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检索条件"任意字段=4th Conference on Learning and Intelligent Optimization"

共 11565 条记录，以下是631-640 订阅

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A ChatGPT-enabled intelligent Assistive Technology for Homecare to Support Activities of Daily Living for People with Mild Dementia 24

A ChatGPT-enabled Intelligent Assistive Technology for Homec...

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4th International conference on Human-Machine Interaction (ICHMI)

作者： Chen, Xiaowei Fan, Guoliang Roberts, Emily Howell, Steven, Jr. Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA Oklahoma State Univ Dept Design & Merchandising Stillwater OK 74078 USA

ISBN: (纸本)9798400716812

People with dementia (PwD) face significant cognitive challenges that hinder their ability to perform daily tasks, referred to as activities of daily living (ADLs), and are particularly impeded when it comes to the more intricate instrumental ADLs (IADLs). Lacking sufficient support, individuals with dementia are at a greater risk of losing their independence and becoming more vulnerable. A range of intelligent assistive technologies (IATs) have been proposed to help individuals with mild dementia perform certain ADLs on their own. However, most current IATs offer limited support for IADLs to PwD, primarily due to the technologies inability to comprehend the unique communication needs of PwD and a lack of user-specific customization. this paper presents a new proposed homecare assistive tool for people with mild dementia, called CATcare (Cognitive Assistive Technology Care). Our CATcare system, utilizing ChatGPT, offers a comprehensive understanding of PwD. Easily operated and configured via smartphone or smart glasses, it allows caregivers to tailor it to the unique IADL requirements of each care recipient. Additionally, it offers step-by-step guidance, prompts, and timely feedback to help PwD complete their desired tasks. Our research leverages the capabilities of transfer learning from AI models in indoor localization, object detection, and Natural Language Processing (NLP) and shows the potential and promise to develop a customizable and personalizable CATcare tool. this CATcare system is intended to improve the quality of life of PwD and reduce the burden of their caregivers.

关键词： ChatGPT transfer learning people with mild dementia intelligent assistive technologies audio/visual prompting indoor localization instrumental activities of daily living

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Improved strategy based sparrow search algorithm for UAV 2D path optimization 4

Improved strategy based sparrow search algorithm for UAV 2D ...

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4th IEEE International conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Guo, Wanli Xinjiang University Urumqi China

ISBN: (纸本)9798350361643

In recent years, swarm intelligence optimization algorithms have demonstrated excellent performance in optimization, achieving favorable results in solving complex practical problems. Sparrow Search Algorithm (SSA) is an intelligent algorithm proposed in recent years. this paper focuses on the two-dimensional path optimization of Unmanned Aerial Vehicles (UAVs). Addressing issues such as the decline in population diversity and susceptibility to local optima in the later iterations of the Sparrow Search Algorithm, a multi-strategy hybrid improved Sparrow Search Algorithm (ISSA) is proposed. ISSA is applied to UAV two-dimensional path optimization planning to find the optimal path for obstacle avoidance. this paper utilizes Piecewise Linear Chaotic Mapping (PWLCM) to optimize the initial settings of the population, enhances population diversity, and effectively controls the balance between global search and local optimization using a nonlinear weight factor. this strengthens the algorithm's global optimization ability and avoids the algorithm from being trapped in local optima. Perturbation is introduced through longitudinal and transverse crossover strategies, introducing new genetic variations among population individuals to increase diversity, helping the algorithm escape local optima and find better solutions. through multi-strategy improvements, a hybrid improved ISSA Sparrow Search Algorithm is formed. Simulation experiments demonstrate that ISSA outperforms five other algorithms including GA, SSA, PSO, DE, and GWO in terms of path optimization performance. © 2024 IEEE.

关键词： Global optimization

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Reinforcement learning Based optimization Algorithm for Resource Offloading and Load Balancing in Connected Vehicles 4

Reinforcement Learning Based Optimization Algorithm for Reso...

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4th International conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Zhao, Qian Duan, Zhanhao Guan, Shaoyun Harbin University of Commerce School of Computer and Information Technology Harbin China

ISBN: (纸本)9798350394375

Edge computing in Internet of vehicles accomplishes the objectives of low latency and energy consumption by offloading tasks to edge computing servers. However, how to reduce the task offloading latency and energy consumption of vehicle terminals while ensuring the priority of server computing and server load balancing is still a challenge. To solve this problem, this paper first constructs the channel model, communication model, delay model, load balancing model, and target optimization model of the Internet of Vehicles edge computing, and uses non-orthogonal multiple access (NOMA) communication technology. then the paper proposes an improved Q-learning algorithm based on genetic factors (GAQ-learning algorithm) to calculate the vehicle task offloading scheme. Finally, comparative experiments were conducted between the proposed solution and various schemes. Simulation results demonstrate that the proposed solution can effectively reduce the average offloading latency of users, optimize the workload between servers, and effectively reduce energy consumption, with improved performance compared to three base offloading schemes. © 2024 IEEE.

关键词： learning algorithms

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A Correlative Survey on Imbalanced Breast Cancer Data Using Ensembled Oversampling Techniques and Deep learning Algorithms 4

A Correlative Survey on Imbalanced Breast Cancer Data Using ...

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4th International conference on Emerging Systems and intelligent Computing, ESIC 2024

作者： Datta, Debaleena Singha, Pinaki Pritam Sen, Uddalok Mallick, Pradeep Kumar Techno India Saltlake Department of Cse Kolkata India University of Engineering and Management Department of Cse Kolkata India Mckv Institute of Engineering Department of Cse Kolkata India Kalinga Institute of Industrial Technology Department of Cse Bhubaneswar India

ISBN: (纸本)9798350349856

Inadequacy of labeled breast cancer data causes imbalance and inaccuracy in classifiers. Our proposed research aims for a computer assisted detection (CAD) system to detect maligned cancer cells using the Wisconsin Breast Cancer dataset through a novel ensembled approach that combines various oversampling methods with standard machine learning and deep learning classifiers. the work is three-folds: (1) Use of 6 oversampling techniques: Random oversampling, Synthetic minority oversampling technique, Borderline SMOTE, k-means SMOTE, Support vector machine SMOTE, Adaptive synthetic minority oversampling, (2) Use of 5 ML models: Logistic Regression, Decision Tree, Random Forest, Adaptive boosting, Extreme gradient boosting, and (3) Use of 3 DL models: Artificial neural network, Multilayer perceptron, Convolutional neural network (CNN). the experimental results deduce that CNN has performed better than the others when assisting the SMOTE family and achieves the maximum accuracy of 98.25%. © 2024 IEEE.

关键词： Multilayer neural networks

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Keyframe Extraction Approaches for intelligent Video Analysis 4

Keyframe Extraction Approaches for Intelligent Video Analysi...

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4th International conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Sabarivasan, N. thangavel, Senthil Kumar Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798331523923

Real-time violence detection is essential for protecting the safety and security of people, especially in college campuses that are dynamic and have crowds. Manual surveillance systems are popular but inefficient as they require human attention all the time and are not capable of quickly identifying and responding to violent *** the exsisting violence detection model takes quite a time for processing the entire video. the proposed model extracts features as keyframes from the video using MobileNet V3 and K-medoids clustering. the redundancy of video data can be avoided and significant violent activities identified by isolating and analyzing critical frames from video data. this enhances the efficiency and responsiveness of surveillance systems. this work primarily contributes a customized violent activities dataset and implementing keyframe extraction algorithm for optimizing the usage of computational resources, and incorporating lightweight algorithms for seamless deployment with existing CCTV networks. the experimental results clearly demonstrate the ability of this approach to detect keyframes, thereby offering a scalable and practical solution. © 2025 IEEE.

关键词： Security systems

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A Review of Machine learning Algorithms for Detection of Breast Cancer 4

A Review of Machine Learning Algorithms for Detection of Bre...

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4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Tale, Abhay Barhate, Aditya Verma, Prateek Gourshettiwar, Palash Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331529635

Breast cancer is still a global health concern, chiefly affecting women, as it is one of the major causes of cancer mortality. For the therapy to be effective, early diagnosis of cancer is critical to raising the survival level and increasing the effectiveness of the procedure. the advancement in the applied technique of Machine learning (ML) has brought further opportunities for early and effective diagnosis of breast cancer. this review paper provides insights into how different ML algorithms including the Support Vector Machine (SVM), Convolutional Neural Network (CNN), Random Forests, and many more have contributed to the detection of breast cancer. these algorithms have proved effective in providing solutions to various medical data and imaging including early diagnosis and treatment. the paper also looks at the use of ML with precision medicine to show that these technologies will help minimize false diagnoses and offer better treatment options. this review consolidates recent works and advancements to emphasize the prospect of ML in breast cancer management and calls for more studies to enhance its implementation in practice © 2024 IEEE.

关键词： Convolutional neural networks

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User-Centered Design and Implementation of an intelligent Wearable Device for Real-Time Health Monitoring in IoT Ecosystems 4

User-Centered Design and Implementation of an Intelligent We...

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4th International conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Li, Zhengdong Wuhan University Bioengineering Art College Wuhan China

ISBN: (纸本)9798331534622

this paper focuses on the user-centered design and development of an intelligent wearable device for real-time health monitoring, within an IoT ecosystem. Emphasizing ergonomics, aesthetic appeal, and ease of use, the product design process incorporates human factors engineering and material selection to ensure a comfortable and durable device. the wearable integrates advanced biosensors for continuous monitoring of key health metrics and employs wireless communication for seamless data transmission. A key innovation lies in the integration of machine learning algorithms that enhance the device's predictive capabilities, offering users valuable health insights. this paper outlines the design iterations, prototyping process, and usability testing that led to a market-ready product, ensuring the device meets both functional and user experience goals. © 2024 IEEE.

关键词： Electronic health record

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Exploring Socioeconomic Influences on Life Expectancy through Machine learning Ensemble Regression Techniques 4

Exploring Socioeconomic Influences on Life Expectancy throug...

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4th International conference on intelligent Technologies, CONIT 2024

作者： Dangety, Sowjanya Kasulu, Kasani Vaddi Swetha, G. Begum, Zareena Bhashyam, Krishna Mohan Lakshmanarao, A. Gmr Institute of Technology Department of Cse A.P Rajam India Eluru College of Engineering and Technology Department of Cse A.P Eluru India Cmr Engineering College Department of It Kandlakoya Medchal Hyderabad India Rvr&jc College of Engineering Department of It A.P Guntur India Aditya University Department of It Surampalem India

ISBN: (纸本)9798350349900

Life expectancy prediction is a critical area in public health research, aiding in policy formulation and resource allocation. In this study, we explored the prediction of life expectancy using a dataset sourced from Kaggle. Initially, the dataset underwent rigorous preprocessing to handle missing values and ensure data quality. Subsequently, a diverse array of regression models, including Multiple Linear Regression (MLR), SVM, Gradient Boosting, Random Forest, XGBoost, and Decision Tree were applied to the dataset. through thorough evaluation, the top-performing models were identified, and a comparative analysis was conducted. Leveraging ensemble learning techniques, specifically stacking and voting ensemble, further refinement was achieved, resulting in a robust predictive model with high R-squared values. the results highlight the effectiveness of ensemble regression methods in forecasting life expectancy, offering valuable insights for public health policy and decision-making. © 2024 IEEE.

关键词： Adversarial machine learning

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Hybrid Deep learning Models with Adversarial Defense Mechanisms for Secure Beamforming Prediction 4

Hybrid Deep Learning Models with Adversarial Defense Mechani...

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4th intelligent Cybersecurity conference, ICSC 2024

作者： Abasi, Ammar Kamal Aloqaily, Moayad Guizani, Mohsen Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350354775

Millimeter-wave (mmWave) communication in 6G networks presents unique challenges, such as susceptibility to blockages and rapidly changing environments. this paper introduces a truly new hybrid deep learning (DL) architecture, which synergistically combines convolutional neural networks (CNNs) and vision transformers (ViTs), to enhance beamforming prediction. CNNs efficiently process spatial data crucial for beamforming, while ViTs model long-range dependencies vital in dynamic 6G scenarios. this integrated approach not only aims to boost prediction accuracy but also incorporates tailored adversarial defenses against attacks like the Fast Gradient Sign Method (FGSM) to fortify security. the key contributions are: 1) Developing an innovative hybrid CNN-ViT architecture tailored for 6 G beamforming prediction;2) Integrating novel adversarial defense mechanisms to enhance security;3) Extensive simulations and real-world validation demonstrating significant performance and security improvements over existing methods. the proposed solution paves the way for robust and secure 6G network deployments by overcoming mmWave propagation complexities while mitigating adversarial threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Advancing Healthcare Accessibility: Development of an AI-Driven Multimodal Chatbot 4

Advancing Healthcare Accessibility: Development of an AI-Dri...

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4th International conference on intelligent Technologies, CONIT 2024

作者： Garimella, Bala Subrahmanyam Garlapati, Hari Sharan Choul, Sriharini Cherukuri, Rajesh Lanke, Pallavi B v Raju Institute of Technology Dept.Computer Science and Engineering Narsapur India

ISBN: (纸本)9798350349900

In today's rapidly evolving healthcare sector, the need for efficient and accessible medical assistance is more crucial than ever. With the advancements in Artificial intelligence (AI), healthcare chatbots have emerged as a promising solution to address this need. these chatbots are capable of providing personalized medical information, scheduling appointments, and offering support to patients, thereby enhancing healthcare accessibility and patient engagement. the purpose of this project is to develop an advanced AI-powered healthcare chatbot that builds upon existing technologies to bring significant improvements in patient interaction and healthcare delivery. By leveraging the capabilities of Natural Language Processing (NLP) algorithms and deep learning techniques, the project aims to enhance the chatbot's functionality by integrating multimodal interaction features, enabling users to interact via text, voice, etc and provide more accurate and relevant responses to user queries. Additionally, the project seeks to create a comprehensive patient portal where users can securely access their medical records, reports, appointments, and engage with the chatbot for personalized assistance. © 2024 IEEE.

关键词： Deep learning

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