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检索条件"任意字段=5th International Conference on Signal Processing and Machine Learning, SPML 2022"

共 783 条记录，以下是441-450 订阅

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ESMAANI: A Static and Dynamic Arabic Sign Language Recognition System Based on machine and Deep learning Models

ESMAANI: A Static and Dynamic Arabic Sign Language Recogniti...

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international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Essam Hisham Sherine Nagy Saleh Computer Engineering Department Arab Academy for Science Technology and Maritime Transport Alexandria Egypt

ISBN: (纸本)9781665482387

As the size of the population of sign language users increased, the importance of breaking the barrier between those who can use sign language and those who can not in the Arabic community increased. In this paper, We present ESMAANI, a computational solution that enables sign language recognition while utilizing machine learning and deep learning techniques. the proposed system aims to contribute to the study of the challenges and complexities associated with sign language recognition, specifically Arabic sign language. the proposed models present a non-intrusive computer vision approach to building a system specialized in Arabic sign language recognition translating the input sign gestures from a camera stream or video input into text output. Supporting static sign language input, which is common in fingerspelling and alphabet representation and dynamic sign language input which is employed for signing at the word level. the paper also presents a person and environment-independent dataset that's capable of generalizing to include further the various versions of ArSL the proposed static sign recognition system achieved an overall accuracy of 99.7%. And For the proposed dynamic sign recognition system achieved maximum recognition validation accuracy of 97% suggesting strong generalization.

关键词： Deep learning Text recognition Sociology signal processing algorithms Gesture recognition Assistive technologies Streaming media

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Proceedings of the 5th international conference on Electronics, Communication and Aerospace Technology, ICECA 2021

Proceedings of the 5th International Conference on Electroni...

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5th international conference on Electronics, Communication and Aerospace Technology, ICECA 2021

ISBN: (纸本)9781665435246

the proceedings contain 318 papers. the topics discussed include: smart intelligent framework for quality inspection digitalization;distributed multi-dimensional data index strategy in cloud computing environment;certain investigation on cause analysis of accuracy metrics in sentimental analysis on news articles;reconstruction of sports industry information software architecture based on mobile app;the construction of a decentralized digital certification system for cultural archives management under the background of big data;blood cancer detection using machine learning;detecting pneumonia using vision transformer and comparing with other techniques;enhancement of power quality through optimized cascaded MLI based shunt active filter;and analysis on SAR signal processing for high-performance flexible system design using signal processing.

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Tool for Assessing Intellectual Quotient Among Children 5th

Tool for Assessing Intellectual Quotient Among Children

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5th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

作者： Vinitha, B. Rishita, K. Pranavi, K. Reddy, R. Pallavi Kalyani, N. Shaikpet Telangana Hyderabad India

ISBN: (纸本)9789811641763

Cognitive assessment (or intelligence testing) is used to evaluate the general thinking and reasoning abilities of a person, often referred to as intellectual functioning or IQ. Higher IQ scores represent higher cognitive performance and lower scores reflect poor cognitive performance. Nevertheless, individual domain scores that provide a more accurate representation of an individual’s cognitive ability than the overall IQ score because the scores between domains differ greatly. Intelligence testing can determine various aspects of a child’s cognitive capacity. Such intellectual skills include verbal awareness, visual and spatial perception, logical reasoning, working memory and speed of processing. A cognitive assessment can also help recognize particular areas of strength or weakness in children using psychometric tests, so that effective approaches can be built to improve learning and growth through the implementation of different machine learning models. Recommendations should be provided with a deeper understanding of how a child thinks to improve weak skills and reinforce areas of strength. Sometimes, an assessment can also help to explain certain behaviors that a child may exhibit. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cognitive assessment Intellectual functioning Intelligence testing IQ score machine learning

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A Comparative Analysis of Euclidean-Support Vector machine 11th

A Comparative Analysis of Euclidean-Support Vector Machine

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11th international conference on Robotics, Vision, signal processing and Power Applications, RoViSP 2021

作者： Hoong, Kenneth Tan Kean Wan, Wong Yee Tien, Michelle Tan Tien Nugroho, Hermawan Electrical and Electronic Engineering Department University of Nottingham Malaysia Semenyih Malaysia

ISBN: (纸本)9789811681288

Support vector machines (SVMs) are a class of machine learning algorithms which use kernel functions to map data into feature space, where a separating hyperplane can be computed to classify the data. Generally, SVMs are reliant on the selection of kernel function and parameters, which can severely downgrade the performance of the classifier if mishandled. In this paper, we compare the performance of the Euclidean-SVM (ESVM), that have low dependency on kernel selection, with conventional SVM. the comparison are conducted with two different datasets. Results show that the ESVM has higher performance consistency as compared to SVM. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Analysis of Diabetes mellitus using machine learning Techniques

Analysis of Diabetes mellitus using Machine Learning Techniq...

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international conference on Multimedia, signal processing and Communication Technologies, (IMPACT)

作者： Salliah Shafi Bhat Venkatesan Selvam Gufran Ahmad Ansari Mohd Dilshad Ansari B. S. AbdurRahman Crescent Institute of Science and Technology Chennai India Faculty of Science MIT World Peace University (MITWPU) Pune India Dept. of Computer Science & Engineering Guru Nanak University Hyderabad India

ISBN: (纸本)9781665476485

Diabetes Mellitus (DM) often known as hyperglycemia is caused by high blood sugar levels. Although DM is a metabolic chronic disease. Treatment and early detection are essential to reducing the risk of serious outcomes. the World Health Organization (WHO) reports that diabetes has a significant mortality rate causing 1.5 million deaths worldwide. the disease can be identified early because to technology tremendous improvements. In order to build a model with a few variables based on the PIMA dataset this research focuses on evaluating diabetes patients as well as diabetes diagnosis using various machine learning Techniques (MLT). Exploratory data analysis is the first step in our process after which the information is transferred for data pre-processing and feature selection. the relevant features are chosen and the data is then training and testing using three different MLT such as Support Vector machine (SVC), Random Forest (RF) and K-Nearest Neighbors (KNN). Amongst all of the classifiers Random Forest has the highest accuracy of 97.75% followed by Support Vector machine (82.25%) and K-Nearest Neighbors (86.25%).

关键词： Training Radio frequency Support vector machine classification Static VAr compensators Organizations signal processing Diabetes

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5th EAI international conference on Advanced Hybrid Information processing, ADHIP 2021

5th EAI International Conference on Advanced Hybrid Informat...

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5th EAI international conference on Advanced Hybrid Information processing, ADHIP 2021

ISBN: (纸本)9783030945534

the proceedings contain 94 papers. the special focus in this conference is on Advanced Hybrid Information processing. the topics include: A Dynamic Monitoring Method for Marathon Athletes Based on Wireless Sensor Network;research on Key Technologies of Analysis of User Emotion Fluctuation Characteristics in Wireless Network Based on Social Information processing;Variable Bandwidth Receiving Method of Civil Aircraft Radar signal Based on FPGA Technology;recognition of Aerobics Movement Posture Based on Multisensor Movement Monitoring;a Real-Time Detection Algorithm for Abnormal Users in Multi Relationship Social Networks Based on Deep Neural Network;research on Personalized Recommendation Algorithm Based on Mobile Social Network Data;building Construction Information Real-Time Sharing Method Based on 3D Scanning Technology and Social Network Analysis;a Mining Algorithm for Relevance of Business Administration Based on Complex Social Information Network;classification Method of Regional Differentiation Characteristics of Enterprise Management;design of Fractal Image Coding Compression and Transmission Model Based on Wavelet Transform;research on Price Stickiness of Consumer Goods Based on Real-Time Social Information Flow;an Algorithm of Employment Resource Allocation for College Students Based on Social Network Mining;an Improved Mobilenetv3-Yolov5 Infrared Target Detection Algorithm Based on Attention Distillation;A Novel Method of Combined Window Function Construction Filter Applied to F-OFDM System;Passive Electromagnetic Field Positioning Method Based on BP Neural Network in Underwater 3-D Space;optimization of Cross-border E-commerce Marketing Strategy During the Pandemic;deep Residual Network with Transfer learning for High Spatial Resolution Remote Sensing Scenes Classification;research on E-commerce Logistics Transportation Route Planning Method Based on Recurrent Neural Network.

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5th EAI international conference on Advanced Hybrid Information processing, ADHIP 2021

5th EAI International Conference on Advanced Hybrid Informat...

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5th EAI international conference on Advanced Hybrid Information processing, ADHIP 2021

ISBN: (纸本)9783030945503

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Performance Analysis of machine learning Algorithms in Intrusion Detection and Classification 5th

Performance Analysis of Machine Learning Algorithms in Intru...

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5th international conference on Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT, ICETCE 2022

作者： Dilip, R. Samanvita, N. Pramodhini, R. Vidhya, S.G. Telkar, Bhagirathi S. Global Academy of Technology Bengaluru India Nitte Meenakshi Institute of Technology Bengaluru India BGS Institute of Technology Mandya India Acharya Institute of Technology Bengaluru India

ISBN: (纸本)9783031070112

Computer security is defined as the defense of computing systems against external threats in order to protect resource confidentiality, integrity, and availability. When an intrusion occurs, both network resources and the victim server are put at risk. When an intrusion occurs in a computer system or network, the Intrusion Detection System (IDS) tracks it and notifies the system administrator, allowing the appropriate action to be taken. People’s faith in the Internet has declined as the frequency of cyber-attacks has increased. A security attack known as Denial of Service (DoS) is quite successful (DoS). When an intrusion detection system (IDS) detects external attacks as well as system abuse or internal attacks, it sends a signal to a central monitoring station. In terms of functioning, an intrusion detection system is similar to a burglar alarm. this article provides a machine learning based intrusion detection system. NSL KDD data set is used as input for experimental work. ANN, SVM and ID3 algorithms are used in analytical investigation. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Intrusion detection

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Leveraging Semi-Connected Devices To Enhance Federated learning

Leveraging Semi-Connected Devices To Enhance Federated Learn...

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international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Hend K. Gedawy Khaled A. Harras Aiman Erbad Carnegie Mellon University Hamad Bin Khalifa University

ISBN: (纸本)9781665482387

Federated learning (FL) was introduced to over-come traditional machine learning data privacy concerns, and thus, enable us to gain access to more data. Data owners, clients, are orchestrated by a central FL-server to train data locally and only share their model weights. FL approaches have mainly relied on Cloud and/or Edge to aggregate these model weights and propagate training knowledge across clients. However, several issues hinder the scalability of these approaches, especially in communication-challenged environments. In this paper, we propose a novel semi-distributed system to improve FL training accuracy and time, as well as resource-efficiency at the clients. We leverage co-located clusters of high-end IoT devices, known as FemtoClouds, to propagate training knowledge beyond the Edge. We only leverage Edge/Cloud opportunistically to prop-agate knowledge across FemtoCloud pools. Our evaluation shows that our semi-distributed FemtoClouds system achieves competitive accuracy to state-of-the-art FL approaches, with up to 95% time savings and up to 84% energy savings.

关键词： Training Data privacy Federated learning Scalability Aggregates signal processing Data models

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theft Cyberattacks Detection in Smart Grids Based on machine learning

Theft Cyberattacks Detection in Smart Grids Based on Machine...

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international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Abdelfatah Ali Mohamed Mokhtar Mostafa F. Shaaban Department of Electrical Engineering South Valley University Qena Egypt Department of Electrical Engineering American University of Sharjah Sharjah UAE Department of Power and Electrical Machines Engineering Ain Shams University Cairo Egypt

ISBN: (纸本)9781665482387

Electricity theft is a worldwide issue that adversely impacts companies and users. this issue disrupts the expansion of utility companies, produces electric dangers, and affects the high-level cost of electricity for users. the extensive penetration of advanced metering infrastructure networks gives a chance to identify theft cyberattacks by examining the collected data of the energy consumption from smart meters. this work presents a detection approach based on statistical and machine learning to measure theft confidence. An anomaly detection approach is adopted, in which, to detect suspicious data, a theft detection unit based on a fine tree regression model is constructed. Historical data of average load consumption per unit area, smart meter readings, and temperature are employed in the training stage of the proposed approach. the error between the true and estimated data is fitted by a probability density function to identify suspicious data and determine the theft confidence. Different electricity theft cyberattacks are studied to evaluate the efficacy of the developed approach. the obtained results demonstrate the effectiveness of the developed detection approach.

关键词： Temperature measurement Training Energy consumption Electric variables measurement machine learning Companies Probability density function

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