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24,371 Article Results

Denigration analysis of Twitter data using cyclic learning rate based long short-term memory

10.11591/ijece.v15i1.pp700-710
Suhas Bharadwaj Rajendra , Sampath Kuzhalvaimozhi , Vedavathi Nagendra Prasad
Technological innovation has given rise to a new form of bullying, often leading to significant harm to one's reputation within social circles. When a single person becomes target to animosity and harassment in a cyberbullying incident, it is termed as denigration. Many different cyberbullying detection techniques are carried out to counter this, concentrating on word-based data and user account features only. The main objective of this research is to enhance the learning rate of long short-term memory (LSTM) using cyclic learning rate (CLR). Therefore, in this research, cyberbullying in social media is detected by developing a framework based on LSTM-CLR which is more stable for enhancing classification accuracy without the need for multiple trials and modifications. The effectiveness of the suggested LSTM-CLR is assessed for identifying cyberbullying using Twitter data. The attained results show that the proposed LSTM-CLR obtains 82% accuracy, 80% precision, 83% recall and 81% F-measure in the classification of cyberbullying tweets, which is superior when compared with the existing multilayer perceptron (MLP) and bidirectional encoder representations from transformers (BERT) models.
Volume: 15
Issue: 1
Page: 700-710
Publish at: 2025-02-01

Robust adaptive integral sliding mode control of a half-bridge bidirectional DC-DC converter

10.11591/ijece.v15i1.pp114-128
Julius Derghe Cham , Francis Lénine Djanna Koffi , Alexandre Teplaira Boum , Ambe Harrison
A novel approach to improving the dynamic response of a half-bridge bidirectional DC-DC converter is presented in this paper, particularly in the face of disturbances from internal or external sources. These converters, which are integral to the operation of DC microgrids, are responsible for stepping up or stepping down voltage as required. To optimize the converter's performance under varying conditions, we propose an adaptive integral sliding mode controller (AISMC) enhanced by particle swarm optimization (PSO). The proposed controller leverages the strengths of both super-twisting sliding mode control (STSMC) and adaptive control, providing a robust and responsive solution to the challenges posed by the converter's nonlinear dynamics. The system's stability is rigorously ensured through the application of Lyapunov stability criteria, which underpin the enhanced performance of the controller. Simulations conducted in the MATLAB/Simulink environment demonstrate that the AISMC-PSO outperforms conventional control strategies, offering superior stability, robustness, and precision. The results clearly indicate that the proposed approach minimizes errors and enhances the overall efficiency and reliability of the bidirectional half-bridge DC-DC converter, making it a highly effective solution for DC microgrid applications.
Volume: 15
Issue: 1
Page: 114-128
Publish at: 2025-02-01

Enhancing online exam security: encryption and authentication in Jordanian and international universities

10.11591/ijece.v15i1.pp719-727
Ali M. Al-Ghonmein , Yahia Alemami , Khaldun G. Al-Moghrabi , Saleh Atiewi
In today's educational landscape, the online examination system has become crucial, particularly due to the challenges posed by the coronavirus disease 2019 (COVID-19) pandemic. Despite its advantages in expediting result dissemination and reducing resource consumption, online examinations face significant security threats like leakage, cheating, fraud, and hacking, which hinder their widespread adoption. This paper addresses these security concerns by proposing integrating advanced security algorithms and biometric devices. It presents a comprehensive literature review on existing online examination systems, focusing on their security mechanisms, and compares these findings with a proposed framework. Additionally, a questionnaire was administered across Jordanian governmental and private universities to explore strategies for safeguarding computerized tests through encryption and authentication methods. The results reveal that Jordanian institutions lack adequate security safeguards and procedural standards. Key recommendations include encrypting the question bank stored in databases and employing biometric identification techniques to enhance the security and effectiveness of student verification. The proposed framework aims to improve the overall security, speed, and secrecy of the online examination process, addressing the critical gaps identified in current systems. This research contributes to developing more secure and reliable online examination systems in higher education.
Volume: 15
Issue: 1
Page: 719-727
Publish at: 2025-02-01

Enhancing monitoring systems with interference-handling radio frequency direction finding: BMKG C-Band weather radar in West Kalimantan Indonesia case study

10.11591/ijece.v15i1.pp414-424
Eka Kusumawardhani , Leonardus Sandy Ade Putra , Ikbal Mawaldi , Ade Kurniawan , Wawan Kurnawan
This study investigates the effectiveness of the interference-handling radio frequency direction finding (RFDF) monitoring system in detecting and localizing interference sources near the BMKG C-Band weather radar in West Kalimantan, Indonesia. We conducted field tests varying interference transmitter power levels (0-30 dBm) and distances (100, 500, and 1,000 m) from the radar. Results indicate the RFDF system's robust performance, consistently detecting interference within 5-20 minutes and accurately localizing sources with minimal deviation from actual positions. The findings confirm the system's superiority over traditional manual methods, offering a reliable solution for interference management in weather radar operations. However, limitations include controlled test conditions and a need for further exploration of the system's efficacy in diverse environmental settings. This research contributes to improving radar reliability and lays the groundwork for future studies to refine RFDF technology for broader meteorological applications.
Volume: 15
Issue: 1
Page: 414-424
Publish at: 2025-02-01

Video conferencing algorithms for enhanced access to mental healthcare services in cloud-powered telepsychiatry

10.11591/ijece.v15i1.pp1142-1151
Rajagopalan Senkamalavalli , Subramaniyan Nesamony Sheela Evangelin Prasad , Mahalingam Shobana , Chellaiyan Bharathi Sri , Rajendar Sandiri , Jayavarapu Karthik , Subbiah Murugan
Exploring the video conferencing algorithms for cloud-powered telepsychiatry to improve mental healthcare access. The goal is to evaluate and optimise these algorithms' latency, bandwidth utilisation, packet loss, and jitter across worldwide locations. To provide a smooth and high-quality virtual consultation between patients and mental health providers. Using performance data to identify areas for development, the effort aims to lower technological hurdles and increase telepsychiatry session dependability. Findings will help create strong, efficient algorithms that can handle different network situations, increasing patient outcomes and extending mental healthcare services. In the 1st instance latent analysis in a sample of 5 cities, the average latency (ms) is 45, the peak latency is 120, the off-peak latency is 30, and the packet loss is 0.5. In another instance, bandwidth utilisation in a sample of 5 sessions ranged from 30 to 120 minutes, with data supplied in MB - 150-600 and received in MB - 160-620, with average bandwidth (Mbps) - 5-15 and maximum bandwidth: 10-20.
Volume: 15
Issue: 1
Page: 1142-1151
Publish at: 2025-02-01

Harnessing deep learning for medicinal plant research: a comprehensive study

10.11591/ijece.v15i1.pp908-920
Vidya Hullekere Ananda , Narasimha Murthy Madiwala Sathyanarayana Rao , Thara Dharmapura Krishnamurthy
In today’s world, people are more prone to diseases due to food adulteration and pollution in the environment, and people have found a way of using herbal medicine as an alternative to allopathic medicine, especially since coronavirus disease 2019 (COVID-19). Medicinal plants are the source of herbal medicines that increase the immunity of humans. Medicinal plants are used in many applications, like pharmaceuticals, cosmetics, and drugs. Medicinal plants are of great importance, and hence this work presents a review of the medicinal plants grown in Karnataka State, India. The work also highlights species identification and disease detection of medicinal plants employing machine learning and deep learning approaches. The paper provides information about datasets available for various medicinal plant leaf images. The deep learning models used for species identification and disease detection in medicinal plants have been discussed along with the results.
Volume: 15
Issue: 1
Page: 908-920
Publish at: 2025-02-01

Predictive modeling for healthcare worker well-being with cloud computing and machine learning for stress management

10.11591/ijece.v15i1.pp1218-1228
Muthukathan Rajendran Sudha , Gnanamuthu Bai Hema Malini , Rangasamy Sankar , Murugaaboopathy Mythily , Piskala Sathiyamurthy Kumaresh , Mageshkumar Naarayanasamy Varadarajan , Shanmugam Sujatha
This paper provides a new method for stress management-focused predictive modeling of healthcare workers' well-being via cloud computing and machine learning. The need for proactive measures to track and assist healthcare workers' mental health is highlighted by the rising expectations placed on them. Using various data sources, our system compiles information from surveys, social media, electronic health records, and wearable devices into a single location for analysis. Predictive models that predict healthcare workers' stress levels and well-being are developed using gradient boosting, a strong machine learning (ML) technique. This work is suitable for gradient boosting due to its resilience to overfitting and capacity to process many kinds of data. Healthcare organizations may improve the health of their employees by using our technology to detect stress patterns and identify the causes of that stress. It can use specific treatments and support systems to alleviate that stress. Widespread adoption and real-time monitoring are made possible by the scalability, flexibility, and accessibility of cloud computing infrastructure. This method shows promise in the direction of proactive solutions driven by data for controlling the stress of healthcare workers and improving their general well-being.
Volume: 15
Issue: 1
Page: 1218-1228
Publish at: 2025-02-01

Product reviews analysis to extract sentimental insights with class confidence rate using self-organizing map neural network

10.11591/ijece.v15i1.pp980-994
Sara Ahsain , Yasyn Elyusufi , M'hamed Ait Kbir
Customer data analysis helps companies to understand customer intentions and behaviors better. This study introduces an analysis of product reviews to help managers adopt a more efficient strategy to extract valuable knowledge and help detect segment of customers that need a special attention and products that need improvement or with the most impact. The used dataset is a set of Amazon reviews divided into multiple categories; each review has a target column called ‘overall’ that takes a value between 1 and 5 (customer's satisfaction). Based on the ‘overall’ column, multiple labeling methods have been used and compared to get a binary target variable, positive or negative, that affects a class to a review. This dataset contains more than one million reviews and can give companies great insight into products’ quality and customers’ retention. This work has materialized by using customer segmentation and competitive learning with self-organizing map (SOM) Model and adopting a new approach to explore the generated network/map, it is based on clustering and map nodes labelling using a majority voting process. The results show that the proposed dual approach combining the prior knowledge, related to supervised learning, and the competitive learning abilities enhances the SOM model’s capabilities.
Volume: 15
Issue: 1
Page: 980-994
Publish at: 2025-02-01

Airport infrastructure and runway precision aids for forecasting flight arrival delays

10.11591/ijece.v15i1.pp1038-1050
Hajar Alla , Youssef Balouki
Recent research has concentrated on using machine learning approaches to forecast flight delays. The majority of prior prediction algorithms were based on simple and standard attributes collected from the database from which the data were pulled. This article is the first attempt to propose novel features linked to airport capacity and infrastructure. The total runways, the total runway intersections, the longest runway length, the shortest runway length, the runway precision rate, the total terminals, and the total gates were all examined. In this paper, we suggest an optimized multilayer perceptron to predict flight arrival retards implementing data for domestic flights operated in United States airports. We employed data normalization, sampling techniques, and hyper-parameter tuning to strengthen the reliability of the suggested model. The experimental findings demonstrated that data normalization, sampling approaches, and Bayesian optimization produced the most accurate model with 92.49% accuracy. The achievements of the study were compared to other benchmark research from literature. The time complexity for the proposed model was computed and presented at the end of the investigation.
Volume: 15
Issue: 1
Page: 1038-1050
Publish at: 2025-02-01

Enhancing the reliance of emergency power supply systems for nuclear facilities using hybrid system

10.11591/ijece.v15i1.pp36-45
Mohammed Saade , Hussein El-Eissawi , Adel S. Nada
The performance of an atomic facility depends on the efficient supply of electricity, particularly emergency loads like monitoring and control equipment, radiation safety systems, and emergency lights. Most nuclear facilities rely on diesel generators to supply emergency loads during grid outages. Due to the diesel generator's imperfections, such as its starting time, it may fail to deliver power because it is unavailable due to maintenance, failure to start, or failure to run and supply the load. It cannot immediately supply the critical loads, resulting in a blackout and the release of radioactive substances into the environment. To address the previous issues, this paper proposes an improved method to enhance the reliability of nuclear facilities for providing electricity to safety and critical consumers during normal and emergency operating modes. The approach incorporates a photovoltaic (PV) system/battery, and its robustness and performance are tested using load flow and transient stability analysis. The simulation results demonstrated the effectiveness and speed of the proposed method when compared to the traditional method, as the emergency consumers were successfully powered within a very short time without fluctuations, and the voltage reduction and frequency were within the nominal values. The electrical transient analyzer program (ETAP) is used to validate these results.
Volume: 15
Issue: 1
Page: 36-45
Publish at: 2025-02-01

Adaptive control techniques for improving anti-lock braking system performance in diverse friction scenarios

10.11591/ijece.v15i1.pp260-279
Mohammed Fadhl Abdullah , Gehad Ali Abdulrahman Qasem , Mazen Farid Ramadhan , Heng Siong Lim , Chin Poo Lee , Nasr Alsakkaf Alsakkaf
Anti-lock braking systems (ABS) enhance vehicle safety by preventing wheel lock-up, but their effectiveness depends on tire-road friction. Traditional braking systems struggle to maintain effective performance due to the risk of wheel lock-up on varying road surfaces, affecting vehicle stability and control. This study presents a novel method to improve ABS efficiency across varying friction conditions. The proposed approach employs a feedback control mechanism to dynamically adjust the braking force of each wheel based on the prevailing friction coefficient. Specifically, we incorporate a P-controller in the input signal and two additional P-controllers as output and input parameters for friction. By manipulating the proportional control values, key parameters such as wheel speed, stopping distance, and slip rate can be effectively managed. Notably, our investigation reveals intriguing interactions between the proportional controls, highlighting the complexity of ABS optimization. The method was evaluated through simulations across various friction conditions, comparing it to conventional ABS in terms of brake performance, stability, and stopping distances. The results indicate that the proposed method significantly enhances ABS performance across varying friction coefficients; however, additional research is warranted to address stopping distance and time issues, particularly in snowy and icy conditions.
Volume: 15
Issue: 1
Page: 260-279
Publish at: 2025-02-01

Predicting academic performance: toward a model based on machine learning and learner’s intelligences

10.11591/ijece.v15i1.pp645-653
Jamal Eddine Rafiq , Zakrani Abdelali , Mohammed Amraouy , Said Nouh , Abdellah Bennane
With the rapid evolution of online learning environments, the ability to predict students' academic performance has become crucial for personalizing and enhancing the educational experience. In this article, we present a predictive model based on machine learning techniques, designed to be integrated into online learning platforms using the competency-based approach. This model leverages features from four key dimensions: demographic, social, emotional, and cognitive, to accurately predict learners' academic performance. We detail the methodology for collecting and processing learning traces, distinguishing between explicit traces, such as demographic data, and implicit traces, which capture learners' interactions and behaviors during their learning process. The analysis of these data not only improves the accuracy of performance predictions but also provides valuable insights into skill acquisition and learners' personal development. The results of this study demonstrate the potential of this model to transform online education by making it more adaptive and focused on individual learners' needs.
Volume: 15
Issue: 1
Page: 645-653
Publish at: 2025-02-01

Backstepping controller for speed loop of permanent magnet synchronous motors integrated with a time-varying disturbance load observer for Metro Nhon-Hanoi Station

10.11591/ijece.v15i1.pp235-242
An Thi Hoai Thu Anh , Lam Quang Thai , Pham Duc Minh
Urban rail systems offer the substantial potential for reducing environmental pollution, alleviating traffic congestion, ensuring safety, and maintaining punctuality. Nevertheless, the operation of urban rail demands substantial electrical energy, and saving energy solutions are crucial to exploiting the full advantages of electric trains. This paper proposes the replacement of traditional traction motors with permanent magnet synchronous motors (PMSMs) due to their superior efficiency, reduced power losses, and compact size compared to direct current (DC) motors or other asynchronous three-phase motors with equivalent power, developing a backstepping controller for the speed loop coupled with a load observer-time-varying disturbance (TVD). The simulation results were conducted in MATLAB/Simulink with parameters collected from the Nhon-Hanoi urban railway line, Vietnam, verifying the proposed algorithms' correctness and effectiveness.
Volume: 15
Issue: 1
Page: 235-242
Publish at: 2025-02-01

Cascaded AC-DC parallel boost-flyback converter for power factor correction

10.11591/ijece.v15i1.pp224-234
Nur Vidia Laksmi B. , Muhammad Syahril Mubarok , Moh. Zaenal Effendi , Widi Aribowo , Tian-Hua Liu
A two-stage power factor correction (PFC) topology achieves a higher power factor quality and lower harmonic distortion than a single-stage converter. This paper introduces a two-stage PFC topology using a parallel boost and flyback converter which is employed as a voltage regulator The main boost converter is used for PFC and the other is the active filter circuit. The filter is implemented to improve the quality of phase-current and eliminate the switching loss. Furthermore, a reaction curve of Ziegler-Nichol’s method determines the controller parameter for cascaded PFC converter circuit. Simulated and experimental results are presented to validate the proposed method. The total harmonic distortion (THD) value decreases significantly from 83.35% become 0.98% in the simulation. In addition, experimental results show that the current response has good performances, including less harmonics, higher power factor, and lower THD value compared to without a PFC circuit. The PF increased from 0.43 become 0.96, the THD value decreased from 49.4% become 16.2%, and contains a small number of harmonics. The proposed controller method has better responses than the conventional one, including small steady-state error, fast rise time and settling time. A microcontroller (MCU), type STM32F407VG, produced by STMicroelectronics is used to execute the proposed control in both converters.
Volume: 15
Issue: 1
Page: 224-234
Publish at: 2025-02-01

Handling class imbalance in education using data-level and deep learning methods

10.11591/ijece.v15i1.pp741-754
Rithesh Kannan , Hu Ng , Timothy Tzen Vun Yap , Lai Kuan Wong , Fang Fang Chua , Vik Tor Goh , Yee Lien Lee , Hwee Ling Wong
In the current field of education, universities must be highly competitive to thrive and grow. Education data mining has helped universities in bringing in new students and retaining old ones. However, there is a major issue in this task, which is the class imbalance between the successful students and at-risk students that causes inaccurate predictions. To address this issue, 12 methods from data-level sampling techniques and 2 methods from deep learning synthesizers were compared against each other and an ideal class balancing method for the dataset was identified. The evaluation was done using the light gradient boosting machine ensemble model, and the metrics included receiver operating characteristic curve, precision, recall and F1 score. The two best methods were Tomek links and neighbourhood cleaning rule from undersampling technique with a F1 score of 0.72 and 0.71 respectively. The results of this paper identified the best class balancing method between the two approaches and identified the limitations of the deep learning approach.
Volume: 15
Issue: 1
Page: 741-754
Publish at: 2025-02-01
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