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

共 13501 条记录，以下是231-240 订阅

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Construction of Probability Graph optimization Model on Basis of Localization Algorithm 4

Construction of Probability Graph Optimization Model on Basi...

引用

4th international conference on Machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Li, Song Jiang, Qiuming Hubei Industrial Polytechic Hubei Shiyan China

Accurate positioning is crucial in fields such as indoor positioning, autonomous driving, and robot navigation. However, traditional base station positioning algorithms are often affected by factors such as sensor errors, environmental noise, and uncertainty, resulting in certain errors in the positioning results. the article constructed a probability graph model using Radio Frequency Identification (RFID) by using position estimation results as variables. In this model, each position estimation result was represented as a node, and the dependency relationship between positions was represented as an edge. this article observed sensor data and uses Bayesian inference methods to update the probability distribution of each node. the experimental results showed that compared to traditional base station positioning algorithms, the probability graph optimization model based on radio frequency identification can significantly improve the accuracy and reliability of positioning. the highest values for both reached 0.989 and 0.95, respectively. this model has significant advantages in positioning tasks and provides an effective solution for practical applications. © 2024 the Authors.

关键词： Graph algorithms

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Enhanced Routing Capabilities for WSN Using QOS optimization Technique 4

Enhanced Routing Capabilities for WSN Using QOS Optimization...

引用

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

作者： Kaushik, Vineet Agarwal, Varsha School of Commerce & Management Iimt University Uttar Pradesh Meerut India Isme - School of Management & Entrepreneurship Atlas Skill Tech University Maharashtra Mumbai India

ISBN: (纸本)9798350333558

Wireless Sensor Networks (WSN) have been implemented in a variety of applications ranging from environmental monitoring and control of industrial processes to healthcare and emergency response. In many cases, routing needs to meet stringent Quality of Service (QoS) requirements due to the time-sensitive nature of the data being transmitted. To address this challenge, several routing approaches have been proposed to enhance the performance of Delivery Ratio and Delay with QoS optimization. this paper presents an overview of different QoS-aware routing architectures and algorithms such as source-routing, hop-by-hop routing, location-aided routing, and random routing, which have been successfully applied to WSNs. We also discuss several QoS metrics such as throughput, delay, and reliability, as well as methods for their optimization. Finally, we provide some examples of current and potential applications where these approaches can be implemented. We conclude by discussing a few open research problems and future research directions in the area of QoS-aware routing for WSNs. © 2023 IEEE.

关键词： Quality of service

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Artificial Intelligence for Cybersecurity: A State of the Art 4

Artificial Intelligence for Cybersecurity: A State of the Ar...

引用

4th IEEE international conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Siam, Abdullah Al Hassan, Md Maruf Bhuiyan, Touhid Daffodil International University Department of Software Engineering Dhaka Bangladesh Southeast University Department of CSE Bangladesh Washington University of Science and Technology School of IT VA United States

ISBN: (纸本)9798331518882

the goal of this study is to analyze the role of artificial intelligence (AI) in cybersecurity by analyzing the strengths of many key AI techniques, including machine learning (ML), deep learning (DL), natural language processing (NLP), and anomaly detection algorithms. We conducted a detailed literature assessment on current research to assess the applicability of AI approaches in cybersecurity. Principal methodologies examined include machine learning for behavioral analytics, deep learning for malware detection, natural language processing for phishing identification, and anomaly detection for intrusion detection. Artificial intelligence approaches significantly enhance cybersecurity, with each strategy providing distinct functionalities. this study's distinctive contributions are its comparative assessment of approaches, emphasizing their strengths and practical applications in cybersecurity while addressing present issues and future avenues for improvement. the research suggests that a combination of AI in cybersecurity that merges these strategies could provide a significant increase in digital security. these findings bring substantial value by identifying the best scenarios for using each AI approach in cybersecurity. © 2025 IEEE.

关键词： Cybersecurity

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LiDAR Point Clouds in Autonomous Driving Integrated with Deep learning: A Tech Prospect 4

LiDAR Point Clouds in Autonomous Driving Integrated with Dee...

引用

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

作者： Vishnupriya Chowdhary, S. Neelima, N. Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798350343670

the combination of LiDAR point clouds and deep learning is transforming autonomous systems. the balance between LiDAR technology and DL methods for accurate point cloud categorization is the topic of this work. Our objectives involve generating effective algorithms for real-time processing, providing strength in a variety of contexts, and improving safety in applications like autonomous driving. the main issues addressed involve LiDAR point clouds, deep learning broad overviews, and data preprocessing approaches. Real-world examples demonstrate effective uses beyond autonomous driving, demonstrating the technology's adaptability. Although there are difficulties, continuing research suggests responses for computing efficiency and model resilience. the conclusion predicts a bright future with versatile and adaptive technologies for wide-scale implementation across developing LiDAR environments. © 2024 IEEE.

关键词： Autonomous Driving Deep learning LiDAR Point Clouds Technology Prospect

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Automated Tuna Freshness Assessment via Gas Sensors and Machine learning algorithms 4

Automated Tuna Freshness Assessment via Gas Sensors and Mach...

引用

4th international conference on Intelligent Cybernetics Technology and applications, ICICyTA 2024

作者： Pratama, Nyoman Raflly Novamizanti, Ledya Wijaya, Dedy Rahman School of Electrical Engineering Telkom University Bandung Indonesia School of Electrical Engineering Center of Excellence Artificial Intelligence for Learning and Optimization Telkom University Bandung Indonesia School of Appllied Science Telkom University Bandung Indonesia

ISBN: (纸本)9798331506490

Ensuring the safety and quality of fish products is essential for public health, especially in countries like Indonesia, where tuna is a popular fishery product. Tuna freshness is a critical indicator of seafood safety, directly influencing both its nutritional value and the risk of contamination. this study explores the application of machine learning models to accurately and efficiently classify tuna freshness based on gas emissions detected by an electronic nose (e-nose) system. the system employs MQ-135, MQ-9, and MQ-2 gas sensors, which are sensitive to spoilage-associated gases such as ammonia, methane, and alcohol. A dataset of 58,389 records was generated to capture the gas profiles released by tuna over time, creating a robust dataset for model training and validation. three machine learning algorithms-K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Naive Bayes-were compared to evaluate classification accuracy and practical applicability. Results indicate that both KNN and SVM achieved high accuracy rates of 99%, while Naive Bayes reached 90%, underscoring the suitability of KNN and SVM for tuna freshness detection. these findings demonstrate the potential of integrating e-nose technology and machine learning to provide a rapid, reliable, and scalable solution for freshness assessment in the fishing industry, enhancing quality control from suppliers to retailers. © 2024 IEEE.

关键词： Fish products

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Particle Swarm optimization of Deep learning Models for the Determination of Vehicle Hijacking Tweets 4

Particle Swarm Optimization of Deep Learning Models for the ...

引用

4th international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Patel, Taahir A. Nyirenda, Clement N. University of the Western Cape Department of Computer Science Cape Town South Africa

ISBN: (纸本)9798350353266

the problem of vehicle hijacking is increasingly prevalent in many parts of the world, including South Africa, causing travelers to live in constant fear of becoming victims. To address this, this work employs the usage of social media data to identify hijacking incidents in urban environments by using the city of Cape Town in South Africa as the case study. More specifically, the incorporated data is obtained from the Twitter social media platform. However, this form of data tends to be informal and unstructured. Hence, this led to the issue of data relevance, which was addressed by using Machine learning algorithms to classify tweets. Two deep learning algorithms were employed: a Convolutional Neural Network (CNN), and a Multi-layer Feed-forward Neural Network (MLFNN). However, due to model complexity, Particle Swarm optimization (PSO) was also implemented to produce the model variations, PSOCNN and PSOMLFNN. It was found that the PSO-optimized models outperformed the standard non-PSO models. the PSOMLFNN approach was also identified to be the best-performing model amongst the employed selection by obtaining an accuracy of 96.15%. Hence, it was successfully employed along with the Tweet data to produce vehicle hijacking maps. In future work, further machine learning and optimization algorithms will be tested to identify the best-performing algorithms for this task as well as extend the hijacking maps to a mobile application for drivers on South African roads. © 2024 IEEE.

关键词： Tweets

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A Comprehensive Analysis of Machine learning Models for the Prediction of Diabetes Mellitus 4

A Comprehensive Analysis of Machine Learning Models for the ...

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4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Muvva, Vijaya Bhaskar Reddy Lokman, Ts. Asiah Binti School of Information & Technology Malaysia University of Science and Technology Petaling Jaya Malaysia

ISBN: (纸本)9798350352931

this survey paper provides an overview of the current state-of-the-art techniques for diabetes diagnosis and prediction using machine learning (ML) methods. Various techniques, including both basic classifiers and deep learning networks, have been applied to solve the PIMA Indian Diabetes Dataset (PIDD) and other similar datasets. these include diagnostic methods that employ deep learning architectures such as interpretable filter convolutional neural networks (IF-CNN), CNN-Bi-Directional LSTM hybrids, and grasshopper optimization algorithms. these approaches have led to improvements in prediction accuracy, often through feature selection techniques such as Principal Component Analysis (PCA) and Polynomial Regression (PR). However, many challenges persist. For example, the large amount of data required to train Spiking Neural Networks (SNNs) is a limitation, while the models are often less robust to noise, with no effective way to monitor network performance. this survey aims to compare these models, highlight their strengths and weaknesses, and provide insights for future advancements in predicting diabetes mellitus. © 2024 IEEE.

关键词： Prediction models

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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...

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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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