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检索条件"任意字段=7th IASTED International Conference on Advances in Computer Science and Technology"

共 1426 条记录，以下是61-70 订阅

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A Study on Integrating Machine Learning Techniques for Waste Management 7

A Study on Integrating Machine Learning Techniques for Waste...

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7th international conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Vaidya, Ruchika Dattangire, Rahul Biradar, Divya Verma, Prateek Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Data Engineering Publicis Sapient HoustonTX77002 United States University of Texas Department of Computer Science ArlingtonTX76019 United States

ISBN: (纸本)9798350372816

the current study focuses on how various Machine Learning (ML) algorithms can be used in waste management and emphasizes the significance of intelligent technologies in addressing contemporary issues. By leading a broad writing survey, the paper features the meaning of incorporating these advances into waste management practices. the system segment subtleties the information assortment, preprocessing, model determination, and assessment measurements utilized in the review. the accuracy and performance metrics of various classifiers, including K Nearest Neighbors, Linear Support Vector Classifier, Logistic Regression, Decision Tree, Multi-Layer Perceptron, and Random Forest, are demonstrated by the results of experiments with them. Especially noteworthy is the MLP classifier's 91.81% accuracy. the discoveries highlight the viability of ML approaches in ssmart waste management. In addition, a comparison table that summarizes the classifiers' accuracy scores provides insight into their suitability for applications in waste management. © 2024 IEEE.

关键词： Adversarial machine learning

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Key Analysis of Integer Factorizing based Public-Key Cryptosystems Using Machine Learning 7

Key Analysis of Integer Factorizing based Public-Key Cryptos...

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7th international conference on advances in Artificial Intelligence, ICAAI 2023

作者： Alia, Mohammad Ahmad Jaradat, Yousef Alshehadeh, Abdul Razzak Faculty of Science and It Al-Zaytoonah University of Jordan Amman Jordan Faculty of Engineering and Technology Al-Zaytoonah University of Jordan Amman Jordan Faculty of Business Al-Zaytoonah University of Jordan Amman Jordan

ISBN: (纸本)9798400708985

this paper discusses the possibilities of using machine learning models to analyze the keys in public key encryption algorithms that are based on various mathematical hard problems. Given their distinct domains within computer science, machine learning and public key cryptosystems may appear unrelated at first glance. Nonetheless, there are intriguing correlations and potential points of convergence between these two domains. In general, machine learning models have the potential to help with differential cryptanalysis, a technique that uses statistical differences between plaintext-ciphertext pairs to deduce parts of the key. As a result, breaking encryption keys using machine learning is a complex and evolving research area that investigates cryptanalysis machine learning techniques. While public key encryption algorithms were designed to be secure against various attacks such as brute force, advances in computing power and machine learning have raised concerns about the potential emergence of new attack vectors. © 2023 ACM.

关键词： Public key cryptography

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Analysis of Digital Twins Implementation in Smart City using Big Data and Deep Learning 7

Analysis of Digital Twins Implementation in Smart City using...

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7th international conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Nayak, Chinmaya Kumar Karunakaran, S. Yamunaa, P. Kayalvili, S. Tiwari, Mohit Unni, Manu Vasudevan Sri Sri University Faculty of Emerging Technologies Sri Sri Vihar Ward No - 3 Godi Sahi Odisha Barang754006 India Computer Technology Kongu Engineering College Erode Tamilnadu Perundurai India Peri Institue of Technology Department of EEE India Computer Science & Engineering Velalar College of Engineering and Technology Erode Tamil Nadu Thindal638012 India Computer Science and Engineering Bharati Vidy Ap Eeth's College of Engineering A-4 Rohtak Road Paschim Vihar Delhi110063 India St. Claret College P. O. Box 1355 MES Ring Rd Sharadhamba Nagar Jalahalli Karnataka Bengaluru560013 India

ISBN: (纸本)9798350397253

Digital twins are becoming more relevant for business and academic users due to advances in IoT, AI, and Big Data. Due to global urbanization, pollution, public safety, traffic congestion, and other challenges have arisen. New technologies make cities smarter to keep up with growth. In the Internet of things (IoT) age, many sensing devices acquire and/or produce a broad range of sensory data over long periods of time for a variety of businesses and applications. the use case determines the device's data stream volume and speed. the efficacy of the analytics process used to analyze these streams of data to learn, predict, and act determines IoT's worth as a business paradigm changer and quality-of-life technology. this study introduces Deep Learning (DL), a family of advanced machine learning techniques, to enhance IoT analytics and teaching. Introducing new results, challenges, and research opportunities. this study may assist academics and newbies comprehend how to use DL to smart cities. Analyzing and summarizing major IoT DL research initiatives. Check out smart IoT devices with DL embedded into their AI. Ultimately, the study will identify issues and suggest additional research. Each chapter concludes with experimental findings and the newest literature review. © 2023 IEEE.

关键词： Smart city

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Assessing Stroke Patients Movements Using Inertial Measurements through the advances of Ensemble Learning technology 23

Assessing Stroke Patients Movements Using Inertial Measureme...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Razfar, Najmeh Kashef, Rasha Mohammadi, Farah Electrical and Computer Engineering Ryerson University TorontoON Canada

ISBN: (纸本)9781665494571

Internet of things (IoT) and wearable sensors enabled the possibility of measuring activities of daily living (ADLs). the accuracy and precision of detecting stroke patients' movements, using wearable sensors and analyzing data by implementing the advancement of machine learning and ensemble learning technology can enhance the stroke patients' remote assessment techniques. therefore, this paper aimed to apply ensemble in addition to the machine learning models on the Xsens sensors dataset derived from wearable sensors and collected from twenty stroke survivors. then, we compare the performance of the single model with the bagging and boosting ensemble learning methods to detect the affected hand of the stroke survivors from the non-affected hand. the results indicated that the ensemble techniques such as GentleBoost and AdaBoost achieved the highest model performance with 91.2% and 89.6% accuracy, respectively in comparison with other single machine learning techniques as well as other ensembles. It was also noted that the subspace ensemble techniques achieved the lowest accuracy, recall, and specificity compared to the Decision Tree. © 2021 IEEE.

关键词： Learning systems Smart cities Stroke (medical condition) Boosting Sensor systems Internet of things Motion measurement

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advances in Fluid Mechanics: Computing and Mathematical Models, Turbulence, Fluid theory Applications 7

Advances in Fluid Mechanics: Computing and Mathematical Mode...

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2023 7th international conference on Fluid Mechanics and Industrial Applications, FMIA 2023

作者： Wei, Jiajun Ouyang, Chunfa Shan, Xiaoqian Gao, Qun Zheng, Kangsheng Luo, Wei Shanghai Institute of Technology University Shanghai China

this paper reviews the application of computing and their mathematical principles in fluid mechanics, turbulence, and fluid mechanics. Specifically, it includes the application of computer software in fluid mechanics, the application of mathematical principles in fluid mechanics, the mathematical model in turbulence, the specific application of turbulence model, the application of fluid mechanics in engineering, and the application of fluid mechanics in simulation technology. the comparison and analysis of the similarities and differences in various applications are made to provide theoretical guidance for the researchers engaged in the research of fluid mechanics. © Published under licence by IOP Publishing Ltd.

关键词： Application programs

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Progress and Analysis of Multimodal Knowledge Graphs 7

Progress and Analysis of Multimodal Knowledge Graphs

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7th international conference on Intelligent Computing and Signal Processing, ICSP 2022

作者： Hu, He Chen, Yujue Xi'an University of Science and Technology School of Computer Science ShaanXi China Qinghai Normal University School of Physical Education Qinghai China

ISBN: (纸本)9781665478571

the important value of knowledge graphs in artificial intelligence applications is becoming increasingly evident. Super large-scale general knowledge graphs have been constructed and widely used in many products such as search, recommendation and intelligent interaction. At the same time, with the deepening of intelligent technologies such as text, speech and vision, knowledge graphs are facing new challenges and opportunities in complex knowledge representation, multimodal semantic understanding techniques and applications. In this paper, we will introduce the latest advances in understanding techniques and applications based on knowledge graphs, from text to multimodal combined knowledge graphs. © 2022 IEEE.

关键词： Knowledge graph

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Electroencephalogram Based Stress Detection Using Machine Learning 7th

Electroencephalogram Based Stress Detection Using Machine Le...

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7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Ohal, Hemlata Tiwari, Abhishek Satote, Kiran Zagade, Sakshi Tule, Vaishnavi Garad, Ajinkya Computer Science and Engineering DVK MIT World Peace University Pune India

ISBN: (纸本)9783031699856

this study analyses the topic of stress, highlighting the vital necessity of efficient coping techniques and mentality-based approaches to identify different stress states in a timely manner. the study focuses on the intricate function of electroencephalogram (EEG) signals in detecting stress for medical professionals’ diagnosis so that treatment of disorders linked to stress could be treated at the earliest. the suggested method begins with preprocessing, feature extraction and further proceeds with development of ensemble model. this work investigates the identification of stress using EEG signals. the ensemble model achieves an amazing 98% accuracy on scaled data after undergoing thorough testing. the study emphasizes the critical importance of stress identification and presents ensemble technique for sophisticated EEG-based stress identification. this underscores the importance of stress identification and how it can affect the way a person perceives and handles stress, which will ultimately lead to an improved quality of life. © IFIP international Federation for Information Processing 2024.

关键词： Electroencephalography

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Cricket Forecast: Unraveling Future Matches’ Outcomes 7th

Cricket Forecast: Unraveling Future Matches’ Outcomes

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Siddharth, Vemula Vivek Vikranth, Pulukuri Shalem Karthik, N. Vani, V. Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal609 609 India

ISBN: (纸本)9783031699856

Identifying important factors that frequently affect a team’s success is very important, that leads to the usage of mathematical models in the sports especially in cricket, in order to predict which side might win a ODI or a T20 match. Many people have started to forecast the outcome of these cricket matches that ultimately adds an extra layer of amusement and entertainment to the game. the project aims to develop a mathematical model that analyses pre match conditions and players previous performance and finally gives which team has a better chance to win. the model using Random Forest Classifier achieved an accuracy of 63% and better precision, recall and f1 score on pre match conditions and Random Forest Regressor acquired r2 score of 88% and 79% for ODI batting, T20 batting and 97% and 98% for ODI bowling and T20 bowling is achieved for different formats of the game based on players performance. Hyperparameter tuning is used to increase accuracy. Cross validation is used for performance evaluation. © IFIP international Federation for Information Processing 2024

关键词： Cricket (Sports)

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Improved Evaluator for Subjective Answers Using Natural Language Processing 7th

Improved Evaluator for Subjective Answers Using Natural Lang...

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Sharif, A. Yasin Ravindhar, N.V. Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9783031699818

Examination is a preliminary process for evaluating our knowledge. While objective examinations consist of multiple-choice questions, which are easy to evaluate using simple methods, whereas subjective examinations, which often require written responses, having a significant challenge in uniform evaluation. this type of answer evaluation is a time-consuming process to undertake. this can lead to inconsistencies and biases in grading due to the stress of examiners having to carry out the same task repeatedly. With the technological advancements in the field of Natural Language Processing, which offers a range of techniques that can be employed to evaluate text responses, we have proposed a novel solution for creating an evaluator that increases the accuracy as near to human evaluators compared to other such existing systems for evaluating subjective answers. this model works by taking inputs like the user response, expected sentences and expected keywords and works with the help of numerical vectors, natural language processing and deep learning approaches and other mathematical calculations to calculate the aggregate score for an answer. © IFIP international Federation for Information Processing 2024.

关键词： Deep learning

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An innovative islanding detection scheme in distributed generation using rate of change of the ratio of negative and positive sequence superimposed voltage components. 7

An innovative islanding detection scheme in distributed gene...

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7th international conference on computer Applications in Electrical Engineering-Recent advances, CERA 2023

作者： Bashir, Javeed Aalam, Mohammad Sajid Kanth, Mir Uzair Jena, Premalata Islamic University of Science and Technology Kashmir Department of Electrical Engineering Awantipora India Iit Roorkee Department of Electrical Engineering Roorkee India

ISBN: (纸本)9798350305005

this article presents a unique method for detecting localized islanding in distributed generation (DG) system which connected to the main power grid, using a passive approach. the proposed methodology involves calculating the cumulative sum of rate of change of ratio of negative and positive sequence superimposed voltage components (RCR21) at the distributed generation (DG) side, which is then employed to detect the occurrence of islanding. the current active methodologies deliberately introduce disruptions to the power system, posing potential issues concerning stability, power quality, and overall reliability of the prevailing grid. Passive methods, on the other hand, has a considerable non-detection zone (NDZ). the technique recommended in this article successfully addresses the NDZ issue without introducing any disturbances. To evaluate its effectiveness, PSCAD/EMTDC is employed for simulating a system. the proposed method undergoes several tests on the said system to assess its performance. the results demonstrate that the suggested approach exhibits the minimum NDZ and can distinguish islanding within a remarkably short period of time, significantly faster than a recloser which typically requires 150 ms for detection. © 2023 IEEE.

关键词： Distributed power generation

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