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共 11827 条记录，以下是121-130 订阅

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Alzheimer's Disease Prediction using VGG16 and Transfer learning 3

Alzheimer's Disease Prediction using VGG16 and Transfer Lear...

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

作者： Uprit, Maharth Monish Khattar, Pranav Brahmbhatt, Malhar Preethiya, T. Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798331527549

Alzheimer’s Disease (AD) prediction is essential for early detection and effective treatment. In this paper, a hybrid deep learning model has been proposed for predicting Alzheimer’s Disease in a patient in their early years. The dataset consists of Magnetic Resonance Imaging (MRI) scans that have four classes namely, non-demented, very mild demented, mildly demented, and moderately demented. In this hybrid model, fine-tuned VGG16 and transfer learning models were used with hyperparameter tuning to adapt it to Alzheimer's Disease prediction. The simulation results show that the proposed hybrid model outperforms the existing VGG16 model with an accuracy of 97.23% © 2025 IEEE.

关键词： Prediction models

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Decision Planning for Autonomous Driving Based on Proximal Policy optimization 24

Decision Planning for Autonomous Driving Based on Proximal P...

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3rd International Symposium on intelligent Unmanned Systems and Artificial Intelligence (SIUSAI)

作者： Li, Shuang Liu, Chunsheng Nie, Zhaoying Qilu Univ Technol Shandong Acad Sci Jinan Peoples R China Shandong Univ Jinan Peoples R China

ISBN: (纸本)9798400710025

Decision planning in autonomous driving tasks can be decomposed into behavioral decision-making and trajectory planning. Based on the pre-trained behavioral decision models through domain generalization and imitation learning, we propose an autonomous driving control theoretical model that optimizes behavioral decisions through hierarchical proximal strategy optimization. We design reward functions based on various factors during the driving process, so that the controlled vehicle can continuously learn self-reinforcement in the simulation environment to achieve autonomous driving decision planning tasks. Firstly, by layering the action space of the Proximal Policy optimization (PPO) algorithm, we reduce the search difficulty of action space through behavioral decision-making and trajectory planning methods. Then, the weights of the end-to-end deep learning network pre-trained based on imitation learning will be applied to the actor network for PPO to accelerate the convergence speed of the model. Finally, we use the open-source autonomous driving simulator CARLA as the simulation platform and compare our algorithm with other algorithms on the CORL2017 and No-rush benchmarks. The experimental results demonstrate the superiority of the algorithm proposed in this paper.

关键词： Decision planning Proximal policy optimization Reward function Autonomous driving

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optimization Strategy for Wind-Solar Complementary Energy Storage Capacity Leveraging Photovoltaic Virtual Energy Storage 3

Optimization Strategy for Wind-Solar Complementary Energy St...

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3rd conference on Fully Actuated System Theory and Applications (FASTA)

作者： Ni, Jiahua Chen, Yuwei Wang, Tong Jin, Wei Powerchina Huadong Engn Corp Limied Hangzhou 311122 Peoples R China Hangzhou City Univ Sch Informat & Elect Engn Hangzhou 311122 Peoples R China

ISBN: (纸本)9798350373707;9798350373691

In this study, we present an integrated optimization model for configuring energy storage capacities in wind-solar energy systems, utilizing an innovative approach of Photovoltaic (PV) Virtual Energy Storage (PV-VES). This model aims to mitigate the high costs associated with conventional electrochemical storage solutions by integrating cost-effective PV components. The proposed optimization framework constructs a cost-minimization objective function, considering the operational characteristics and constraints of wind turbines, PV arrays, and energy storage units. By calculating the optimal installation capacities for both electrochemical and virtual energy storage, the model provides selective cost advantages in investment for renewable energy storage infrastructures. The results demonstrate that PV-VES can enhance the economic feasibility of wind-solar complementary storage systems.

关键词： capacity optimization electrochemical storage PV virtual energy storage

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Deep learning-based Multimodal Fusion for Improved Object Recognition Accuracy 3

Deep Learning-based Multimodal Fusion for Improved Object Re...

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

作者： Wang, Qi Cheng, Xiaohan Gao, Zijun Gu, Wenjun Mei, Taiyuan Xia, Haohao Northeastern Univ Boston MA 02115 USA Johns Hopkins Univ Baltimore MD USA Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9798350352634;9798350352627

This paper explores the application of deep learning and multimodal information fusion to enhance image recognition capabilities. Multimodal fusion, integrating information from different sources like audio and images, offers a deeper understanding of objects compared to unimodal approaches. Deep learning techniques are employed to achieve this fusion, specifically through an enhanced Inception network. This research investigates the impact of multimodal information on recognition accuracy by comparing purely image-based, purely sound-based, and multimodal fusion scenarios. The results demonstrate a significant increase in average recognition accuracy for moving objects through multimodal fusion. This advancement holds considerable value in various fields, including autonomous driving. The findings contribute to the development of more accurate object recognition systems across diverse applications.

关键词： Deep learning Semantic Segmentation Multi-Modal Fusion Image Recognition Voice Recognition

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

Application and practice of artificial intelligence in conte...

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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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Skin cancer severity analysis and prediction framework based on deep learning 24

Skin cancer severity analysis and prediction framework based...

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3rd International conference on Artificial Intelligence and intelligent Information Processing, AIIIP 2024

作者： Huang, Ruijie Wu, Haoran AI of FST Beijing Normal University-Hong Kong Baptist University United International College Guangdong Zhuhai China

ISBN: (纸本)9798400707308

This paper provides a Convolutional Neural Network (CNN) based skin cancer detection model that categorizes skin lesions as benign or malignant. To increase its resilience, the model makes use of picture enhancing strategies such data augmentation and normalization. The approach automatically collects characteristics from pictures via the construction of a deep convolutional network, hence enabling efficient categorization. The dataset was divided into training and validation sets inside the experimental framework, using convolutional layers, max pooling, and fully connected layers—common deep learning architectures. We used the Adam optimizer for performance optimization and binary cross-entropy as the loss function in a binary classification challenge. We assessed the model’s performance during training by looking at parameters like accuracy and AUC (Area Under the Curve). The testing results show that the model detects skin cancer with excellent accuracy and reliability;on the test set, both accuracy and AUC approach substantial values. In addition, we used a confusion matrix to determine the model’s sensitivity, specificity, and positive predictive value, which allowed for a more thorough examination of its categorization capabilities. The results indicate that the CNN-based model has a great deal of promise for use in skin cancer diagnosis, providing a theoretical framework for later practical application. © 2024 Copyright held by the owner/author(s).

关键词： Benign and malignant classification

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intelligent sensing and adaptive modulation: the key technologies of future communication systems 24

Intelligent sensing and adaptive modulation: the key technol...

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3rd International conference on Frontiers of Artificial Intelligence and Machine learning (FAIML)

作者： Zhang, Pengqin Zhu, Hongjie Guangzhou Coll Technol & Business Guangzhou 510850 Guangdong Peoples R China

ISBN: (纸本)9798400709777

With the continuous progress of the times and technology, the way of communication between people has evolved into an efficient way of information transmission based on the Internet, and the future communication system can only be more efficient and accurate. Therefore, this paper believes that intelligent perception and adaptive modulation can become the main technologies of future communication development, because intelligent perception can efficiently collect information, and adaptive modulation can independently choose the best modulation format, thereby improving bandwidth utilization. In addition, this paper also verifies the idea of this paper through comparative experiments at the end, and the experimental index to measure the performance is the data transmission rate. In the experiments repeated up to 50 times, the average data transmission efficiency of the intelligent communication system using the proposed method is 96.93Mdbs, while that of the ordinary communication system is 66.78Mdbs. It can be seen that there is a big gap between the two, which fully reflects the great improvement of the work efficiency of the communication system brought by the method in this paper.

关键词： intelligent sensing Adaptive modulation Future communication Data transmission rate Signal-to-noise ratio

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E-RL:Vehicle Driving Strategy learning with Environmental Reinforcement learning Framework 3

E-RL:Vehicle Driving Strategy Learning with Environmental Re...

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3rd International conference on Electronics and Information Technology, EIT 2024

作者： Shuai, Yanshu Zheng, Jiaoling Wang, Haoquan Zhang, Sihui Liu, Yiming Guo, Peng Zeng, Yu Chengdu University of Information Technology School of Software Engineering Sichuan Chengdu China Sichuan Yifang Intelligent Technology Co. Sichuan Chengdu China

ISBN: (纸本)9798350369151

To address urban traffic congestion simulation after Traffic Management Initiatives (TMI) implementation, a method combining deep reinforcement learning with environmental optimization is proposed, optimizing the environmental strategy through a fixed agent policy. To tackle the challenges posed by road network topology changes post-TMI, the method designs an environmental Markov reinforcement learning framework and introduces graph neural networks to dynamically calculate road resistance in urban areas. Combined with the Dijkstra algorithm, the vehicle routing strategy is determined. This strategy is then applied to the road network after TMI to predict road resistance post-TMI. Experiments validate that this method achieves good congestion replication in the original road network environment and effectively predicts congestion after TMI implementation. © 2024 IEEE.

关键词： Traffic congestion

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3rd International conference on intelligent Computing and optimization, ICO 2020

3rd International Conference on Intelligent Computing and Op...

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3rd International conference on intelligent Computing and optimization, ICO 2020

ISBN: (纸本)9783030681531

The proceedings contain 113 papers. The special focus in this conference is on intelligent Computing and optimization. The topics include: Intellectualized Control System of Technological Processes of an Experimental Biogas Plant with Improved System for Preliminary Preparation of Initial Waste;way for Intensifying the Process of Anaerobic Bioconversion by Preliminary Hydrolysis and Increasing Solid Retention Time;evaluation of Technical Damage Caused by Failures of Electric Motors;development of a Prototype Dry Heat Sterilizer for Pharmaceuticals Industry;optimization of Parameters of Pre-sowing Seed Treatment in Magnetic Field;development of a Fast Response Combustion Performance Monitoring, Prediction, and optimization Tool for Power Plants;Industry 4.0 Approaches for Supply Chains Facing COVID-19: A Brief Literature Review;ontological Aspects of Developing Robust Control Systems for Technological Objects;a New Initial Basis for Solving the Blending Problem Without Using Artificial Variables;review of the Information that is Previously Needed to Include Traceability in a Global Supply Chain;modeling of the Thermal State of Systems of Solar-Thermal Regeneration of Adsorbents;online Technology: Effective Contributor to Academic Writing;a Secured Electronic Voting System Using Blockchain;preconditions for Optimizing Primary Milk Processing;optimization of Compost Production Technology;economic Aspects and Factors of Solar Energy Development in Ukraine;a Method for Ensuring Technical Feasibility of Distributed Balancing in Power Systems, Considering Peer-to-Peer Balancing Energy Trade;the Results of a Compromise Solution, Which Were Obtained on the Basis of the Method of Uncertain Lagrange Multipliers to Determine the Influence of Design Factors of the Elastic-Damping Mechanism in the Tractor Transmission.

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Research on Maintenance and Fault Prediction Model of intelligent Building Driven by Internet of Things 3

Research on Maintenance and Fault Prediction Model of Intell...

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3rd International conference on Data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Li, Xin Gao, Lingyu Liu, Yuding Harbin Huade University Heilongjiang Harbin150025 China

ISBN: (纸本)9798331519032

With the rapid development of Internet of Things (IoT) technology, its application in the field of intelligent buildings is increasingly extensive, which significantly improves the energy efficiency management and fault prediction capabilities of intelligent buildings. The purpose of this paper is to discuss the construction and optimization method of maintenance and fault prediction model of intelligent building driven by IoT. Through in-depth analysis of sensor data in intelligent buildings, statistical characteristics, time series characteristics, frequency characteristics and correlation characteristics are extracted, and the running state and potential faults of building systems are fully captured. On this basis, this paper proposes a fault prediction model based on ensemble learning, which combines random forest and AdaBoost algorithm to effectively improve the prediction accuracy and model stability. The experimental results show that the model performs well in fault prediction tasks in typical intelligent building scenes, with high accuracy, recall and F1 score, and its performance is stable under different data sets. In addition, by introducing L1 and L2 regularization, the model complexity is effectively controlled and the risk of over-fitting is reduced. The research in this paper not only provides a new solution for the maintenance and fault prediction of intelligent buildings, but also provides a useful reference for the future development of intelligent building management systems. © 2024 IEEE.

关键词： Prediction models

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