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检索条件"任意字段=International Conference on Engineering of Reconfigurable Systems and Algorithms"

共 89770 条记录，以下是291-300 订阅

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Effectiveness of Llama 2 in addressing career development needs: A comprehensive review 1st

Effectiveness of Llama 2 in addressing career development ne...

引用

1st international conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Singh, Anuradha Sharma, Himanshu Chaudhary, Ankur Arora, Mandeep Kaur Department of CSE Noida Institute of Engineering and Technology Greater Noida India Department of ECE Noida Institute of Engineering and Technology Greater Noida India Department of Psychology Kamla Nehru College University of Delhi New Delhi India

ISBN: (纸本)9781032874036

Career guidance instruments play a vital role in guiding an individual to select the most suitable career in making career choices. This study research the future contribution of Llama 2 in enhancing the career advisory systems by including more sophisticated learning algorithms along with data analytics in the recommendation process. In the next sections, it details how Llama 2 can be useful to deliver personalized career advice as well as solve complex professional demands. The discussion also includes the effects Llama 2 had on user experience, matched recommendations, and what to expect for future professional development. © 2025 the Author(s).

关键词： Artificial intelligence

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Classification and Analysis of EEG Montage

Classification and Analysis of EEG Montage

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Sivaranjani, S. Sneha, R. Sowmiga, B. Sruthikaa, K.V. Yuvashree, R. M. Kumarasamy College of Engineering Department of Electronics and Communication Engineering Thalavapalayam Tamilnadu Karur639113 India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Electroencephalography (EEG) has gained considerable traction in diverse research and clinical applications due to its non-invasive nature, real-time brain activity monitoring capabilities, and increasing affordability. This literature survey explores the applications and advancements of EEG technology in fields such as sleep studies, seizure detection, Alzheimer's disease diagnostics, user authentication, intensive care monitoring, and brain-computer interfaces (BCIs). Innovations such as reduced-channel setups and subdermal electrodes have improved EEG accessibility, while machine learning algorithms have enhanced signal interpretation and diagnostic accuracy. For instance, simplified EEG configurations show promise in seizure detection and home sleep monitoring, while TMS-EEG-based methods improve Alzheimer's diagnostics. EEG-based user authentication methods present a unique approach to secure identification, though privacy remains a concern. Additionally, EEG-driven BCIs are evolving, offering new ways for individuals with motor impairments to interact with technology. This review highlights EEG's transformative potential across these domains, identifies current limitations, and suggests future research directions, particularly in enhancing signal quality, data privacy, and diagnostic accuracy. © 2025 IEEE.

关键词： Clinical research

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Machine Learning in Healthcare: A Review of Applications, Opportunities and Challenges 2

Machine Learning in Healthcare: A Review of Applications, Op...

引用

2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Thute, Trupti Saratkar, Saniya Chaudhari, Aarti Raut, Rohini Thakare, Gayatri Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

This article explores the potential of machine learning (ML) in transforming healthcare. It provides an overview of ML and its various categories, highlighting how supervised and unsupervised learning algorithms can be utilized for tasks like disease diagnosis, treatment personalization, and resource allocation. The article delves into specific applications of ML in healthcare, including image analysis for early disease detection, visualization of complex medical data, and personalized medicine approaches. It further explores the exciting opportunities presented by ML, such as improved diagnostics and treatment, increased efficiency and cost reduction, and advancements in research and development. However, the article also acknowledges the challenges associated with implementing ML in healthcare, including data privacy concerns, real-time data requirements, and the complexity of certain models. Overall, the article emphasizes the multifaceted landscape of ML in healthcare, showcasing its potential to revolutionize the industry while acknowledging the hurdles that need to be addressed for successful integration. © 2025 IEEE.

关键词： Theranostics

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Electric Vehicle Fault Battery Analyzer Using Internet of Things

Electric Vehicle Fault Battery Analyzer Using Internet of Th...

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Kamatchi Kannan, V. Muniraj, C. Sankarganesh, R. Parimalsundar, E. Thrinath, B.V. Sai Ponmurugan, P. Knowledge Institute of Technology Salem India K.S.R. College of Engineering Tiruchengode India Mohan Babu University Tirupati India Chennai600078 India

ISBN: (纸本)9798331509828

This research work focuses on a comprehensive solution that evaluates and monitors the health of electric vehicle batteries in real time is the Internet of Things (IoT)-based Electric Vehicle (EV) Battery Fault Analyzer. A micro controller, an IoT module, sensors, a cloud platform, and a user interface are all included in the framework to facilitate efficient data collection, investigation, and client alerting. The core components are individual battery cell management and monitoring, a micro controller for data acquisition, and an Internet of Things module for seamless cloud connectivity. A few of the sensors that provide crucial information about the state of the battery include accelerometers, voltage, current, and temperature. After this, data has been securely transmitted, the condition of the battery is evaluated by fault detection algorithms and data analysis processes on a cloud platform. © 2025 IEEE.

关键词： Fault detection

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Blockchain-Based Secure Cloud Storage for IoT Healthcare Using BiGRU and BiLSTM Models 3

Blockchain-Based Secure Cloud Storage for IoT Healthcare Usi...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Chougule, Prachi Arihant Sarumathi, S. Kant, Ravi Gupta, Raj Kumar Latha, U. Pushpa Perada, Anvesh Dkte Society's Tetile and Engineering Institute Department of Electrical Engineering Ichalkaranji India Hkbk College of Engineering Department of Computer Science and Engineering Bangalore India Ysaicds Shoolini University Department of Computer and Data Sciences Solan India Sardar Vallabhbhai Patel College Bhabua Vksu Department of Physics Ara India Department of English Hyderabad India Drexel University Department of Electrical and Computer Engineering Philadelphia United States

ISBN: (纸本)9798331508456

Machine Learning (ML) algorithms have experienced a significant increase in popularity owing to the digitisation of analogue processes and other technological advancements, like the Internet of Things (IoT). Dependable datasets are crucial for the precise forecasting and resolution abilities of these algorithms, which are indispensable in healthcare, cloud computing, engineering, and finance. Conversely, training datasets are vulnerable to manipulation, thereby skewing the results. To address this issue, blockchain-based approaches have been proposed to enhance the reliability and security of cloud-stored E-Health data generated by the Internet of Things. To improve the accuracy and efficacy of healthcare prediction models, our study focusses on an advanced strategy that integrates feature selection with model training. Optimal input data is refined by feature selection employing entropy and correlation coefficient methodologies. The proposed model, employing BiGRU and BiLSTM, surpasses conventional CNNs and BiRNNs, with an average accuracy of 94.57%. When integrated with the Internet of Things (IoT), blockchain technology ensures the secure storage and transmission of electronic health records (EHRs). This strategy enhances predictive accuracy and safeguards sensitive health data in cloud infrastructures by amalgamating feature selection techniques with robust deep learning models. © 2025 IEEE.

关键词： Electronic health record

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A Comparative Analysis Between Support Vector Machine and Linear Regression for Improving Accuracy of Medal Tally in Asian Games

A Comparative Analysis Between Support Vector Machine and Li...

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Muthulingam, Gurusigaamani Ayyanar Ponnuchamy, Kardeepa Kumar, Gulla Pavan Mahesh, Jalla Karthik, G.N.H.B. Krishna, J. Venkata Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509828

Medal Prediction is a kind of art to predict the future champion with the aid of various past performing competitors. This prediction has so hard to find a world champion in terms of different categories of medals such as gold, silver, and bronze. The process and analysis function has efficiently evolved with the usage of Machine learning algorithms. Support Vector Machine (SVM) and Linear Regression (LR) are the best examples of deep machine learning approaches to improve the accuracy and efficacy of the proposed system. This proposed system has refined the accuracy difference between the SVM and LP with the high rate of datasets. The Global medal prediction has changed into more successful with the aid of two faces of machine learning processes. The various parameters of comparative studies have proven in an effective manner. © 2025 IEEE.

关键词： Support vector regression

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Novel Civil engineering Construction Site Safety Monitoring and Warning System based on Machine Vision 3

Novel Civil Engineering Construction Site Safety Monitoring ...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Zhang, Qiankun Cheng, Qibin Zhejiang Construction Investment Innovation Technology Co. Ltd. Zhejiang Hangzhou310000 China Zhejiang Construction Engineering Group Co. Ltd. Zhejiang Hangzhou310013 China

ISBN: (纸本)9798331509675

Machine vision is widely used in the engineering field, especially in the intelligent monitoring, safety warning and information recognition scenarios. In this study, the novel civil engineering construction site safety monitoring and warning system based on machine vision is proposed. As a pioneering research, this system realizes all-weather real-time security monitoring by combining the daytime and nighttime image processing technology. In daytime image processing scenarios, this study proposes a new image de-hazing algorithm. This algorithm models the imaging process by improving the atmospheric scattering model. At the same time, the guided filtering algorithm is used for optimization, which significantly improves the image clarity in low-visibility environments. In the night image processing scenario, this study uses infrared imaging technology and filtering algorithms for image enhancement. This operation can effectively solve the challenges of image noise and detail loss in low-light environments. As the core of the model, this study proposes a safety warning framework based on image anomaly detection to meet the special needs of construction sites. This innovative architecture includes human posture rotation detection and image color deviation anomaly recognition. Through such operations, automatic recognition and warning of dangerous behaviors are achieved. Experimental results prove the effectiveness of the recognition algorithm. © 2025 IEEE.

关键词： Machine vision

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Evaluating the Security of Distributed and Cooperative Media algorithms in Networks 5th

Evaluating the Security of Distributed and Cooperative Media...

引用

5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Yadav, Rakesh Kumar Sharma, Deeplata Mishra, Amba Krishnamoorthy, Ramkumar Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Chemistry Vivekananda Global University Jaipur India Department of Information Technology and M.Tech Integrated Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9789819780426

This paper discusses the modern-day safety-demanding situations associated with distributed and cooperative media algorithms in networks. It gives an overview of the safety protocols and algorithms used to ensure that records are shared securely from one node to another in those networks. The authors outline the proposed security protocols that have been evolved to mitigate this danger and examine their effectiveness, performance, and scalability. They also speak about the capacity threats and assaults that might be completed to compromise the security measures at the application layer. The paper concludes with hints for developing extra cozy, disbursed, cooperative media algorithms and networks. The technical summary discusses the significance of comparing the security of allotted and cooperative media algorithms in PC networks, which may be used to address numerous communication-demanding situations and to put into effect cozy conversation structures. The current nation of cozy communique systems relies closely on cozy, distributed, and cooperative media algorithms designed for paintings in extensive community environments. Those algorithms should be carefully analyzed in an excellent way to determine the security and robustness of the machine. The number one dreams of comparing disbursed and cooperative media algorithms are to detect protection vulnerabilities, check the computational complexity of the algorithms, analyze the communication payloads, and validate the effectiveness of the security implementations. Moreover, equipment has to be used to monitor the security houses of the algorithms that can perceive weaknesses or exploits. Similarly, assessing the security of distributed and cooperative media algorithms calls for robust trying out and simulation. Checking out should account for network dynamics, penetration trying out, unauthorized entry, and malicious attack situations. Simulation should ensure that the algorithm is successfully carried out and that

关键词： Comfortable Guard Networks Statistics Structures

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A Smart Agrisense IoT System with ML Integration

A Smart Agrisense IoT System with ML Integration

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2025 international conference on Computer, Electrical and Communication engineering, ICCECE 2025

作者： Kumar, P. Vishnu Kumar, Gorantla Rohith Rithwik, Sirigiri Deepesh, Gurram G. Pullaiah College of Engineering and Technology Department of Internet of Things Andhra Pradesh Kurnool India

ISBN: (纸本)9798331513894

This paper presents an advanced agricultural monitoring system utilizing Internet of Things (IoT) technology to provide precise soil and nutrient analysis. The core of the system is the ESP32 microcontroller, which processes data from various sensors to facilitate seamless operation. The focus is on improving crop yield and soil health by continuously monitoring nitrogen, phosphorus, and potassium (NPK) levels. The system explores recent advancements, including machine learning algorithms for accurate crop recommendations and edge computing for real-time data processing. Challenges faced in implementing such systems-such as sensor calibration, maintaining data consistency, and integrating predictive models-are addressed. Despite these obstacles, field tests demonstrate the system's reliability in analyzing soil fertility and offering tailored crop suggestions. The system consistently provides actionable insights to identify suitable crops, ensuring efficient resource use. This proposed agricultural monitoring system is designed to overcome these challenges and significantly advance precision farming. It is innovative, affordable, and easy to operate, making it a valuable tool for modern agriculture. © 2025 IEEE.

关键词： Smart agriculture

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Research on the Application of Multimodal Sensing Technology in Surveying and Mapping Data Collection 5

Research on the Application of Multimodal Sensing Technology...

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5th IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Bo, Liu Jianying, Weng School of Architectural Engineering Shandong University of Engineering and Vocational Technology Jinan Shandong China

ISBN: (纸本)9798331533694

This paper proposes an innovative CNN and RNN fusion algorithm to optimize the application of multimodal sensing technology in surveying and mapping data collection. The algorithm combines the data of multiple sensors such as lidar, RGB camera and infrared sensor, uses CNN to extract the spatial features of the image, and uses RNN to analyze the temporal relationship between different modal data, so as to achieve efficient fusion of multimodal data. Experimental results show that the proposed algorithm improves the data accuracy by about 18% in multiple surveying and mapping scenarios compared with the traditional single-modal sensor method, and the processing efficiency is improved by 15%. In addition, the algorithm shows significant robustness in complex environments, can effectively cope with the challenges brought by environmental changes, and improves the accuracy and stability of data. The research in this paper provides technical support for the development of intelligent surveying and mapping systems and promotes the popularization of multimodal data fusion algorithms in practical applications. © 2025 IEEE.

关键词： Spatio-temporal data

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