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

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Investigation on COVID-19 by using Machine learning Techniques 4

Investigation on COVID-19 by using Machine Learning Techniqu...

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4th IEEE international conference on Computing, Power and Communication Technologies, GUCON 2021

作者： Suresh, K. Mukhopadhyay, Adwitiya Amrita Vishwa Vidyapeetham Amrita School of Arts and Sciences Department of Computer Science Mysuru India

ISBN: (纸本)9781728199511

the COVID-19 pandemic had brought about a standstill to many activities across the world. Health experts, doctors and academic investigators across the globe have been attempting to come to terms with the trying demands posed on the human population due to the pandemic. this paper attempts to develop a precise model for examination of and forecasting effective measures to be implemented during different situations to limit the impact of COVID-19. It also addresses various trials and tests faced while using machine learning algorithms. For the experimental analysis different parameters such as countries (China, America, India, South Africa and Italy), month, types of measures to be undertaken (awareness campaigns, economic measures, domestic travel restrictions, health screening at airports and psychological assistance involving medical social work) and date of implementation details are considered. COVID-19 epidemic determent procedures by recognizing, evaluating danger situation and probable paths of epidemic using a machine-learning technique have been explored. A proposed methodology to forecast extension of lockdown in order to exterminate COVID-19 is presented wherein SVM regression technique is used for prediction of actual extension of lockdown during the pandemic situation. © 2021 IEEE.

关键词： COVID-19

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Pragmatic Way of Analyzing Malware Attacks Detection in IoT Devices Using Deep learning 4th

Pragmatic Way of Analyzing Malware Attacks Detection in IoT ...

引用

4th international conference on Communication and Intelligent Systems, ICCIS 2022

作者： Barman, Moushumi Sharma, Bobby Department of CSE Assam Don Bosco University Assam Guwahati India

ISBN: (纸本)9789819920990

Internet of things (IoT) plays a vital role in transforming the world from telephone to smartphone, typewriter to laptop then notebook, normal home to smart home, hard work to smart work. the tremendous use of IoT in numerous applications not only eases our life but also saves time for innovative work. But everything has its negative influence. there is a ruinous-effect of using IoT in large-scale. It encourages the cyber-attacks to target the network and system which further leads to various kinds of cyber-attacks. Among all the cyber-attacks, malware proves themselves the most dangerous attack to bring interruption to daily operation and threaten the user to think twice before clicking on any link. this paper comprises the different types of malware attacks along with the IoT architecture. However, researchers have proposed different approaches to detect malware attacks on IoT using deep learning. there are some security issues which are still working on it. Further, this paper describes such a type of security issue that proves that IoT devices are still vulnerable to malware attacks. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Machine learning and Artificial Intelligence for Enhanced Alzheimer's Disease Detection: A State-of-the-Art Review

Machine Learning and Artificial Intelligence for Enhanced Al...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), international conference on

作者： Shehu Mohammed Neha Malhotra Department of Computer Applications Lovely Professional University Phagwara India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Alzheimer's disease (AD) is a substantial public health challenge due to its rising prevalence on a global scale. Timely identification and tailored treatment are essential for enhancing patient results and maximizing healthcare resources. this paper offers a thorough examination of the latest progress in applying machine learning (ML) and artificial intelligence (AI) methods to improve Alzheimer's disease (AD) detection and customize care tactics. We will first discuss the etiology of Alzheimer's disease and emphasize the need of early identification. We will explore different machine learning and artificial intelligence techniques such as deep learning, support vector machines, and ensemble approaches used for Alzheimer's disease detection and prognosis. We investigate combining many types of data sources, including neuroimaging, genetic markers, and clinical assessments, to improve predictive models. We also examine how AI-driven decision support systems help create tailored care plans and optimize treatment for people with Alzheimer's disease. We discuss present obstacles and prospective paths in the area, such as data diversity, model explainability, and ethical concerns. this study seeks to offer insights into the current state of machine learning (ML) and artificial intelligence (AI) applications in Alzheimer's disease (AD) research and clinical practice, with the goal of advancing early diagnosis and individualized management strategies for this severe neurological ailment.

关键词： Support vector machines Ethics Machine learning algorithms Reviews Soft sensors Medical treatment Collaboration Machine learning Ubiquitous computing Alzheimer's disease

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Metal Oxide Classification Based on SVM 19th

Metal Oxide Classification Based on SVM

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19th international conference on Advanced Intelligent Computing Technology and applications (ICIC)

作者： Xiao, Kai Wang, Zhuo Bao, Wenzheng Xuzhou Univ Technol Sch Informat Engn Xuzhou 221018 Jiangsu Peoples R China

ISBN: (纸本)9789819947546;9789819947553

Oxides are classified into various oxides according to their constituent elements, among which there are binary and quaternary oxides. Binary oxides are widely used in industry, and quaternary oxides have great potential in this aspect of electrode materials. this paper focuses on a support vector machine based classification method for oxides, redefining the method for describing metal oxides in order to facilitate computation, and using principal component analysis to reduce the dimensionality of the data, evaluating the classification results using a variety of metrics, and comparing the classification results of multiple kernel functions and the number of principal components to select the best algorithm. the results show that the support vector machine with the number of principal components of 4 and a Gaussian kernel function has better classification results and is important for the identification of oxides.

关键词： oxides classification algorithms machine learning principal component analysis support vector machines

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Blockchain-powered Federated learning: A Review on Current Practices

Blockchain-powered Federated Learning: A Review on Current P...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Neda Fatima Usha Batra Mukesh Prasad Hasim Ali School for Computer Applications Manav Rachna International Institute of Research and Studies in Faridabad India University of Technology Sydney Australia Computer Science Engineering Skill Department of CS and IT Shri Vishwakarma Skill University India School of Computer Science UTS Australia

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

With improvements in machine learning technology, viable methods for processing the massive amounts of data created in real life are now available. However, data protection and scalability concerns will slow down the further development of machine learning algorithms. Federated learning (FL) prevents data leaks by distributing training jobs to numerous clients and thus isolating the central server from the local devices. Nevertheless, FL still has issues like malicious data and single points of failure. Blockchain Technology’s debut offers a secure and practical solution for FL deployment. this research study presents a comprehensive evaluation of the blockchain-based FL. First, the concept of Federated learning is extensively dwelled upon. then, the limitations of Federated learning were analyzed, which led to the adoption of BC-FL. Some real-world uses of BCFL are also analyzed. Subsequently, the integration of BC with FL has been studied. Finally, the architecture of BC-FL has been developed and described. BC-FL is an upcoming research field that is comparatively unexplored. the paper offers insights into its development and how the technology can be implemented by addressing data privacy and security issues.

关键词： Training Systematics Federated learning Reviews Social networking (online) Scalability Smart contracts

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Data-Driven SOH Estimation for Lithium-Ion Battery Based on CEEMDAN-BiLSTM and Bayesian optimization

Data-Driven SOH Estimation for Lithium-Ion Battery Based on ...

引用

Advanced algorithms and Control Engineering (ICAACE), international conference on

作者： Weiwei Zhang Yundong Gu Xueyuan Li School of Mathematics and Physics North China Electric Power University Beijing P.R. China School of Control and Computer Engineering North China Electric Power University Beijing P.R. China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Accurate estimation of State of Health (SOH) in lithium-ion batteries is critical for ensuring the safety, reliability, and operational efficiency of energy storage systems in electric vehicles and grid applications. However, battery degradation exhibits complex nonlinear behaviors, while capacity regeneration and noise introduce significant uncertainties into traditional SOH estimation methods. this paper introduces a novel hybrid framework, CEEMDAN-BiLSTM-BO, designed to address these challenges through three core components: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for robust signal decomposition, Bidirectional Long Short-Term Memory (BiLSTM) networks for bidirectional temporal feature learning, and Bayesian optimization (BO) for optimal weight tuning. CEEMDAN decomposes raw capacity data into Intrinsic Mode Functions (IMFs), effectively isolating global degradation trends from noise and capacity regeneration components. BiLSTM networks capture bidirectional temporal dependencies in each IMF, while BO dynamically optimizes IMF weights to mitigate interference from non-stationary signals. Experimental validation on multiple battery datasets demonstrates that CEEMDAN-BiLSTM-BO outperforms existing methods by leveraging adaptive noise suppression, data-driven optimization, and bidirectional sequence learning.

关键词： Degradation Lithium-ion batteries Uncertainty Accuracy Noise Estimation Bidirectional long short term memory Bayes methods Reliability optimization

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Next-Gen Healthcare: Blockchain-Fuelled Federated learning for IoMT Security

Next-Gen Healthcare: Blockchain-Fuelled Federated Learning f...

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Artificial Intelligence and Signal Processing (AISP), INDIA, international conference on

作者： Subasish Mohapatra Ayashkant Mishra Subhadarshini Mohanty School Of Computer Sciences OUTR Bhubaneswar

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

the Internet of Medical things (IoMT) is revolutionising the healthcare landscape by seamlessly integrating medical devices, sensors, and healthcare information systems. this interconnected network of devices is designed to improve patient outcomes, enhance healthcare delivery, and streamline medical processes. However, as IoMT continues to evolve, it introduces new challenges related to data security, privacy, and interoperability. Blockchain technology has emerged as a promising solution to address these challenges, offering a decentralised and secure framework for managing health-related data in IoMT applications. this research aims to implement a blockchain-enabled network within a Federated learning-based Internet of Medical things (IoMT) environment. the proposed framework features a centralized server hosting a global machine learning model. IoMT devices operate with local models that run concurrently with the global model, incorporating device-specific data. Simulations and comparisons have been conducted on the predominant consensus models, namely Proof of Stake and Proof of Work. these ongoing initiatives aspire to play a role in enhancing the security and privacy aspects of the latest developments in the Internet of Medical things.

关键词： Proof of stake Privacy Signal processing algorithms Internet of Medical things Signal processing Proof of Work Data models Sensor systems Sensors Servers

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Research on the optimization of flight control technology for UAVs based on reinforcement learning on moving platform

Research on the optimization of flight control technology fo...

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Computers and Artificial Intelligence Technologies (CAIT), international conference on

作者： Xunfei Yu Weijun Hu Jiale Quan Ruichang Wang Northwestern Polytechnical University Xi'an China Xi'an Jiaotong University Xi'an China

With the rapid development of drone technology, the application of drones in military and civilian fields is becoming increasingly widespread. However, accurately controlling the flight attitude of drones during takeoff on a moving platform has always been a challenging problem. this paper takes a vehicle-mounted UAV as an example and proposes a drone flight control technique based on reinforcement learning. Introducing the Proximal Policy optimization (PPO) algorithm to improve the control method based on the traditional PID control aims to enhance the accuracy and performance of attitude control. Experimental results show that this algorithm can effectively enhance the attitude stability and control accuracy of drones during takeoff on a moving platform, enabling the drone to maintain posture stability during the takeoff process at a speed of 20 km/h.

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Surrogate-Assisted Level-Based learning Particle Swarm Optimizer Based on Expected Value Evaluation under High Noise optimization

Surrogate-Assisted Level-Based Learning Particle Swarm Optim...

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Machine Intelligence theory and applications (MiTA), international conference on

作者： Peng-Cheng Yan Xiao-Min Hu Sang-Woon Jeon Xiao-Feng Liao School of Computer Science and Technology Guangdong University of Technology Guangzhou P.R. China Department of Electrical and Electronic Engineering Hanyang University Ansan South Korea Chongqing University Chongqing P.R. China

ISBN: (数字)9798350385670

ISBN: (纸本)9798350385687

Surrogate-assisted evolutionary algorithms (SAEAs) have emerged as an effective approach for addressing expensive optimization problems. However, in scenarios where uncertain factors such as evaluation noises exist, the performance and reliability of most SAEAs are compromised due to inaccuracies and uncertainties, such as biases and measurement errors, which may vary. To mitigate these challenges, this paper proposes a particle re-evaluation technique based on a classifier-assisted approach. Specifically, the technique leverages a classifier-assisted level-based learning swarm optimizer to enhance the algorithm’s performance and reliability. Moreover, it explores various reevaluation rules tailored for uncertain conditions. Experimental results demonstrate that re-evaluation significantly enhances the classifier-assisted level-based learning swarm optimizers’ ability to improve performance and reliability, particularly in environments where objective fitness is sensitive to disturbances. Additionally, the experiments show that re-evaluation substantially boosts the optimizers’ capacity to discover superior solutions and enhances particle trustworthiness in uncertain situations.

关键词： Measurement errors Uncertainty Noise Market research Robustness Resource management Iterative methods Particle swarm optimization optimization Machine intelligence

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Layer-wise based Adabelief optimization Algorithm for Deep learning 22

Layer-wise based Adabelief Optimization Algorithm for Deep L...

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Proceedings of the 4th international conference on Advanced Information Science and System

作者： Zhiyong Qiu Zhenhua Guo Li Wang Yaqian Zhao Rengang Li State Key Laboratory of High-end Server and Storage Technology Shandong massive information technology research institute China State Key Laboratory of High-end Server and Storage Technology Inspur Electronic Information Industry Co. Ltd. China

ISBN: (纸本)9781450397933

For the optimization problem of deep learning, it is important to formulate a optimization method that can improve the convergence rate without sacrificing generalization ability. this paper proposes a layer-wise based Adabelief optimization algorithm to solve the deep learning optimization problems more efficiently. In the proposed algorithm, each layer of the deep neural network is set different learning rate appropriately in order to achieve a faster convergence rate. We also give the theorems that can guarantee the convergence property of Layer-wised AdaBelief method. Finally, we evaluate the effectiveness and efficiency of the proposed algorithm on experimental examples. Experimental results show that the converges speed of the layer-wised AdaBelief algorithm is the fastest compared with the mainstream algorithms. Besides, the new algorithm also maintaining an excellent convergence result in all numerical examples.

关键词： Adaptive learning Rate

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