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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

共 13584 条记录，以下是741-750 订阅

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Swarm Intelligence Based Feature Selection and Machine learning Methods for Future Railway Traffic Prediction 4

Swarm Intelligence Based Feature Selection and Machine Learn...

引用

4th international conference on Intelligent Technologies, CONIT 2024

作者： Radhika, C. Hanirex, D.Kerana Bharath Institute of Higher Education and Research Department of Computer Applications Tamil Nadu Chennai India Bharath Institute of Higher Education and Research Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350349900

An essential prerequisite for proactive and anticipatory real-time regulation of railroad traffic is the accurate forecast of train delays, or deviations from the schedule. To manage the viability of timetable realization, traffic controllers must forecast the trains' arrival times in (or approaching) their region. Rescheduled travel, traffic control, data on passengers, and effective route design all rely upon the ability to predict train locations in both space and time in real time. the study introduces a data mining system designed to forecast train traffic. the system utilizes a dataset that includes the scheduled times of all trains and the relationships between them, along with weather characteristics. In order to improve the system's accuracy in predicting future traffic, research was proposed that would use Swarm Intelligence-based feature selection techniques as Harris Hawk optimization (HHO). To ascertain the best predictive models in terms of predictive capability, machine learning techniques namely Bi-directional Long Short-Term Memory (Bi-LSTM) with entity embedding are utilized. It has been demonstrated that the suggested algorithm for processing and analyzing rail traffic is more efficient than alternative approaches and has shown an accuracy rate of 92.3% and precision of 93%. © 2024 IEEE.

关键词： Swarm intelligence

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Ideological and Political Education in the Mobile Internet Era: A Survey 4

Ideological and Political Education in the Mobile Internet E...

引用

4th international conference on Educational Technology, ICET 2024

作者： Liu, Qing Tang, Zhongjian Zhang, Simin Chongqing Youth Vocational & Technical College School of Artificial Intelligence Chongqing China

ISBN: (纸本)9798350376944

Ideological and political education is a crucial part of the education system for university students in China. However, traditional teaching methods face challenges such as weak classroom interaction and insufficient student self-learning ability. In order to improve the teaching effectiveness of ideological and political courses, new technologies like mobile Internet have been employed. In order to better understand the applications of mobile Internet technology in ideological and political education and guide the development of mobile learning systems, this paper summarizes existing mobile Internet applications designed for ideological and political teaching and analyzes main technical platforms used for building mobile learning systems. Meanwhile, in-depth discussions are conducted on the challenges and possible future research directions. the research in this paper can provide guidance for the design of ideological and political courses. © 2024 IEEE.

关键词： education ideological and political mobile internet mobile learning survey

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DGPO: Discovering Multiple Strategies with Diversity-Guided Policy optimization 38

DGPO: Discovering Multiple Strategies with Diversity-Guided ...

引用

38th AAAI conference on Artificial Intelligence (AAAI) / 36th conference on Innovative applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence

作者： Chen, Wentse Huang, Shiyu Chiang, Yuan Pearce, Tim Tu, Wei-Wei Chen, Ting Zhu, Jun Carnegie Mellon Univ Pittsburgh PA USA 4Paradigm Inc Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Microsoft Res Cambridge England

ISBN: (纸本)1577358872

Most reinforcement learning algorithms seek a single optimal strategy that solves a given task. However, it can often be valuable to learn a diverse set of solutions, for instance, to make an agent's interaction with users more engaging, or improve the robustness of a policy to an unexpected perturbance. We propose Diversity-Guided Policy optimization (DGPO), an on-policy algorithm that discovers multiple strategies for solving a given task. Unlike prior work, it achieves this with a shared policy network trained over a single run. Specifically, we design an intrinsic reward based on an information-theoretic diversity objective. Our final objective alternately constraints on the diversity of the strategies and on the extrinsic reward. We solve the constrained optimization problem by casting it as a probabilistic inference task and use policy iteration to maximize the derived lower bound. Experimental results show that our method efficiently discovers diverse strategies in a wide variety of reinforcement learning tasks. Compared to baseline methods, DGPO achieves comparable rewards, while discovering more diverse strategies, and often with better sample efficiency.

关键词： Reinforcement learning

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An Ensembled Grid based Machine learning Approach For PD Classification From MRI Images 4

An Ensembled Grid based Machine Learning Approach For PD Cla...

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4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Redhya, M. Jayalakshmi, M. Kalasalingam Academy of Research and Education Department of Computer Applications Tamil Nadu India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350343670

Parkinson's disease (PD) is a neuro-degenerative disease caused due to breakdown of brain cells in the central-part of the nervous system. As symptoms of PD appear only after 60% or more of these cells are destroyed, early detection is a tedious task. Several studies have proven that PD can be effectively diagnosed from MR-Images (MRI). this paper provides an ensembled machine learning model, Stacked-EG model, for MRI-image based PD classification. the dataset was obtained from PPMI (Parkinson's Progression Markers Initiative) online repository which included 134 PD subjects (80 males and 54 females) and 126 healthy patients (60 males and 66 females) around 60 years old on average. Feature extraction is performed through the application of filters, including wavelet transformations and intensity-based enhancements. Feature selection is conducted using optimised XGBoost model's feature significance scores. For classification, a Stacking Classifier which combines the strengths of XGBoost, SVM, and a Random Forest classifier was used. the model obtained an accuracy of 98.41% in classifying PD from healthy controls. © 2024 IEEE.

关键词： GridSearch optimization Magnetic Resonance Imaging (MRI) Random Forest Stacking SVM XGBoost

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Mastering Beaglebone AI & AI-64 on its Multifaceted applications 4

Mastering Beaglebone AI & AI-64 on its Multifaceted Applicat...

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4th international conference on Soft Computing for Security applications, ICSCSA 2024

作者： Sivakumar, P. Premkumar, T. Ani Sowntharya, A. Navin, S. Pavithra, T. PSG College of Technology Department of EEE Coimbatore India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515720

the rapid growth of embedded systems has led to the integration of artificial intelligence capabilities at the edge of computer devices. the Beagle-Bone AI and AI64 platform is a potent development board intended to make AI and machine learning activities easier in embedded environments. this study investigates the device's diverse possibilities. the paper provides a comprehensive guide about configuring the Beagle-Bone AI and AI64, offering insights into hardware specifications, software setup, and communication interfaces. Beyond technical configuration, this paper delves into a wide spectrum of applications and beyond. through case studies and practical implementations, this paper demonstrates the platform's utility as a catalyst for innovation and research at the intersection of edge computing and artificial intelligence. © 2024 IEEE.

关键词： Application programs

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Uncertainty-Aware Voltage Control for Distribution Substations Based on Bayesian Deep learning and Probabilistic Graph Self-Encoder Fusion 4

Uncertainty-Aware Voltage Control for Distribution Substatio...

引用

4th international conference on Intelligent Power and Systems, ICIPS 2024

作者： Wei, Jun Ma, Jiong Hu, Jian Zhu, Fei Hangzhou Electric Power Design Institute Co. Ltd. Hangzhou China

ISBN: (纸本)9798331532765

Voltage control in low-observable distribution networks faces significant uncertainty challenges. In this paper, an uncertainty-aware voltage control method based on the fusion of Bayesian deep learning and probabilistic graph self-encoder is proposed. the method first utilizes Bayesian deep learning to establish a probabilistic model of the network topology and inter-node dependencies, and then realizes efficient encoding and decoding of the system state through a probabilistic graph self-encoder. this fusion approach combines the uncertainty quantification capability of Bayesian inference and the structure learning advantage of graph self-encoders to achieve multi-scale uncertainty modeling and dynamic decision optimization. Specifically, this paper develops a Bayesian deep learning model augmented by graph attention network to effectively capture the complex topology of the network;meanwhile, an adaptive conditional probabilistic generative adversarial network is proposed to generate realistic and diverse probabilistic scenarios. By fusing these two techniques, this method achieves adaptive probabilistic scenario generation and screening, as well as uncertainty-based dynamic control strategy optimization. Experimental results show that the fusion method significantly improves the robustness and adaptability of voltage control in low-observable distribution networks, and provides a new idea for solving the uncertainty problem of complex distribution systems. © 2024 IEEE.

关键词： Bayesian networks

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Deep learning Based Intrusion Detection for IoT Networks 4

Deep Learning Based Intrusion Detection for IoT Networks

引用

4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Ananthi, P. Nirmaladevi, K. Mothika, G. Kongu Engineering College Department of Computer Technology-PG Tamilnadu Erode638060 India Madanapalle Institute of Technology & Science Department of Computer Applications India Kongu Engineering College Erode India

ISBN: (纸本)9798331529635

Intrusion detection is crucial for securing IoT networks amid the rapid proliferation of devices, posing significant security challenges. this study offers a unique intrusion detection model for IoT deep learning methods used in networks include Convolutional Neural Networks (CNN), TabNet, and the Transient Search optimization (TSO) method. the project starts by developing and evaluating distinct intrusion detection components. CNN, specializing in feature extraction, analyzes network traffic for potential intrusions. TabNet, known for interpretability and effectiveness with structured data, captures intricate relationships. the novel TSO algorithm improves the system's ability to detect temporary anomalies. Each component's performance is assessed individually, highlighting unique strengths. TabNet excels in structured IoT data pattern identification, surpassing CNN. the TSO algorithm strengthens temporary anomaly recognition, enhancing IoT network security. An ensemble technique combines CNN and TabNet predictions, reducing false positives and negatives to elevate overall detection accuracy. the ensemble, using both CNN and TabNet, achieves an impressive accuracy rate exceeding 99%, demonstrating significant gains in intrusion detection performance. © 2024 IEEE.

关键词： Network intrusion

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Estimation of Optimum Route Calculation via Multi Objective optimization Technique for IoT Enabled WSN 4

Estimation of Optimum Route Calculation via Multi Objective ...

引用

4th IEEE Annual Flagship India Council international Subsections conference, INDISCON 2023

作者： Kumar, Sandeep Desai, Meena School of Basic Science and Technology Iimt University Uttar Pradesh Meerut India Isme - School of Management & Entrepreneurship Atlas Skill Tech University Maharashtra Mumbai India

ISBN: (纸本)9798350333558

the Internet of things (IoT) enabled Wireless Sensor Network (WSN) has been rapidly growing in popularity due to its ability to quickly connect various sensor nodes and provide a network of connected devices. Intelligent mechanisms are required to effectively manage such networks, and Multi-Objective optimization is an emerging technique used in this regard. this technique is focused on exploring the combination of objectives that would optimize the system performance. this can include metrics such as energy consumption, scalability, cost, security, and data accuracy. By combining optimization objectives into a single problem, multi-objective optimization allows for a more comprehensive approach for managing these networks. this paper goes over the major aspects of Multi-Objective optimization for an IoT enabled WSN, such as the impact on performance, and the different optimization techniques that are used. Finally, it explores the potential applications and future prospects of this technique in the field of IoT. © 2023 IEEE.

关键词： Internet of things

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Deep Reinforcement learning Based Collaborative Energy Management for Base Station and Microgrid 4

Deep Reinforcement Learning Based Collaborative Energy Manag...

引用

4th international conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Xie, Dongxue Li, Haitao Beijing University of Technology School of Information Science and Technology Beijing China

ISBN: (纸本)9798331534622

With the rapid expansion of 5G networks, the number of base stations and their energy consumption have significantly increased, making energy efficiency a critical challenge. To address this issue, this paper proposes a collaborative energy management model for 5G base stations and microgrids. By introducing the FL-PPO algorithm, the model achieves collaborative optimization while protecting base station data privacy. the base stations utilize the FL-PPO algorithm to implement dynamic sleep strategies, effectively reducing energy consumption, while the microgrid adopts the Dueling DQN algorithm for energy management, enhancing operational efficiency. Experimental results demonstrate that the proposed method significantly reduces base station energy consumption while increasing microgrid revenue. © 2024 IEEE.

关键词： Deep reinforcement learning

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Evolution of Wearable Healthcare Technology: Opportunities, Challenges and applications 4

Evolution of Wearable Healthcare Technology: Opportunities, ...

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4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Bokde, Rohit Dongare, Pragati Datta Meghe College of Pharmacy Datta Megha Institute of Higher Education and Research Wardha India

ISBN: (纸本)9798331523923

the rapid development and increasing popularity of wearable technologies have drawn substantial attention from firms seeking to expand into new markets and innovate in the healthcare industry. As electronic devices embedded in clothing and accessories, wearable technologies have farreaching applications, with healthcare being a very promising area. Wearable technologies allow real-time monitoring of health through intelligent sensors that send personal health information to connected devices, enabling timely medical interventions. As wearable health technologies move from personal convenience to vital community health devices, their uptake has accelerated among consumers and companies alike. this research discusses the market prospects of wearable health devices. the research considers the innovation of smartwatches and smart medical devices, investigating their technological advancements, market expansion, and prospects for the future. As consumers increasingly seek products that improve quality of life, provide real-time health information, and enable preventive care, the wearable healthcare device market offers substantial opportunities for healthcare providers and manufacturers. © 2025 IEEE.

关键词： Electronic health record

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