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共 11827 条记录，以下是171-180 订阅

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Big Data Analysis and Applications in International Trade: Decision optimization Based on intelligent Algorithms 3

Big Data Analysis and Applications in International Trade: D...

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3rd IEEE World conference on Applied Intelligence and Computing, AIC 2024

作者： Yang, Wenwen Jiangxi College of Foreign Studies Nanchang330096 China

ISBN: (纸本)9798350384598

In the field of international trade, due to its wide range and complex interest relations, it is necessary to synthesize various information for analysis and decision-making. However, traditional data processing methods have been unable to meet the needs of enterprise development. As a new, efficient, safe and reliable solution, large database technology has been widely used in various fields and achieved good results. The purpose of this paper is to discuss and study the cross-department collaboration problem driven by intelligent algorithm through application examples. This paper first introduces the basic concepts, characteristics and relevant theoretical basis of inter-departmental collaboration. By studying the application of intelligent algorithms in interdepartmental collaboration, effective decision support tools are provided to deal with the complex situations in international trade. After that, this paper conducts a simulation test on the intelligent algorithm-driven decision optimization model, and the test results show that the test range of this model for the risk-return rate in trade is 0.04-0.09. © 2024 IEEE.

关键词： big data analysis decision optimization intelligent algorithms international trade

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Optimizing Symptom Prediction in Healthcare: An Integrative Machine learning Approach to Regression and Classification Models 3

Optimizing Symptom Prediction in Healthcare: An Integrative ...

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3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Goddilla, Nagarjuna Reddy Sivasangari, A. Sathyabama Institute of Science and Technology Dept of Cse Chennai India

ISBN: (纸本)9798331515911

This research explores the application of combined machine learning techniques for predicting disease symptoms, aiming to support early diagnosis and improve healthcare outcomes. By analyzing both regression and classification models, the study evaluates their respective performance, strengths, and challenges in predicting symptoms. Using a comprehensive dataset with medical records, demographic data, and clinical variables, we developed predictive models to capture the complexities of healthcare data. To enhance accuracy and manage issues like data imbalance and feature diversity, ensemble methods and feature engineering were applied, with the Random Forest classifier achieving a leading accuracy of 91.84%. Additionally, the study emphasizes model interpretability, which is crucial for healthcare professionals to trust and adopt these predictive tools. The findings provide valuable guidance for healthcare professionals and data scientists in choosing machine learning approaches suited to specific medical applications. By bridging the roles of regression and classification models, this research offers a well-rounded perspective on the potential of predictive analytics in healthcare. © 2025 IEEE.

关键词： Adversarial machine learning

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Human-AI Text Classifier Using Feature Vectorization and ML Models 3

Human-AI Text Classifier Using Feature Vectorization and ML ...

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3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kumbharkar, P.B. Korade, Nilesh B. Kuche, Saurabh Chaudhari, Mayuresh Paraskar, Abhishek Patil, Sahil Pune India

ISBN: (纸本)9798331527549

The ability to discern between text produced by artificial intelligence (AI) and that created by humans has become a crucial challenge in domains including academia, media, and digital communications due to the increasing popularity of AI and NLP technologies. With an emphasis on human response identification, this paper provides an extensive overview of the state-of-the-art techniques and algorithms for identifying AI-generated material. This paper assesses important detection methods, such as deep learning models, attention mechanisms, and feature extraction algorithms, and identifies research needs through a thorough literature review. To shed light on the continuous difficulties in AI-generated text identification, we also investigate the precision, resilience, and practicality of these techniques. © 2025 IEEE.

关键词： Deep learning

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Design and Development of an AI Big Model for Elderly Education 3

Design and Development of an AI Big Model for Elderly Educat...

引用

3rd International conference on Artificial Intelligence and Computer Information Technology, AICIT 2024

作者： Tang, Jian Yiyang Open University Yiyang Education College No. 5 Kangfu North Road Hunan Province Yiyang China

ISBN: (纸本)9798350365443

With the development of technology and social progress, artificial intelligence (AI) has gradually penetrated into various fields of our lives. In the field of education, the application of AI is becoming increasingly widespread, especially in elderly education, where the application of AI is of great significance. Design and develop an AI model for elderly education, aiming to provide personalized, intelligent, and convenient learning experiences for the elderly. Utilizing artificial intelligence technology to provide customized learning content and methods by analyzing the learning needs and interests of the elderly;Through real-time language communication and intelligent tutoring functions, help elderly people solve problems and confusions in learning;Through data analysis and mining, provide support and services for elderly people in areas such as health management and social activities. In the future, technologies such as virtual reality and augmented reality can be combined to create a more diverse and interactive learning experience. © 2024 IEEE.

关键词： Contrastive learning

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Application of image classification in forest fire detection using UAV 3

Application of image classification in forest fire detection...

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3rd International conference on Robotics, Artificial Intelligence and intelligent Control (RAIIC)

作者： Geng, Bowen Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9798350352634;9798350352627

This paper focuses on the application of image classification in forest fire detection using unmanned aerial vehicles (UAVs), discussing the development history of UAV image classification and the significance of machine vision in fire monitoring. Initially, the dataset used for fire detection and the data processing and enhancement techniques are introduced. Subsequently, the construction and architecture of the image classification model are detailed. The core of this study is to enhance the accuracy of model image recognition in complex forest environments by replacing optimizers, modifying the model architecture, and adding modules. Various models and optimizers are compared and analyzed, and the operations and significance of enhancement methods and attention mechanisms are explored. The aim is to improve training effectiveness through these strategies, thereby effectively supporting UAVs in forest fire detection.

关键词： UAV Forest fire detection Image classification Machine vision Model optimization

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optimization of Electrical Equipment in intelligent Transmission Engineering Under Fuzzy Neural Network Algorithm 3

Optimization of Electrical Equipment in Intelligent Transmis...

引用

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

作者： Li, Xiping Wang, Jitao School Of Intelligent And Electrical Electronic Engineering Harbin Cambridge College Heilongjiang Harbin China School Of Electrical And Electronic Engineering Harbin Institute Of Technology Heilongjiang Harbin China

ISBN: (纸本)9798350318609

Traditional optimization analysis of electrical equipment in transmission engineering is often based on experience and rules, lacking intelligent methods. This leads to limitations in the adaptability and effectiveness of traditional methods when facing complex and variable power system conditions. By introducing the fuzzy neural network (FNN) algorithm, this paper fully utilizes its adaptability to nonlinear and complex correlations, thereby improving the intelligence level of optimization analysis of electrical equipment in transmission engineering. It collects actual power system operation data information and preprocesses the data to ensure the quality of the data. This article constructs a FNN model to effectively handle fuzzy and uncertain information, and applies it to the complex environment of power systems. The experimental results show that the average RMSE of the FNN for electrical equipment optimization prediction is 0.07, and the optimized voltage is very stable. The application of FNN algorithm can effectively improve the optimization effect of electrical equipment. © 2024 IEEE.

关键词： Fuzzy neural networks

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Visionary Cardioscan: Heart Risk Detection through Retinal Imaging 3

Visionary Cardioscan: Heart Risk Detection through Retinal I...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Arun Kumar, S. Dharshini, B. Hemanth, M. Vignesh, S. Saratha, M. Department of Artificial Intelligence and Machine Learning M.Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Heart attacks and hypertension are two cardiovascular disease risk factors that have a major impact on the microvascular system's structure and function. Fundus camera images can detect abnormalities in retinal blood vessels that indicate vascular injury. Using machine learning techniques, preclinical signs of damage, which may not be visible to an observer, can be detected. It proposes a system that uses retinal images to predict heart attack risk. The model leverages vessel segmentation to extract morphological details of retinal blood vessels. The results can assist in identifying heart disease early, especially in young patients. © 2025 IEEE.

关键词： Ophthalmology

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optimization of Visual SLAM for Mobile Robots in Complex Environments: Tight Coupling of Visual-Inertial Fusion and Efficient Loop Closure Detection Strategies 3

Optimization of Visual SLAM for Mobile Robots in Complex Env...

引用

3rd International conference on Robotics, Artificial Intelligence and intelligent Control (RAIIC)

作者： Fu, Bo Wei, Wei Guo, Zhiyuan Ren, Yuhan Dept Xian Univ Technol Xian Shaanxi Peoples R China

ISBN: (纸本)9798350352634;9798350352627

This study tackles mobile robots' environmental perception challenges in complexity, presenting an advanced Visual-Inertial SLAM (VSLAM) technique that enhances accuracy, robustness, and real-time functionality. It bolsters visual odometry with ORB features for meticulous matching and selects keyframes, integrating IMU data for robust motion handling in low-textured scenes. Rear-end operations employ graph optimization and a bag-of-words-based loop closure, utilizing sliding window optimization and marginalization to curb accumulative errors, ensuring trajectory and map consistency. Experiments on KITTI, EuRoC, and TUM datasets surpass conventional methods like ORB-SLAM2 and VINS-Mono, trimming trajectory and mapping inaccuracies by 23.8% and 41.7%, respectively, with robust adaptability across motion modes and environments. System module timing analysis paves the way for real-time deployment on less powerful hardware. Future research directions include direct visual odometry, dynamic environment adaptation, multi-sensor fusion, and large-scale scene comprehension, pushing SLAM's frontier in intricate dynamics and empowering autonomous robot navigation.

关键词： Mobile Robots Visual SLAM Graph optimization Environmental Perception

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Machine learning-Driven Strategies to Mitigate Mobile Banking Adoption Challenges in Rural Areas 3

Machine Learning-Driven Strategies to Mitigate Mobile Bankin...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Shridhar, B.R. Vinoth, S. Chinnasamy, Gopalakrishnan Cms Business School Jain Deemed-to-be University Faculty of Management Bengaluru India

ISBN: (纸本)9798331515911

Mobile banking has huge potentials to promote financial inclusions, especially in the rural areas. However, the adoptions in these areas are hampered by factors relating to security, misunderstanding, limitation in technology, and trust. This research, therefore, tries to study problems and advocates a solution for the same based on machine learning. Demographic features, current usage, and key impediments to mobile banking adoptions are studied with a large database of rural respondents. The paper uses classification decision trees, basic content analysis, random forest allocation, and multiple regression to identify the major concepts driving customer satisfaction and behavior. Security, competition, and user interface complexity issues have emerged as particularly strongly influencing factors in the results. The conclusion derived is that a focus on machine learning solutions effectively addresses implementation issues, further extending to a bettering of overall user experience. © 2025 IEEE.

关键词： Adversarial machine learning

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Intracranial Hemorrhage Detection using Deep learning and optimization Techniques 3

Intracranial Hemorrhage Detection using Deep Learning and Op...

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3rd International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2023

作者： Balipa, Mamatha Kundapur, Poornima P. Adithya Nmam Institute of Technology Department of Mca Karnataka Nitte India Manipal Institute of Technology Mahe Department of Ds and Ca Manipal India

ISBN: (纸本)9798350306989

Intracranial Hemorrhage (ICH) is a critical medical condition characterized by bleeding within the skull, specifically in the brain. Timely detection and accurate classification of ICH from computed tomography (CT) scan pictures are essential for successful treatment plan and improved patient outcomes. This study presents a novel approach for ICH detection using Deep learning and Machine learning techniques. A hybrid model is proposed, leveraging the power of ResN et50, a state-of-the-art deep learning architecture, together with different machine learning methods. The hybrid models include ResN et50 with Support Vector Machines(SVM), Artificial Neural Networks(ANN), ANN optimized using the Grasshopper optimization Algorithm, and ANN optimized using the Genetic optimization Algorithm. By harnessing the learned features of ResN et50 and the optimization capabilities of these algorithms, the models aim to accurately identify and classify hemorrhage patterns in CT scans. Experimental evaluations on a dataset of CT scan images demonstrate the superiority and efficacy of the proposed hybrid models in achieving high classification accuracy. The results highlight the potential of this approach for aiding clinicians in early diagnosis and treatment planning of ICH, thus contributing to improved patient care in cases of intracranial hemorrhage. © 2023 IEEE.

关键词： Support vector machines

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