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25,002 Article Results

Attention deficit and hyperactivity disorder classification in quantitative EEG signals using machine learning algorithms

10.11591/ijeecs.v37.i3.pp1580-1587
Syifani Ihfadza Aliyah , Sastra Kusuma Wijaya , Yetty Ramli
Attention deficit and hyperactivity disorder (ADHD) classification method as a quantitative observation has been continually improved to assist medical practitioners. Currently, machine learning algorithms such as k-nearest neighbors (KNN), multilayer perceptron (MLP), and support vector machine (SVM) are widely used. This study proposed a feature extraction method for quantitative electroencephalography (qEEG) data derived from the continuous wavelet transform (CWT) to classify children with ADHD versus healthy subjects. Subsequently, this study compared the performance of the classification pipeline before and after the implementation of principal component analysis (PCA) on the features prior to processing with machine learning algorithms. The results revealed that the overall performance of the classifiers consistently improved after the implementation of PCA. The results highlight the varying impact of PCA on classifier performance, with KNN showing an improvement in testing accuracy from 61.84% to 69.21% following PCA implementation, while the other classifiers showed deterioration in performance. These findings suggest that while PCA may be beneficial for some classifiers, its impact on performance varies depending on the specific characteristics of the dataset and the classifier utilized. Moreover, this study provides insight for future implementation of the classification method for ADHD patients across a more specific clinical range of the spectrum.
Volume: 37
Issue: 3
Page: 1580-1587
Publish at: 2025-03-01

Leveraging 3D convolutional networks for effective video feature extraction in video summarization

10.11591/ijeecs.v37.i3.pp1616-1625
Bhakti Deepak Kadam , Ashwini Mangesh Deshpande
Video feature extraction is pivotal in video processing, as it encompasses the extraction of pertinent information from video data. This process enables a more streamlined representation, analysis, and comprehension of video content. Given its advantages, feature extraction has become a crucial step in numerous video understanding tasks. This study investigates the generation of video representations utilizing three-dimensional (3D) convolutional neural networks (CNNs) for the task of video summarization. The feature vectors are extracted from the video sequences using pretrained two-dimensional (2D) networks such as GoogleNet and ResNet, along with 3D networks like 3D Convolutional Network (C3D) and Two-Stream Inflated 3D Convolutional Network (I3D). To assess the effectiveness of video representations, F1-scores are computed with the generated 2D and 3D video representations for chosen generic and query-focused video summarization techniques. The experimental results show that using feature vectors from 3D networks improves F1-scores, highlighting the effectiveness of 3D networks in video representation. It is demonstrated that 3D networks, unlike 2D ones, incorporate the time dimension to capture spatiotemporal features, providing better temporal processing and offering comprehensive video representation.
Volume: 37
Issue: 3
Page: 1616-1625
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

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

Optimized dense convolutional network with conditional autoregressive value-at-risk for chronic kidney disease detection through group-based search

10.11591/ijeecs.v37.i3.pp2009-2020
Chetan Nimba Aher , Archana Rajesh Date , Shridevi S. Vasekar , Priyanka Tupe-Waghmare , Amrapali Shivajirao Chavan
Chronic kidney disease (CKD) is the gradual decrease in renal functionality that leads to kidney failure or damage. This disease is the most severe worldwide health condition that kills numerous people every year as an outcome of hereditary factors and worse lifestyles. As CKD progresses, it becomes difficult to diagnose. Utilizing regular doctor consultation data for evaluating diverse phases of CKD can assist in earlier detection and timely inference. Furthermore, effectual detection methods are vital owing to an increased count of patients with CKD. Here, group search conditional autoregressive value-at-risk based dense convolutional network (GSCAViaR-DenseNet) is introduced for CKD detection. Firstly, chronic data is acquired from the dataset and Min-Max normalization is utilized to pre-process considered chronic kidney data. Thereafter, feature selection (FS) is performed based on Topsoe similarity. Lastly, CKD detection is executed by dense convolutional network (DenseNet) and group search conditional autoregressive value-at-risk (GSCAViaR) is employed to train DenseNet. However, GSCAViaR is designed by incorporating a group search optimizer (GSO) with a conditional autoregressive value-at-risk (CAViaR) model. Additionally, GSCAViaR-DenseNet acquired a maximal accuracy of about 91.5%, sensitivity of about 92.8% and specificity of about 90.7%.
Volume: 37
Issue: 3
Page: 2009-2020
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

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

Multifaceted approach for anticipating learner performance using parameter weightage and ensemble algorithm fusion

10.11591/ijeecs.v37.i3.pp2032-2043
Shabnam Ara S Jahagirdar , Tanuja Ramachandraiah
Anticipating student performance has garnered significant attention in education research for offering early insights that enable timely interventions and personalized support, ultimately improving student success and retention rates. This research focuses on enhancing the accuracy and efficiency of student performance prediction models by employing a hybrid ensemble framework that integrates weighted feature selection with meta-learner-based approaches. A weighted feature selection method was employed to prioritize the most influential of the 23 parameters in the dataset, enhancing prediction accuracy while reducing the computational burden. These parameters were then used to build a hybrid ensemble model by combining base learners with meta-learners, systematically tuned using hyperparameter optimization. This approach aimed to further improve prediction accuracy by fusing multiple base learners, leveraging the strengths of different algorithms for more accurate predictions. The proposed hybrid model was validated across different features selected based on feature importance using random forest (RF). An accuracy of 98.38% was achieved when all 23 features were considered and an accuracy of 97.13 % was achieved when the top 10 features were used. The research highlights the significance of early prediction for prompt intervention and demonstrates how feature weighting can boost model efficacy.
Volume: 37
Issue: 3
Page: 2032-2043
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

Analysis of cryptographic methods for ensuring security in the field of internet of things

10.11591/ijeecs.v37.i3.pp1596-1606
Temirbekova Zhanerke Erlanovna , Abdiakhmetova Zukhra Muratovna , Tynymbayev Sakhybay
The number of internet of things (IoT) devices continues to grow, and so do the associated concerns regarding their security and privacy. Evaluating the efficacy of cryptographic solutions within IoT systems emerges as a crucial endeavor to uphold the integrity and reliability of these systems. Amidst the rapid evolution of IoT technology, safeguarding the confidentiality, integrity, and availability of data emerges as a top priority. This article underscores the significance of deploying robust cryptographic algorithms to fortify IoT devices against a myriad of potential threats. Effective evaluation of cryptographic solutions within IoT systems entails a comprehensive analysis and comparison of diverse algorithms, coupled with an assessment of their performance, resilience against attacks, and resource utilization. Central to evaluating the effectiveness of cryptographic solutions within IoT systems is a consideration of various factors including computational complexity, power consumption of devices, ease of implementation, and compatibility with existing infrastructures. This article reviews a number of cryptographic solutions including Rivest–Shamir–Adleman (RSA), El-Gamal, Paillier. These algorithms are implemented on the ATmega2560 microcontroller, which allows for a comprehensive assessment of key parameters such as efficiency in terms of encryption and decryption time, power consumption, and memory usage of IoT devices.
Volume: 37
Issue: 3
Page: 1596-1606
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

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

Low-noise amplifier with pre-distortion architecture for ultra-wide band application in radio frequency

10.11591/ijres.v14.i1.pp208-220
Pradeep Kumar Siddanna , Parameshachari Bidare Divakarachari
Ultra-wide band (UWB) is a wireless technology deployed for transmitting data at high rates over short distances. Similar to Wi-Fi and Bluetooth, UWB is a radio frequency (RF) technology that operates via radio waves. To remove minor noise and glitches, low noise amplifier (LNA) is required because it amplifies weak signals without significantly adding noise. However, UWB has multiple frequencies that require coefficient change due to frequency variations. When low-pass filter (LPF) is employed to solve this, updates are necessary to manage delay and power because the LPF feedback is connected to LNA to increase delay and power consumption. In this research, LNA with a pre-distortion architecture is proposed to remove minor noises and small glitches. It is processed by using pre-distortion as an active component which reduces delay and power consumption in UWB. The pre-distortion process operates in the subthreshold voltage range by providing a transistor to each frequency as input, inturn effectively reducing the noise. The proposed LNA with pre-distortion architecture is developed on 180-nm complementary metal-oxide semiconductor (CMOS) technology using Cadense ASIC tool. The proposed architecture achieves a noise figure (NF) of 2.16 dB and less power consumption of 43.06×10-6 W when compared to the existing techniques namely, cascade amplifiers, W-band LNA, reflectionless receiver (RX), and broadband RF receiver front-end circuits.
Volume: 14
Issue: 1
Page: 208-220
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
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