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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Rawat, Ayush Rawat, Vandana Singh, Neelam Kuchhal, Navya Barmola, Jigyasa Negi, Harendra Singh Graphic Era Deemed to Be University Department of Computer Application Dehradun India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350341737

YouTube has become the dominant online video platform, fueling demand for downloading content. In response, numerous third-party tools have emerged to enable downloading YouTube videos, though this violates their terms of service. This research presents the development and evaluation of a custom YouTube video downloader application in Python. The motivation is building an easy-to-use yet customizable solution for saving YouTube videos locally. The tool combines a graphical user interface using PySimpleGUI with the yt-dlp media extraction library. Key innovations include supporting flexible customization of download settings, bulk playlist/channel downloads, a modular architecture, cross-platform packaging, and open source release. Quantitative performance evaluations reveal efficient resource usage, throughput, and scalability when downloading high resolution videos in bulk. User studies highlight satisfaction with the extensive features compared to alternative download tools. However, increased access poses ethical considerations around copyright compliance and fair use versus abuse. While technical and usability goals are largely achieved, fostering responsible use relies on user discretion. Ongoing interdisciplinary research can help guide ethical innovation in this controversial domain. The main contributions are the functional open source tool itself, performance bench marking, user experience evaluations, analysis of legal/ethical impacts, and promotion of community enhancement. © 2023 IEEE.

关键词： Python

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Improved Particle Swarm optimization Task Scheduling Algorithm Based on Multiple Swarm Molecular Dynamics Theory 24

Improved Particle Swarm optimization Task Scheduling Algorit...

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5th International Conference on computer Information and Big Data applications, CIBDA 2024

作者： Liu, Zilai Yu, Liang Li, Qiqian College of Computer Science Guangdong University of Science and Technology Guangdong AIoT Application Innovation Joint Laboratory AIoT Edge Computing Engineering Technology Research Center Dongguan523000 China

ISBN: (纸本)9798400718106

In light of the objective to optimize task scheduling in Cyber-physical systems (CPS) by establishing an efficient mapping between system resources and user tasks, a novel particle swarm optimization task scheduling algorithm is proposed. This algorithm incorporates the principles of multiple swarm molecular dynamics theory, thereby enhancing its performance. By integrating molecular dynamics theory and KMTOA into the particle swarm optimization algorithm, the proposed approach aims to achieve improved task time span and load balancing capabilities. To achieve this, the algorithm initially divides the particle population into distinct sub-populations. Subsequently, different gravitational rules are applied to guide the evolution of particles in each sub-population. This division and rule implementation greatly enhance the diversity of the population in the early stages and enhance the local search ability in the later stages. Simulation and comparative experiments have been conducted to evaluate the effectiveness of the improved task scheduling algorithm. The results demonstrate that the algorithm exhibits superior task time span and load balancing capabilities, effectively enhancing the efficiency of resource mapping. © 2024 ACM.

关键词： Cyber Physical System

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Visual abstraction of dynamic network via improved multi-class blue noise sampling

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Frontiers of computer science 2023年第1期17卷 171-185页

作者： Yanni PENG Xiaoping FAN Rong CHEN Ziyao YU Shi LIU Yunpeng CHEN Ying ZHAO Fangfang ZHOU School of Computer Science and Engineering Central South UniversityChangsha 410083China Institute of Big Data Hunan University of Finance and EconomicsChangsha 410205China Rail Data Research and Application Key Laboratory of Hunan Province Changsha 410083China

Massive sequence view (MSV) is a classic timeline-based dynamic network visualization approach. However, it is vulnerable to visual clutter caused by overlapping edges, thereby leading to unexpected misunderstanding of time-varying trends of network communications. This study presents a new edge sampling algorithm called edge-based multi-class blue noise (E-MCBN) to reduce visual clutter in MSV. Our main idea is inspired by the multi-class blue noise (MCBN) sampling algorithm, commonly used in multi-class scatterplot decluttering. First, we take a node pair as an edge class, which can be regarded as an analogy to classes in multi-class scatterplots. Second, we propose two indicators, namely, class overlap and inter-class conflict degrees, to measure the overlapping degree and mutual exclusion, respectively, between edge classes. These indicators help construct the foundation of migrating the MCBN sampling from multi-class scatterplots to dynamic network samplings. Finally, we propose three strategies to accelerate MCBN sampling and a partitioning strategy to preserve local high-density edges in the MSV. The result shows that our approach can effectively reduce visual clutters and improve the readability of MSV. Moreover, our approach can also overcome the disadvantages of the MCBN sampling (i.e., long-running and failure to preserve local high-density communication areas in MSV). This study is the first that introduces MCBN sampling into a dynamic network sampling.

关键词： dynamic network visualization massive sequence view multi-class blue noise sampling visual abstraction

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Real-Time Heartbeat Sensing with Face Video using a Webcam and OpenCV

Real-Time Heartbeat Sensing with Face Video using a Webcam a...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Rawat, Vandana Pathak, Mukul Jain, Ishika Semwal, Nayan Jain, Neelam Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India

ISBN: (纸本)9798350323917

People's heart rates can be measured for a variety of purposes, including telemedicine, the Internet of Things (IoT), sports, security, etc. The traditional method of monitoring HR can, however, be challenging at times or it may not scale. This study offers a solution that can simultaneously assess the heart rates of various persons using live video broadcasts. By recording the color changes brought on by blood circulation in facial skin, heart rate can be measured. An indicator that correlates to heart rate is represented by the typical hue in the area of interest (ROI) selected on the face. A pulse frequency is frequently retrieved from this signal using signal processing. This method is reasonably accurate and stable. A webcam is used to simultaneously detect the faces of persons and measure their heart rates, yielding a Heart rate slip rate of 3-5 percent. Our solution uses object tracking along with face detection, which requires less computing resources and enables greater scaling. © 2023 IEEE.

关键词： Face recognition

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Research Trends in Supply Chain Management: A Machine Learning Perspective

Research Trends in Supply Chain Management: A Machine Learni...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Singh, Neelam Mittal, Varsha Rawat, Vandana Malik, Preeti Rana, Aayushi Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India

ISBN: (纸本)9798350323917

Machine Learning has been considered as one of the most sought-after technologies that plays a pivotal role in almost every area of research and development. It has been employed to multitude of areas including disease prediction, recommendation systems, online fraud detection and Supply Chain Management. For the last two decades research in machine learning (ML) and supply chain management (SCM) have gained a huge impetus. Previously there was insufficient data which is being replaced by huge data influx for supply chain management (SCM). With Big Data into picture entire SCM analysis process has been changed. SCM requires agile and efficient processes and techniques to handle these changing requirements. Machine Learning is identified as one of the leading disciplines to meet the purpose of SCM. Machine Learning techniques can prove beneficial to fulfill various goals of SCM like customer value creation, strategic planning, forecasting future requirements, competitive trends and financial strategies. This article provides a detailed usage of various machine learning techniques for various phase of SCM. Analysis of contributions and implications of machine learning to accomplish SCM goals is also identified. Latest research trends and challenges in the area of SCM and Machine Learning are also listed and discussed. This study aims at providing an inclusive assessment of ML applications in SCM. © 2023 IEEE.

关键词： Supply chain management

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AI-Powered Job Market Analysis: Transforming Workforce Trends and Employment Predictions 5

AI-Powered Job Market Analysis: Transforming Workforce Trend...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Purohit, Sanjana Shuja, Mirza Walia, Sahil Kumar, Mukesh Bajaj, Karan Bhardwaj, Vivek Jecrc University School of Computer Applications Rajasthan Jaipur India Lovely Professional University School of Computer Application Punjab Phagwara India Snowflake Inc. AtlantaGA United States Chandigarh Group of Colleges Chandigarh School of Business Department of Computer Application Punjab Mohali140307 India Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India Manipal University Department of Computer Science and Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350376685

This analysis delves into the "AI-Powered Job Market Insights"dataset, offering a comprehensive exploration of how artificial intelligence (AI) and automation are influencing the modern job market. The dataset provides a snapshot of job listings across various industries, highlighting factors such as AI adoption levels, automation risk, and salary ranges. By examining these elements, we aim to uncover key trends and insights into how AI is reshaping employment opportunities and job market dynamics. The primary objective of this analysis is to identify and understand key patterns related to salaries, required skills, remote work, and job market trends influenced by AI. By employing various analytical techniques, we seek to reveal how AI adoption impacts compensation, skill requirements, and job growth, as well as how it correlates with the likelihood of remote work. © 2024 IEEE.

关键词： Job analysis

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Parameters Research of Facial Emotion Detection Algorithm Based on Machine Learning

Parameters Research of Facial Emotion Detection Algorithm Ba...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Zhou, Ting Yadav, Amit Shi, Xiao Jiang Khan, Asif Chengdu Neusoft University Department of Computer Science and Technology Chengdu611844 China Charles Darwin University College of Engineering IT and Environment Australia Integral University Department of Computer Application Lucknow India

ISBN: (纸本)9798350361537

The purpose of emotional state recognition is to let computers have the ability to analyze and understand human emotions and intentions, and deeply analyze human psychological activities, so as to play a role in the fields of entertainment, education, intelligent medical treatment and so on. Different emotion recognition algorithms and different parameters of the same algorithm have different recognition effects. Among them, muscle-based feature model and 68 feature point calibration are two common face emotion recognition methods. The former uses deep learning to judge the emotional state by analyzing the movement of face muscles, while the latter calculates various feature parameters through the position relationship of 68 key points of the face, and then judges the emotional state. This paper mainly discusses the calibration method of 68 feature points. Through the use of two machine learning algorithms (KNN, SVM) and the study of different parameters in the algorithm, the influence of different parameters on the emotion recognition effect is compared and analyzed. The experiment proves that by detecting 68 key points of face, we can find the optimal parameter value in the current classification task. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Thyroid Disease Diagnosis Through Machine Learning: A Classification-Based Approach 5

Enhancing Thyroid Disease Diagnosis Through Machine Learning...

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5th International Conference on Sustainable Communication Networks and application, ICSCNA 2024

作者： Verma, Shanti Modi, Hetal Desai, Jaynesh H Rami, Khyati Lokmanya Institute of Management and Computer Application Gujarat Technological University Gujarat Ahmedabad India Bhagwan Mahavir College of Computer Application Bhagwan Mahavir University Gujarat Surat India Vidyadeep University Gujarat Surat India SAL Institute of Technology & Engineering Research Department of Computer Science Gujarat Ahmedabad India

ISBN: (纸本)9798331530013

Thyroid disease has recently adopted the related classification which has been worthy in the contemporary system of healthcare delivery because of the advances in diagnostics. This work proposes an automated diagnostic system for thyroid disease using machine learning approach to improve the diagnostic accuracy of the disease. The presented model also uses the features selection and classifiers like SVM and Random Forest for the classification of different thyroid disorders including hypothyroidism, hyperthyroidism, and euthyroidism. Missing values in the dataset are treated to provide actual worth in the results while imbalance is also dealt with. The process involves feeding its input a well-constructed data set after which it is checked for its performance through a validation process. The performance of the model is evaluated based on thyroid disease related parameters and the findings show a better performance compared to conventional diagnosis techniques. In this study, the authors also present their future work on how to possibly incorporate this machine learning framework into clinical practice although the study shows how this framework will help the healthcare workers to make better decisions in relation to thyroid diseases. © 2024 IEEE.

关键词： Adversarial machine learning

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PROMO: PROactive MObility-support model for task scheduling in fog computing

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International Journal of computers and applications 2022年第11期44卷 1092-1101页

作者： Kaur, Navjeet Kumar, Ashok Kumar, Rajesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Application Lovely Professional University Phagwara India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

In fog computing, closer proximity of fog devices to the end-user provides several benefits, such as reduced End-to-End (E2E) delay and better Quality of Service (QoS). Although, fog computing gives promising benefits of uninterrupted services to mobile users. The critical challenge is to decide when and where to re-schedule tasks according to the user's mobility. This paper targets this issue and proposes a Proactive Mobility-support (PROMO) model for re-scheduling tasks to mitigate the issues that influence QoS in mobility. The PROMO model is implemented and validated on MobFogSim, which significantly improves delay with 37% and around 17% downtime during task re-scheduling. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Fog computing

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A Multimodal Deep Learning Approach for Advancing Liver Disease Diagnosis and Prognosis Prediction 2

A Multimodal Deep Learning Approach for Advancing Liver Dise...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Kumar, Mukesh Shelke, Nitin Arvind Singh, Jagendra Sharma, Kamal Nain Sharma, Rajat Kumar, Rakesh The Technological Institute of Textile & Sciences Electronics and Communication Engineering Bhiwani India Bennett University School of Computer Science Engineering & Technology Greater Noida India Lovely Professional University School of Computer Application Jalandhar India G D Goenka University Department of Computer Science and Engineering - SOES Gurugram India Magadh University Department of Computer Science and I.T. Bodh Gaya India

ISBN: (纸本)9798350368109

Using a multimodal deep learning framework, we have demonstrated the possibility to predict liver disease subtyping and prognosis with high accuracy. The framework includes over 1600 patients, for which medical imaging and clinical data have been collected. Convolutional Neural Networks (CNNs), Recurrent Neural Network (RNNs), and Long short-term memory (LSTMs) have been combined in the work to extract features from the input data and demonstrate correlation between input and output and model complexity. Such an approach allowed analyzing images and predicting liver diseases based on structural abnormality analysis with the use of CNNs. RNNs and LSTMs, in their turn, allowed combining features for multimodal data source analysis and sequential clinical data analysis providing information about patient history. The obtained results were accurate for each feature extraction and modeling, with the best performance demonstrated by CNN with RNN where the accuracy was 97.8% and another CNN with LSTM where the accuracy was around 94.5%. The model proved to be feasible and accurate in the context of its application to hepatology since multiple data sources were combined in the work. This approach allows accounting for all modes of data source and offers a comprehensive assessment including disease diagnosis and prognosis. The obtained results can be also applied to clinical practice where doctors can add data about their patients to receive more information on diagnosis or prognosis for better decision-making. Finally, the idea to use a multilayered feature from multimodal data sources can be transferable to other types of sequential and structural data and applied to other diseases for their diagnosis and prognosis. However, it is essential to test the framework in other settings to refine it or find limitations to its application. © 2024 IEEE.

关键词： Convolutional neural networks

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