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Informatica (Slovenia) 2025年第2期49卷 375-396页

作者： Kumar, Guru Dayal Tyagi, Shekhar Pradhan, Kalandi Charan Shah, Akshat Migration and Development Research Group School of Humanities and Social Sciences Indian Institute of Technology Indore452020 India Department of Computer Science and Engineering Indian Institute of Technology Indore452020 India Department of Computer Science and Engineering Shri Ramdeobaba College of Engineering and Management Nagpur440001 India

This research presents a novel methodology for cloudburst forecasting using the XGBoost (Extreme Gradient Boosting) machine learning algorithm. With the escalating impact of climate change, accurately predicting extreme weather events like cloudbursts is crucial due to their potential to trigger floods. Cloudburst events were identified from daily rainfall data. Our study leverages historical weather data, focusing on intricate rainfall patterns, to forecast future cloudburst occurrences. Comparative analysis against Random Forest and LSTM models confirmed XGBoost’s effectiveness, consistently outperforming alternatives across multiple performance metrics. The XGBoost model, known for its ability to handle complex datasets, demonstrated strong predictive performance, with an RMSE of 0.12 and an MAE of 0.09, indicating high accuracy. This research provides a reliable tool for advanced weather forecasting and early warning systems, offering valuable support to policymakers, disaster management teams, and agricultural planners in mitigating risks associated with extreme rainfall events. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Acid rain

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Computational Analysis of Surface Pressure Distribution over a 2D Wedge in the Supersonic and Hypersonic Flow Regimes

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Fluid Dynamics & Materials Processing 2023年第6期19卷 1637-1653页

作者： Javed S.Shaikh Krishna Kumar Khizar A.Pathan Sher A.Khan MIT School of Engineering MIT ADT UniversityPune412201India Trinity College of Engineering and Research Pune411048India Department of Mechanical Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia

The complex fluid-dynamic instabilities and shock waves occurring along the surface of a two-dimensional wedge at high values of the Mach number are studied here through numerical solution of the governing ***,a regression model is implemented to determine the pressure distribution for various Mach numbers and angles of *** Mach number spans the interval from 1.5 to *** wedge angles(θ)are from 5°to 25°.The pressure ratio(P2/P1)is reported at various locations(x/L)along the 2D *** results of the numerical simulations are compared with the regression model showing good agreement.

关键词： CFD analysis supersonic wedge angle Mach number

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An Intelligent Blockchain-Oriented Digital Voting System Using NEAR Protocol

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SN computer Science 2023年第5期4卷 643页

作者： Snehlata Shukla, Pallavi Singh, Ashutosh Kumar Tiwari, Saloni Rishabh Dwivedi, Vijay Kumar Department of Computer Science and Engineering United College of Engineering & Research UP Prayagraj 211010 India

According to today’s social environment, implementing a secure digital system is a challenging task;a traditional voting system (i.e., ballot system) does not offer ambiguity regarding the counting of electoral votes. Voting frauds may fail because of many reasons like pooling booth fraud, and fake voter IDs, so to solve this problem, we need a secured digital voting system, and the best solution implemented is Blockchain. This technique conquers all the problems of the traditional voting system. And this technology is used to provide a highly secure, decentralized, and provide P2P network to verify, process, and record all persons. This opens all possibilities to construct a secure network as well as a decentralized system that does not need third-party involvement. To the best of our knowledge, this is the first attempt to implement blockchain NEAR protocol for digital voting systems. In this paper, our main aim is to build a secure digital voting system based on this blockchain protocol which offers the privacy of a person, security, flexibility, and fairness of the current voting scheme. It also decreases the cost of hosting a countrywide election. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Digital voting NEAR protocol Security

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Strategies for Effective EEG Preprocessing 22

Strategies for Effective EEG Preprocessing

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22nd International Learning and Technology Conference, L and T 2025

作者： Salem, Nema Alamoudi, Nora Mustafa, Lama Bagais, Fatima Effat University Electrical Research Laboratory College of Engineering Jeddah Saudi Arabia Effat University College of Engineering Department of Electrical and Computer Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331532215

The administration of anesthesia is critical for ensuring the safety of surgical procedures, yet it comes with inherent risks associated with medication mistakes. Monitoring EEG signals during anesthesia is crucial for understanding patient physiology;however, the accuracy of this information heavily relies on effectively eliminating noise. Decreasing noise in EEG signals is vital for accurately interpreting data, ultimately enhancing patient safety and refining anesthesia management. This research concentrates on an advanced preprocessing technique for EEG signals, employing segmentation, detrending, variational mode decomposition (VMD), and noise reduction through Gaussian filtering, moving averages, Independent Component Analysis (ICA), and Discrete Wavelet Transform (DWT). The study utilized EEG data collected during surgeries and applied these preprocessing approaches to reduce noise and artifacts. Our results indicate that employing the Discrete Wavelet Transform (DWT) for denoising is robust and produces superior outcomes compared to other techniques investigated, achieving a high Signal-To-Noise Ratio (SNR) of 43.23 dB. © 2025 IEEE.

关键词： Anesthesia Discrete Wavelet Transform EEG preprocessing Gaussian Independent Component Analysis Moving average Noise removal SDG 17

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Life Expectancy Prediction using Machine Learning Technique - KNN 3

Life Expectancy Prediction using Machine Learning Technique ...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Narooka, Preeti Vashistha, Aarushi Mitra, Priyanka Derashri, Gulisha School of Computer Science Engineering Manipal University Department of Artificial Intelligence and Machine Learning Rajasthan Jaipur303007 India Jaipur Engineering College and Research Centre Department of Computer Science and Engineering Jaipur303905 India Jaipur Engineering College and Research Centre Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350384598

In the modern world, life expectancy turns out to be the most valuable factor in determining population's health. Today, it is more crucial to predict the future because life is more precarious due to a variety of circumstances, including social, economic, environmental, educational, lifestyle, and health conditions. The machine learning algorithm KNN for life expectancy is presented in this work. The dataset gathered for this study is separated into a training set (70%) and a test set (30%), and the forecasts precision is examined. To avoid misclassification, KNN needs a larger sample size, and the best K are chosen more in accordance with cross-validation experience than with strong statistical theory. © 2024 IEEE.

关键词： Adversarial machine learning

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Dermo classify: A dermatologist skin disease detection and classification using DCNN

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research on Biomedical engineering 2025年第1期41卷 1-18页

作者： Sudar, K. Muthamil Nagaraj, P. Muneeswaran, V. Panda, Baidyanath Bhoi, Akash Kumar Department of Computer Science and Engineering Mepco Schlenk Engineering College Tamilnadu Sivakasi India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Krishnankoil India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Virudhunagar Krishnankoil India LTIMindtree 1 American Row 3 Floor HartfordCT06103 United States Directorate of Research Sikkim Manipal University Gangtok Sikkim737102 India

Purpose: In current circumstances, investigators who are doing exploration in various regions remote from their old neighborhood are experiencing different skin issues like dermatitis, dry skin, and psoriasis because of environment or weather condition changes, food sensitivities, and water changes. This results in itching and dry patching on their skin. An affected individual should investigate and analyze the skin sickness, take appropriate precautions to keep the illness from spreading and seek therapy from a skin expert as quickly as time permits. Since the investigators come from different parts of the country, they are unfamiliar with hospitals and places, so they avoid such skin diseases without realizing the severity of the sickness. To address this, we propose a computer-aided system that detects and classifies skin diseases early, helping individuals to identify their condition and seek appropriate treatment quickly. Methods: To detect skin disease at an early stage, this work epitomizes a strategy using a computer-aided model according to sophisticated deep learning neural network techniques such as convolutional neural network (CNN), Residual neural networks, and Xception neural network to detect skin disease. We use the HAM-10000 data set, which contains data on seven different types of skin diseases. Results: The CNN model produced the most significant performance accuracy of 98.3718% in the experiments. Conclusion: The paper presents a promising approach for skin disease detection and classification using DCNNs. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2024.

关键词： Convolutional neural networks

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Effectiveness of Advanced ECG Noise Reduction Filters 22

Effectiveness of Advanced ECG Noise Reduction Filters

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22nd International Learning and Technology Conference, L and T 2025

作者： Salem, Nema Jamjoom, Jude Alzahrani, Maria Qashqari, Maha Effat University Electrical Engineering Research Laboratory Effat College of Engineering Jeddah Saudi Arabia Effat University Effat College of Engineering Electrical and Computer Engineering Department Jeddah Saudi Arabia

ISBN: (纸本)9798331532215

The study examined various filtering methods applied to ECG signals;the MIT-BIH Arrhythmia dataset included two channels to reduce noise and artifacts while maintaining critical waveform characteristics. Initial filtering included a notch filter and a bandpass filter. Advanced filters such as Savitzky-Golay, Wavelet, Kalman, Wiener, and combined Wiener-Golay were used for noise reduction. Results showed that the Kalman filter performed best with an average SNR of 21.40 dB, while the Wavelet method worked well on channel 2. The Wiener filter was less effective due to its limited adaptability to changing noise. The combined Wiener-Savitzky-Golay filter showed a balanced performance but did not outperform the Kalman filter. Visual inspections of ECG signals from five patients revealed that the Kalman and Wavelet filters successfully maintained crucial morphological characteristics. The Savitzky-Golay filter offered effective smoothing but sometimes dampened the QRS complex, while the Wiener filter introduced high-frequency artifacts. © 2025 IEEE.

关键词： Wiener filtering

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ViT-SENet-Tom: machine learning-based novel hybrid squeeze–excitation network and vision transformer framework for tomato fruits classification

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Neural Computing and Applications 2025年第9期37卷 6583-6600页

作者： Swapno, S M Masfequier Rahman Nobel, S. M. Nuruzzaman Islam, Md Babul Bhattacharya, Pronaya Mattar, Ebrahim A. Department of CSE Bangladesh University of Business and Technology Dhaka1216 Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Department of Computer Science and Engineering Amity School of Engineering and Technology and Research and Innovation Cell Amity University West Bengal Kolkata India Robotics - Cybernetic College of Engineering University of Bahrain Manama Bahrain

Tomatoes are essential fruits in numerous nations for their vast demand. It is very important to maintain the freshness of tomatoes. One of the primary challenges in the recent culinary landscape is accurately identifying healthy tomatoes while effectively eliminating damaged or rejected ones. Existing approaches employ various strategies for categorizing tomato fruit, but they often suffer from inaccuracies, slow detection, and suboptimal performance. Thus, motivated by this gap, in this paper, we propose a novel machine learning (ML) framework, ViT-SENet-Tom, which is a hybrid vision transformer (ViT) model with squeeze and excitation (SENet) block network for fast, accurate, and efficient tomato fruit classification. The framework works on three tomato classes, respectively, the ripe, unripe, and reject. In developing the proposed model, we utilized advanced and newly designed layers and functions. This integration created a more complex and sophisticated neural network, significantly enhancing efficiency and contributing to the model’s novelty. Our chosen dataset was small initially, but we implemented augmentation techniques to increase its size. This approach made our system more reliable, efficient, and effective. The hybrid ViT-SENet framework employs encoders and self-attention networks with squeeze and excitation channel functions to allow precise, robust, fast, and efficient tomato classification. In simulation, the framework achieves a training accuracy of 99.87% and validation accuracy of 93.87%, indicating the precise classification of tomatoes. Besides, this work tests accuracy using fivefold cross-validation. The highest accuracy seen at fold-5 is 99.90%. These testing results demonstrate the efficacy of the proposed framework in real-deployment scenarios. The implementation has the potential to provide enhanced and more sustainable food security and safety in future. © The Author(s) 2025.

关键词： Fruits

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A Novel Approach to Improving Distributed Deep Neural Networks over Cloud Computing

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第17期17卷 94-107页

作者： Ali, Muhtada Zuhair Muttair, Karrar Shakir Department of Computer Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Nanotechnology and Advanced Materials Research Unit Faculty of Engineering University of Kufa Najaf Iraq

In recent years, deep distributed neural networks (DDNNs) and neural networks (NN) have excelled in an extensive list of applications. For example, deep convolutional neural networks (DCNNs) are constantly gaining new features in various tasks in computer vision. At the same time, the number of end devices, including Internet of Things (IoT) devices has increased prominently. These devices are attractive targets for machine learning applications because they are often directly connected to sensors. For example (cameras, microphones, and gyroscopes) that record large amounts of input data in a stream mode. This study presents the design of a DDNN with end devices, edges, and clouds that spans computer hierarchies. The idea presented is considered one of the new ideas because it depends on two layers to distinguish, namely the convolutional layer and the pooling layer. The main objective behind using these two layers in one proposal is to provide and obtain the best results. Finally, we discovered that the proposed technique produced the best results in terms of accuracy and cost, with the precision of the definition reaching 99 % and the cost being quite affordable at 25. As a result, we conclude that these results are far superior to those achieved by the researchers in their ideas provided in previous recent literature. © 2023 by the authors of this article. Published under CC-BY.

关键词： Cloud computing

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A Comprehensive Survey of Cybercrime and Cybersecurity 5

A Comprehensive Survey of Cybercrime and Cybersecurity

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5th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Shindagi, Kunaal Shirish Koppad, Khushi Vikram Jayakkanavar, Abhilasha Gorabal, S.V. Jain College of Engineering and Research Department of Computer Science and Engineering Belagavi India Jain College of Engineering and Research Department of Mechanical and Engineering Belagavi India

ISBN: (纸本)9798350305449

In the futuristic context, 'cybercrime' has become more hazardous to the nation's youth because felons and cybercrime victims are reported worldwide with false geographic information. any person in any area with a region is susceptible to a cyber threat, according to the futuristic scenario because protecting data has become one of the largest concerns in the modern world, cybersecurity is essential in the field of information technology. Cyber security is crucial to protect businesses and individuals from spammers and cyber culprits because one security branch Can make the private information of millions of people available. These branches can lead to a loss of client trust and have a single financial impact on the business. Cybercrime is expanding daily and it is the first thing that comes to mind when we think of cybersecurity. It is crucial to be aware of the threat that information technology (IT) poses and how to defend against it. Knowing what cyber security is and being able to use it effectively are essential in the modern world, which is run almost entirely by information technology. Cyber security seeks to shield users from harm, secure their safety, and safeguard personal information while they browse the internet. This research aims the acknowledge the extent of awareness among youth about cybercrime and cybersecurity. © 2023 IEEE.

关键词： Cybersecurity

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