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29,939 Article Results

Harmonic reduction techniques in renewable energy distribution systems using cascaded multilevel inverters: a comparative analysis

10.11591/ijpeds.v16.i1.pp76-85
Nayana Gangadhara , Savita D. Torvi
Penetration of renewable energy in distribution generation increases power quality in the output. The harmonics inherent in the inverters are a major contributor to the power quality issues in the distribution system. Multilevel inverters are used to get rid of the harmonics inherent in the inverter output. Among the multilevel inverter topology cascaded multilevel inverters have taken center stage due to their simple topology and control with lesser components. This paper reviews different multilevel inverter topologies that have led to cascaded multilevel inverter topology and applies pulse width modulation (PWM) techniques to the topology. Phase disposition PWM technique is applied on the cascaded H-bridge multilevel inverter (MLI) topology for 5-level, 7-level, and 9-level inverter topologies. The total harmonic distortion (THD) obtained for these topologies is compared with and without the use of an LC filter in the inverter output. PWM techniques including phase disposition, for five-level, seven-level, and nine-level MLI methods are applied on the cascaded multilevel inverter and results are compared for harmonic reduction in the inverter output.
Volume: 16
Issue: 1
Page: 76-85
Publish at: 2025-03-01

Internet of things (IoT) based monitoring system for hybrid powered E-bike charging station

10.11591/ijpeds.v16.i1.pp243-250
Mohammad Noor Hidayat , Aji Nugroho , Abdullah Faiq Munir , Ratna Ika Putri
The internet of things (IoT) has become an important foundation in the development of web-based and remote technologies. In the implementation of renewable energy in hybrid E-bike systems, IoT-based monitoring system integration has made a significant contribution to monitoring activities. One of the latest innovations in the development of IoT in E-bike systems is the application of power prediction and the Coulomb counting method to estimate the charging time for a battery with a capacity of 200 AH, so that users can know the time needed to charge the battery efficiently. The IoT E-bike system is designed with user data display and monitoring features via the website, such as data on voltage, current, light intensity, battery percentage, power prediction, and prediction of the resulting battery charging time. Experimental results were obtained during the battery charging period, increasing the battery percentage from 50.43% (10 volts) to 71.769% (11.3 volts) in 4.5 hours with a battery charging charge of 153,866.4 C.
Volume: 16
Issue: 1
Page: 243-250
Publish at: 2025-03-01

Quantitation of new arbitrary view dynamic human action recognition framework

10.11591/ijeecs.v37.i3.pp1797-1803
Anh-Dung Ho , Huong-Giang Doan
Dynamic action recognition has attracted many researchers due to its applications. Nevertheless, it is still a challenging problem because the diversity of camera setups in the training phases are not similar to the testing phases, and/or the arbitrary view actions are captured from multiple viewpoints of cameras. In fact, some recent dynamic gesture approaches focus on multiview action recognition, but they are not resolved in novel viewpoints. In this research, we propose a novel end-to-end framework for dynamic gesture recognition from an unknown viewpoint. It consists of three main components: (i) a synthetic video generation with generative adversarial network (GAN)-based architecture named ArVi-MoCoGAN model; (i) a feature extractor part which is evaluated and compared by various 3D CNN backbones; and (iii) a channel and spatial attention module. The ArVi-MoCoGAN generates the synthetic videos at multiple fixed viewpoints from a real dynamic gesture at an arbitrary viewpoint. These synthetic videos will be extracted in the next component by various three-dimensional (3D) convolutional neural network (CNN) models. These feature vectors are then processed in the final part to focus on the attention features of dynamic actions. Our proposed framework is compared to the SOTA approaches in accuracy that is extensively discussed and evaluated on four standard dynamic action datasets. The experimental results of our proposed method are higher than the recent solutions, from 0.01% to 9.59% for arbitrary view action recognition.
Volume: 37
Issue: 3
Page: 1797-1803
Publish at: 2025-03-01

Experimental study on the use of Savonius combined blade rotors as wind turbines and hydrokinetic turbines

10.11591/ijpeds.v16.i1.pp555-563
Arifin Sanusi , Jahirwan Ut Jasron , Sudirman Syam
Renewable energy development is increasingly important to anticipate the limited use of fossil energy and its impact on the environment. The Savonius turbine is a vertical axis turbine that can utilize flow from all directions with simple construction, so it has the potential to be developed as a wind turbine and hydrokinetic to generate electricity. This paper aims to conduct an experimental studied the same Savonius combined blade rotor as a wind turbine used in a wind tunnel and a hydrokinetic turbine in an irrigation channel. The experimental results show that the Savonius turbine can function well as a wind and hydrokinetic turbine. The Savonius combined blade turbine improves the performance of conventional Savonius blade turbines, including its use as a hydrokinetic turbine, which is affected by flow velocity. The performance of the Savonius turbine is indicated by the power coefficient Cp and torque coefficient (Ct) values based on the fluid flow velocity. At the same wind speed (4 m/s), the combined blades can increase the performance Cp by up to 11% compared to conventional blades. The use of the same combined blades tested as a hydrokinetic turbine resulted in an increase in Cp and a decrease in Ct with an increase in tip speed ratio (TSR).
Volume: 16
Issue: 1
Page: 555-563
Publish at: 2025-03-01

Tomato leaf disease detection using Taguchi-based Pareto optimized lightweight CNN

10.11591/ijeecs.v37.i3.pp1772-1784
Bappaditya Das , C. S. Raghuvanshi
The prospect of food security becoming a global danger by 2050 due to the exponential growth of the world population. An increase in production is indispensable to satisfy the escalating demand for food. Considering the scarcity of arable land, safeguarding crops against disease is the best alternative to maximize agricultural output. The conventional method of visually detecting agricultural diseases by skilled farmers is time-consuming and vulnerable to inaccuracies. Technology-driven agriculture is an integral strategy for effectively addressing this matter. However, orthodox lightweight convolutional neural network (CNN) models for early crop disease detection require fine-tuning to enhance the precision and robustness of the models. Discovering the optimal combination of several hyperparameters might be an exhaustive process. Most researchers use trial and error to set hyperparameters in deep learning (DL) networks. This study introduces a new systematic approach for developing a less sensitive CNN for crop leaf disease detection by hyperparameter tuning in DL networks. Hyperparameter tuning using a Taguchi-based orthogonal array (OA) emphasizes the S/N ratio as a performance metric primarily dependent on the model’s accuracy. The multi-objective Pareto optimization technique accomplished the selection of a robust model. The experimental results demonstrated that the suggested approach achieved a high level of accuracy of 99.846% for tomato leaf disease detection. This approach can generate a set of optimal CNN models’ configurations to classify leaf disease with limited resources accurately.
Volume: 37
Issue: 3
Page: 1772-1784
Publish at: 2025-03-01

Approach for modelling and controlling of autonomous cruise control system through machine learning algorithms

10.11591/ijeecs.v37.i3.pp1532-1542
R. Kiruba , S. Prince Samuel , N. Kavitha , K. Srinivasan , V. Radhika
Automated cruise control installation is one of the utmost significant phases in the auto industry's pursuit of autonomous vehicles. The controller of choice is one of the key factors in determining whether a design will be durable and cost-effective. The model-based controller and a cutting-edge algorithmic optimization method are both presented inside the framework of this proposed study. The suggested controller may achieve the desired characteristics of the design, including a faster rise time, a faster settle time, a smaller peak overshoot, and a smaller steady-state error. A MATLAB-executed and -simulated system model using a control method based on a hybrid genetic algorithm and reinforcement learning has been used to effectively and automatically regulate the vehicle's velocity in compliance with all design parameters.
Volume: 37
Issue: 3
Page: 1532-1542
Publish at: 2025-03-01

Advancing solar energy harvesting: Artificial intelligence approaches to maximum power point tracking

10.11591/ijpeds.v16.i1.pp55-69
Meriem Boudouane , Lahoussine Elmahni , Rachid Zriouile , Soufyane Ait El Ouahab
This paper presents a comparative study of five maximum power point tracking (MPPT) control techniques in photovoltaic (PV) systems. The algorithms evaluated include classical methods, such as perturb and observe (P&O) and incremental conductance (IC), as well as intelligent approaches such as fuzzy logic (FL), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference system (ANFIS). Intelligent methods provide faster response times and fewer oscillations around the maximum power point (MPP). The structure of the PV system includes a PV generator, load, and DC/DC boost converter driven by an MPPT controller. The performance of these techniques is analyzed under identical climatic conditions (same irradiation and temperature) in terms of efficiency, response time, response curve, accuracy in tracking the MPP, and others considered in this work. Simulations were performed using MATLAB-Simulink software, demonstrating that ANNs and ANFIS outperform traditional methods in dynamic environments, with FL being computationally intensive. P&O exhibited significant oscillations, while IC a showed slower tracking speed.
Volume: 16
Issue: 1
Page: 55-69
Publish at: 2025-03-01

Utilizing logistic regression in machine learning for categorizing social media advertisement

10.11591/ijeecs.v37.i3.pp1954-1963
Hari Gonaygunta , Geeta Sandeep Nadella , Karthik Meduri
The purpose of this paper is to investigate the use of logistic regression in machine learning to distinguish the types of social media advertisements. Since the logistic regression algorithm is designed to classify data with a target variable that has categorical results, it is the one selected. As a result, this research intends to measure the efficiency of logistic regression for the classification of social media advertisements. This research centers on the social media advertisements dataset and employs logistic regression for classification purposes. The model is evaluated against performance metrics to measure the extent to which it can categorize social media advertisements. As a result, the findings of this study show that logistic regression is fit for classifying social media advertisements. Logistic regression is important for machine learning when it comes to classifying social media advertisements because it supports categorizing advertisements according to their characteristics and precisely predicts the categorical results.
Volume: 37
Issue: 3
Page: 1954-1963
Publish at: 2025-03-01

Machine learning based optimized sea vessel location detection to identify illegal fishing

10.11591/ijeecs.v37.i3.pp1626-1636
Ajay Kumar , Kakoli Banerjee , Pradeep Kumar , Harsha K. G. , Vinooth P. , Pankaj Kumar , Saumya Saumya , Nishant Nishant , Satyam Verma , Vidushi Bhardwaj
Illegal fishing is a pervasive and destructive global issue that poses a significant threat to maritime ecosystems and the resilience of fisheries. Illegal, unregulated, and unreported (IUU) fishing leads to the extinction of the fishing population. Many researchers have presented various approaches to detect illegal fishing, for example, using sensors, image recognition, and convolutional neural networks (CNNs) but each one has some limitations. Our research aims to compare different vessel gear types to select the best vessel container that can be easily monitored and less prone to illegal activities. To achieve this, our research proposed an optimization method that involves hyperparameter selection using a genetic algorithm instead of a grid search. Using the crossover method of the genetic algorithm our model is compatible with larger datasets and unknown search space which is not possible in the baseline algorithm i.e. grid search. Moreover, after applying the genetic hyperparameter optimization technique, the overall accuracy, recall, and F1 score is increased for all vessel types significantly. While comparing our optimized model with the existing model with different evaluation metrics, our model’s performance is outstanding.
Volume: 37
Issue: 3
Page: 1626-1636
Publish at: 2025-03-01

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

A novel dataset and part-of-speech tagging approach for enhancing sentiment analysis in Kannada

10.11591/ijeecs.v37.i3.pp1661-1671
Sunil Mugalihalli Eshwarappa , Vinay Shivasubramanyan
The problem addressed in this research is the limited availability of labelled datasets and effective sentiment analysis tools for the Kannada language. Existing challenges include linguistic variations, cultural diversities, and the absence of comprehensive datasets designed specifically for sentiment analysis in Kannada. This research aims to enhance sentiment analysis capabilities for the Kannada language, addressing challenges posed by linguistic variations and limited labelled datasets. A novel Kannada dataset derived from SemEval 2014 task 4 was created using a conversion process. The dataset was processed using part-of-speech tagging, and a specialized model called K-BERT (Kannada bidirectional encoder representations from transformers) was introduced and implemented using Python within the Anaconda environment. Performance evaluation results showcased K-BERT's superiority over traditional machine learning (ML) algorithms and the BERT model, achieving an accuracy of 0.98, precision of 0.97, recall of 0.97, and F-score of 0.98 in sentiment classification for Kannada text data. This work contributes a unique Kannada dataset, introduces the K-BERT model specifically designed for Kannada sentiment analysis, and emphasizes the importance of collaborative efforts in advancing natural language processing (NLP) research for multilingual environments.
Volume: 37
Issue: 3
Page: 1661-1671
Publish at: 2025-03-01

Sustainable supply chain modeling: a review based on the application of the system dynamics approach

10.11591/ijeecs.v37.i3.pp1637-1649
Julia Kurniasih , Zuraida Abal Abas , Siti Azirah Asmai , Agung Budhi Wibowo
Sustainable supply chains, evolving with supply chain 5.0 revolution, are crucial for achieving sustainable development goals (SDGs) by balancing economic growth, environmental protection, and social responsibility. They help reduce environmental impacts, promote ethical labor practices, and ensure financial viability. Sustainable supply chains involve complex interactions and external influences. The system dynamics approach effectively captures these intricate interactions through feedback loops and non-linear relationships. This review seeks to identify issues in modeling sustainable supply chains using system dynamics and offer insights for developing sustainable, flexible, responsive, and resilient models. This paper reviews literature from 2020 to 2023 using thematic analysis. It examines dynamics, behaviors, management, sustainability strategies, decision-making, and future directions for sustainable supply chain modeling. The findings suggest that a comprehensive framework can enhance management practices, support policymaking, and promote sustainability. Integrated risk management is essential for resilient, adaptable supply chains, while financial viability and scalability are essential for the widespread adoption of sustainability practices. Understanding the roles of various actors and integrating supply chain components can improve support systems, and exploring green energy, technology adoption, and consumer behavior can advance sustainability goals. Future research should also better integrate sustainability aspects and explore a broader range of literature for deeper insights.
Volume: 37
Issue: 3
Page: 1637-1649
Publish at: 2025-03-01

Authenticated image encryption using robust chaotic maps and enhanced advanced encryption standard

10.11591/ijeecs.v37.i3.pp1543-1554
Rupaliben V. Chothe , Sunita P. Ugale , Dinesh M. Chandwadkar , Shraddha V. Shelke
The ability of advanced encryption standard (AES) algorithm to protect information systems has given cryptography a new dimension. Recent encryption approaches to enhance randomness include the use of chaotic algorithms, which provide resistance to differential attacks. We have proposed the application of robust chaotic maps in the block cipher to design a secure authenticated encryption scheme to get advantages of both. The chaotic sequence is generated using hyperbolic tangent map and added to input image initially to increase randomness. The basic 256-bit AES key is generated using the robust Renyi modulo map. An additional 128-bit key enhances security. Instead of static values used in AES, dynamic initialization vector (IV), different for every image will be generated. The results are mathematically verified using various security parameters. The algorithm provides lower values of peak signal-to-noise ratio (PSNR) (7.81 to 9.10 dB) for encrypted images and higher dissimilarities between input and encrypted image histograms. Thus, it is highly resistant to statistical attacks. The experimental results and their comparison prove the superiority of our proposed cryptosystem against statistical, differential and brute-force attacks. Thus, the novel multi-chaotic AES-GCM (galois/counter mode) algorithm can be used for color image encryption in military and industrial applications demanding high data security and authentication.
Volume: 37
Issue: 3
Page: 1543-1554
Publish at: 2025-03-01

Detecting network security incidents in wireless sensor networks using machine learning

10.11591/ijeecs.v37.i3.pp1650-1660
Tamara Zhukabayeva , Atdhe Buja , Melinda Pacolli , Yerik Mardenov
This study enhances the domain of cybersecurity within wireless sensor networks (WSNs) through the integration of sophisticated artificial intelligence (AI) and machine learning (ML) techniques. By conducting an exploratory data analysis (EDA), this research reveals critical insights into network behavior, facilitating the development of predictive models for anomaly detection. The application of ML algorithms decision trees (DT) and random forest (RF) demonstrated dominant performance in identifying potential security threats, as evidenced by metrics accuracy, precision, recall, and F1 scores. This work not only enhances the security framework for WSNs but also contributes to the extensive field of network security, offering a robust analytical and predictive methodology for future cybersecurity initiatives. The advanced model can be deployed in other WSN and internet of things (IoT) based applications.
Volume: 37
Issue: 3
Page: 1650-1660
Publish at: 2025-03-01

TechTrolley-enhancing the retail experience

10.11591/ijeecs.v37.i3.pp1476-1486
Dhananjay Rajendra Chavan , Roshan Mahadev Sherekar , Sarthak Praveen Khudbhaiye , Jaya Zalte
In the modern era, convenience and efficiency have become essential aspects of daily life, and grocery shopping is no exception. The traditional shopping experience, characterized by long queues and time-consuming checkout processes, can be frustrating and inefficient. To address these challenges, the TechTrolley has emerged as an innovative solution, leveraging Bluetooth and radio frequency identification (RFID) technology to revolutionize the grocery shopping experience. With the help of TechTrolley, customer can seamlessly complete the shopping by scanning and purchasing the products, controlling the trolley with the use of controller integrated in application, getting details of the products and price in the application and over LCD display embedded on the trolley, complete the checkout process at billing counter. With the need to implement, we need an RFID tag, ESP32, LCD display, L298N motor driver and battery to implement the motion features of a trolley, database for storing the user and product details, a bridge network through router to establish the network between admin, user and the trolley in order to invoke the real time updates.
Volume: 37
Issue: 3
Page: 1476-1486
Publish at: 2025-03-01
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