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31,456 Article Results

Prospects of using organic Rankine cycle for geothermal power generation

10.11591/ijpeds.v16.i1.pp575-583
Zhanat Tulenbayev , Aizhan Zhanpeisova , Ardak Omarova , Akmaral Tleshova , Nazym Abdlakhatova
The relevance of this study stems from the desire to develop efficient and sustainable methods of energy extraction from low-temperature geothermal resources, which is of key importance in the context of finding alternative energy sources and reducing dependence on conventional, often non-renewable sources. The purpose of this study was to analyze the organic Rankine cycle (ORC) to improve the efficiency of energy recovery from low-temperature geothermal sources. The present study employed the analytical method, the deduction method, the induction method, the functional method, the classification method, the synthesis method. ORC applications for geothermal energy were comprehensively analyzed, with a focus on the investigation of low-temperature resources. The best cycle performance parameters were determined, considering diverse operating conditions. Concrete technical recommendations were developed for the selection of organic working media to improve system efficiency. The summarized findings highlight the potential of the ORC in enhancing the sustainability and efficiency of geothermal systems.
Volume: 16
Issue: 1
Page: 575-583
Publish at: 2025-03-01

Machine learning models in renewable energy forecasting: a systematic literature review

10.11591/ijeecs.v37.i3.pp1874-1886
Mohamed Yassine Rhafes , Omar Moussaoui , Maria Simona Raboaca
During the past years, the convergence of machine learning (ML) technologies with renewable energy sectors has become a significant key area of innovation as a key area of innovation, enhancing the efficiency and predictability of sustainable energy sources. ML algorithms, adept at handling complex data, have become essential in forecasting energy outputs from variable sources like solar and wind. This integration has led to the development of smarter, more adaptive grid systems, capable of efficiently managing the variability of renewable energy sources. This review paper focuses on several key areas: firstly, it provides a summary of related work, specifically focusing on ML in the renewable energy field. Secondly, it delves into ML models and evaluation metrics used for solar and wind energy forecasting. Thirdly, it analyzes 21 studies published from 2019 to 2023, primarily centered on solar energy (60%) and wind energy (40%), with an emphasis on various forecasting horizons, highlighting the results of the ML algorithms used and the performance metrics to evaluate their effectiveness. Finally, it identifies gaps and opportunities in this field. The state-of-the-art review and its findings can offer a solid foundation for future research initiatives.
Volume: 37
Issue: 3
Page: 1874-1886
Publish at: 2025-03-01

Analysis and implementation of peak armature current (Imax) of a chopper-fed DC-DC motor drive in DCM

10.11591/ijpeds.v16.i1.pp96-105
Mohamad Nazir Abdullah , Muhammad Hafeez Mohamed Hariri , Mohd Khairunaz Mat Desa , Mohd Nadzri Mamat , Suardi Kaharuddin
At low frequencies of operation in a chopper-fed direct current (DC) motor drive, the armature current may become discontinuous thus the controller operates in discontinuous conduction mode (DCM). Since the minimum armature current is zero in DCM, the analysis of peak armature current (Imax) is to investigate the ripple content in armature current at different values of duty cycle which will help in decreasing the peaky current of DC motor during operation. The simulation was carried out using MATLAB-Simulink software and the laboratory setup was based on Atmega 328 microcontroller board. In this paper, the theoretical and experimental analysis of peak armature current were performed at fix low frequency in DCM and variable duty cycles to provide full control of DC motor speed. The results show that the peak armature current changes with the change of duty cycles and its magnitude is decreased almost 50% at higher duty cycle values.
Volume: 16
Issue: 1
Page: 96-105
Publish at: 2025-03-01

Marrying deep learning within blockchain technology for credit card fraud prevention

10.11591/ijeecs.v37.i3.pp1985-1998
Imane Karkaba , El Mehdi Adnani , Mohammed Erritali
Over the last decade or so, an excessive turnout on e-financial transactions by companies and customers results in a pinnacle growth of credit card fraudulent acts, leading them to lose frequently huge amounts of money. In their trial to find the key to this issue, specialists and experts have founded a bunch of fraud detection and prevention models relied on data mining, machine learning and deep learning. Yet, the outcomes were not effective nor optimal. Thereupon, to foster these prototypes’ function, Blockchain -a safe, decentralized and unchangeable database- was deployed to ban any sort of anomaly or data alteration after storing. For identifying malicious financial behaviours, our work managed to intermingle a pre-designed deep learning prototype with Blockchain. That is to say -for the sake of preventing fraudulence that concerns credit card- we applied the former prototype in Blockchain system. Still, Blockchain showed impotence in terms of using off-chain data, which embeds deep learning pattern, specifically through smart contract. Hence, we activated chainlink boosting our model to surmount this obstacle.
Volume: 37
Issue: 3
Page: 1985-1998
Publish at: 2025-03-01

Comparative analysis of edge detection image processing techniques for efficient traffic signal management system

10.11591/ijeecs.v37.i3.pp1555-1568
Niranjana Chandrasekara Bharathy , Manju Shivanna Dasappanavar , Anne Gowda Aleri Byre Gowda , Jijesh Jisha Janardhanan
Traffic monitoring and control pose significant challenges, particularly in metropolitan areas worldwide. Traditional methods such as timers, traffic police control, and sensor-based systems have become increasingly ineffective due to escalating traffic volumes. Image processing emerges as a promising technology for enhancing traffic control systems. This research aims to address the inefficiencies of current traffic management systems (TMS) by conducting a detailed comparative analysis of conventional image processing techniques, focusing on edge-based methods. Utilizing the open source computer vision (OpenCV) Library, we evaluated various edge detection techniques based on qualitative parameters like environmental lighting, object detection, and size, as well as quantitative parameters such as average traffic density, sharpness ratio (SR), abruptness, sensitivity factors (SF), processing time, frame processing time, and average frames per second (FPS). The study finds that the Canny edge detection technique outperforms others, with an average traffic density of 30.12 across 10 frames, superior SR, and minimal processing time of 99 seconds. This makes it highly effective for traffic control applications by generating high-quality edge maps with minimal noise. Our findings suggest that improving conventional image processing techniques and integrating deep learning algorithms can further enhance TMS, leading to more efficient urban mobility and reduced environmental impact.
Volume: 37
Issue: 3
Page: 1555-1568
Publish at: 2025-03-01

Value group classifier model for ethical decision-making

10.11591/ijeecs.v37.i3.pp1899-1907
Shweta Bhatnagar , Rashmi Agrawal
Decision-makers refer to ethics or moral philosophy during times of ethical dilemma. Dilemmas are situations of inner conflict, which require a methodical approach. Diversity in viewpoints on moral decisions ensures there cannot be a fixed solution for ethical dilemmas as in the case of numerical problems. Existing ethical and sustainable decision models for businesses are not automated because of a lack of a comprehensive list of dilemmas. To resolve this gap, an AI model was trained to classify all dilemmas into three value groups by using a support vector classifier (SVC). The model provided scaffolding to the ethical decision-maker by suggesting relevant human values applicable to the dilemma. The design works on the ethical theory of stakeholder management, which includes sustainable business goals. The study was conducted with 30 students and 30 adults to identify their dilemmas. The dilemma dataset was used to train an ethical decision-support tool, called the value group classification (VGC) model. The model achieved a score of 0.52 on performance. The VGC model overcomes the black-box biases of similar machine-learning models by allowing human autonomy in ethical decisions.
Volume: 37
Issue: 3
Page: 1899-1907
Publish at: 2025-03-01

Forecasting bitcoin price fluctuations: a time series analysis approach for predictive modelling

10.11591/ijeecs.v37.i3.pp1964-1975
Amine Batsi , Mohamed Biniz , Rachid El Ayachi
The recent fluctuations in the price of Bitcoin, marked by both significant increases and subsequent decreases, has attracted media and public attention. Consequently, many researchers have explored various factors influencing Bitcoin’s price and the underlying patterns of its fluctuations. This paper aims to predict and analyses the factors affecting Bitcoin’s price by creating a unique dataset with nearly 40 features and deriving two child datasets using correlation and mutual information as feature selection techniques. Additionally, we train machine learning models, including linear regression (LR), extreme gradient boosting (XGBoost), support vector regression (SVR), Facebook Prophet (FB Prophet), and bidirectional gated recurrent unit (BI-GRU), to predict Bitcoin’s next-day price. The model’s performance is evaluated using mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 score metrics. Our findings indicate that machine learning techniques are effective in predicting Bitcoin’s price and could be valuable for investors seeking to maximize profits.
Volume: 37
Issue: 3
Page: 1964-1975
Publish at: 2025-03-01

Rounding function-based zero crossing detection for a sensorless BLDC motor control

10.11591/ijpeds.v16.i1.pp106-116
Musa Mohammed Gujja , Dahaman Ishak , Muhammad Najwan Hamidi , Mohamed Salem , Mohamad Nazir Abdullah , Khalil Alluhaybi
Permanent magnet brushless DC (PMBLDC) motors are favored for their low maintenance, high reliability, and efficiency, making them ideal for industrial, domestic, military, aerospace, and robotics applications. Sensor less control is the most preferred technique for PMBLDC motors due to its reliability and cost-effectiveness, eliminating the need for physical sensors. A crucial aspect of sensor less control is accurately detecting the point of zero crossing of the back electromotive force (BEMF) signals. Traditional methods, such as rotor position estimation, input observers, and AI-based strategies, can suffer from high ripples and computational inefficiencies. This paper introduces an approach using the rounding function to determine the point of zero crossing, aiming to enhance precision and reduce computational overhead. The rounding function converts continuous BEMF signals into discrete signals, minimizing ripples and facilitating accurate zero-crossing detection. This method improves detection accuracy while simplifying computation demands. Validation was performed through a MATLAB Simulink simulation and an experiment using the F28379D microcontroller, gate driver, and a six-switch inverter. The results demonstrate the effectiveness of the proposed approach, showing agreement between experimental and simulation outcomes.
Volume: 16
Issue: 1
Page: 106-116
Publish at: 2025-03-01

Neuro-adaptive hierarchical sliding mode control for a 3-wheeled mobile robot with disturbance rejection

10.11591/ijeecs.v37.i3.pp1515-1531
Jose Carlos Vizarreta Mamani , Bladimir Leonel Vera Huamani , Raul Sulla Torres
High tracking accuracy and fast convergence are essential features in the control of wheeled mobile robots for autonomous navigation applications. However, the design of a control system for these robots faces significant challenges due to the inherent complexity of their nonlinear dynamics and their non-holonomic underactuated nature. This article introduces a novel control framework for trajectory tracking of a three-wheeled mobile robot (3WMR), considering external and internal disturbances. To address the uncertain nonlinear and underactuated non-holonomic dynamics of the 3WMR, an adaptive hierarchical fast terminal sliding mode control (AHFTSMC) strategy is proposed. In this approach, an adaptive neural network scheme adjusts the sliding surface coefficients in real time to minimize tracking errors and mitigate the chattering phenomenon. In addition, a finite time disturbance observer (FTDO) is designed to accurately estimate and compensate for unknown lumped disturbances, which helps to improve the disturbance rejection capability. The stability of the closed-loop system is demonstrated using Lyapunov theory. The proposed control approach is validated by numerical simulations, and a comparative analysis of recent hierarchical sliding mode controllers (SMC) is performed. The results demonstrate that the proposed approach achieves superior performance in terms of fast convergence and tracking accuracy, which are crucial features for the autonomous navigation of mobile robots.
Volume: 37
Issue: 3
Page: 1515-1531
Publish at: 2025-03-01

Convolutional neural network for speech emotion recognition in the Moroccan Arabic dialect language

10.11591/ijeecs.v37.i3.pp1588-1595
Soufiyan Ouali , Said El Garouani
Extracting the speaker's emotional state has become an active research topic lately due to the demand for more human interactive applications. This field of research has noted significant advancement, especially in the English language, owing to the availability of massive speech-labeled corpora. However, the progress of analogous methodologies in the Arabic language is still in its infancy stages. This paper presents a new massive natural speech emotion dataset and a speech recognition model for the Moroccan Arabic language. Four primary emotion labels were selected: happy, sad, angry, and neutral. Various spectral features, such as the mel-frequency cepstral coefficient (MFCC), were extracted and tested to determine the optimal feature combination. A convolutional neural networks (CNNs) model was built and trained on our dataset. The results were compared between spectral features individually and combined with the CNN model resulting in the selection of MFCC, root-mean-square (RMS), mel-scaled spectrogram, and spectral, as optimal spectral features for our dataset. This selection yielded significant results, with an accuracy of 99.55% for emotion recognition, outperforming the existing research.
Volume: 37
Issue: 3
Page: 1588-1595
Publish at: 2025-03-01

Implementation and analysis of temperature and gas sensor datalogger in multi-stage condenser pyrolysis

10.11591/ijeecs.v37.i3.pp1497-1505
Muhammad Fathuddin Noor , Sumardi Hadi Sumarlan , Yusuf Hendrawan , Bambang Dwi Argo , Hartawan Abdillah
The development of alternative energy sources is crucial for addressing contemporary energy and environmental challenges. This study presents the implementation and analysis of a temperature and gas sensor datalogger within a multi-stage condenser pyrolysis system, designed to assess the potential of pyrolytic liquid smoke derived from Cerbera odollam (bintaro) fruit waste. The datalogger system was developed to continuously capture and retain data on temperature, air humidity, and non-condensable gases throughout the pyrolysis process. The experimental research focused on evaluating the impact of varying reactor temperatures (250 °C, 300 °C, and 350 °C) and cooling fluid flow rates on the performance of the condenser and the production of bio-oil. Results indicated that reactor temperature significantly affects bio-oil yield, with the highest output of 190 mL obtained at 350 °C. Additionally, the temperature of the smoke entering each condenser and the cooling water’s temperature were found to influence the composition of the condensates produced by each stage. This study highlights the importance of integrating sensor technologies to optimize pyrolysis conditions, thereby enhancing the efficiency of energy production from bintaro fruit waste.
Volume: 37
Issue: 3
Page: 1497-1505
Publish at: 2025-03-01

Communication induced checkpointing based fault tolerance mechanism using deep-learning in IoT applications

10.11591/ijeecs.v37.i3.pp1785-1796
Sowjanya Lakshmi A , Vanipriya Ch
Internet of things (IoT) is increasingly used in diverse environments such as healthcare, industry and agriculture. They carry a risk of adverse effects if they make decisions based on faulty information. Software faults, especially transient faults are a primary contributor to deficient decision-making. The existing fault tolerant mechanisms often suffer from checkpoint overheads as checkpoints are placed in all the nodes. This paper describes a novel communication induced checkpointing based fault tolerance mechanism (CIC-FTM) designed to efficiently recover from transient faults, while minimizing useless and forced checkpoints. Long short-term memory (LSTM) based deep learning algorithm is used in our approach to predict fault occurrences and strategically place checkpoints. The proposed method also in turn improve system reliability and performance. Experimental results demonstrate the effectiveness of proposed CIC-FTM in IoT environment by minimizing the practicable operating time for checkpointing and back propagation, compared to traditional fault-tolerance mechanisms.
Volume: 37
Issue: 3
Page: 1785-1796
Publish at: 2025-03-01

A reliable and secure demand side control for an IoT-enabled smart power system using machine learning

10.11591/ijeecs.v37.i3.pp1428-1434
Vemulapalli Harika , Gudavalli Madhavi , Hanumantha Rao Battu , Rambabu Kasukurthi , Pradeep Jangir , John T Mesia Dhas
As the adoption of IoT-enabled smart power systems grows, the necessity for reliable and secure demand-side control becomes paramount. This paper introduces a robust demand-side management (DSM) engine that leverages machine learning to enhance both the reliability and security of smart grids. This paper presents a novel demand-side control system leveraging advanced machine learning techniques to optimize energy usage in smart power systems. The proposed system integrates IoT devices for data acquisition and employs machine learning algorithms to forecast energy demand, detect anomalies, and enable adaptive control strategies. By harnessing predictive analytics, the system anticipates consumption patterns and adjusts power distribution to maintain stability and prevent overloads. Moreover, robust security protocols are incorporated to protect the system against cyber threats and unauthorized access, ensuring data integrity and user privacy. Extensive simulation results demonstrate the system’s efficacy in reducing energy wastage, improving load balancing, and enhancing the overall reliability of the power grid. This research underscores the potential of combining IoT and machine learning to develop resilient and secure energy management solutions, paving the way for more sustainable and smart power systems.
Volume: 37
Issue: 3
Page: 1428-1434
Publish at: 2025-03-01

Classification of traditional Joha rice using deep neural network

10.11591/ijeecs.v37.i3.pp1682-1691
Hemanta Kalita , Mirzanur Rahman
Rice is the major food for the most of the people in the world. It is grown comfortably in a rain fed area. Rice is called a kharif crop in India. According to the International Rice Research Institute (IRRI) classification rice is classified as Ahu, Sali, Boro and Hill rice. In north eastern India the Joha rice is mainly cultivated in the state of Assam. There are many varieties of Joha rice like Kunkuni, Rampal, Manipuri, Tulshi, and Keteki. But it is quite complex for the farmers as well as the common people to differentiate these type of Joha rice due to their morphological structure. Initially this type of classification done by naked eyes or used some laboratory experiments. But due to the tiredness and some external factors it is not fruitful. Some of the image classification technologies are used in classification as well identification of rice in different research work using MATLAB. At present deep learning plays a tremendous role in image analysis in agricultural domain. Here in this paper we take two verities of Joha rice which are closer in morphological structure and cannot be separated by our naked eyes. Using deep neural network, binary classification is done in these Joha rice.
Volume: 37
Issue: 3
Page: 1682-1691
Publish at: 2025-03-01

Discovering solutions of economic load dispatch problem by war strategy optimization algorithm

10.11591/ijeecs.v37.i3.pp1407-1414
Hung Duc Nguyen , Ly Huu Pham
This paper suggested two methods, called war strategy optimization algorithm (WSO) and tunicate swarm algorithm (TSA), to find solutions for economic load dispatch problem (ELD). Various test systems with complex restrictions and discontinuous objective functions are used to assess the efficacy and resilience of the applied methods. The test cases are ranked from the simplest to the most complicated ones, in which the last with load demands are changed from the minimum power to the maximum power of total power of all units. The result comparison indicated that WSO can always reach the best cost for all test systems, but TSA cannot achieve similar values. Namely, WSO can reduced smaller cost than TSA by $ 0.565 and $ 121,325 for the first and second test systems, respectively. In comparison to other previous methods, the results found by WSO are equal to or better than those from others; however, the searchability of WSO is faster. Consequently, WSO is highly effective for handling ELD problem and can be considered for applying different problems in the engineering domain.
Volume: 37
Issue: 3
Page: 1407-1414
Publish at: 2025-03-01
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