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Triple voting: hybrid cardiovascular diseases prediction model

International Journal of Applied Decision Sciences 2025年第3期18卷 257-281页

作者： Solomon, Dahlak Daniel Kumar, Karan Sonia Kanwar, Kushal Polat, Kemal Yogananda School of AI Computers and Data Science Shoolini University Himachal Pradesh Solan India Mullana Ambala133207 India Department of Computer Science and Engineering and Information Technology Jaypee University of Information and Technology Himachal Pradish Solan India Department of Electrical and Electronics Engineering Bolu Abant Izzet Baysal University Bolu Turkey

Currently, cardiovascular diseases are a high-risk cause of death in both developed and developing countries. Thus, heart disease prognosis has received substantial interest in the medical field worldwide. The incidence of heart disorders is escalating at an alarming rate, and it is crucial and worrisome to anticipate their occurrence. Predicting and detecting cardiovascular disease using machine learning and data mining might be clinically useful, but difficult. There are numerous machine learning algorithms accessible, several studies have developed machine learning algorithms for early cardiac disease prediction to help physicians suggest medical treatments. The accuracy of the model will be evaluated to determine whether the performance of the model is accurate or not. Seven machine learning methods are compared in this study, with the data obtained from the UCI Laboratory’s cardiovascular patient database. In essence, this research presents a majority voting-based hybrid model which is called triple voting. The hybrid model uses voting of Naïve Bayes, logistic regression (LR) and support vector machines (SVM) experimental outcomes show the proposed triple voting model’s accuracy is 89%, which is higher than the individual models and other proposed hybrid models. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Pulmonary diseases

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FPGA Implementation of XOR-MUX based Full Adder and Truncated Multiplier for Signal Processing Applications 15

FPGA Implementation of XOR-MUX based Full Adder and Truncate...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pavithara, P. Raghapriya, N.R. Dinesh, P.M. Gowtham, S. Viji, D. Kavin Kumar, K. Chandrasekaran, Gokul Department of Electronics and Communication Engineering Kongu Engineering College Perundurai Erode638060 India Department of Computer Science and Engineering Erode Sengunthar Engineering College Thuduppathi638057 India Department of Electronics and Communication Engineering Sona College of Technology Salem636005 India Department of Electrical and Electronics Engineering KSR College of Engineering Tiruchengode637215 India Department of Artificial Intelligence and Data Science Erode Sengunthar Engineering College Thuduppathi638057 India Department of Electronics and Communication Engineering Karpagam Institute of Technology Coimbatore India

ISBN: (纸本)9798350370249

There has been considerable advancement in the digital world, particularly in image processing applications, in recent years. The most important requirements are area efficiency, minimal delay, low power consumption, and fast speed. Due to the arithmetic operations of adders and multipliers, digital devices are prone to have increase in the size of logic because they take up more space in VLSI implementation. The proposed method of XOR-MUX based full adder and truncated multiplier concentrates on reducing the delay and area. Replacing normal multipliers with truncated multipliers computes n bit output instead of 2n bit outputs that is, the MSB bit of the truncated multiplier are retained whereas the LSB bits are truncated thereby preserving the information. This is suitable for certain applications of FIR filters that do not concentrate on lower bit length. In this project, the conventional full adders are replaced by XOR-MUX based full adders and are utilized in truncated multipliers which results in area reduction by 1 2 . 6 6 % and delay reduction by 11.68 % with a modest increase in power consumption. This reduction in truncated multipliers contributes towards the design of high performance FIR filters in signal processing applications. ©2024 IEEE.

关键词： FIR filters

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Design and Evaluation of Generative Adversarial Network for Automated Image Colorization System 5

Design and Evaluation of Generative Adversarial Network for ...

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5th International Conference on Smart electronics and Communication, ICOSEC 2024

作者： Vidyalakshmi, R. Gayathri, N. Aarthi, M.S. Kathirvel, N. Bhuvaneshvari, K. Sivaramkrishnan, M. Karpagam College of Engineering Department of Electrical and Electronics Engineering Coimbatore India Sri Eshwar College of Engineering Department of Computer Science Engineering Coimbatore India P.A. College of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Information Technology Chennai India

ISBN: (纸本)9798331504403

For the preservation of historical materials and the enhancement of visual representation, image colorization is crucial. Image colorization adds color to black-and-white or grayscale images, revitalizing old photographs and papers. It affords us a more immersive and relatable experience of history. Additionally, colorization imparts realism and liveliness to visual content. It facilitates audience engagement, emotional expression, and the development of visually captivating content that elicits a response and imparts a memory. The process of image colorization utilizing Deep Learning (DL) models is investigated in this study. The pre-processing techniques utilized to ensure data integrity and consistency are sourced from Kaggle. The image colorization challenge is built using DL models, notably Generative Adversarial Networks (GANs) and Convolutional Autoencoders (CAEs). These algorithms learn to colorize grayscale images through the use of training data. Peak Signal-to-Noise Ratio (PSNR), Learned Perceptual Image Patch Similarity (LPIPS), Structural Similarity Index (SSIM), and Fréchet Inception Distance (FID) are some of the metrics utilized to assess the models. The evaluation results indicate that the GAN model exhibits superior performance in image colorization compared to the CAE model. This is supported by higher PSNR and SSIM values, as well as lower values for LPIPS and FID. © 2024 IEEE.

关键词： Generative adversarial networks

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Comparative Analysis of Machine Learning Algorithms for Transmission Line Fault Detection 10

Comparative Analysis of Machine Learning Algorithms for Tran...

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10th IEEE International Conference on Power Systems, ICPS 2023

作者： Jyoti, Oishi Hossain, Md. Munawar Nahid, Emama Ismail Siddique, Md. Abu Rajshahi University of Engineering & Technology Department of Electrical & Computer Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350318739

Unforeseen failures in the electrical power transmission line may arise due to various unpredictable factors. The occurrence of power failures on transmission lines has the potential to cause significant damage to the existing power grid unless prompt detection and correction of faults are carried out. The primary objective of this paper is to address the detection and classification of faults occurring on electrical power transmission lines. Specifically, the study focuses on a dataset consisting of 12,000 data points, each characterized by six distinct features. To achieve this, artificial neural networks are employed as the chosen methodology. The classification of transmission line faults was performed using various machine learning algorithms, including Linear Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, AdaBoost, Bagging Classifier, and XGBoost. The algorithms were evaluated based on parameters including accuracy, error rate, prediction speed, and training time. The findings suggest that tree-based classifiers have exhibited superior performance, achieving 100% accuracy in the classification of transmission line faults. On the other hand, Logistic Regression and Support Vector Machines (SVM) have yielded average results in this regard. © 2023 IEEE.

关键词： Decision trees

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Open-Source Gesture-Powered Augmented Reality-Based Remote Assistance Tool for Industrial Application: Challenges and Improvisation 3rd

Open-Source Gesture-Powered Augmented Reality-Based Remote A...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Sharma, Chitra Sharma, Kanika Kumar, Manni Garg, Pardeep Goyal, Nitin Department of Electronics and Communication Engineering National Institute of Technical Teachers Training and Research Chandigarh India Department of Computer Science and Engineering Chandigarh University Punjab Mohali India Department of Electronics and Communication Engineering Jaypee University of Information Technology Himachal Pradesh Waknaghat Solan India Department of Computer Science and Engineering School of Engineering and Technology Central University of Haryana Haryana Mahendragarh123031 India

ISBN: (纸本)9789819986453

In this paper, development theory and procedure are to be presented to inspect and enhance the pre-existing gesture interaction with mobile Augmented Reality applications, as well as the application of gesture-enabled, augmented reality systems in the industry. The testing of the prototype has been done by utilizing the standard manipulation tasks that are typical in the Augmented Reality context. Also, a comparative analysis is performed with the previously existing gesture-based and touch-based input techniques used in various Augmented Reality applications. The task performance data has been evaluated on the basis of in-person user feedback. When tested, the gesture input methods introduced via the current research were found to be better than the existing interaction techniques. Most of the participants preferred to use the gesture interaction methodology explained within the current research paper for further Augmented Reality based research and development work. Quick response time and a straightforward approach to implementation were some of the most common feedback obtained by the user. The research paper mainly explains the thorough notion behind current and upcoming gesture-based interaction technologies. Also, a brief discussion targeted toward the future implications of this research work has been attached herewith for later reference and usage. The limitations and the obstacles in the pathway of the implementation of augmented reality in industries have been discussed within this research paper and attempts have been made to minimize or eliminate the drawbacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Augmented reality

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Design of Hemispherical Patch Antenna with Conformally Spaced Log-Periodic Elements for Multi-Directional and Multi-Band Operation

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Progress In Electromagnetics Research C 2022年 118卷 187-198页

作者： Prasad, Venkatesh Nuthan Gowda, Eshwar Dhanush Indira, Krishnappa Kodukula, Ananya Arora, Diksha Department of Electronics and Communication Engineering Ramaiah Institute of Technology Bangalore India Robot Bosch India Department of Computer Science and Engineering Ramaiah Institute of Technology India Department of Mechanical Engineering Ramaiah Institute of Technology India

In this paper, the design of an Ultra Wide Band (UWB) hemispherical antenna with Log-Periodic Elements (LPEs) capable of operating at multiple resonating frequencies lying in L, S, C, X, and Ku frequency bands is presented. The design consists of a complex structure of silver hemisphere with LPE mounted on an FR-4 substrate fed by a 50 Ω microstrip line. The dependency of the inclination of log-periodic elements mounted on the hemisphere is analyzed with parametric study. The proposed miniaturized antenna uses LPEs to obtain an impedance bandwidth of above 100% and a multi-directional radiation pattern. The measured results show that a wide operating band of 12.63 GHz (1.68 GHz–14.31 GHz) (8.52: 1) has been achieved with a multi-directional radiation pattern with a peak realized gain of 8.12 dBi. © 2022, Electromagnetics Academy. All rights reserved.

关键词： Kurchatovium

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An Innovative Method to Detect Cyberbullying on Social Media 3

An Innovative Method to Detect Cyberbullying on Social Media

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3rd International Conference on electronics and Renewable Systems, ICEARS 2025

作者： Pandiaraja, P. Muthumanickam, K. Geetha, M. Mahesh, P.C. Senthil Vedhashree, S.K. Selvarathi, C. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai600062 India Kongunadu College of Engineering and Technology Department of Information Technology Tholurpatti Tamil Nadu Trichy621215 India M.Kumarasamy College of Engineering Department of Computer Science and Engineering Karur639113 India Excel Engineering College Department of Computer Science and Engineering Tamilnadu Komarapalayam637303 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore641021 India

ISBN: (纸本)9798331509675

Today social media has fundamentally altered global communication and information exchange. However, as these platforms have become more widely used, so has cyber-hatred which is a significant problem that has caught academics' attention. This article has been addressed by a variety of methods from deep learning and machine learning, including recurrent neural networks, convolutional neural networks, logistic regression, and Naive Bayes. These techniques distinguish between various classes using mathematical concepts. However, sentiment-oriented data offers an accurate picture of how the public reads communications online, accurate classification necessitates a more "critical thinking"perspective. Two machine learning classifiers were employed in this study, one of which was built on a review of the literature to look at efficient classification techniques. Four online hatred datasets using logistic regression and multinomial naive Bayes. To better grasp the text in the datasets, the classifier results were enhanced by using XG-Boost algorithm. © 2025 IEEE.

关键词： Adversarial machine learning

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Squeeze and Excitation Attention Meets Modified EfficientNetB0 Architecture: Multi-Class Brain Tumor Classification Using Explainable Artifical Intelligence 26

Squeeze and Excitation Attention Meets Modified EfficientNet...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Shakin, Md. Musab Us Saber Akter, Farzana Hasan, S. M. Mahedy Hossain, Md. Rakib Srizon, Azmain Yakin Islam, Rifatul Akter, Sheikh Shraboni Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350359015

Brain tumors, spanning a spectrum from benign to malignant, pose a formidable medical challenge due to their potential to disrupt critical brain functions. Traditional diagnostic methods, relying on human interpretation of MRI images, frequently suffer from accuracy and efficiency limitations. Despite promising research, it encounters challenges in feature extraction and model interpretability. In this study, an innovative approach to brain tumor classification was introduced. A modified variant of the EfficientNetB0 architecture was leveraged, and reinforced by a Squeeze and Excitation Attention (SEA) block. The upper layers of the original EfficientNetB0 architecture were fine-tuned and customized to suit the specific investigation. Subsequently, the tailored EfficientNetB0 was employed to extract features and seamlessly integrate the SEA block. This integration amplified the emphasis on pertinent features while dampening irrelevant ones. The approach underwent a comprehensive empirical assessment on a combined dataset, yielding a remarkable accuracy rate of 99.70%, surpassing other state-of-the-art methods. Moreover, Grad-CAM visualization was employed to precisely pinpoint areas within the images that exerted a substantial influence on the model's predictions. © 2023 IEEE.

关键词： Brain MRI Images Brain Tumor Classification Squeeze and Excitation Attention Transfer Learning

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Leveraging Deep Learning Methodologies in Data Science Practices: A Systematic analysis 15

Leveraging Deep Learning Methodologies in Data Science Pract...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Gupta, Eesha Senthilkumar, K.K. Gaikwad, Vidya S. Nagpal, Manish Singh, Akhilendra Pratap Sharma, Sonal Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Department of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

This paper proposes a systematic analysis of using deep mastering methodologies in statistics technology practices for the cause of leveraging the strength of deep mastering algorithms. It presents an overview of the exceptional types of deep gaining knowledge of methodologies and architectures in addition to their application within the various areas of records technology. Similarly, it critiques the prevailing literature and famous several useful findings regarding the usage of deep mastering in facts science and also the potential demanding situations and possibilities that could come in conjunction with it. It details the coding and implementation of numerous deep mastering fashions such as convolutional neural networks, deep belief networks, recurrent neural networks, and lengthy quick term memory (LSTM). Additionally, it presents an in-intensity analysis of the professionals and cons of the usage of deep gaining knowledge of for information technology. Eventually, it gives guidelines on a way to effectively use deep studying in facts technological know-how. © 2024 IEEE.

关键词： Fashions Guidelines Mastering Networks Technological

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ENERGY-EFFICIENT ANT ROUTING ALGORITHM FOR OPTIMIZED PATH SELECTION NETWORK LONGEVITY IN WIRELESS SENSOR NETWORKS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第2期84卷 21-42页

作者： Zelani, Shaik Khader Babu, D. Nagabhushana Surendra, Doddaga Rahman, Mohammad Zia Ur Department of Electronics and Communication Engineering Sri Vasavi Institute of Engineering and Technology-Autonomous Pedana India Department of Data Science Malla Reddy University Telangana Hyderabad India Department of Computer Science and Applications Koneru Lakshmaiah Education Foundation K L Deemed to be University Vaddeswaram Andhra Pradesh India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation K L Deemed to be University Vaddeswaram Andhra Pradesh India

The exponential growth of wireless sensor networks (WSNs) in various applications underscores the critical need for energy-efficient routing protocols to extend network lifespan. This paper introduces the Energy-Efficient Ant Routing Algorithm, a novel bioinspired routing strategy derived from ant colony optimization (ACO), which integrates traditional pheromone mechanisms with dynamic energy-aware adaptations to prioritize routes through nodes with higher residual energy levels, thereby balancing load and prolonging network functionality. The model’s adaptive pheromone updating mechanism, which considers both the cumulative path energy cost and the minimum residual energy of nodes along the path, encourages the use of energy-efficient routes and mitigates rapid energy depletion in critical network areas. This approach not only extends operational network lifetime but also supports higher data reliability and throughput in challenging WSN scenarios. The results of the proposed model demonstrated significant improvements over traditional ACO and other common energy-aware routing protocols. The network lifespan is extended by ~ 25% compared to standard ACO and by 15% compared to the best-performing conventional energy-aware routing protocol under the same conditions. Furthermore, EEARA achieved a 20% reduction in average energy consumption per node and enhanced the data delivery ratio by 10% due to more stable routing paths. The results underline the model’s potential as a scalable, efficient routing solution for energy-limited networks, promising substantial improvements in sustainable network operations. © 2025 by Begell House, Inc.

关键词： Ant colony optimization

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