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检索条件"任意字段=3rd Conference on Learning and Intelligent Optimization"

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Instant Fingerprint Recognition using Optimized Machine learning Models by Corona Virus optimization Algorithm 3

Instant Fingerprint Recognition using Optimized Machine Lear...

引用

3rd International conference on intelligent Computing and Next Generation Networks, ICNGN 2024

作者： Kadhim, Saif Mohanad Siaw Paw, Johnny Koh Tak, Yaw Chong Abd Al-Latief, Shahad Thamear Selangor Malaysia Selangor Malaysia

ISBN: (纸本)9798331529222

Fingerprint recognition has become an invaluable and rapidly advancing technology that plays a crucial role in various daily applications. From identity authentications to justice systems, mobile payment, and even access control and security measures. Despite the remarkable progress, fingerprint recognition is still a dynamic research field and confronts several challenges. One of the main challenges is the high variability in fingerprint images due to factors like fingerprint aging, central rotation, obliteration, z-cut, occlusion, orientation, and fingerprint device noises. Additionally, the computational complexity and the time concerns surrounding fingerprint recognition systems have raised considerations that need to be addressed. This study presents a brand-new, very quick, and extremely accurate fingerprint identification system built on a sophisticated, bioinspired coronavirus optimization algorithm. Through hyperparameter tuning of machine learning algorithms and the use of Enhancement of the Coronavirus method, the principal objective of the suggested configuration is to choose and optimize the best characteristics of fingerprint photos and improve the accuracy of recognition. Six machine learning algorithms are utilized, and the proposed system has been evaluated on three datasets (i.e., NIST, CASIA Fingerprint 5.0, SOCOfing). The suggested system provides excellent recognition accuracy with little computing effort and complexity in uncontrolled contexts, with an average accuracy of 99% in 1.03 ms, as demonstrated by the comparison of experimental findings with current approaches, and time, respectively. ©2024 IEEE.

关键词： Coronavirus

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Fault Diagnosis for Distributed Systems Based on RF-LGBM 3

Fault Diagnosis for Distributed Systems Based on RF-LGBM

引用

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

作者： Qiu, Juncheng Zhou, Ziqi Wang, Haoze Cai, Changchun Hohai Univ Coll Artificial Intelligence & Automat Changzhou Jiangsu Peoples R China

ISBN: (纸本)9798350352634;9798350352627

Distributed systems are critical infrastructures for many enterprises and organizations, yet they also present challenges in fault diagnosis. To improve the efficiency and accuracy of fault diagnosis in distributed systems, this paper proposes a fault diagnosis model based on the RF-LGBM algorithm. The model first utilizes LGBM for initial diagnosis and then employs RF to reclassify the two fault types with lower accuracy. The results indicate that the RF-LGBM model achieves an AUC greater than 0.96, effectively diagnosing faults in distributed systems.

关键词： distributed systems RF-LGBM fault diagnosis Bayesian optimization Gini

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Research on Multimodal optimization Mechanism based on Machine learning 3

Research on Multimodal Optimization Mechanism based on Machi...

引用

3rd International conference on Cloud Computing, Big Data Application and Software Engineering, CBASE 2024

作者： Chen, Qianyu Tang, Runxuan School of Information Science and Technology ShanghaiTech University Shanghai China School of Information Science and Technology Shanghai Normal University Shanghai China

ISBN: (纸本)9798331506582

As modern engineering optimisation problems become increasingly complex, a significant number of them exhibit multimodal characteristics. This implies that there are multiple local optimal solutions and a single global optimal solution. This not only increases the difficulty of problem-solving, but also necessitates the development of more sophisticated algorithms for optimisation. In this work, by constructing intelligent agents to continuously optimize their behavior strategies in the search process of multimodal optimization problems, they use environmental feedback to continuously optimize their behavior strategies, and at the same time simulate the process of natural selection and genetic variation to generate multiple candidate solutions, so as to conduct extensive searches on a global scale. Deep neural networks are used to build policy networks and value networks for reinforcement learning agents, enabling agents to accurately predict the potential benefits of different search strategies through a large amount of training data. Through real-time monitoring and analysis of feedback information, the model dynamically adjusts the search strategy to balance exploration and utilization, avoid falling into the local optimal solution, and finally find the global optimal solution. Experimental analysis shows that the model performs well in dealing with complex multimodal optimization problems, and significantly improves the efficiency and effect of solving. © 2024 IEEE.

关键词： Adversarial machine learning

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Computer-Aided Railway Operations Human Resources optimization and Performance Evaluation 24

Computer-Aided Railway Operations Human Resources Optimizati...

引用

3rd International conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Feng, Xiaojie Shuohuang Railway Development Co. Ltd. Chengdu China

ISBN: (纸本)9798400718120

With the increasingly complex business environment, the rational allocation of human resources and the improvement of personnel utilization efficiency have become key factors for enterprises to maintain competitiveness. In this paper, a computer-aided human resources optimization solution is proposed to address this issue. This solution establishes a scientific optimization model, designs efficient hybrid intelligent optimization algorithms, and develops a complete optimization management system. Through in-depth analysis of human resources and business data, combined with actual enterprise constraints, the system can automatically solve the optimal human resource allocation scheme, providing decision support for enterprise implementation. In trials conducted in multiple enterprises, this solution significantly optimized the allocation of human resources, greatly improved resource utilization efficiency, and achieved significant economic benefits. © 2024 Copyright held by the owner/author(s).

关键词： Human resources optimization

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Clustering Federated learning with Differentially Private optimization on Transformer 24

Clustering Federated Learning with Differentially Private Op...

引用

3rd International conference on Networks, Communications and Information Technology (CNCIT)

作者： Zhi, Yajing Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798400717048

This study explores the integration of federated learning and Transformer models to enable collaborative and privacy-preserving deep learning for vision tasks. Federated learning is a distributed learning paradigm that allows training models on distributed data without sharing the original data, and Transformer models have demonstrated impressive performance in a variety of applications. We leverage the strengths of federated learning and Transformer models and apply the clustering techniques to address the challenge of privacy protection in large-scale deep learning. By combining these approaches, this research aims to develop efficient and private deep learning systems for vision applications, providing data privacy guarantees and maintaining the practicality of trained models. In the experiments, we show the proposed model can successfully identify clustering membership and perform local tasks with satisfactory accuracy and efficiency.

关键词： Federated learning Model Clustering Vision Transformer Differential Privacy

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ASBO-MAT-BiLSTM: Social Media Network Anomaly Detection using Optimized Multihead Attention Transformer based Model 3

ASBO-MAT-BiLSTM: Social Media Network Anomaly Detection usin...

引用

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

作者： Lunawat, Sonali Rao, Jyoti Patil, Pramod Dr. D. Y. Patil Institute of Technology Pimpri Pune India

ISBN: (纸本)9798331527549

Currently, attacks in the networks are the most vital issue in modern society and the networks from minor to huge networks are susceptible to network threats. Various approaches exist with several advantages and limitations in detecting anomaly, such as computational complexity, overfitting, low level of accuracy in detection, and so on. To overcome these issues and detect network anomaly effectively Adaptive Secretary Bird optimization algorithm (ASBO) is integrated with the RAnked Minority Oversampling in Boosting (RAMOB) model to resolve the issue of data imbalance through making the data in a balanced manner. The Multihead Attention Transformer (MAT) mechanism and ASBO algorithm enable the BiLSTM model to effectively detect anomaly by solving the issues of overfitting and complexities in terms of both time and computation. The experimental outcomes display that the ASBO-MAT-BiLSTM model reaches an accuracy of 97.116% using NSL-KDD Dataset and accuracy of 96.54%, using CIC-IDS2017 Dataset, which reveals that our model attains higher efficiency in detecting anomalies in the networks. © 2025 IEEE.

关键词： Deep learning

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Artificial Intelligence-Powered Sleep optimization: Enhancing SEL Interventions to Reduce Bedtime Procrastination 3

Artificial Intelligence-Powered Sleep Optimization: Enhancin...

引用

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

作者： Pawar, Vilis Vhatkar, Abhijit Chavan, Pravin Gawankar, Siddhi Dr. D. Y. Patil Vidyapeeth Global Business School and Research Centre Pune India

ISBN: (纸本)9798331515911

This paper explores the integration of Artificial Intelligence (AI) with that of Social-Emotional learning (SEL) interventions to address bedtime procrastination and promote better sleep hygiene. Bedtime procrastination, a prevalent issue among various age groups, often leads to insufficient sleep, negatively impacting overall health and well-being. By leveraging AI's adaptive capabilities and SEL's focus on emotional regulation and decision-making, we propose a comprehensive framework for smart sleep strategies. Our approach includes personalized interventions designed to foster better sleep routines, enhance self-discipline, and mitigate procrastination behaviors. The study evaluates the effectiveness of these AI-driven SEL interventions through a series of controlled experiments and surveys, demonstrating significant improvements in sleep patterns and overall sleep quality. These findings suggest that AI-enhanced SEL interventions offer a promising solution for reducing bedtime procrastination and improving sleep hygiene, contributing to better health outcomes and quality of life. © 2025 IEEE.

关键词： Sleep research

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Research on Short-term Electricity Demand Forecasting Based on Deep learning Algorithms 24

Research on Short-term Electricity Demand Forecasting Based ...

引用

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

作者： Fan, Zhuoni Gao, Yuan College of Economics and Management Beijing Jiaotong University Beijing China Shandong Energy Group Materials Co. Ltd. Luzhong Branch Shandong Taian China

ISBN: (纸本)9798400707308

This paper introduces an advanced short-term electricity demand forecasting model based on Recurrent Neural Networks (RNN) combined with Long Short-Term Memory (LSTM) units. This hybrid model effectively captures the temporal dynamics in electricity consumption, incorporating features such as past energy use, temperature, humidity, and calendar variables. By utilizing a rolling prediction technique, the model addresses long-term prediction challenges and enhances accuracy. Extensive model evaluation shows a marked reduction in error metrics, including Mean Absolute Error and Root Mean Squared Error. The proposed method demonstrates significant potential for real-time energy demand forecasting, supporting grid optimization and energy management. This approach highlights the model’s applicability in smart grid systems, providing more reliable predictions crucial for operational efficiency and sustainability. © 2024 Copyright held by the owner/author(s).

关键词： Demand response

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Multi-spherical robot formation control based on pigeon swarm optimization 3

Multi-spherical robot formation control based on pigeon swar...

引用

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

作者： He, Xinyu Lu, Yuzhou Wang, Qiguan Lu, Haojie Southwest Univ Sci & Technol Sch Informat Engn Mianyang Sichuan Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing Peoples R China

ISBN: (纸本)9798350352634;9798350352627

In order to realize tight formation control of spherical robots, a formation control method of multi-spherical robot system was proposed based on optimization of pigeon swarm algorithm. Based on the kinematics model of spherical robots, the roles of spherical robots were divided into leader and follower. A PID controller was designed for stability control in x and y direction channels. According to the desired position and velocity, a corresponding fitness function is uniformly set to optimize the optimal parameters of the formation controller. Finally, the stable control of the multi-spherical robot formation is realized in the simulation environment.

关键词： Multi-spherical robot system Tight formation control Pigeon swarm algorithm

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Augmented Reality (AR) and Virtual Reality (VR) for Personalized learning 3

Augmented Reality (AR) and Virtual Reality (VR) for Personal...

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

作者： Gaikwad, Aditya Rahul Gaikwad, Rohan Maruti Jodh, Dasharathraje Naganath Khiani, Simran Department of Computer Engineering G. H Raisoni College of Engineering and Management Pune India

ISBN: (纸本)9798331527549

To develop a specific learning environment using augmented reality (AR) and virtual reality (VR) technology, we create immersive and interactive educational experiences that enhance engagement, improve understanding of complex concepts, and cater to diverse learning styles for students. The system will be able to meet the needs of each learner by gathering data on their learning objectives, preferences, and past knowledge. Interactive multiple-choice questions will be used to evaluate students' performance and identify areas for development. Based on the findings, the website will recommend relevant study guides and classes. AR and VR technology will be used to develop immersive learning environments that include interactive simulations, 3D visualizations, and collaborative virtual worlds. To improve learning results and student engagement, the platform's architecture will prioritize data security, accessibility, and usability. © 2025 IEEE.

关键词： Virtual environments

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