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共 3731 条记录，以下是3691-3700 订阅

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ESO-HUIM: Enhanced Swarm Optimization for High-Utility Item-Set Mining in Critical Product Identification from Transaction Logs

ESO-HUIM: Enhanced Swarm Optimization for High-Utility Item-...

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Advances in Computing, Communication and Networking (ICAC2N), international conference on

作者： Yogesh Juyal Sonal Sharma Uttaranchal School of Computing Sciences Uttaranchal University Dehradun Uttarakhand India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

In recent times, High-Utility Item-Set Mining (HUIM) has garnered significant attention for its ability to identify successful products by simultaneously considering both quantity and profit, diverging from traditional methods such as frequent item-set mining or association rule mining. Addressing the challenges posed by vast item sets and sizable databases, utility-driven mining has emerged as a robust data mining approach, seeking meaningful insights from diverse *** research introduces a novel swarm-based algorithm for mining High-Utility Item-Sets (HUIs). The algorithm is characterized by unique features that demonstrate its effectiveness in extracting high-utility data from large datasets. Beyond the algorithmic contribution, this thesis includes the application of the proposed method to mine high-utility data from real-world datasets, adding empirical evidence to its practical *** presented research significantly advances the field of High-Utility Mining by developing highly efficient algorithms and showcasing their practical applicability. The emphasis on the usefulness of items underscores the importance of deriving valuable insights from data. The title "ESO-HUIM: Enhanced Swarm Optimization for High-Utility Item-Set Mining in Critical Product Identification from Transaction Logs" encapsulates the essence of this research, highlighting the algorithm's enhanced swarm optimization approach for critical product identification through HUIM in transaction logs.

关键词： Databases Feature extraction Robustness Association rule learning Data mining Particle swarm optimization Convergence

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Visual Safety Monitoring Detecting Deviations in Industrial Environments

Visual Safety Monitoring Detecting Deviations in Industrial ...

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Green Industrial Electronics and Sustainable Technologies (GIEst), IEEE international conference on

作者： Arun. C. A G.R Kanagachidambaresan Relangi Sanjay Manohar Shaik Shameer S Thenappan Aditya Sharma Dept of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Dept of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331540142

ISBN: (纸本)9798331540159

Safety in the work place is essential for protecting workers from workplace accidents and maintaining a safe and efficient workplace culture. Traditional industrial safety monitoring methods are time-consuming and subject to human error. In recent years, industries addresses this issue by utilizing computer vision and marking safety violations in real-time. The proposed system detects safety deviations such as missing safety helmets, vests, and masks, allowing for proactive intervention to avert accidents. Processing live video feeds from CCTV cameras via the Coral dev board with suitable Deep learning algorithm the proposed system ensures real-time detection of safety violations. In the proposed model, YOLOv8x algorithm is used, it achieves the precision of 0.985, recall of 0.874, and accuracy of 0.893 by utilizing the dataset comprising 2,801 images and videos combined collected from various sources. Also, the proposed system's flexibility in altering detection parameters depending on unique work site requirements makes it applicable to various industrial shop floor and safety regulations. The data visualization dashboard offers real-time notifications. It enhances workplace safety, saves time, and improves operational efficiency by detecting deviations and providing early alerts for safety violations using the powerful algorithms, and seamless communication.

关键词： Deep learning Visualization Employment Data visualization streaming media Cameras Real-time systems Safety Feeds Monitoring

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learning to stabilize online reinforcement learning in unbounded state spaces 24

Learning to stabilize online reinforcement learning in unbou...

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Proceedings of the 41st international conference on machine learning

作者： Brahma S. Pavse Matthew Zurek Yudong Chen Qiaomin Xie Josiah P. Hanna University of Wisconsin-Madison

In many reinforcement learning (RL) applications, we want policies that reach desired states and then keep the controlled system within an acceptable region around the desired states over an indefinite period of time. This latter objective is called stability and is especially important when the state space is unbounded, such that the states can be arbitrarily far from each other and the agent can drift far away from the desired states. For example, in stochastic queuing networks, where queues of waiting jobs can grow without bound, the desired state is all-zero queue lengths. Here, a stable policy ensures queue lengths are finite while an optimal policy minimizes queue lengths. Since an optimal policy is also stable, one would expect that RL algorithms would implicitly give us stable policies. However, in this work, we find that deep RL algorithms that directly minimize the distance to the desired state during online training often result in unstable policies, i.e., policies that drift far away from the desired state. We attribute this instability to poor credit-assignment for destabilizing actions. We then introduce an approach based on two ideas: 1) a Lyapunov-based cost-shaping technique and 2) state transformations to the unbounded state space. We conduct an empirical study on various queuing networks and traffic signal control problems and find that our approach performs competitively against strong baselines with knowledge of the transition dynamics. Our code is available here: https://***/Badger-RL/stOP.

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Buck Boost Converter Control in an IoT-Based Wind System Using Supervised learning Technique

Buck Boost Converter Control in an IoT-Based Wind System Usi...

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Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (IstEMS), international conference on

作者： N. Anbuselvan S. Hemalatha Suresh Lakshminarayanan E. Sivanantham N. Mohankumar Thamizhamuthu R Department of Electrical and Electronics Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Tagore Engineering College Chennai Tamil Nadu India Department of Engineering & Electronics Oman South Al Batina University of Technology and Applied Sciences - Almusanna Oman Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering B.V Raju Institute of Technology Telangana India Department of Computing Technologies Kattankulathur Campus-Chengalpattu SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350384246

ISBN: (纸本)9798350384253

In recent years, distributed energy resources (DERs) have gained significance as a way of introducing a substantial quantity of renewable energy sources (RESs) into power grids. Wind turbines are accessible in windy areas, giving wind energy systems (WES) a realistic alternative for clients. This work attempts to increase the effectiveness and adaptability of WES by presenting a novel way of managing DC-DC buck-boost converters. The suggested method combines an Internet of Things (IoT) system for monitoring with supervised learning algorithms to make energy conversion more stable, efficient, and able to adapt in real time to changing wind conditions. In this research, creating a reliable supervised learning control method is the primary goal. The second goal is to optimize the most important parameters of the converter. Using the new supervised learning algorithm, the study changes variables like duty cycle, switching frequency, and inductance to get the best results based on past performance data and how the system is working now. It investigates the influence of these parameters on overall system efficiency and stability. The incorporation of an IoT monitoring system into the WES is an essential component of the study. Various sensors monitor variables such as wind speed, converter input and output voltages, and currents in real-time and are shown as results.

关键词： Wind energy Wind speed Switching frequency Supervised learning Real-time systems stability analysis Wind turbines

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Advancing Sustainable Manufacturing with IoT and Deep Reinforcement learning in the Industry 4.0 Era

Advancing Sustainable Manufacturing with IoT and Deep Reinfo...

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Green Industrial Electronics and Sustainable Technologies (GIEst), IEEE international conference on

作者： Franciskus Antonius Alijoyo Divya Nimma Saravanan B Jangam Divya P N V Syamala Rao M Sankari V Center for Risk Management and Sustainability Indonesia School of Business and Information Technology STMIK LIKMI Bandung Indonesia Data Analyst in UMMC University of Southern Mississippi USA Department of CSE Sri Eshwar College of Engineering Coimbatore India A.V. College of Arts Science and Commerce Hyderabad India Department of CSE SRM University AP Amaravati Andhra Pradesh India Department of AI&DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331540142

ISBN: (纸本)9798331540159

Nowadays with the emergence of Industry 4. 0, there is therefore the need to have sustainable manufacturing in order to increase operational efficiency as well as relieve the pressure on the environment. A potential weakness of conventional approaches is time-dependency while processing real-time data to prevent dysfunctions in the company’s resource management, energy consumption control, and predictive maintenance. Thus, in this work it is proposed an innovative approach to address these problems with the use of Deep Reinforcement learning (DRL) and Internet of Things (IoT) technology. Energy consumption, machine performance and environmental data are collected by IoT sensors in real-time and fed into a DRL model. This concept effectively eliminates the vices that are characteristic of conventional approaches in that it seeks to optimize production processes by cell design. The approach suggested above attains an unprecedented 98. 2% accuracy rate. This is in a bid to exemplify its capability in predicting these phenomenal and balance forecasts, to improve the overall performance and sustainability of manufacturing. Thereby, the method offers a robust answer to the acute questions arising in front-line manufacturing today and sets a brand-new reference for intelligent and sustainable behavior in the context of the fourth industrial revolution 4.0.

关键词： Green manufacturing Energy consumption Production Sensor phenomena and characterization Deep reinforcement learning Real-time systems Fourth Industrial Revolution Internet of Things Resource management Sustainable development

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Craftax: a lightning-fast benchmark for open-ended reinforcement learning 24

Craftax: a lightning-fast benchmark for open-ended reinforce...

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Proceedings of the 41st international conference on machine learning

作者： Michael Matthews Michael Beukman Benjamin Ellis Mikayel Samvelyan Matthew Jackson Samuel Coward Jakob Foerster University of Oxford University College London

Benchmarks play a crucial role in the development and analysis of reinforcement learning (RL) algorithms. We identify that existing benchmarks used for research into open-ended learning fall into one of two categories. Either they are too slow for meaningful research to be performed without enormous computational resources, like Crafter, NetHack and Minecraft, or they are not complex enough to pose a significant challenge, like Minigrid and Procgen. To remedy this, we first present Craftax-Classic: a ground-up rewrite of Crafter in JAX that runs up to 250x faster than the Python-native original. A run of PPO using 1 billion environment interactions finishes in under an hour using only a single GPU and averages 90% of the optimal reward. To provide a more compelling challenge we present the main Craftax benchmark, a significant extension of the Crafter mechanics with elements inspired from NetHack. Solving Craftax requires deep exploration, long term planning and memory, as well as continual adaptation to novel situations as more of the world is discovered. We show that existing methods including global and episodic exploration, as well as unsupervised environment design fail to make material progress on the benchmark. We believe that Craftax can for the first time allow researchers to experiment in a complex, open-ended environment with limited computational resources.

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Credit Card Fraud Detection Using machine learning

Credit Card Fraud Detection Using Machine Learning

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Advances in Computing, Communication and Networking (ICAC2N), international conference on

作者： Alok Kumar Nishad Nitesh Pandey Vinay Kumar Pandey School of Computing Science and Engineering Galgotias University U.P. India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Credit card fraud continues to pose significant financial risk to consumers and any financial institution. To meet this task, this approach looks at machines that are particularly active in credit card fraud detection techniques, mainly focusing on random forest version Using a comprehensive dataset of credit card transactions we see how customized technologies can improve the accuracy of fraud detection. Our research aims to increase our technical understanding of techniques like decision trees, neural networks, and logistic regression. High accuracy deceptive interest detection was demonstrated by the random forest model, which had outstanding power. Beyond only comparing the different models in our study, this example shows how adaptable and trustworthy the model is when handling credit card theft. To investigate the models employed and emphasize the benefits and drawbacks of each technique for identifying fraudulent messages, we offer a thorough study of the system. Our results demonstrate the potential of machine recognition to improve fraud detection and establish random forest versions as predictors in this regard. This information has more concrete implications because it provides a reliable and powerful means of reducing economic losses and increasing the security of the financial system and of every individual. This study also suggests future directions, and it says that a combination of similar developments and models could also lead to more robust fraud detection systems. Credit card fraud continues, and is a constant *** conclusion, our research offers valuable information about credit card fraud detection, and the Random Forest version of the tool is effective, accurate, and gives better protection against fraud even in the face of ongoing attacks.

关键词： Adaptation models Accuracy Neural networks Credit cards Fraud Security stakeholders Reliability Protection Random forests

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System for Managing Online Crime and Enhancing Public Services

System for Managing Online Crime and Enhancing Public Servic...

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Advances in Computing, Communication and Networking (ICAC2N), international conference on

作者： P. Kaushik J. Josphin Mary Dhivyabharathi D Monica Thongam Nakra Hritik Awasthi Rupendra Kumar Dept of CSE(AIML-CSE) Chandigarh University Chandigarh India Dept. of Computer Science Meenakshi A.H.E.R Chennai India Dept. of CSE Dhanalakshmi Srinivasan College of Engineering Coimbatore India Dept. of Psychology Lingayas Vidyapeeth Faridabad India Dept. of CSE Chandigarh University Chandigarh India Dept. of Computer Science J.S. University Shikohabad India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

An Online Crime Management System has been developed to enhance the effectiveness and efficiency of criminal investigations and data management for law enforcement agencies. This web-based platform provides multiple access levels, including administrative roles for police officers who can review and analyze user reports, and functionalities for users to report crimes, manage cases, track and analyze evidence, and communicate with other participants in investigations. The system is designed to securely and efficiently handle criminal investigation data, featuring a user-friendly interface that facilitates easy navigation and information retrieval. The platform requires specific technical specifications for both hardware and software to ensure optimal performance. Overall, the Online Crime Management System represents a valuable tool for law enforcement agencies, improving the management and execution of criminal investigations.

关键词： Law enforcement Reviews Navigation machine learning Market research Information retrieval Software Public security Recording Monitoring

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Unlocking the Potential of Artificial Intelligence: Revolutionizing Financial Risk Management with Enhanced Decision-Making and Mitigated Risks

Unlocking the Potential of Artificial Intelligence: Revoluti...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), international conference on

作者： Avni Garg Centre for Interdisciplinary Research for Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

The growing significance of intelligent technology (AI) in the context of handling financial risks is examined in this study. The essay analyses the drawbacks of using conventional risk management techniques and gives a summary of the theoretical underpinnings of financial risk management. The literature research section sheds light on the various ways artificial intelligence is being used in the field of managing financial risk. Credit risk, market risk, organisational risk, and liquidity risk are some of these uses. The essay also discusses the advantages and difficulties of using artificial intelligence to financial risk management. The benefits of using AI include improved speed and accuracy in risk identification, surveillance, and mitigation. The study also highlights a number of difficulties, including the requirement for extremely high-quality data, the possibility of computational bias, and the existence of societal issues. According to the research, intelligent technology (AI) has the potential to drastically alter the financial risk management industry by improving capacity for decision-making, minimising risks, and improving efficiency. However, to fully leverage these advantages, it is essential to skillfully negotiate the obstacles associated with the incorporation of AI into the domain of financial risk control. This study provides a thorough examination of artificial intelligence's function in financial risk management, stressing both the benefits and drawbacks of its application. For decision-makers, risk managers, and financial institutions looking to integrate AI into their risk management plans, this paper is an invaluable resource.

关键词： Industries Ethics Surveillance Prevention and mitigation Decision making machine learning Risk management Artificial intelligence standards Risk mitigation

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Bridging Biomimetic Designs with Advanced Deep learning for Next-Generation Sustainable Wearable Technologies

Bridging Biomimetic Designs with Advanced Deep Learning for ...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), international conference on

作者： Priyameet Kaur Keer N Lakshmi Deepthi Ginni Nijhawan Nandini Shirish Boob Vivek Kumar C Taqi Mohammed Khattab Al-Rubaye Master of Business Administration New Horizon College of Engineering Bangalore India Computer Science and Engineering(Data Science) Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Department of Civil GRIET Bachupally Hyderabad Telangana India Department of Medical Laboratory Technology College of Medical Technologies The Islamic University Najaf Iraq

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

Demand for smart gadgets and ecologically friendly solutions has spurred new ideas. This study uses biomimetic designs and powerful deep learning to forecast a major change in next-generation electronics. Portable technology is developing worldwide, and the opening emphasizes the need to consider the environment when producing them. A novel strategy combining biomimicry and deep learning might increase smart device accuracy, energy efficiency, and biomimicry scores. This project meticulously integrates biomimetic design with cutting-edge deep learning. Biomimicry improves wearable tech by taking ideas from nature. strong deep learning algorithms based on artificial neural networks provide predictive analytics and user adaption. The project was designed to investigate biomimetic settings and user behaviors in a controlled environment, and the datasets were carefully selected. Across several success metrics, the proposed method performs better. The accuracy increases greatly, demonstrating how effectively biomimetic concepts and deep learning techniques work together to improve accuracy and reliability. Energy efficiency increases, indicating that biomimetic methods prioritize environmental protection. Biomimicry indices indicate that mimicking natural systems works, proving the method is effective. This study provides an eco-friendly technique to produce smart gadgets. Smoothly combining biomimicry and deep learning improves devices and satisfies environmental demands. This study shows how nature-driven, nature-friendly technology may change things. As smart tech evolves, this study ensures that new technologies are cutting-edge and environmentally friendly. Biomimetic designs and deep learning have revolutionized sustainable technologies. It promotes a more compassionate and eco-friendly future.

关键词： Deep learning Technological innovation Accuracy Biomimetics Biological system modeling Energy efficiency Wearable devices Sustainable development Smart devices Next generation networking

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