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检索条件"任意字段=International Conference on Advances in Science, Engineering and Robotics Technology"

共 47833 条记录，以下是951-960 订阅

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A Comparative Study on Reinforcement Learning in Disease Prediction on Medical Data

A Comparative Study on Reinforcement Learning in Disease Pre...

引用

2024 international conference on advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Nijana, V. Rajendran, P. Selvi Hindustan Institute Of Technology And Science Computer Application Department India Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering India

ISBN: (纸本)9798350364828

Medical imaging has been used extensively in healthcare in recent years for a variety of purposes, including disease diagnosis, treatment planning, and tracking the course of an illness. These applications entail taking pictures of the afflicted organ using a variety of modalities. Image segmentation and classification is two important process performed in disease diagnostic application. Deep reinforcement learning approach applied in many gaming application. DRL provides the accurate result in many real world applications, so researchers pay attention in DRL in medical data that helps the physicians in treating the patient. This study focus on surveying the various applications such as Image registration, lesion localization, image segmentation, image classification and Landmark detection using deep reinforcement learning in healthcare. In addition, this study investigates on the various DRL algorithms (Qlearning, deep deterministic policy gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TDDDPG), Deep Q Network (DQN) and Soft Actor-Critic (SAC) in Alzheimer's disease prediction. Among the DRL methods the DDPG achieved the highest accuracy in detecting the Alzheimer's disease with 97%. © 2024 IEEE.

关键词： Forecasting

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CVN: A Collision Value Network for Crowd Navigation

CVN: A Collision Value Network for Crowd Navigation

引用

2024 IEEE international conference on robotics and Biomimetics, ROBIO 2024

作者： Zhang, Huiwen Gao, Qing Leng, Yuquan Institute of CVTE Research Guangdong China School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen518107 China Southern University of Science and Technology Department of Mechinical and Energy Engineering Shenzhen China

ISBN: (纸本)9781665481090

Dense crowd scenes are highly dynamic and un-certain, posing significant challenges to robot navigation. Recent studies have begun to utilize deep neural networks to encode interaction behaviors among agents, followed by reinforcement learning to develop obstacle avoidance strategies. However, accurately encoding the status of complex crowds remains a significant challenge, often leading to frequent interference with agents during navigation, and greatly reduces the experience of traffic participants. To this end, this paper proposes the collision value network (CVN). CVN leverages the multi-head attention mechanism in the transformer to encode the interaction behaviors among different agents, and then addresses the crowd navigation problem with reinforcement learning. Based on the designed reward function, the value iteration method is used to obtain the approximate value function and obstacle avoidance policy. Experiments show that the proposed method can enable the robot to make reasonable obstacle avoidance decisions quickly and accurately in complex scenarios. Across 500 rounds of random navigation tests, the navigation success rate reaches 98 %, the collision probability is 2 %, and the navigation time is 10.21s, showcasing superior performance compared to existing methods. © 2024 IEEE.

关键词： Reinforcement learning

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Numerical Optimization and Performance Evaluation of Lead-Free BaZrS3-Based Perovskite Solar Cells with Consideration of Structural and Operating Parameters 4

Numerical Optimization and Performance Evaluation of Lead-Fr...

引用

4th international conference on robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Shimul, Asadul Islam Hossain, Md Maruf Sarker, Swarna Rani Shahriare, Atiq Alam, Shakidul Dipa, Safia Aktar Bangabandhu Sheikh Mujibur Rahman Science and Technology University Department of Electrical and Electronic Engineering Gopalganj8100 Bangladesh University of Missouri-Columbia Department of Electrical Engineering and Computer Science MO65211 United States

ISBN: (纸本)9798331530938

Lead-free BaZrS3 is considered a highly promising substitute for lead-based halide perovskites. This research involved designing and simulating a perovskite solar cell using the Glass/ITO/ZnO/BaZrS3/Cu2O/Ni structure. Zirconium halide perovskite was employed to address lead halides' stability and toxicity issues. The solar cell's performance was evaluated using the one-dimensional simulation program SCAPS-1D. The analysis focused on essential parameters such as absorber thickness, absorber defect density, hole transport layer (HTL) thickness, HTL acceptor density, electron transport layer (ETL) thickness, ETL donor density, and operating temperature. At a current density (JSC) of 21.49 mA/cm2, an open-circuit voltage (VOC) of 1.37 V, and a fill factor (FF) of 89.98%, the results indicate a maximum efficiency of 26.53%. These results establish the possibility of creating ecologically sustainable and highly effective PSCs using barium zirconium sulfide (BaZrS3). © 2025 IEEE.

关键词： Perovskite

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Smart Music Player Integrating Facial Emotion Recognition and Music Mood Recommendation 15

Smart Music Player Integrating Facial Emotion Recognition an...

引用

15th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Manjunath, K.G. Pramod, Shreedevi Chiranth, T.P. Siddaganga Institute of Technology Tumkur Department of Computer Science and Engineering Karnataka Tumkuru India Brindavan College of Engineering Bagaluru main Road Bangalore India

ISBN: (纸本)9798331300579

We came across many applications that are serve solutions and assistance for the music playlist creation recommendation. To provide more facility and the solutions to the music creation and recommendation of music and numerous technique approaches have followedin our proposed system. To determine the approached and developing the current state of human emotion and playing through the mood determined by the system. Our proposed system mainly focuses on playing and recommending the songs according to end user current mood or betterment of the mood. © Grenze Scientific Society, 2024.

关键词： Music

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Exploring and Analysing Deep Reinforcement Learning Based Algorithms for Object Detection and Autonomous Navigation

Exploring and Analysing Deep Reinforcement Learning Based Al...

引用

2024 international conference on advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Simenthy, Joseph Rish Mathana, J.M. Hindustan Institute Of Technology And Science Department Of Electronics And Communication Engineering Tamilnadu Chennai603103 India Jayasakthi Engineering College Department Of Biomedical Engineering Tamilnadu India

ISBN: (纸本)9798350364828

Object recognition algorithms need to be very accurate and efficient in order for autonomous navigation systems to see and securely interact with their surroundings. Although conventional computer vision techniques have made significant advancements in this area, they sometimes have trouble with complicated situations, occlusions, and dynamic settings. This research study introduces and investigates deep reinforcement learning (DRL) methodologies to enhance object recognition in the context of autonomous navigation. A potent paradigm for addressing challenging decision-making issues is deep reinforcement learning. The purpose of this study is to show how agents may learn to identify and categorize objects in a variety of demanding situations by defining object detection as a reinforcement learning problem. © 2024 IEEE.

关键词： Object detection

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Performance Analysis & Evaluation of Wireless Sensor Network using LEACH & HEED 15

Performance Analysis & Evaluation of Wireless Sensor Network...

引用

15th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Hirwe, Angeeta Sindal, Ravi Jain, Nitin Kumar Institute of Engineering & Technology DAVV Indore India IPS Academy Institute of Engineering & Science Indore India

ISBN: (纸本)9798331300579

In Wireless sensor network (WSN) lifetime of sensors is a critical issue because the sensor nodes having limited power. Therefore, organization of nodes in a proper manner (clustering) becomes one of the major techniques to expand the lifetime and improve the throughput of the network. In this paper we propose a Hybrid Energy Efficient Distributed Protocol (HEED) for clustering in Sensor Network to improve the lifetime of network. The simulation result show that the propose scheme has higher network throughput in terms of higher data packets received than existing LEACH protocol. © Grenze Scientific Society, 2024.

关键词： Sensor nodes

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Web-Based Deep Learning Model for Zero Day Vulnerability Detection using FastAPI

Web-Based Deep Learning Model for Zero Day Vulnerability Det...

引用

2024 international conference on advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Suresh Babu, C.V. Surendar, V. Sriram, E. Subhash, S. Information Technology Hindustan Institute Of Technology And Science Chennai India

ISBN: (纸本)9798350364828

In the evolving world of technology, the growing number of security weaknesses is becoming a serious concern, for our online safety. This research presents a method leveraging LSTM technology to identify both familiar and undiscovered vulnerabilities in log files of web application frameworks. The platform features a user interface developed using ReactJS with upcoming upgrades focused on enhancing appeal and simplifying the log file upload process, for vulnerability assessment. By enabling users to effortlessly upload logs the system creates reports that outline forms of attacks offering valuable insights, into possible security risks. Recent live testing of the system showed its ability to quickly identify threats showcasing its potential as a proactive tool for enhancing cybersecurity defenses. Moreover, the model's prediction accuracy stands at a 98% proving its strength and dependability in identifying weaknesses. These results underscore the significance of security measures, in dealing with the evolving landscape of digital threats. © 2024 IEEE.

关键词： Anomaly detection

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Smart Grid Solar Tracking Optimization using Deep Reinforcement Learning Algorithm

Smart Grid Solar Tracking Optimization using Deep Reinforcem...

引用

2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Kavitha, H.S. Anu, H. Komala, M. Pavithra, G.S. Mallikarjunaswamy, S. Sharmila, N. Naveen, K.B. Sheela, S. Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Rns Institute of Technology Bengaluru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Bgs Institute of Technology Adichunchanagiri University Dept of Electronics and Electrical Engineering BG Nagar India Global Academy of Technology Dept of Computer Sceinec and Engineering Bengaluru India

ISBN: (纸本)9798350388602

The integration of solar power into smart grids necessitates efficient solar tracking to maximize energy capture. Conventional methods like Single-Axis Trackers (SAT) and Dual-Axis Trackers (DAT) have limitations, including high maintenance costs, limited adaptability, and inefficiency under variable weather conditions. These drawbacks result in suboptimal energy generation, hindering the performance of smart grids. The proposed Smart Grid Solar Tracking Optimization using Deep Reinforcement Learning Algorithm (SGST-DRLA) addresses these issues by utilizing a Deep Reinforcement Learning (DRL) approach to dynamically optimize solar panel orientation. Unlike static control strategies of SAT and DAT, SGST-DRLA continuously learns from realtime data, adjusting the tracker's position to maximize sunlight exposure and energy capture. This adaptive method enhances tracking precision and reduces operational costs. SGST-DRLA significantly improves performance metrics, achieving a 0.25% increase in energy capture efficiency, a 0.20% reduction in operational costs, and a 0.30% improvement in adaptability to varying weather conditions compared to traditional methods. This approach not only optimizes solar tracking in smart grids but also supports sustainable and efficient renewable energy integration, making it a viable solution for overcoming the limitations of existing tracking systems. © 2024 IEEE.

关键词： Deep reinforcement learning

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Textual Therapies: Harnessing Language Models for Mental Health Insights

Textual Therapies: Harnessing Language Models for Mental Hea...

引用

2024 international conference on advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Bookanakere, Pranav Sharanya, S. Saniya, Syeda Arjun, R. Srinivas, K.S. Pes University Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350388800

Recent years have seen a drastic rise in the occurrence of mental health issues, emphasizing the critical need for scalable approaches to identify those who are impacted. This paper introduces a novel approach that utilizes Large Language Models (LLMs), to determine whether a person is suffering from a mental illness. Specifically, it focuses on our implementation of OpenAI's Generative Pre-Trained Transformer 2 (GPT-2) model, comprising of 1.5 billion parameters. Furthermore, it has been quantized using the BaseQuantizeConfig class. We carefully examine the potential use of LLM -based technologies in the prediction of the presence of a mental illness. Our study aims to elucidate the feasibility and accuracy surrounding the deployment of LLM-based approaches in predictive analysis of mental health. We explore real-world circumstances, assessing the potential pros and cons that emerge when integrating these technologies into existing mental health screening frameworks. Through this exploration, we aim to contribute to the ongoing research on the combination of artificial intelligence and language models in mental health care, striving for a future where innovative technologies play a pivotal role in improving mental health outcomes. © 2024 IEEE.

关键词： Electronic health record

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Enhanced Radiological Anomaly Detection using Optimized YOLO-NAS Model

Enhanced Radiological Anomaly Detection using Optimized YOLO...

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2024 international conference on advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Leka Shree, J. Joy, Emmanuel Sri Ram, M.S. Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Coimbatore India Division Of Artificial Intelligence And Machine Learning Karunya Institute Of Technology And Sciences Coimbatore India

ISBN: (纸本)9798350371567

Chest X-rays are commonly used to detect lung diseases. Chest radiographs often have a lot of perceptual errors. The application of image processing in diagnosing lung diseases has improved medical imaging. This has also enabled the creation of databases for classifying chest radiographs. However, current methods are not accurate enough to reliably detect anomalies. Aimed at improving the shortcomings of existing techniques, the proposed approach uses the Automated Neural Architecture Construction (AutoNAC) engine, a proprietary of Neural Architecture Search (NAS). Comparative evaluations against established models, including YOLOv3, YOLOv5, YOLOv7, and YOLOv8, showcase the superiority of YOLO-NAS in mean Average Precision (mAP@0.5), precision, and recall metrics. The originality of this study lies in introducing NAS specifically AutoNAC, to optimize neural network architectures for chest X-ray anomaly detection, marking a significant advancement in diagnostic capabilities in medical imaging. © 2024 IEEE.

关键词： Anomaly detection

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