detection with genetic algorithms and generative adversarial networks

10.11591/ijece.v15i3.pp3064-3074
Abid Dhiya Eddine , Ghazli Abdelkader , Bouache Mourad
Malware detection is a critical task in cybersecurity, necessitating the creation of robust and accurate detection models. Our proposal employs a holistic methodology for identifying and mitigating malware using deep learning techniques. Initially, a customized genetic algorithm is employed for feature selection, reducing dimensionality and enhancing the discriminatory power of the dataset. Subsequently, a deep neural network is trained on the selected features, achieving high accuracy and robust performance in distinguishing between malware and benign data. Generative adversarial networks are also utilized to evaluate model effectiveness on unseen data and ensure the model's robustness and generalization capabilities. Evaluation of the proposed model demonstrates accurate malware detection with high generalization capabilities. Furthermore, future research should focus on developing and deploying practical tools or systems that implement the proposed model for real-time malware detection in operational environments. This research makes a significant contribution to the field of malware detection and provides excellent opportunities for practical implementation in the field of cybersecurity.
Volume: 15
Issue: 3
Page: 3064-3074
Publish at: 2025-06-01

Exploring the effectiveness of hybrid artificial bee PyCaret classifier in delay tolerant network against intrusions

10.11591/ijece.v15i3.pp3149-3161
Rajashri Chaudhari , Manoj Deshpande
In challenging environments with intermittent connectivity and the absence of end-to-end paths, delay tolerant networks (DTNs) require robust security measures to safeguard against potential threats. This study addresses these issues by implementing an intrusion detection system (IDS) enhanced with machine learning techniques. Common threats such as distributed denial-of-service (DDoS) and flood attacks are tackled using datasets like network intrusion detection (NID) and flood attack datasets. Multiple machines learning methods, including k-nearest neighbors (K-NN), decision trees (DT), logistic regression (LR), and others, are utilized to improve detection accuracy. A PyCaret-based approach is developed to increase efficiency while preserving attack detection accuracy in DTNs. Comparative research demonstrates that PyCaret outperforms Scikit-learn models, and the artificial bee PyCaret classifier (ABPC) optimizes hyperparameters to improve model performance. NS2 simulation shows the system's ability to thwart attacks, offering useful insights into DTN security and improving communication reliability in various situations.
Volume: 15
Issue: 3
Page: 3149-3161
Publish at: 2025-06-01

Monkey detection using deep learning for monkey-repellent

10.11591/ijece.v15i3.pp3238-3245
Nur Latif Azyze Mohd Shaari Azyze , Teow Khimi Quan , Ida Syafiza Md Isa , Muhammad Afif Husman
Animal intrusion has caused many issues for human beings, especially monkeys. Monkeys have caused many problems such as yield crop damage, damage to human facilities and assets and stealing food. This study aims to investigate the use of deep learning to detect monkey presence accurately and use a proper repellent system to scare them away. A deep learning algorithm is constructed with supervised learning, which includes the monkey dataset with appropriate labelling of the object of interest. The detection of the monkey comes with a bounding box with respective confidence of detection. The result is then used to evaluate the accuracy of monkey detection. The accuracy of the trained model is assessed by evaluating its performance under varying conditions of camera quality and distances. The study focuses on proving the reliability of deep learning to detect monkeys and automatically perform corresponding actions like repelling monkeys. Hence this may reduce the reliance of manpower to secure a large space and improve safety issues.
Volume: 15
Issue: 3
Page: 3238-3245
Publish at: 2025-06-01

Demographic determinants of patronage of medicine hawkers by commercial vehicle passengers in Ghana

10.11591/ijphs.v14i2.24606
Joy Ato Nyarko , Kofi Osei Akuoko , Jonathan Mensah Dapaah , Nana Yaa Serwaa Akuoko , Egwolo Perpetual Iyengunmwena
Medicine hawking is one of the major public health problems of the global south. This present study examined the demographic determinants of patronage of the services of medicine hawkers among commercial vehicle passengers in Kumasi, Ghana. A cross-sectional study was carried out from February 2022 to March 2022 at major bus terminals in Kumasi. Data were descriptively and inferentially analysed. The survey revealed that 55% of the respondents had bought medicines from medicine hawkers before. There was a significant relationship between having bought from a medicine hawker before and the intention to buy from them again in the future. Also, age, religion and education contributed significantly to patronising the services of medicine hawkers. We recommend that government intensifies its public health education on the implications of seeking health care services from these medicine hawkers.
Volume: 14
Issue: 2
Page: 912-918
Publish at: 2025-06-01

Security analysis of Indonesia e-commerce platform against the risk of phishing attacks

10.11591/ijaas.v14.i2.pp533-541
Gede Arna Jude Saskara , Made Ody Gita Permana , I Made Gede Sunarya
This research analyses the security of e-commerce platforms in Indonesia against the risk of phishing attacks using the social-engineer toolkit (SET) application. Of the 31 platforms tested, it was found that 22 platforms have a low-security level because they can be easily replicated to carry out phishing attacks. In contrast, 9 platforms showed a high level of security, as they implemented the step-wise authentication and embedded login methods, which proved effective in protecting the platform from phishing attacks. The effectiveness rate of the SET application in conducting tests was recorded at 70.9%; the percentage is included in the high category. This research also identified that most low-security platforms still use the single-page login method or a special URL for login, making them very vulnerable to phishing attacks. The action research method was used as the research framework, involving five stages: diagnosis, planning, action, evaluation, and learning. The results of this study provide important guidance for platform owners to improve security mechanisms, how to build a login page to avoid the risk of misuse by cybercrime actors to conduct phishing attacks, and for users as a reference to choose a more secure e-commerce platform.
Volume: 14
Issue: 2
Page: 533-541
Publish at: 2025-06-01

The influence of sentiment analysis in enhancing early warning system model for credit risk mitigation

10.11591/ijai.v14.i3.pp1829-1838
Angel Karentia , Derwin Suhartono
One important source of bank income is interest income from credit activities, another part of which is obtained from fee-based income. Rapid credit growth is directly proportional to an increase in potential credit risk (counterparty default). In addition to comprehensive credit assessment at the initial stage of credit initiation, banks need to monitor the condition of existing debtors. Empirically, difficulties in handling non-performing loans often occur due to delays in detection and preparation of action plans. In this case, losses due to non-performing loans can have implications for the bank's reputation and worsen its financial performance. This research aims to determine the effect of sentiment analysis (external sentiment prediction model [positive, neutral, and negative] with certain keywords) on the level of accuracy of the early warning system (EWS) model in predicting the credit quality of bank debtors in the coming months. This study found that upgrading EWS with sentiment analysis will give better accuracy levels compared to traditional EWS models. In addition, the predictive power of EWS (traditional and upgraded) is inversely proportional to the prediction period, the longer the target prediction time, and the less predictive power of the EWS model.
Volume: 14
Issue: 3
Page: 1829-1838
Publish at: 2025-06-01

Harnessing speed breakers potentials for electricity generation: a case study of Covenant University

10.11591/ijece.v15i3.pp2669-2680
Hope Evwieroghene Orovwode , John Amanesi Abubakar , Olutunde Oluwatimileyin Josiah , Ademola Abdullkareem
The global imperative to transition towards sustainable energy sources has sparked innovative solutions for energy generation and environmental conservation challenges. As fossil fuel usage for power generation continues to raise environmental concerns, converting kinetic energy from vehicular motion via speed breakers presents a unique avenue for renewable power production. This study explores the concept of utilizing speed breakers as a means of electricity generation to power little power-consuming but critical load, with Covenant University serving as a pertinent case study. This research investigates the technical, economic, and environmental implications of implementing speed breaker-based electricity generation within Covenant University. Analyzing the university's energy consumption patterns showed that some loads do not require much power but are critical. Street lighting is one of such loads. This study discerns the potential contribution of speed breaker-generated electricity to address energy demands by simulation and constructing a prototype. Advanced engineering tools, such as simulation software Fusion 360 and Proteus 8.0, were employed to model and integrate the roller speed breaker mechanism with the electrical infrastructure. The findings offer valuable insights into the viability of speed breaker-generated electricity as an alternative energy source, paving the way for sustainable energy practices in educational institutions and beyond.
Volume: 15
Issue: 3
Page: 2669-2680
Publish at: 2025-06-01

Improving breast cancer classification with a novel VGG19-based ensemble learning approach

10.11591/ijece.v15i3.pp2809-2819
Chaymae Taib , Adnan El Ahmadi , Otman Abdoun , El Khatir Haimoudi
Breast cancer is one of the most life-threatening diseases, particularly affecting women, highlighting the importance of early detection for improving survival rates. In this study, we propose a novel diagnostic framework that combines a modified VGG19 architecture with Bagging ensemble learning, using three base classifiers: decision tree (DT), logistic regression (LR), and support vector machine (SVM). We also compare this approach with twenty-four hybrid models, integrating various convolutional neural network (CNN) architectures (ResNet50, VGG19, ConvNextBase, DenseNet121, EfficientNetV2B0, EfficientNetB0, MobileNet, and NasNetMobile) with Bagging ensemble methods. Our results show that the proposed model outperforms all other architectures, especially when combined with SVM, achieving accuracy of 97% on the fine needle aspiration cytology (FNAC) dataset and 90% on the International Conference on Image Analysis and Recognition (ICIAR) dataset. This framework demonstrates strong potential for improving early breast cancer diagnosis.
Volume: 15
Issue: 3
Page: 2809-2819
Publish at: 2025-06-01

Enhancing artificial neural network performance for energy efficiency in laboratories through principal component analysis

10.11591/ijaas.v14.i2.pp310-321
Desmira Desmira , Norazhar Abu Bakar , Mustofa Abi Hamid , Muhammad Hakiki , Affero Ismail , Radinal Fadli
This study investigates energy efficiency challenges during laboratory activities. Inefficient energy use in the practicum phase remains a critical issue, prompting the exploration of innovative forecasting models. This research employs artificial neural network (ANN) models integrated with principal component analysis (PCA) to predict energy consumption and optimize usage. The findings reveal that PCA components, including eigenvalues, eigenvectors, and matrix covariance values, significantly influence the ANN model's performance in forecasting energy production. The ANN training achieved a high correlation coefficient (R=1) with a mean squared error (MSE) of 0.045931 after 200,000 epochs, demonstrating the model's robustness. While testing results showed a moderate correlation (R=0.46169), the models demonstrated potential for refinement and scalability. This integration of ANN and PCA models provides a reliable framework for accurately forecasting energy usage, offering an effective strategy to enhance energy efficiency in laboratory settings. By optimizing energy consumption, this approach has the potential to reduce operational costs and environmental impact. The strong performance metrics highlight the practical utility of these models in educational contexts, contributing to sustainable energy management and better resource allocation. Furthermore, the reduction in energy-related environmental impacts underscores the broader applicability of these models for fostering sustainable development in similar contexts.
Volume: 14
Issue: 2
Page: 310-321
Publish at: 2025-06-01
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