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28,812 Article Results

Hybrid artificial intelligence approach to counterfeit currency detection

10.11591/ijece.v15i6.pp5804-5814
Monther Tarawneh
The use of physical money continues, posing ongoing challenges in the form of counterfeit money. This problem not only poses a threat to economic stability but also undermines confidence in the financial systems in use. Traditional methods such as manual inspections and testing of security features have become ineffective in detecting advanced counterfeiting techniques on an ongoing basis. This study proposes a hybrid model that harnesses the power of artificial intelligence, combining convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and support vector machines (SVMs) for counterfeit detection. The proposed model leverages the diverse strengths of a number of artificial intelligence techniques, combining the ability to detect counterfeiting, analyse visual aspects, and sequences of banknotes. The proposed model was tested using real Jordanian currency sets of different denominations and datasets generated using generative adversarial networks (GANs). The results showed that the model was able to detect counterfeiting with high accuracy of 98.6%. and minimal errors compared to other methods. This outstanding performance demonstrates the benefits of integrating artificial intelligence (AI) technologies and that there is room for development and solutions that can keep up with advanced counterfeiting strategies. The study demonstrates the importance of integrating AI in maintaining the integrity of physical currency transactions.
Volume: 15
Issue: 6
Page: 5804-5814
Publish at: 2025-12-01

Modified differential evolution algorithm to finding optimal solution for AC transmission expansion planning problem

10.11591/ijece.v15i6.pp5045-5054
Thanh Long Duong , Nguyen Duc Huy Bui
The transmission expansion planning (TEP) problem primarily aims to determine the appropriate number and location of additional lines required to meet the increasing power demand at the lowest possible investment cost while meeting the operation constraints. Most of the research in the past solved the TEP problem using the direct current (DC) model instead of the alternating current (AC) model because of its non-linear and non-convex nature. In order to improve the effectiveness of solving the AC transmission expansion planning (ACTEP) problem, a modified version of the differential evolution (DE) is proposed in this paper. The main idea of the modification is to limit the randomness of the mutation process by focusing on the first, second, and third-best individuals. To prove the effectiveness of the suggested method, the ACTEP problem considering fuel costs is solved in the Graver 6 bus system and the IEEE 24 bus system. Moreover, the result of each system is compared to the original DE algorithm and state-of-the-art methods such as the one-to-one-based optimizer (OOBO), the artificial hummingbird algorithm (AHA), the dandelion optimizer (DO), the tuna swarm optimization (TSO), and the chaos game optimization (CGO). The results show that the proposed algorithm is more effective than the original DE algorithm by 1.86% in solving the ACTEP problem.
Volume: 15
Issue: 6
Page: 5045-5054
Publish at: 2025-12-01

Trends of unmanned aerial vehicles in smart farming: a bibliometric analysis

10.11591/ijece.v15i6.pp5746-5758
Alfred Thaga Kgopa , Sikhosonke Manyela , Bessie Baakanyang Monchusi
This paper presents a review of the trends of unmanned aerial vehicles (UAV) in agriculture using a bibliometric analysis. This bibliometric analysis shows that 1676 articles were accessed from the Elsevier Scopus database between 2013 and 2023. Our findings indicate research related to UAVs in agriculture has surged over the years, but the adoption and acceptance of smart farming technology in sub-Saharan Africa remains inert. This study employed VosViewer in data analysis and bibliometrics. Our findings show that China leads all countries and followed by the United States on UAV publications in smart farming research foci. Our findings indicate that UAVs are impactful in improving crop growth, crop health monitoring, and may be beneficial to small-holder farmers with increased yields. We recommend that sub-Saharan Africa nations accelerate collaboration with China and United States in advancing climate smart agriculture practices to mitigate food insecurity risks.
Volume: 15
Issue: 6
Page: 5746-5758
Publish at: 2025-12-01

Bibliometric analysis to highlight the impacts of digitalization, artificial intelligence, and modern optimization on the human environment during international armed conflicts

10.11591/ijece.v15i6.pp5815-5826
Hegazy Rezk , Montaser Mahmoud
This research is conducted to explore the impacts of digitalization and artificial intelligence (AI) on the human environment during international armed conflicts, aiming to identify trends, challenges, and potential solutions to improve humanitarian aid, decision-making, and conflict resolution strategies. To identify the main research issues about the effects of AI and digitalization on the human environment in conflict situations, this work employs a bibliometric analysis. A bibliometric analysis of the impacts of digitalization and artificial intelligence on the human environment during armed conflicts by examining 544 selected papers from Scopus database has been conducted. Knowledge mapping techniques involving collaboration analysis, co-citation analysis, and keywords co-occurrence analysis methods are adopted in bibliometric analysis. Based on a comprehensive analysis of literature, this work attempts to pinpoint the key areas of interest, knowledge gaps, and new problems in this domain. The findings of this bibliometric analysis contribute to a better understanding of the complex interactions between technology, armed conflicts, and the human environment, with implications for humanitarian action, international law, and conflict resolution efforts. The bibliometric analysis reveals that the United States of America (USA) is by far the leading country in research within this field, with a substantial frequency of 181 documents. It significantly surpasses that of other countries, indicating its dominant position in the research landscape. In sum, the work offers suggestions for further research and policy intervention.
Volume: 15
Issue: 6
Page: 5815-5826
Publish at: 2025-12-01

Enhanced ankle physiotherapy robot with electromyography - triggered ankle velocity control

10.11591/ijece.v15i6.pp5314-5326
Dimas Adiputra , Radithya Anjar Nismara , Muhammad Rafli Ramadhan Lubis , Nur Aliffah Rizkianingtyas , Kensora Bintang Panji Satrio , Rangga Roospratama Arif , Annisa Salsabila
Previous ankle physiotherapy robots, called picobot rely on predefined trajectories continuous passive movement without considering patient intent, limiting the encouragement of user-intent motion. This study then integrates electromyography (EMG) signals as triggers into picobot with an ankle velocity-based control system. The upgraded robot activates movement in specific gait phases based on muscle activity, synchronizing therapy with the patient’s intent. Functionality test on 7 young male healthy subjects investigates leg muscles, such as Tibialis Anterior, Soleus, and Gastrocnemius muscles for the most significantly contribute to ankle movements. Then, the muscle is tested to trigger picobot movements. Functionality tests revealed the Tibialis muscle significantly contributes to gait phases 2, the Soleus is prominent in phases 3 and 4, and gastrocnemius is active on phase 1. The robot successfully performs plantarflexion when EMG signals exceed a 1.58 V threshold, reaching a target position of -0.11 rad at a constant velocity of -0.62 rad/s. These findings establish a foundation for future trials since patient testing has not yet been conducted. By promoting active participation, this innovation has the potential to enhance rehabilitation outcomes. Incorporating user-intent triggers may accelerate recovery and improve healthcare accessibility in Indonesia, offering a significant advancement in physiotherapy technologies.
Volume: 15
Issue: 6
Page: 5314-5326
Publish at: 2025-12-01

An ensemble machine learning based model for prediction and diagnosis of diabetes mellitus

10.11591/ijece.v15i6.pp5347-5359
Moataz Mohamed El Sherbiny , Asmaa Hamdy Rabie , Mohamed Gamal Abdel Fattah , Ali Elsherbiny Taki Eldin , Hossam El-Din Mostafa
Diabetes mellitus (DM) is a chronic metabolic disorder that poses significant health risks and global economic burdens. Early prediction and accurate diagnosis are crucial for effective management and treatment. This study presents an ensemble machine learning-based model designed to predict and diagnose Diabetes Mellitus using clinical and demographic data. The proposed approach integrates multiple machine learning algorithms, including random forest (RF), extreme gradient boosting (XGB), and logistic regression (LR), to leverage their individual strengths and enhance the entire performance. The ensemble model was trained and validated on multiple comprehensive datasets. Performance measures demonstrate the robustness of proposed model and its reliability in distinguishing diabetic cases from non-diabetic cases after applying several preprocessing steps. This work ensures the capability of machine learning in advancing healthcare by providing efficient, data-driven tools for diabetes management, aiding clinicians in early diagnosis, and contributing to personalized treatment strategies. Comparative analysis against standalone models highlights the superior predictive capabilities of the ensemble approach. Results had shown that ensemble model achieved an accuracy of 96.88% and precision of 89.85% outperforming individual classifiers.
Volume: 15
Issue: 6
Page: 5347-5359
Publish at: 2025-12-01

Adaptive tilt acceleration derivative filter control based artificial pancreas for robust glucose regulation in type-I diabetes mellitus patient

10.11591/ijece.v15i6.pp5297-5313
Smitta Ranjan Dutta , Akshaya Kumar Patra , Alok Kumar Mishra , Ramachandra Agrawal , Dillip Kumar Subudhi , Lalit Mohan Satapathy , Sanjeeb Kumar Kar
This study proposes an Aquila optimization–based tilt acceleration derivative filter (AO-TADF) controller for robust regulation of blood glucose (BG) levels in patients with type-I diabetes mellitus (TIDM) using an artificial pancreas (AP). The primary objective is to develop a controller that ensures normo-glycemia (70–120 mg/dl) while enhancing stability, accuracy, and robustness under physiological uncertainties and external disturbances. The AO algorithm tunes the control gains of the TADF controller to minimize the integral time absolute error (ITAE), ensuring optimal insulin infusion in real time. The AO-TADF controller introduces a filtered structure to improve the dynamic response and noise rejection capability, effectively handling the nonlinear nature of glucose-insulin dynamics. Simulation results demonstrate that the proposed approach achieves a faster settling time (230 minutes), lower peak overshoot (3.9 mg/dl), and reduced noise (1%) compared to conventional proportional integral derivative (PID), fuzzy, sliding mode (SM), linear quadratic gaussian (LQG), and H∞ controllers. The closed-loop system achieves a stable glucose level of 81 mg/dl under varying meal and exercise disturbances, validating the superior performance and robustness of the AO-TADF approach.
Volume: 15
Issue: 6
Page: 5297-5313
Publish at: 2025-12-01

Machine learning model for accurate prediction of coronary artery disease by incorporating error reduction methodologies

10.11591/ijece.v15i6.pp5655-5666
Santhosh Gupta Dogiparthi , Jayanthi K. , Ajith Ananthakrishna Pillai , K. Nakkeeran
Coronary artery disease (CAD) remains a leading cause of mortality worldwide, with an especially high burden in developing countries such as India. In light of increasing patient loads and limited medical resources, there is an urgent need for accurate and reliable diagnostic support systems. This study introduces a machine learning (ML) framework that aims to enhance CAD prediction accuracy by specifically addressing the reduction of false negatives (FN), which are critical in medical diagnostics. Utilizing a stacked ensemble model comprising five base classifiers and a meta-classifier, the framework integrates cost-sensitive learning, classification threshold tuning, engineered features, and manual weighting strategies. The model was developed using a clinically acquired dataset from the Jawaharlal Institute of postgraduate medical education and research (JIPMER), consisting of 428 patient records with 36 original features. Evaluation metrics show that the proposed model achieved an accuracy of 92.19%, sensitivity of 98%, and an F1-score of 95.15%. These improvements are significant in a clinical context, potentially reducing missed diagnoses and improving patient outcomes. The model is intended for deployment in cardiology outpatient settings and demonstrates a scalable, adaptable approach to medical diagnostics.
Volume: 15
Issue: 6
Page: 5655-5666
Publish at: 2025-12-01

Optimizing short-term energy demand forecasting: a comprehensive analysis using autoregressive integrated moving average method

10.11591/ijece.v15i6.pp5924-5933
Firman Aziz , Jeffry Jeffry , Misbahuddin Buang , Supriyadi La Wungo , Nasruddin Nasruddin
This study addresses the critical gap in short-term electricity demand forecasting in South Sulawesi, where inconsistencies between projected and actual peak loads hinder daily operational planning, system stability, and investment efficiency. While previous studies have applied approaches such as fuzzy logic, ARIMA-ANN, and hybrid models, few have focused on simple, robust ARIMA-based models validated across different time spans for daily operational use. To address this, the autoregressive integrated moving average (ARIMA) model is implemented within the Box-Jenkins framework, using automated model selection through the pmdarima library and Akaike’s information criterion (AIC) to identify optimal parameter configurations. The study analyzes daily peak load data from 2018 to 2023, producing realistic forecasts with high accuracy. The selected ARIMA model achieves a mean absolute percentage error (MAPE) of 1.91% and a root mean square error (RMSE) of 38.123, demonstrating its effectiveness in capturing short-term load trends. These results confirm the suitability of ARIMA for short-term forecasting in energy systems and its potential to enhance operational decision-making, reduce forecasting errors, and improve investment planning. The study also establishes a methodological foundation for future development, including the integration of ARIMA with machine learning and the use of extended datasets to support strategic energy management.
Volume: 15
Issue: 6
Page: 5924-5933
Publish at: 2025-12-01

On big data predictive analytics-trends, perspectives, and challenges

10.11591/ijece.v15i6.pp5978-5985
Yassine Benlachmi , Abdelaziz El Yazidi , Abdallah Rhattoy , Moulay Lahcen Hasnaoui
The world is experiencing explosive growth in numerous sectors such as healthcare, engineering, scientific studies, business, social networking. This growth is causing an immense surge in data generation too. And with the emergence of technologies like internet of things (IoT), Mobile, and cloud computing, the volume of data being produced is skyrocketing. However, making sense of this colossal amount of data is a daunting challenge. Enter big data computing, a new paradigm that blends large datasets with advanced analytical techniques. Big data is characterized by the three V's: Volume, velocity, and variety, and refers to massive datasets. By processing this data, we can uncover new opportunities and gain valuable insights into market trends. Traditional techniques are simply not equipped to handle the scale of Big Data. The purpose of this article is to gather reviews of various predictive analytics applications related to big data and the advantages of using big data analytics across various decision-making domains.
Volume: 15
Issue: 6
Page: 5978-5985
Publish at: 2025-12-01

Detection of breast cancer with ensemble learning using magnetic resonance imaging

10.11591/ijece.v15i6.pp5371-5379
Swati Nadkarni , Kevin Noronha
Despite notable progress in medicine along with technology, the deaths due to breast cancer are increasing steadily. This paper proposes a framework to aid the early detection of lesions in breast with magnetic resonance imaging (MRI). The work has been carried out using diffusion weighted imaging (DWI) and dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI). Data augmentation has been incorporated to enlarge the data set collected from a reputed hospital. Deep learning has been implemented using the ensemble of convolutional neural network (CNN). Amongst the individual CNN models, the you only look once (YOLO) CNN yielded the highest performance with an accuracy of 93.4%, sensitivity of 93.44%, specificity of 93.33%, and F1-score of 93.44%. Using Hungarian optimization, appropriate selection of individual CNN architectures to form the ensemble of CNN was possible. The ensemble model enhanced performance with 95.87% accuracy, 95.08% sensitivity, 96.67% specificity, and F1-score of 95.87%.
Volume: 15
Issue: 6
Page: 5371-5379
Publish at: 2025-12-01

Optimized passive and active shielding of magnetic induction generated by ultra-high-voltage overhead power lines

10.11591/ijece.v15i6.pp5144-5161
Salah-Eddine Houicher , Rabah Djekidel , Sid Ahmed Bessidek
This paper presents computational modeling to assess and limit the magnetic induction levels emitted by an extra-high-voltage (EHV) overhead transmission line of 750 kV using the fundamental principle of Biot-Savart law in magnetostatics. An optimization technique based on the grey wolf optimizer (GWO) algorithm is employed to determine the appropriate location of the passive and active loop conductors, and the associated parameters to shielding to achieve better compensation of magnetic induction in an interest zone. The resulting magnetic induction of the ultra high voltage (UHV) overhead power line exhibits a crest value of 27.78 μT at the middle of the right-of-way, which can be considered unacceptable by strict protection standards. Generally, the magnetic compensation loops optimally located under the phase conductors of the power transmission system reduce the magnetic induction levels along the transmission line corridor. The passive loop attenuates the maximum magnetic induction by a rate of 29.7%. Therefore, the performance of the active loop is better; it provides a greater reduction with a rate reaching 53.24%. The simulation results were tested with those derived by the elliptical polarization process. An excellent concordance was found, which made it possible to ensure the adopted method.
Volume: 15
Issue: 6
Page: 5144-5161
Publish at: 2025-12-01

Performance analysis of D2D network in heterogeneous multitier interference scenarios

10.11591/ijict.v14i3.pp811-821
Dhilipkumar Santhakumar , Arunachalaperumal Chellaperumal , Jenifer Suriya Lazer Jessie , Jerlin Arulpragasam
The trade-off between boosting network throughput and minimizing interference is a critical issue in fifth generation (5G) networks. Diverting the data traffic around the network access point in device-to-device (D2D) communication is an important step in realizing the vision of 5G. Though the D2D network improves the network performance, they complicate the interference management process. Interference is an invisible physical phenomenon occurring in wireless communication which happens when multiple transmissions happen simultaneously over a general wireless medium. Enormous growth in usage of mobile phone and other wireless gadgets in recent years has paved the way for significant role in Interference analysis over multi-tier network. Interference could affect communication systems performance and it might even prevent systems functioning properly. 3G and 4G wireless devices coexisted with reverse compatibility in a coverage area. Also, after their widespread adoption, 5G devices have become prevalent across the globe and this reaffirms interference coexistence as a significant field of research. Multiple systems operating in a region will cause severe interference and ultimately reduce the quality of received signal. A simulation environment for cellular standards coexistence considering real-time parameters is created and experimented. Various research works earlier addresses the interference mitigation techniques in multi-tier networks but none of them present the analysis of scenarios and interfering signal power levels in the respective contexts. In this paper various scenarios with different network interference coexistence were studied, simulated, and analyzed quantitatively.
Volume: 14
Issue: 3
Page: 811-821
Publish at: 2025-12-01

Design of miniaturized dual-band bandpass filter with enhanced selectivity for GPS and RFID applications

10.11591/ijict.v14i3.pp993-1001
Thupalli Shaik Mahammed Basha , Arun Raaza , Vishakha Bhujbal , Meena Mathivanan
This article presents a miniaturized interdigital coupled dual-band bandpass filter with multiple transmission zeros/poles. Stepped impedance resonators, interdigital coupled lines, and series coupled lines make up the proposed filter design. A circuit simulator is used to analyze a proposed filter, and the magnitude and bandwidth shifts have been investigated. To confirm the proposed filter design, equations for transmission zero frequencies have been constructed and verified based on even-odd mode analysis and lossless transmission line theory. A working prototype for 2.2 GHz (RFID) and 1.38 GHz (GPS) applications is made and tested. With λg representing the guided wavelength at the first band (1.38GHz), the finished prototype is compact, measuring 0.32 λg×0.27 λg. According to the experimental findings, there is strong selectivity in the first and second passbands, with roll-off rates of 190 and 168 dB/GHz, respectively. Good isolation between the two passbands is indicated by an insertion loss of less than 20 dB.
Volume: 14
Issue: 3
Page: 993-1001
Publish at: 2025-12-01

Advancements in brain tumor classification: a survey of transfer learning techniques

10.11591/ijict.v14i3.pp1002-1014
Snehal Jadhav , Smita Bharne , Vaibhav Narawade
This survey article presents a critical review of the state-of-the-art transfer learning (TL) methodologies applied in the field of brain tumor classification, with a special emphasis on their various contributions and associated performance metrics. We will discuss various pre-processing approaches, the underlying fine-tuning strategies, whether used purely or in an end-to-end training manner, and multi-modal applications. The current study specifically highlights the application of VGG16 and residual network (ResNet) methods for feature extraction, demonstrating that leveraging highorder features in magnetic resonance imaging (MRI) images can enhance accuracy while reducing training. We further analyze fine-tuning methods in relation to their role in optimizing model layers for small, domain-specific datasets, finding them particularly effective in enhancing performance on the small brain tumor dataset. It will look into end-to-end training, which means fine-tuning models that have already been trained on large datasets to make them better. It will also present multimodal TL as a way to use both MRI and computed tomography (CT) scan data to get better classification results. Comparing different pre-trained models can provide a better understanding of the strengths and weaknesses associated with the particular brain tumor classification task. This review aims to analyze the advancements in TL for medical image analysis and explore potential avenues for future research and development in this crucial field of medical diagnostics.
Volume: 14
Issue: 3
Page: 1002-1014
Publish at: 2025-12-01
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