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Application of artificial intelligence and machine learning in expert systems for the mining industry: modern methods and technologies

10.11591/ijece.v15i3.pp3291-3308
Natalya Mutovina , Margulan Nurtay , Alexey Kalinin , Aleksandr Tomilov , Nadezhda Tomilova
The mining industry has changed significantly in recent decades with the introduction of advanced technologies such as artificial intelligence (AI) and machine learning (ML). These innovations contribute to the creation of expert systems that help in optimizing processes, increasing the safety and sustainability of operations. This article is a literature review of modern AI and ML methods and technologies used in the mining industry. Discusses various intelligent and expert systems used to improve productivity, reduce operating costs, improve occupational safety, environmental sustainability, machine automation, predictive analytics, quality monitoring and control, and inventory and logistics management. The advantages and disadvantages of different approaches are analyzed, as well as their potential impact on the future of the mining industry. The review highlights the importance of integrating AI and ML into mining processes to achieve more efficient and safer solutions.
Volume: 15
Issue: 3
Page: 3291-3308
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

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

Exploring the effectiveness of multiclass decision jungle for internet of things security

10.11591/ijece.v15i3.pp3095-3106
Smitha Rajagopal , Abhik Sarkar , Venkat Narayanan Manjunath
Network intrusion detection systems (NIDS) are vital in protecting computer networks against cyber security incidents. The relationship between NIDS and internet of things (IoT) security is pivotal and NIDS plays a significant role in ensuring the security and reliability of IoT ecosystems. Ensuring the security of IoT devices is critical for several reasons. It helps safeguard sensitive information, guarantees the dependability of crucial infrastructure, meets regulatory obligations, and fosters user confidence. As the IoT ecosystem expands, prioritizing security is essential to minimize risks and maximize the benefits of connected devices. Given the ever-expanding cyber threat landscape, the multiclass classification task is essential to empower the NIDS with an ability to distinguish between various attack patterns in less computational time. The multiclass decision jungle algorithm is investigated to optimize the performance of NIDS. The research has considered permutation feature importance to include only the relevant features from the data. Using a contemporary dataset such as CICIOT 2023, the study has demonstrated an impressive attack detection rate of over 90% for 20 modern attack types. This research has investigated the effectiveness of IoT security measures and its prospective contributions to the field of cyber security.
Volume: 15
Issue: 3
Page: 3095-3106
Publish at: 2025-06-01

Sentiment analysis of vaccine data using enhanced deep learning algorithms

10.11591/ijaas.v14.i2.pp562-579
Monika Verma , Sandeep Monga
This paper investigates and experiments with an approach to improve sentiment analysis on vaccine datasets with deep learning. It evaluates random forest (RF), naïve Bayes (NB), and recurrent neural network (RNN) models across a variety of configurations, i.e., vector dimensions, pooling techniques, as well as evaluation methods, hierarchical SoftMax vs negative sampling. The results show that the model we proposed prevailed with an accuracy of 99.05% on a learning rate equal to 0.001, outperforming all other models based on metrics including precision, recall, and F1-score for benign/malignant cases. The results suggest that higher vector dimensions, average pooling, lowering the dropout rate, and employing hierarchical SoftMax for output significantly improve model performance. Hierarchical SoftMax performs better than negative sampling, whereas a lower dropout rate decreases overfitting and leads to improved generalization. Our results demonstrate the necessity to apply more sophisticated deep-learning tools around capturing nuances of public vaccine-related sentiment, which may be crucial for informing communication strategies and supporting decision-making in a real-world health emergency. The findings indicate that the performance of sentiment analysis with regard to COVID-19 vaccine deployment policy design and public monitoring could be enhanced by advanced deep learning algorithms.
Volume: 14
Issue: 2
Page: 562-579
Publish at: 2025-06-01

Susceptibility of Aedes aegypti to malathion and permethrin insecticides in Enrekang Regency: an experimental study

10.11591/ijaas.v14.i2.pp291-299
Sulasmi Sulasmi , Hamsir Ahmad , Juherah Juherah , Iwan Suryadi , Siti Rachmawati
Insecticide resistance in Aedes mosquitoes can undermine arbovirus control efforts. Malathion and permethrin insecticides belong to the group of insecticides used for control and if used continuously will cause immunity of target mosquitoes. This study aims to assess the level of susceptibility of Aedes aegypti to insecticides commonly used in public health in the Enrekang Regency. The type of research used was experimental research. Female Aedes aegypti were collected from rearing results with a total sample size of 240 mosquitoes which were divided into 120 mosquitoes each in 4 treatments and 2 controls on malathion 0.8% and permethrin 0.25% insecticides. The results obtained from the research on insecticide susceptibility test results using malathion 0.8% in 60 minutes of exposure averaged 55% dead and exposure for 24 hours averaged 90% mosquito death, while permethrin 0.25% insecticide in 60 minutes of exposure averaged 90% dead mosquitoes and 24 hours exposure averaged 100% mosquito death, while for the control all live. The conclusion of the study was the susceptibility test of Aedes aegypti mosquitoes to malathion 0.8% insecticide in the category of moderate resistance while permethrin 0.25% insecticide in the category of susceptible.
Volume: 14
Issue: 2
Page: 291-299
Publish at: 2025-06-01

A prediction of coconut and coconut leaf disease using MobileNetV2 based classification

10.11591/ijece.v15i3.pp2834-2844
Kavitha Magadi Gopalakrishna , Raviprakash Madenur Lingaraju
This research is aimed at effectively predicting coconut and coconut leaf disease using enhanced MobileNetV2 and ResNet50 methods. The stages involved in this implemented method are data collection, pre-processing, feature extraction, and classification. At first, data is collected from coconut and coconut leaf datasets. Gaussian filter and data augmentation techniques are applied on these images to eliminate noise during the pre-processing phase. Then, features are extracted using ResNet50 technique, while the diseases are classified using MobileNetV2 approach. In comparison to the existing methods namely, EfficientDet-D2, DL-assisted whitefly detection model (DL-WDM), and modified inception net-based hyper tuning support vector machine (MIN-SVM), the proposed method achieves superior classification values with 99.99% and 99.2% accuracy for coconut leaf and for coconuts, respectively.
Volume: 15
Issue: 3
Page: 2834-2844
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

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

The future of healthcare: exploring internet of things and artificial intelligence applications, challenges, and opportunities

10.11591/ijece.v15i3.pp3075-3083
Kamal Elhattab , Driss Naji , Abdelouahed Ait ider , Karim Abouelmehdi
The internet of things (IoT) refers to a network of physical devices embedded with sensors, software, and communication tools, which allow for seamless exchange and collection of data. This technology enables automation, continuous monitoring, and data-driven decision-making across a variety of fields. In the healthcare sector, the integration of IoT with artificial intelligence (AI) is transforming how patient care is delivered, providing real-time health monitoring, personalized treatment options, and more efficient management of healthcare resources. This study investigates the significant influence of the IoT and AI on the healthcare system, focusing on how these technologies improve patient outcomes and streamline healthcare operations. It also highlights emerging challenges in the adoption of these technologies and suggests potential solutions to address these obstacles and enhance healthcare delivery. The research is based on an in-depth review of AI and IoT applications in healthcare, uncovering advancements in patient monitoring, disease management, and operational efficiency, while also identifying key challenges such as data privacy concerns and issues with system interoperability.
Volume: 15
Issue: 3
Page: 3075-3083
Publish at: 2025-06-01

Adaptive hybrid particle swarm optimization and fuzzy logic controller for a solar-wind hybrid power system

10.11591/ijape.v14.i2.pp498-512
G. B. Arjun Kumar , M. Balamurugan , K. N. Sunil Kumar , Ravi Gatti
This paper presents the best modeling and control strategies for a grid-connected hybrid wind-solar power system to maximize energy production. For variable wind speeds, determine the optimal power point using fuzzy logic control, adopt an adaptive hill climb searching method, and compare it with an optimal torque control method for large inertia wind turbine (WT). The role of fuzzy logic controller (FLC) is to adjust the hill climbing search (HCS) technique's step-size according to the operating point. The doubly-fed induction generator (DFIG) control system has two subsystems: rotor-side and grid-side converters. The active and reactive power have been indirectly regulated by adjusting the current on the d-q axis. The rotor side converter (RSC) controllers are responsible for controlling the WTs rotational speed to achieve the maximum power output. The grid side converter (GSC) manages the voltage at the DC link and keeps a unity power factor between the grid and GSC. Optimal hybrid power point tracking technique for use with photovoltaic systems in both constant and variable shade circumstances, based on particle swarm optimization (PSO) and perturb and observe (P&O). The optimal power point tracking (OPPT) approach is compared to three other methods: PSO, P&O, and hybrid P&O-PSO. The model has a total capacity of 2.249 MW, with wind capacity of 2 MW and solar capacity of 0.249 MW, and its efficiency is analyzed.
Volume: 14
Issue: 2
Page: 498-512
Publish at: 2025-06-01

Techno-economic analysis and optimization of solar energy systems: a case study at Ar-Raniry State Islamic University

10.11591/ijaas.v14.i2.pp322-335
Fahmy Rinanda Saputri , Ricardo Linelson , Muhammad Salehuddin , Muhammad Dzaky Al-Haidar
This research examines the implementation of a solar power generation system at Ar-Raniry State Islamic University (UIN Ar-Raniry), specifically focusing on the Faculty of Tarbiyah and Keguruan building. The study aims to enhance energy efficiency, assess economic feasibility, and reduce environmental impacts by optimizing solar energy potential through variables such as local meteorological conditions, panel orientation, tilt angles, and system efficiencies. Utilizing PVSyst software for simulations, the research evaluates technical performance, life cycle costs, and carbon dioxide (CO₂) emission reductions. The results indicate that the solar Photovoltaic (PV) system can generate 251,214 kWh annually while reducing CO₂ emissions by 173,095 kg. Economically, the investment is deemed feasible, with a payback period of 7.8 years, a lower cost of energy (LCOE) compared to State Electricity Company (PLN) tariffs, a positive net present value (NPV), and a high internal rate of return (IRR). Although there are minor losses in thermal and module quality, the system remains effective. This study contributes significantly to sustainable energy policies in higher education and recommends further long-term performance monitoring and exploration of additional renewable energy technologies on campus.
Volume: 14
Issue: 2
Page: 322-335
Publish at: 2025-06-01

Optimized control strategy for enhanced stability in grid-connected photovoltaic-wind hybrid energy systems

10.11591/ijaas.v14.i2.pp609-617
Madhu Babu Thiruveedula , Asokan Kaliyamoorthy , Kosara Sravani , Murgam Sharath Yadav , Petturi Satish Kumar , Routhu Akash
To improve stability in grid-connected photovoltaic-wind (PV-wind) hybrid energy systems, this research presents optimized model predictive control (MPC) and proportional resonant (PR) control algorithms. The proposed MPC strategy enhances power management by forecasting future system behavior and optimizing control actions accordingly, while the PR controller effectively handles grid-synchronized voltage regulation and harmonic compensation. Together, these advanced control techniques significantly improve grid stability, ensure optimal utilization of renewable energy resources (RER), and maintain power quality under varying operating conditions. The performance of the hybrid system is evaluated through extensive simulations that consider a range of real-world scenarios, including fluctuating load demands and diverse climatic conditions. The results confirm the effectiveness of the proposed MPC and PR-based control in dynamically adjusting power output from wind and photovoltaic sources, thereby ensuring reliable and efficient grid integration. These findings highlight the potential of intelligent control systems in enabling the secure, stable, and long-term adoption of renewable energy within modern power grids.
Volume: 14
Issue: 2
Page: 609-617
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

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
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