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Automation, computing and Renewable Systems (ICACRS), International Conference on

作者： B. Rubini S. Pradeep Kumar M. Suganiya Department of Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computing Technologies SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9781665460859

Recent population crises have increased pollution in urban areas due to garbage overflow. Garbage collection has recently become a significant issue in urban areas. However, collecting and segregating garbage is a complicated process. Because of population growth, every individual, animal, and bird should impact the environment. People use an excess of polythene bags and medical and chemical waste outside dustbins; they are not properly disposing of their waste. Because of the garbage system harms the environment, resulting in foul odors and unsanitary conditions in people's surroundings. As a result, the proposed system explains how to solve this problem using an intelligent dustbin monitoring system. On the top of the dustbin, ultrasonic sensors are to be installed to measure the fill level. When the dustbin overflows, the GSM modem is activated and connected to an Arduino Uno board with an ultrasonic sensor via a microcontroller-based platform. In this intelligent dustbin, the threshold level is high. When this level reaches ultrasonic sensors, activate, until the garbage collection dustbin is empty GSM Modem is continuously alerted to the appropriate authorities.

关键词： GSM Costs Ultrasonic variables measurement Urban areas Sociology Modems Acoustics

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Fingerspelling Classification for Robot Control

Fingerspelling Classification for Robot Control

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Kevin McCready Sonya Coleman Dermot Kerr Nazmul Siddique Emmett Kerr Yiannis Aloimonos Cornelia Fermüller School of Computing Engineering and Intelligent Systems Ulster University Londonderry Northern Ireland Department of Electronic and Mechanical Engineering Atlantic Technological University Letterkenny Ireland Institute for Advanced Computer Studies University of Maryland College Park Maryland USA

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Improvements to human-robot interaction methods could increase the ease of use of robots in manufacturing environments. Many of these environments are noisy and therefore preclude the use of audio communication between humans or in human-robot interactions. Therefore, this paper proposes using a gesture based communication system for robot control. To that end, the VGG16 and VGG19 convolutional neural network (CNN) structures are used for gesture classification along with 3 datasets of American Sign Language (ASL) fingerspelling images. The model performance is evaluated, and modifications made to their parameters to improve performance, before applying them to robot control tasks. The results show that with parameter tuning, test accuracies of up to, 100% are achievable.

关键词： Sign language Accuracy Computational modeling Robot control Human-robot interaction Convolutional neural networks Noise measurement Tuning Testing Residual neural networks

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Artificial Intelligence Techniques for the Performance Analysis of Indian Mutual Fund Industry and Market Trends

Artificial Intelligence Techniques for the Performance Analy...

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Mobile computing and Sustainable Informatics (ICMCSI), International Conference on

作者： S. Silvia Priscila M. Sakthivanitha M. Yogeshwari T. Shirley Devakirubai M. Mohamed Sirajudeen Department of Computer Science Bharath Institute of Higher Education and Research Tamil Nadu India Department of Information Technology VEL'S Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India Department of Data Analyst Saveetha Liberal Arts and Science College Chennai Tamil Nadu India Department of Commerce Madras Christian College Chennai Tamil Nadu India Department of Computer Science Nilgiri College of Arts and Science (Autonomous) The Nilgiris Tamil Nadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

A basic examination of a mutual fund's strategy (growth or value), median market cap, rolling returns, standard deviation, and maybe a portfolio breakdown by industry, geography, and other factors make up a typical mutual fund analysis. Mutual fund analysis typically involves calculating the amount of management contribution to the portfolio and ranking equities according to industry. Analyzing a mutual fund portfolio involves a few phases, one of which comprehends the fund's design. This study examine the basic mutual fund parameters and explore the use of artificial intelligence (AI) methods, including Data Mining, machine learning(ML), blockchain and predictive analytics in predicting market trends and assessing mutual fund performance. The purpose of this study is to give users a clear, concise, and professional overview of how to analyze the performance of mutual funds using AI Techniques

关键词： Industries Mutual funds Benchmark testing Market research Performance analysis Artificial intelligence Predictive analytics Portfolios Standards Investment

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A Novel Method for Reducing Risk with Optimized Anomaly Classifier 7

A Novel Method for Reducing Risk with Optimized Anomaly Clas...

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7th International Conference on advanced computing and Communication Systems, ICACCS 2021

作者： Kavitha, C. Jayakarthick, R. Nidhya, M.S. Madurai Kamaraj University College Dept of Computer Science Tamil Nadu Madurai India Vels Institute of Science Technology and Advanced Studies School of Computing Sciences Dept.of. Computer Science Tamil Nadu Chennai India PeriyarManiammai Institute of Science Technology Dept of Software Engineering TamilNadu Thanjavur India

ISBN: (纸本)9781665405201

Deciding on an uncertain event may lead to risk. Uncertainty occurs due to the lack of knowledge of a particular event or a situation. The only way to avoid this is to analyze the Risk. The risk analyzed properly will reduce the impacts to a great extent. Here the risk management dealt with the anomaly detection mechanism, which is carried out by clustering the data to find the outlier as the *** proposed method employs the PCA mechanism for dimensionality reduction which is further clustered with K means algorithm and classified with decision tree algorithm. © 2021 IEEE.

关键词： Uncertainty analysis

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Exploring light-cone distribution amplitudes from quantum computing

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science China(Physics,Mechanics & Astronomy) 2023年第8期66卷 57-65页

作者： Tianyin Li Xingyu Guo Wai Kin Lai Xiaohui Liu Enke Wang Hongxi Xing Dan-Bo Zhang Shi-Liang Zhu Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum MatterSouth China Normal UniversityGuangzhou 510006China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University.Guangzhou 510006China Department of Physics and Astronomy University of CaliforniaLos Angeles 90095USA Center of Advanced Quantum Studies Department of PhysicsBeijing Normal UniversityBejing 100875China Center for High Energy Physics Peking UniversityBeijing 100871China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Frontier Research Institute for PhysicsSouth China Normal UniversityGuangzhou 510006China Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials School of Physics and Telecommunication EngineeringSouth China Normal UniversityGuangzhou 510006China

Light-cone distribution amplitudes(LCDAs)are essential nonperturbative quantities for theoretical predictions of exclusive highenergy processes in quantum chromodynamics(QCD).We demonstrate the prospect of calculating LCDAs on a quantum computer by applying a recently proposed quantum algorithm,with staggered fermions,to the simulation of the LCDA in the(1+1)-dimensional Nambu-Jona-Lasinio(NJL)model on classical *** agreement between the result from the classical simulation of the quantum algorithm and that from exact diagonalization justifies the proposed quantum *** find that the resulting LCDA in the NJL model exhibits features shared with the LCDAs obtained from the QCD.

关键词： light-cone distribution amplitude quantum algorithm NJL model

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Optimizing Sentiment Analysis on Twitter for Improved Customer Insights: Integrating Bagged CNN and Flamingo Search

Optimizing Sentiment Analysis on Twitter for Improved Custom...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： C. Umarani Jyoti Metan Piyush Kumar Pareek Mahantesh Mathapati Department of Computer Science and Applications Christ Academy Institute for Advanced Studies Bengaluru India Department of ISE Atria Institute of Technology Bengaluru India Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India Department of CSE Amruta Institute of Engineering & Management Sciences Bengaluru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Twitter helps you understand public opinion by revealing what people think, feel, and believe. This study provides a framework for businesses, particularly food delivery companies, to analyse social media data competitively and gain actionable insights. A novel combination of Flamingo Search Optimisation (FSA) and a one-dimensional bagged CNN is applied to Twitter data to improve customer satisfaction research. Our method uses advanced machine learning to analyse and interpret consumer attitudes and trends using Twitter's massive, real-time customer feedback stream. We found that sentiment analysis can now better predict consumer preferences and satisfaction. The study suggests developing more advanced optimisation algorithms, cross-platform sentiment analysis, real-time processing, and natural language processing techniques. Average kappa score: 0.9941, sensitivity: 0.9969, specificity: 0.9972, accuracy: 0.9971, precision: 0.9973. This study prepares companies to improve customer engagement and respond quickly to digital consumer sentiments.

关键词： Sentiment analysis Accuracy Sensitivity Social networking (online) Blogs Customer satisfaction Companies Real-time systems Convolutional neural networks Optimization

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Video Frame Prediction Using Convolutional LSTM Encoder and Decoder

Video Frame Prediction Using Convolutional LSTM Encoder and ...

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Data, Computation and Communication (ICDCC), International Conference on

作者： G. Revathy Pavithra Jagadeesan R. Vajubunnisa Begum Vijay Anand P H. Jasmin S. Gayathri Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India Department of Computer Science and Engineering Karpaga vinayaga college of engineering and technology Chennai India Department of Electronics and Communication Science Justice Basheer Ahmed Sayeed College for Women Affiliated by University of Madras Teynampet Chennai Department of Artificial Intelligence and Data Science CMR Institute of Technology Bengaluru India Department of Computer Science and Engineering K. Ramakrishnan College of Technology Trichy Tamilnadu India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

With rapid growth of audiovisual content now spreading through social networking sites, chances of exposure towards violent and mature content by the youth are growing. This project addresses the risk of detection of such content using advanced video frame prediction techniques. We present here an encoder-decoder model of Convolutional LSTMs in the film for violent and obscene sequence detection. The spatiotemporal nature of video data encourages our algorithm to exploit accurate predictions of future frames from past content, which can be used in real time for dangerous material detection. The study relies on recent literature that highlights the psychological implications arising from the viewing of violent content by teenagers, thus making the need for efficient automated detection mechanisms ever more pertinent. Furthermore, we present a complete definition of “violence” for the standardized detection mechanisms and to cross-compare various studies. Using Violent Scene Detection, our results indicate high improvements in the detection accuracy and therefore become effective in this context for parental control and content moderation. Such results help safeguard children in the increasingly digital environment: traditional as well as currently ever-increasing.

关键词： Accuracy Social networking (online) Psychology Machine learning Streaming media Prediction algorithms Real-time systems Spatiotemporal phenomena Decoding Long short term memory

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A Comprehensive Review of Human-Centric AI, Regulatory Frameworks, and Their Role in Shaping Industry 5.0 6

A Comprehensive Review of Human-Centric AI, Regulatory Frame...

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6th International Conference on Futuristic Trends in Networks and computing Technologies, FTNCT 2024

作者： Misra, Sanjay Barik, Kousik Kvalvik, Petter Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Sciences Universidad de Alcalá Alcalá de Henares Madrid Spain JIS Institute of Advanced Studies and Research JIS University Kolkata India Institute for Energy Technology Halden Norway

This paper examines the core principles and design approaches that define Human-Centric AI (HCAI) and the enhancement of human-machine collaborations with aligning goals with Industry 5.0. Through a comprehensive analysis of existing studies, this paper systematically determines the key challenges of harmonizing HCAI principles with the regulatory demands of Industry 5.0, using a PRISMA chart to select relevant studies from four databases. The study highlighted the major global regulatory frameworks governing AI and how they address the ethical, legal, and social challenges HCAI poses. This work aims to inspire further exploration and development in assessing the potential benefits and challenges of integrating Human-Centric AI into Industry 5.0 by identifying research gaps and proposing future study areas. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Human engineering

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How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing 41

How Universal Polynomial Bases Enhance Spectral Graph Neural...

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41st International Conference on Machine Learning, ICML 2024

作者： Huang, Keke Wang, Yu Guang Li, Ming Liò, Pietro School of Computing National University of Singapore Singapore Institute of Natural Sciences School of Mathematical Sciences Zhangjiang Institute for Advanced Study Shanghai Jiao Tong University Shanghai China Shanghai AI Laboratory Shanghai China School of Mathematics and Statistics University of New South Wales Sydney Australia Zhejiang Institute of Optoelectronics Jinhua China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Department of Computer Science and Technology Cambridge University Cambridge United Kingdom

Spectral Graph Neural Networks (GNNs), alternatively known as graph filters, have gained increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian eigendecomposition for Fourier transform. In an attempt to avert prohibitive computations, numerous polynomial filters have been proposed. However, polynomials in the majority of these filters are predefined and remain fixed across different graphs, failing to accommodate the varying degrees of heterophily. Addressing this gap, we demystify the intrinsic correlation between the spectral property of desired polynomial bases and the heterophily degrees via thorough theoretical analyses. Subsequently, we develop a novel adaptive heterophily basis wherein the basis vectors mutually form angles reflecting the heterophily degree of the graph. We integrate this heterophily basis with the homophily basis to construct a universal polynomial basis UniBasis, which devises a polynomial filter-based graph neural network - UniFilter. It optimizes the convolution and propagation in GNN, thus effectively limiting over-smoothing and alleviating over-squashing. Our extensive experiments, conducted on datasets with a diverse range of heterophily, support the superiority of UniBasis in the universality but also its proficiency in graph explanation. Copyright 2024 by the author(s)

关键词： Wiener filtering

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Future Trends and Short-Review on Fish Species Classification Models Based on Deep Learning Approaches

Future Trends and Short-Review on Fish Species Classificatio...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： M. Bhanumathi B. Arthi Department of Computer Science and Engineering SRM Institute of Science and Technology Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies(VISTAS) Department of Computer Science and Engineering Faculty of Engineering and Technology College of Engineering and Technology SRM Institute of Science and Technology

ISBN: (纸本)9781665489638

An automatic fish species classification system is highly essential to track, classify and detect marine species and fishes in complex underwater backgrounds without any manual help. Existing computer vision-based approaches didn't offer effective performance rates underwater because the textural features and shape of fish species are not apparent. The data-driven classification approach needsa superior amount of labeled data;otherwise, they leadtooverfitting at the time of data training, and also, the unseen test data are not utilized for ***, researchers, taxonomists, and geneticists from different biological fields wished to accept fish as a significant element in their research. Theywere discouraged from finding ichthyology,a highly complex task. In fish-based studies, failing to recognize fishes as distinct biological units can lead to thewrong diagnosis. Hence, this survey paper tries to discuss and clarify the recent research on species identification with their algorithmic categorization. This review explores the performance measures compared, the number of fish families/species recognized, datasets used, and tools utilized for implementation. Further, future research directions and compensation for current research gaps are discussed. This review of state-of-the-art fish identification tools shows their potential for providing the right solution in real-life situations.

关键词： Deep learning Training Machine learning algorithms Shape Current measurement Manuals Fish

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