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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

共 13501 条记录，以下是4881-4890 订阅

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Longitudinal Collision Avoidance in Self-Driving Vehicles Using Deep learning and Particle Swarm optimization 4

Longitudinal Collision Avoidance in Self-Driving Vehicles Us...

引用

4th IEEE Pune Section international conference, PuneCon 2021

作者： Kelhe, Tejashri Nair, Devika Kulkarni, Gayatri Deshpande, Ashwini M. MKSSS's Cummins College of Engineering for Women Department of E&TC Engineering Pune India

ISBN: (纸本)9781665444798

Fast and efficient object detection and collision avoidance is an increasingly significant task for autonomous driving technology. this paper proposes a deep learning and swarm intelligence based approach in the automotive domain to detect objects and subsequently avoid collisions. By combining them, improvement can be achieved in the speed and accuracy of self-driving cars to avoid longitudinal collisions. Our proposed approach uses a highly accurate and well-suited deep learning technique for object detection to detect objects in real-time using algorithms and methods such as Mask Region-Based Convolutional Neural Networks (Mask R-CNN) and different versions of You Only Look Once (YOLO). Particle Swarm optimization (PSO) is used to optimize and predict the parameters (velocity and acceleration) required for the self driving car to avoid colliding with the detected object. © 2021 IEEE.

关键词： Particle swarm optimization (PSO)

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Deep learning Based Modal for Predicting Flooding Pattern and Climate Shifts

Deep Learning Based Modal for Predicting Flooding Pattern an...

引用

Innovative Practices in Technology and Management (ICIPTM), international conference on

作者： Lalita Kumari Umesh Kumar Niranjan Kumar Amity School of Engineering Amity University Patna India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

this research presents an integrated deep learning model that combines climate pattern prediction and flood forecasting. Understanding and mitigating climate change and its impact on flooding are critical environmental concerns. Our methodology harmonizes climate and flood datasets, utilizing a neural network architecture and advanced optimization techniques. the compiled model successfully predicts climate patterns and flood events, shedding light on their potential interrelationships. this research offers a novel approach to studying climate and flooding, with significant implications for climate change mitigation and flood risk management.

关键词： Deep learning Climate change Analytical models Adaptation models Weather forecasting Predictive models Data models

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Blockchain and Machine learning Empowered Internet of things applications: Current Issues, Challenges and Future Research Opportunities

Blockchain and Machine Learning Empowered Internet of Things...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： L. Gomathi Anand Kumar Mishra Amit Kumar Tyagi School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India CSE NIIT University Neemrana India Department of Fashion Technology National Institute of Fashion Technology New Delhi India

Two advanced technologies that have the potential to revolutionise the Internet of things (IoT) ecosystem are blockchain and machine learning, as they address a wide range of issues, including data security, interoperability, and scalability. However, there are a number of barriers to integrating these technologies, such as issues with trust, reliability, and governance. this research study summarises the current state of the BML-IoT applications and identifies the challenges and issues encountered in developing these applications. It also discusses possible directions for future research on BML-IoT applications for smart cities, energy management, smart homes, smart agriculture, smart education, and smart healthcare. Overall, this study contributes to a better understanding of the endless possibilities of BML-IoT applications and the research challenges that need to be addressed to fully realise their potential.

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An Online Scheduling Approach for Cloud Computing on Mobile 4

An Online Scheduling Approach for Cloud Computing on Mobile

引用

4th international conference on Frontiers Technology of Information and Computer, ICFTIC 2022

作者： Tao, Tao Li, Hao Zhang, SiHan Xu, RuiLing Huang, Jun Zheng, Xiao School of Computer Science and Technology Anhui University of Technology Ma'anshan China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma'anshan China

ISBN: (纸本)9798350321951

In the mobile cloud computing (MCC) environment, the distribution of user computing activities often suffers from excessive round-trip latency and low processing efficiency. Much of the present research is focused on MCC system-wide task assignment issues, with minimal focus on personalized computational task scheduling optimization. As a result, we present a new dynamic assignment approach for online user computing tasks in our work, which turns the dynamic task scheduling issue into a contextual multi-armed bandit (CMAB) problem. So we use the thompson algorithm to perceive current contextual information. In terms of computing efficiency and latency performance, simulation results show that the suggested strategy surpasses other contextual and context-free benchmarkers. © 2022 IEEE.

关键词： Mobile cloud computing

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MFANet: Multi-feature Aggregation Network for Domain Generalized Stereo Matching 20th

MFANet: Multi-feature Aggregation Network for Domain General...

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20th international conference on Intelligent Computing (ICIC)

作者： Yang, Jinlong Wang, Gang Wu, Cheng Chen, Dong Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Minist Educ Engn Res Ctr Integrat & Applicat Digital Learning Beijing 100039 Peoples R China

ISBN: (纸本)9789819756117;9789819756124

Stereo matching models based on Deep Neural Networks (DNNs) and trained on synthetic domains often struggle to generalize effectively to the imperceptible real domain. Presently, numerous stereo matching approaches focused on domain generalization solely involve a straightforward sequence of multi-scale semantic features. Unfortunately, this simplistic approach can lead to domain shift challenges owing to the omission of crucial detailed information. Simultaneously, this study asserts that texture features play a pivotal role in influencing domain generalization. this is due to the tendency of stereomatching algorithms to seek out the most dominant texture information for matching within texture-rich regions, often disregarding other critical data. To address this, this paper introduces adaptive semantic feature aggregation as well as multi-scale texture feature aggregation, effectively leveraging both the superficial texture data and profound semantic insights derived from the convolutional feature extraction network. this approach mitigates the risk of feature over-specialization while diminishing the influence of cross-domain disparities on the stereo matching network. Furthermore, the model incorporates Recurrent Neural Networks (RNNs) to instruct the cost aggregation network in information synthesis. the performance of our proposed MFANet in stereo matching attains the state-of-the-art level when trained on synthetic datasets and subsequently generalized to four distinct real datasets.

关键词： Stereo Matching Domain Generalization Feature Aggregation Cost Aggregation

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Intelligent Generation and optimization of New Media Art Driven by AIGC 24

Intelligent Generation and Optimization of New Media Art Dri...

引用

Proceedings of the 2024 4th international conference on Big Data, Artificial Intelligence and Risk Management

作者： Lijun Xu Caixian Ye Jun Tang Department of Artificial Intelligence and Data Science Guangzhou Xinhua University Guangzhou Guangdong China School of Information and Intelligent Engineer Guangzhou Xinhua University Guangzhou Guangdong China

ISBN: (纸本)9798400709753

this study starts from the current development status and future trends of intelligent generation in new media art, using a combination of quantitative analysis and experimental simulation methods to focus on the generation and optimization process of new media art works driven by generative AI (AIGC). through comparative analysis of every 100 basic content produced of intelligent art content generation and traditional content production methods, the research results show that an intelligent generation approach that adapts to different user needs can significantly improve the creative quality and audience satisfaction of new media art works. Furthermore, this article delves into the application of the machine learning in the field of art intelligent generation, establishing a user-centered intelligent generation model to promote the continuous optimization of new media art. Building upon this, the article develops an effective dimensional evaluation framework for automatically generating art, providing systematic theoretical support and practical guidance for the field of intelligent generation in new media art. Research in this field will increasingly focus on interdisciplinary integration to promote the deep application of artificial intelligence in artistic creation, meeting the growing demand for personalized cultural consumption.

关键词： AIGC

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Performance Analysis of Machine learning algorithms for Prediction of Liver Disease 4

Performance Analysis of Machine Learning Algorithms for Pred...

引用

4th IEEE international conference on Computing, Power and Communication Technologies, GUCON 2021

作者： Singh, Vinayak Gourisaria, Mahendra Kumar Das, Himansu School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India

ISBN: (纸本)9781728199511

Liver diseases like fatty liver disease, chronic active hepatitis, and cirrhosis are the major cause of mortality in India. Alcohol consumption, inhalation of harmful toxic gases, improper consumption of contaminated pickles, drugs, and foods are the major cause of diseases in the liver. Diagnosis of liver disease needs high accuracy and precise results for predicting whether a person is suffering from liver disease or not. Major disastrous repercussions can be the result of minor errors in the diagnosis of liver diseases. the major goal of this paper is for the detection of liver disease at right time and helping the doctors and combating the increasing number of cases. In this paper, we implemented various machine learning techniques like logistic regression, KNN, XG-Boost, SVM, Gaussian NB, Random forest, Decision tree, Gradient Boosting, CatBoost, AdaBoost, and LightGBM on selected features from the dataset for predicting liver disease and it was found that Random Forest performed best among all the technique and gained high accuracy and performed outstandingly in all metric evaluations. © 2021 IEEE.

关键词： Random forests

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Integrated Computer Network Security System: Intrusion Detection and threat Prediction Using Machine learning algorithms

Integrated Computer Network Security System: Intrusion Detec...

引用

Smart Information Systems and Technologies (SIST), IEEE international conference on

作者： Mariya Utarbayeva Makpal Mukanova Nazarbayev Intellectual School Nazarbayev Intellectual School Almaty Kazakhstan Department of Computer Science Nazarbayev Intellectual School Nazarbayev Intellectual School Almaty Kazakhstan

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

the escalating use of computer networks and associated applications has made cybersecurity a critical concern. this research presents a comprehensive approach to addressing network security issues by developing an Intrusion Detection System (IDS) based on the dataset from the Security Information and Event Management System (SIEM) using various machine learning algorithms. the study includes a detailed comparison of models such as Logistic Regression, K-Nearest Neighbors, Gusain Naive Bayes, Support Vector Machine, Decision Tree, Random Forest, XGBoost, and Artificial Neural Networks. the results demonstrated using histograms and tables show the effectiveness of Random Forest and PCA Random Forest, emphasizing their accurate traffic classification. the research evaluates the model’s performance across various cyber-security datasets containing multiple categories of cyber-attacks. It evaluates efficiency by utilizing criteria such as accuracy, precision, and recall. By applying a multilevel approach aligned with the latest trends in machine learning, the study aims to facilitate swift and precise threat analysis and response, ultimately enhancing the overall effectiveness of the cybersecurity system. the study is an effective educational resource that introduces IT students to innovative machine learning and cybersecurity ideas. through the integration of the results into IT curriculum, educators may close the knowledge gap between theory and practical application by offering students real-world experience in addressing cyber threats. this combined emphasis on innovation and education seeks to develop a new generation of IT professionals with the expertise required for enhanced cybersecurity.

关键词： Surveys Support vector machines Technological innovation Accuracy Machine learning algorithms Education Intrusion detection

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Graphene-Based Wearable E-Textiles Integrated with Deep learning for Real-Time Sweat Analysis in Military and Sports applications

Graphene-Based Wearable E-Textiles Integrated with Deep Lear...

引用

Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： V Ceronmani Sharmila B Sai Varun P A Alvis Josh Aashrith Atmuri K Prasanth Department of Electronics and Communication Engineeering Faculty of Engineering and Technology SRM institute of Technology Kattankulathur India Department of Electronics and Communication Engineeering SRM institute of Technology Kattankulathur India Department of Electronics and Communication Engineeering Communication Engineeering SRM Institute of technology Kattankulathur India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Graphene-based conductive fabrics integrated with deep learning algorithms enable real-time sweat analysis for military and sports applications. these graphene-based e-textiles, renowned for their flexibility, durability, and bio-signal capturing capabilities, are screen-printed onto fabric to create non-invasive sweat sensors. the sensors measure physiological data such as electrolyte balance, hydration levels, and sweat rates, essential for monitoring physical performance under various conditions. A deep learning model, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, processes the collected data to classify sweat composition and recommend optimal clothing based on environmental and physical stressors. Performance analysis demonstrates CNN's superior convergence speed and lower loss values compared to LSTM. this integrated system offers real-time feedback, helping to prevent dehydration, fatigue, and heat-related illnesses, thus providing a transformative approach to personalized healthcare and performance monitoring in demanding environments.

关键词： Deep learning Training Analytical models Accuracy Real-time systems Convolutional neural networks Biomedical monitoring Long short term memory Monitoring Sports

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ICRSA 2021 - 2021 4th international conference on Robot Systems and applications

ICRSA 2021 - 2021 4th International Conference on Robot Syst...

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4th international conference on Robot Systems and applications, ICRSA 2021

ISBN: (纸本)9781450384940

the proceedings contain 14 papers. the topics discussed include: end-to-end lane detection: a key point approach;skin cancer classification using convolutional neural network with autoregressive integrated moving average;human error influence on the system sensitivity of the laser-assisted navigation calibration instrument;a novel human parsing method driven by multi-scale feature blend network;research on intelligent station layout optimization of air defense radar network;machine learning-based predictive model for the prognosis of human papillomavirus (HPV) vaccination attrition;fast parallel constrained Viterbi algorithm for big data with applications to financial time series;motion planning of a macro-micro manipulator for flexible micromanipulation;and design of an open source anthropomorphic robotic finger for telepresence robot.

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