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Construction of Diabetes Prediction Model Based on machine Learning 3

Construction of Diabetes Prediction Model Based on Machine L...

引用

3rd international conference on Intelligent Computing and Next Generation Networks, ICNGN 2024

作者： Xinping, Shang Ke, Jian Xuliang, Yang Yi, Wang Artificial Intelligence Dongguan City University Guangdong Dongguan China

ISBN: (纸本)9798331529222

Diabetes is a global epidemic of chronic diseases;early identification of high-risk groups can effectively reduce the incidence of diabetes and reduce the risk of complications. In recent years, the predictive model based on machine learning has been gradually applied to the early screening and prediction of diabetes due to its efficient data processing ability. In this study, a diabetes prediction model was constructed by analyzing and comparing common machine learning algorithms such as logistic regression, K-nearest neighbor, decision tree, random forest, neural network and XGBoost. The experimental results show that random forest and XGBoost have significant advantages in predicting diabetes mellitus with high prediction accuracy. This study provides an effective tool for clinical applications and provides a reference for the development of future medical prediction models based on machine learning. ©2024 IEEE.

关键词： Prediction models

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Data Science Applications in IoT for Electric Vehicles: Leveraging Artificial intelligence and machine Learning 3

Data Science Applications in IoT for Electric Vehicles: Leve...

引用

3rd international conference for Advancement in Technology, ICONAT 2024

作者： Jujjuvarapu, Ravi Kumar Gaddala, Subose Chandrabose Space & Cyber Technologies Beacon Red EDGE Group Abu Dhabi United Arab Emirates Nanda Technologies LLC DallasTX United States

ISBN: (纸本)9798350354171

The integration of Internet of Things (IoT) technology in Electric Vehicles (EVs) has opened new avenues for optimizing vehicle performance, enhancing user experience, and improving operational efficiency. This paper explores the application of Artificial intelligence (AI) and machine Learning (ML) in IoT-enabled EVs, focusing on key interface areas where data-driven insights can be leveraged. By analyzing current IoT architectures, this research identifies potential areas for AI and ML intervention, such as predictive battery management, autonomous driving algorithms, predictive maintenance, and energy consumption optimization. The proposed framework demonstrates how machine learning models can be utilized to predict battery life and charging patterns, improve autonomous driving decisions, and optimize energy efficiency based on real-time data. The results indicate significant improvements in both vehicle performance and user satisfaction through the integration of AI and ML technologies. This paper provides a comprehensive overview of the challenges and limitations associated with implementing these advanced technologies in EVs, while also highlighting the potential for future research in this rapidly evolving field. © 2024 IEEE.

关键词： ANN Artificial intelligence Data Science Electric Vehicles IoT machine Learning

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Deep Learning based Object Tracking and Detection for Autonomous Drones using YOLOv3 3

Deep Learning based Object Tracking and Detection for Autono...

引用

3rd international conference on Applied Artificial intelligence and Computing, ICAAIC 2024

作者： Leela, G. Naga Varun, U. Uma, M. Srinath, P. Sharan, A. Srinivasa Sree Srinivasa Ramanujan Institute of Technology Dept of Cse Anantapur India

ISBN: (纸本)9798350375190

Object detection plays a vital role in enabling drones to perceive and interact intelligently with their surroundings. The process involves data collection, selecting appropriate deep learning architectures for accurate detection. In our proposed system, we explore the integration of advanced deep learning techniques into autonomous drones for object detection. The trained models combined with onboard software integration efficiently predict object presence in real-time imagery. The project aims to significantly improve the accuracy and reliability of object recognition, enabling drones to distinguish between different objects and navigate their environment with increased precision. The research outcomes hold the potential to enhance drone applications in different fields such as surveillance, search and rescue. © 2024 IEEE.

关键词： Drones

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Gaze Object Detection Based on 3D Body Posture 3

Gaze Object Detection Based on 3D Body Posture

引用

3rd international conference on Artificial intelligence, Internet of Things and Cloud Computing Technology, AIoTC 2024

作者： Feng, Xiao Chen, Yi Xiao, Junwen School of Computer Science Central China Normal University Wuhan430079 China

ISBN: (纸本)9798331530280

Gaze object detection in computer vision is challenging, particularly in scenes with motion blur, multiple overlapping objects, and unclear object boundaries. Traditional methods that primarily rely on head movements struggle to perform well in such complex environments. To address these challenges, our dual-attention multimodal architecture enhances accuracy by establishing a connection between the body orientation and the gaze object while also incorporating depth information for a more precise analysis. Additionally, we introduce a novel IoC (Intersection over Confidence) metric, which provides better differentiation between gaze objects and non-targets. Our approach demonstrates superior performance on both the GOO and Gazefollow datasets, outperforming existing methods in these challenging conditions. © 2024 IEEE.

关键词： Object recognition

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The Application of Artificial intelligence Technology in Optimizing Translation Systems Supported by ChatGPT 3

The Application of Artificial Intelligence Technology in Opt...

引用

3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Li, Jingtai School Of Foreign Languages Jiaying University Guangdong Meizhou China

ISBN: (纸本)9798350318609

In recent years, the large models represented by ChatGPT have brought opportunities and challenges to the teaching of translation courses. The correct application of these tools can enhance learning effectiveness and cultivate student abilities, achieving 'human-machine cooperation'. It can be said that the rise of Chat GPT large-scale language models has led artificial intelligence to a new stage of development, bringing tremendous changes to the field of language teaching. This article focuses on the current situation of modern English education and the specific application strategies of using artificial intelligence technology in optimizing translation systems supported by ChatGPT, proposing exploration suggestions for ChatGPT in the field of translation teaching, which has a certain practical reference significance for the improvement of English teaching theory under the background of the continuous enhancement in the field of artificial intelligence. © 2024 IEEE.

关键词： Artificial intelligence

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A Review on Meta-Learning: How Artificial intelligence and machine Learning Can Learn to Adapt Quickly 3

A Review on Meta-Learning: How Artificial Intelligence and M...

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

作者： Khadse, Shrikant Gourshettiwar, Palash Pawar, Adesh Faculty of Engineering and Technology Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Wardha442001 India Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Meta-learning aims to create Artificial intelligence (AI) systems that can adapt to new tasks and improve their performance over time without extensive retraining. The advent of meta-learning paradigms has fundamentally changed how Artificial intelligence (AI) and machine Learning (ML) are handled, constantly reshaping these fields. Research study reviews the broad field of meta-learning, highlighting its critical function in bridging the AI-ML divide to improve adaptability. The study starts with a review of past developments, following the development of meta-learning algorithms and their underpinning theories. The historical view lays the groundwork for understanding the course of this dynamic regulation, spanning from early efforts in probabilistic program induction to modern discoveries in model-agnostic meta-learning. The study explores multiple meta-learning applications in AI and ML, revealing how they affect financial forecasting, computer vision, natural language processing, autonomous cars, health informatics, robotics, and personalized recommendation systems. When seen from the perspective of adaptive intelligence, meta-learning may be effectively applied to conditions with limited data, hyperparameter optimization, few-shot learning, and quick adaption in reinforcement learning settings. © 2025 IEEE.

关键词： Contrastive Learning

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Intelligent Fire Detection and Alert System with Sensing Capabilities Powered by Artificial intelligence 3

Intelligent Fire Detection and Alert System with Sensing Cap...

引用

3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Yasir Abdullah, R. Hariharan, V.C. Nithin, K.M. Swetha, V. Vijayaalayan, A. Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India

ISBN: (纸本)9798350322842

Artificial intelligence (AI) has become a popular tool to perform video surveillance in order to detect and identify humans, vehicles, objects, and events. By analyzing audio and images via computer software programs, AI model can identify people's faces and other emerging technologies that are commonly used in security monitoring systems. The main purpose of this study is to explore the different areas, where Artificial intelligence (AI) can be used to detect and prevent fires. © 2023 IEEE.

关键词： Face recognition

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Enhancing the accuracy of fruit freshness detection by utilizing transfer learning and customizing convolutional neural network(CNN). 3

Enhancing the accuracy of fruit freshness detection by utili...

引用

3rd international conference on Computing and machine intelligence (ICMI)

作者： Rahman, Nabila Arefin, Mahira Rahman, Sabila Islam, Md Shamiul Khatun, Tammy Akter, Usha Bangladesh Univ Business & Technol Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9798350372977;9798350372984

Food quality and safety are paramount concerns in our modern world and perishable goods, especially fruits and vegetables, stand at the intersection of these concerns. The ability to accurately determine the freshness of these products not only impacts food safety but also holds the key to reducing waste in our food supply chain. To enhance the mean lifespan of humans, it is imperative to eradicate the potential for infectious illnesses. The majority of a high-risk community's diet consists of fruits and vegetables. Consequently, differentiating spoiled fruits from viable ones is critical for their preservation. Automation technology is an indispensable component of daily existence. The principal source of wealth is agriculture in the modern world. Daily growth is observed in the sales volume of fresh produce. People who prioritize their health select only high-quality, nutritious fresh fruits. In this paper, we present a novel approach that leverages state-of-the-art artificial intelligence and computer vision techniques, including Convolutional Neural Networks (CNN), ResNet50, VGG16 and InceptionV3 to tackle the challenge of assessing the quality of fruits and vegetables. By automating the evaluation process, our method goes beyond the traditional, subjective, and time-consuming ones. Using the power of deep learning, we present a complete framework that can tell with unprecedented accuracy whether a wide range of produce items are fresh and safe to eat.

关键词： Fresh Fruit CNN InceptionV3 RseNet50 Rotten Fruit Agriculture

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Application and practice of artificial intelligence in contemporary computer network technology 24

Application and practice of artificial intelligence in conte...

引用

3rd international conference on Cryptography, Network Security and Communication Technology (CNSCT)

作者： Zheng, Tong Feng, Xinshuo Shenyang Univ Chem Technol Sch Humanities & Arts Shenyang Peoples R China Shenyang Univ Chem Technol Sch Chem & Engn Shenyang Peoples R China

ISBN: (纸本)9798400716959

In the era of big data, artificial intelligence as a new technology is developing rapidly and gradually penetrating into various fields, including computer networks. This paper aims to summarize the development of artificial intelligence and computer network technology in the era of big data, and explore the advantages and applications of artificial intelligence in computer networks. First of all, this paper summarizes the general characteristics of artificial intelligence and computer network technology in the era of big data. Secondly, the advantages of artificial intelligence in computer networks are discussed. Artificial intelligence can provide intelligent network management and optimization schemes. Ai can also be applied in the field of cyber security to provide better cyber security protection by automatically identifying and blocking malicious attacks. In addition, AI can also be used for network fault diagnosis and prediction, providing more timely and accurate fault handling. Artificial immunity technology can detect and respond to cyber attacks by building a network immune system. Problem solving technology can use machine learning algorithms to solve complex problems in the network, and data mining technology can mine valuable information and patterns from big data to provide support for network decision-making and optimization. Finally, this paper will further elaborate the application of artificial intelligence in computer networks through practical research. Through these studies, the advantages and potential of artificial intelligence in computer networks can be more fully understood.

关键词： Artificial intelligent Computer network technology Artificial Neural Networks Artificial Immune Systems

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Assisted Landscape Design Model Based on Deep Lab v3+and Computer Vision 24

Assisted Landscape Design Model Based on Deep Lab v3+and Com...

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3rd international conference on Frontiers of Artificial intelligence and machine Learning (FAIML)

作者： He, Xiaoli Zhang, Hong Shandong Inst Commerce & Technol Rural Revitalizat Ind Coll Jinan Shandong Peoples R China

ISBN: (纸本)9798400709777

Reasonable landscape design of urban public space is the key to urbanization transformation. In order to create a green, intelligent and humanistic urban image and enhance the overall beauty of the urban environment, the study provides an extraction method of landscape elements for landscape design with the help of computer vision detection technology and image segmentation model. The experimental results show that the method designed by the study has a better balance of precision and recall, with a precision of 0.9 and a recall of 0.97. The loss function curve of the method converges to a minimum value of 0.50, which is faster, and the average intersection and merger ratio reaches the level of 0.5 at the early iteration, which is better than other models. When applied to landscape design examples, the method performs well in terms of subjective evaluation indexes and panoramic quality compared to traditional design. The study of assisted landscape design method based on Deep Lab v3+ and computer vision provides new ideas for the digital transformation of landscape design, and provides theoretical support and technical guidance for the diversified and high-quality design needs of landscape design.

关键词： Computer vision Deep Lab v3+ Landscape design Image segmentation Landscape elements

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