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共 13702 条记录，以下是241-250 订阅

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A Comprehensive Review on Bio-Inspired optimization Techniques 5th

A Comprehensive Review on Bio-Inspired Optimization Techniqu...

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5th international conference on Data Science, Machine learning and applications

作者： Nimmala, Satyanarayana Samya, Banoth Reddy, Jakkala Ravichandra Ramana, T. Venkata Ch, Ravikumar CVR Coll Engn Dept CSE DS Hyderabad Telangana India CVR Coll Engn Dept Comp Sci & Engn Hyderabad Telangana India Vijay Rural Engn Coll Dept CSE Nizamabad Telangana India CVR Coll Engn Dept Comp Sci & Engn AIML Hyderabad Telangana India Chaitanya Bharathi Inst Technol Dept Artificial Intelligence & Data Sci Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

the concept of optimization has gained widespread recognition in numerous industries worldwide. To maximize resources like time, money, distance, options, and searches, people and organizations are actively looking for the best answers to particular problems. the traditional methods of tackling optimization problems are no longer considered adequate since they frequently produce suboptimal results or take too long. Using heuristic and meta-heuristic methods is now necessary to solve optimization problems. To avoid becoming trapped in locally optimal solutions, meta-heuristic approaches are made to thoroughly investigate and utilize the entire search space. Meta-heuristic algorithms are remarkably adept at solving optimization issues;they take their cues from the behavior of real organisms such as fish, birds, insects, and bees. In almost every modern field, optimization is a crucial factor to take into account while attempting to resolve real-world issues. An in-depth analysis of bio-inspired optimization methods and their real-world applications for a variety of optimization problems is presented in this paper.

关键词： optimization Heuristic methods Meta-heuristic methods Trade-off Bio-inspired optimization

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PRP protocol optimization method based on Q-learning under the Linux operating system 4

PRP protocol optimization method based on Q-learning under t...

引用

2024 4th international conference on Artificial Intelligence and Industrial Technology applications, AIITA 2024

作者： Wang, Ruini Hu, Xianlang Kong, Xiangying Jiangsu Automation Research Institute Lianyungang China

In the rapid development of industrial networks, in the face of complex and changeable network environments, the demand for reliable and efficient communication systems is becoming increasingly prominent. In this paper, a PRP protocol optimization method based on Q-learning under the Linux operating system is proposed. By introducing a Q-learning algorithm, the adaptive learning of a dynamic network environment is realized through Q-value update rules, to adjust PRP protocol parameters. the experimental results show that the optimization of PRP protocol based on Q-learning can improve the performance of PRP protocol under various network conditions and load changes, thus improving communication reliability. © Published under licence by IOP Publishing Ltd.

关键词： Linux

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Detecting and Mitigating Data Integrity Attacks on Distributed algorithms for Optimal Power Flow using Machine learning 57

Detecting and Mitigating Data Integrity Attacks on Distribut...

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57th Hawaii international conference on System Sciences (HICSS)

作者： Harris, Rachel Molzahn, Daniel K. Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9780998133171

Using distributed algorithms, multiple computing agents can coordinate their operations by jointly solving optimal power flow problems. However, cyberattacks on the data communicated among agents may maliciously alter the behavior of a distributed algorithm. To improve cybersecurity, this paper proposes a machine learning method for detecting and mitigating data integrity attacks on distributed algorithms for solving optimal power flow problems. In an offline stage with trustworthy data, agents train and share machine learning models of their local subproblems. During online execution, each agent uses the trained models from neighboring agents to detect cyberattacks using a reputation system and then mitigate their impacts. Numerical results show that this method reliably, accurately, and quickly detects data integrity attacks and effectively mitigates their impacts to achieve near-feasible and near-optimal operating points.

关键词： Cybersecurity Distributed optimization Optimal Power Flow Data Integrity Attack

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Enabling Smart Cities: AI-Powered Prediction Models for Urban Traffic optimization 4

Enabling Smart Cities: AI-Powered Prediction Models for Urba...

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

作者： Revathy, G. thangavel, M. Senthilvadivu, S. Savithri, M.C. Sastra Deemed University Srinivasa Ramanujan Centre Department of Cse Thanjavur India Department of Mca Erode India Saveetha University Saveetha School of Engineering Department of Cse Chennai India J.K.K Munirajah College of Technology Department of Cse Erode India

ISBN: (纸本)9798331523923

Efficient traffic flow is essential for sustainable growth, environmental health, and enhanced quality of life in contemporary urban settings. the intricacy and dynamic character of urban traffic can make traditional traffic control techniques inadequate. In order to optimize urban traffic, this study suggests an AI-powered prediction model that combines real-time data from many IoT-enabled sources, including sensors, GPS data, and mobile devices, with machine learning algorithms. the suggested model predicts traffic patterns and makes adaptive recommendations to maximize traffic flow, lessen congestion, and cut down on delays by utilizing deep learning and reinforcement learning. When compared to conventional techniques, simulations employing real-world information from many urban locations show notable reductions in traffic and travel time. the model is a great tool for smart city efforts because of its scalability and adaptability, which provide traffic authorities and city planners the capacity to make decisions in real time. this study advances the infrastructure of smart cities and applies AI to address the problems of urban transportation. © 2025 IEEE.

关键词： Prediction models

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Deep Conditional Measure Quantization 3rd

Deep Conditional Measure Quantization

引用

3rd international conference on optimization, learning algorithms and applications (OL2A)

作者： Turinici, Gabriel Univ Paris Dauphine PSL CEREMADE Paris France

ISBN: (纸本)9783031530357;9783031530364

Quantization of a probability measure means representing it with a finite set of Dirac masses that approximates the input distribution well enough (in some metric space of probability measures). Various methods exists to do so, but the situation of quantizing a conditional law has been less explored. We propose a method, called DCMQ, involving a Huber-energy kernel-based approach coupled with a deep neural network architecture. the method is tested on several examples and obtains promising results.

关键词： conditional generative algorithms optimization conditional quantization stochastic optimization vector quantization

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Social Media Intelligence: Development of a Parsing Framework 4

Social Media Intelligence: Development of a Parsing Framewor...

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4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Gandhi, Gomtesh Solanki, Devisha Paliwal, Ishita Kushwaha, Sarthak Gupta, Neha Mishra, Durgesh Symbiosis University of Applied Sciences Indore India Symbiosis University of Applied Sciences Scsit Indore India

ISBN: (纸本)9798331518981

Social media platforms are treasure troves of unstructured data, offering unparalleled opportunities for intelligence gathering. However, challenges such as data diversity, evolving APIs, and privacy regulations hinder effective data utilization. the Social Media Intelligence Parser (SMIP) is a versatile cross-platform tool designed to extract, structure, and analyze social media data using advanced machine learning algorithms. It provides real-time insights into suspicious activities and patterns, catering primarily to investigative and counter-terrorism needs. By integrating features such as sentiment analysis, multimedia processing, and dynamic API adaptation, SMIP transforms raw social media data into actionable intelligence. this paper presents the architecture, methodologies, and potential applications of SMIP, emphasizing its scalability and adaptability to evolving digital landscapes. © 2024 IEEE.

关键词： Adversarial machine learning

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Advancements And Difficulties Of Machine learning applications In Customised Medicine 4

Advancements And Difficulties Of Machine Learning Applicatio...

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4th international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Manikandan, M. Venkatesh, P. Ilakkiya, T. Krishnamoorthi, M. Ramu, M. Senthilnathan, C.R. Sri Sairam Engineering College Chennai India Sri Sairam Institute of Technology India Kristu Jayanti College Bengaluru Faculty of Commerce and Management Bengaluru India Sri Sairam Institute of Technology Chennai India

ISBN: (纸本)9798331508845

Machine learning has been a huge boon to the healthcare industry's progress toward individualized medication. this article provides a comprehensive evaluation of the current situation, with a particular emphasis on the applications, benefits, challenges, and possible advances of ML in customized healthcare. through a detailed examination of the literature, we demonstrate the extensive range of machine learning algorithms utilized in disciplines such as illness prediction, diagnosis, therapy selection, prognosis, and patient monitoring. In addition, we look at the possibility that machine learning models could be improved upon by combining data from several sources, such as genetic information, imaging data, electronic health records, and data collected by wearable sensors. Furthermore, we explore the ethical, regulatory, and privacy difficulties associated with ML in healthcare, as well as approaches for overcoming these challenges. After reviewing current trends, we outline potential research prospects in using machine learning for customized medicine, emphasizing the necessity of multidisciplinary collaboration and continual inventiveness. © 2024 IEEE.

关键词： Electronic health record

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Handling outliers in swarm algorithms: a review 4

Handling outliers in swarm algorithms: a review

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4th international Workshop of IT-Professionals on Artificial Intelligence, ProfIT AI 2024

作者： Uzlov, Dmytro Havryliuk, Yehor Hushchyn, Ivan Strukov, Volodymyr Yakovlev, Sergiy V.N. Karazin Kharkiv National University 4 Svobody Sq. Kharkiv61022 Ukraine

Swarm optimization algorithms, inspired by the collective behavior of biological swarms, are a promising tool for solving the problem of optimizing complex systems where traditional methods are often ineffective. However, the problem of outliers can significantly affect the process of finding an optimal solution. therefore, the study of methods for detecting and processing outliers in swarm algorithms, such as the particle swarm optimization (PSO), is an urgent task that has significant potential to improve the efficiency and reliability of these algorithms in various practical applications, such as drone control systems, financial systems, environmental control and modeling systems. the article deals with the problem of outliers in swarm optimization algorithms such as PSO. An overview of existing approaches to managing outliers, including adaptive methods, methods using swarm topologies, hybrid algorithms, and others, is provided. the advantages and disadvantages of each approach are analyzed. Particular attention is paid to new promising areas, such as the combination of neural networks and reinforcement learning, to develop more efficient and adaptive swarm algorithms. the article is aimed at researchers and practitioners in the field of optimization who are interested in improving the efficiency and reliability of swarm algorithms. © 2024 Copyright for this paper by its authors.

关键词： Particle swarm optimization (PSO)

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Situational Portfolio Forecasting and Allocation with Deep-learning Approach 4

Situational Portfolio Forecasting and Allocation with Deep-L...

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4th international conference on Communication Systems, Computing and IT applications, CSCITA 2023

作者： Joshi, Mrityunjay Deshpande, Amol Ambawade, Dayanand Department of Electronics and Telecommunications Sardar Patel Institute of Technology Mumbai58 India

ISBN: (纸本)9781665459877

Portfolio optimization is selecting the best set of possible weights for a group of assets where the objective is to maximize the returns and risk-return ratio and minimize the risks and volatility. this research aims to develop and test ARIMA and LSTM as forecasting techniques and subsequently perform portfolio optimization using a custom optimization methodology leveraging the forecasted returns from the models mentioned earlier. the intention is to develop a portfolio that dynamically allocates weights to the assets for the optimum investment strategy. the portfolio considers an initial investment of 100 units of currency, allowing uncomplicated interpretation of results and data. © 2023 IEEE.

关键词： Forecasting

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A Review of Machine learning algorithms in Diabetes Management 4

A Review of Machine Learning Algorithms in Diabetes Manageme...

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

作者： Gayaki, Unnati Daronde, Subodh Tale, Abhay Barhate, Aditya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798331523923

Diabetes is a prevalent and chronic disease affecting millions worldwide, posing significant challenges in its management and treatment. this review article aims to explore the current and potential future roles of machine learning (ML) in diabetes management, focusing on progress, applications, and challenges. It discusses various ML techniques, including support vector machines (SVM), logistic regression, decision trees, ensemble methods, and advanced deep learning approaches such as long short-term memory (LSTM) networks and convolutional neural networks (CNN). these methods have demonstrated benefits in analytics, patient monitoring, decision support systems, personalised treatment, and patient engagement. However, significant challenges persist, such as computational complexity, data quality, limited interoperability of systems, and ethical concerns regarding AI use in healthcare. the article addresses these challenges by fostering collaboration between AI implementers and health care professionals. this collaboration is essential to develop long-term, legally compliant, and solution-oriented frameworks that enhance patient care and diabetes management and pave the way for more efficient and ethical AI applications in healthcare. © 2025 IEEE.

关键词： Support vector machines

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