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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211-220 订阅

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Effectively Reducing the Data Transmission Time by Swarm optimization Algorithm with Genetic Algorithm in Cloud Computing 4

Effectively Reducing the Data Transmission Time by Swarm Opt...

引用

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

作者： Ansari, Mohd. Juned Homavazir, Zulieka College of Law Iimt University Uttar Pradesh Meerut India Isme - School of Management & Entrepreneurship Atlas Skill Tech University Maharashtra Mumbai India

ISBN: (纸本)9798350333558

Swarm optimization algorithms are becoming increasingly popular for data transmission over wireless networks. this is due to the ability of these algorithms to reduce data transmission time as compared to traditional algorithms. Swarm optimization algorithms take advantage of an efficient use of resources and dynamic adaptation to changing conditions. With these features, they are able to optimize network resources and reduce data transmission time, resulting in faster data transfer. Swarm optimization algorithms have been used to provide solutions to various problems, such as network routing, network load balancing, and congestion control. they are also attractive solutions for increasing the efficiency of data transmission over wireless networks. By utilizing these algorithms, the data transmission times can be reduced significantly, resulting in a more efficient system. © 2023 IEEE.

关键词： Data transfer

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

4th International Conference on Machine Learning and Big Dat...

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

the proceedings contain 150 papers. the topics discussed include: support vector machines principles and actually example;continuous optimization of business environment for power grid timing control;assessment of new energy consumption capacity of grid based on edge computing;evaluation and power grid investment efficiency under high quality development;design of real-time measurement and intelligent path selection system for network quality under big data;enterprise level data warehouse system based on hive in big data environment;research on performance prediction method based on gaussian process regression;design of information management system based on random leapfrog band selection algorithm;simulation and optimization system of automated e-commerce logistics warehouse allocation network based on intelligent algorithm;and intelligent analysis method of e-commerce data based on multiple machine learning algorithms.

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Detection of Eye Disease Using Deep learning algorithms 4

Detection of Eye Disease Using Deep Learning Algorithms

引用

4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Patel, Omkumar J. Verma, Kshitij Vyas, Chirayu Pandey, Trilok Nath Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Applications Kiit Deemed to be University Bhubaneswar India

ISBN: (纸本)9798331540364

the human eye is a complex organ that can perceive a wide variety of visual inputs. However, its functioning can be severely impaired by diseases such as cataract and diabetic retinopathy which have the potential to cause permanent blindness which not only disrupts the productivity and mobility of patients but also causes a socio-economic impact that decreases the quality of life. this paper reviews the recent advancements in deep learning for diagnosing retinal diseases such as diabetic retinopathy (DR), cataract and glaucoma. the paper presents an extensive comparative analysis of 4 definite deep learning models - Vision Transformers (ViT), ResNet, GoogLeNet and Convolutional Neural Network (CNN). the results from the conducted experiments show that ViT has the highest accuracy followed by CNN. the outcomes highlights the potential of deep learning models in revolutionizing eye disease detection technology. © 2024 IEEE.

关键词： Convolutional neural networks

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Sustainable Crop Yield Forecasting with the Dual-Branch Geometric Progressive Feedback Cosine Convolutional Network Model 4

Sustainable Crop Yield Forecasting with the Dual-Branch Geom...

引用

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

作者： Ramkumar, M. Siva Nivetha, R. Gopan, Gokul Kanan, Mohammad Baig, Rahmath Ulla Giri, Jayant Sns College of Technology Dept of Ece India Kumaraguru College of Technology Computer Science and Engineering India Saveetha University Department of Mechanical Engineering Chennai602105 India College of Engineering University of Business and Technology Department of Industrial Engineering Jeddah21448 Saudi Arabia College of Engineering Zarqa University Department of Mechanical Engineering Zarqa Jordan Cambridge Institute of Technology Department of Artificial Intelligence and Machine Learning Bangalore560086 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798331523923

Using deep learning algorithms to extract important agricultural traits has made crop production prediction based on environmental, soil, water, and crop parameters an important area of research. But conventional approaches have trouble creating a straight linear or non-linear mapping between yield values and raw data, and the quality of the characteristics that are extracted has a significant impact on how well they work. Deep reinforcement learning builds a strong prediction framework by combining the intelligence of deep learning with reinforcement learning to get beyond these drawbacks. In order to anticipate agricultural yields accurately and sustainably, this work aims to create a Dual-Branch Geometric Progressive Feedback Cosine Convolutional Network with Skill optimization Algorithm (DB-GPFCNet-SOA). Robust Double-Weighted Guided Filtering (RDWGF) is used for data preprocessing in order to minimize noise and improve important components. To ensure a complete prediction model, the N-Branch retrieves local neighbor-based information while the C-Branch records global geometric properties. Moreover, the accuracy is enhanced by the SOA, which enhances loss parameters. the suggested model has RMSE of 0.25 and MAE of 0.15 with exceptional accuracy of 99.2%, precision of 99.1%, recall of 99%, and F1-score of 99%. these results validate the performance of the model, which renders it a suitable approach for real-time agricultural applications. © 2025 IEEE.

关键词： Deep reinforcement learning

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Overcoming Class Imbalance in Network Intrusion Detection: A Gaussian Mixture Model and ADASYN Augmented Deep learning Framework 24

Overcoming Class Imbalance in Network Intrusion Detection: A...

引用

4th international conference on Internet of things and Machine learning

作者： Chen, Xin Gong, Zhongyuan Huang, Dixin Jiang, Nan Zhang, Yuejin East China Jiaotong Univ Sch Informat & Software Engn Nanchang Peoples R China

ISBN: (纸本)9798400710353

Intrusion detection in computer networks, as a critical component for maintaining network security, has become increasingly essential in the face of the widespread and evolving threats within the complex internet environment. In recent years, the widespread application of machine learning and deep learning technologies has emerged as a crucial means to counteract network threats. Despite significant advances in classification accuracy achieved by these algorithms, challenges persist in addressing the minority class problem within imbalanced datasets. this study proposes an innovative approach that combines the Adaptive Synthetic (ADASYN) sampling method with a Gaussian Mixture Model (GMM) clustering-based sampling method termed AGM. Building upon this, we enhance traditional deep learning models by adopting a comprehensive network architecture known as C3BANet, which integrates Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Channel-Attention mechanisms. we meticulously preprocess the UNSW-NB15 dataset to eliminate noise, inconsistencies, and incompleteness. Subsequently, the dataset undergoes carefully designed sampling using the AGM method to address the issue of poor performance on minority classes caused by the majority class prediction bias. Ultimately, we validate the effectiveness of the enhanced C3BANet model on the UNSW-NB15 dataset. Experimental results demonstrate that, after AGM sampling, the model achieves multiclass detection rates of 96.82%, respectively, on the UNSW-NB15 dataset, outperforming current mainstream intrusion detection algorithms. this research not only introduces methodological innovations but also exhibits significant advantages in practical applications.

关键词： Network intrusion detection Gaussian mixture model Class imbalance UNSW-NB15

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Mitigating Adversarial threats: Safeguarding AI-Driven Fraud Detection Systems Against Malicious Exploits 4

Mitigating Adversarial Threats: Safeguarding AI-Driven Fraud...

引用

4th international conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Kumar, Atul Guleria, Kalpna Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350364729

the proliferation of AI-driven fraud detection systems increases the capability of organizations to a large extent in terms of fraud detection and prevention. these systems, however, are fast becoming the targets of adversarial threats that look to exploit their vulnerabilities. this paper focuses on the various machine learning algorithms such as Logistic Regression, Random Forest, and Decision Tree, that are deployed to mitigate such adversarial threats. Comparative analysis may show that, among the various algorithms considered, decision trees are most robust against malicious exploits. Particularly, the hierarchical structure of a decision tree and its split criteria provide resistance through the effective segregation of anomalous patterns of data and treating them differently. Our results indicate that, compared with others, the Decision Tree algorithm does a better job in fraudulent activity detection, performing well in stability and reliability under attacks. these findings suggest the importance of choosing appropriate machine learning models to strengthen AI-driven fraud detection systems against new types of cyber threats so they are both potent and trustworthy in real-world applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Transformative Potential of Consumer Insights into Sustainable Business Growth: Machine learning Integrated with Marketing Analytics 4

Transformative Potential of Consumer Insights into Sustainab...

引用

4th international conference on Soft Computing for Security applications, ICSCSA 2024

作者： Bist, Ayush Singh, Gurpreet Srivastava, Shilpee Chandigarh University Department of Mathematics Chandigarh India Chandigarh University Department of CSE Chandigarh India

ISBN: (纸本)9798331515720

Machine learning and marketing analytics have emerged as a well-known tool for transforming consumer insights into substantial business growth. through the consumption of advanced procedures and data analytics methodologies, enterprises gain the ability to extract invaluable insights from extensive consumer data sets, leading to refined marketing strategies and enriched customer experiences. this research study analyzes the synergy between machine learning and marketing analytics, emphasizing their profound impact on consumer insights and subsequent business expansion. It explains a range of machine learning algorithms and data analytics tools pertinent to marketing endeavors, including predictive modeling, customer segmentation, and recommendation systems. providing businesses with robust tools for decision-making and strategy formulation. Additionally, this research study explores the real-world examples showcasing the transformative potential of machine learning and marketing analytics in optimizing marketing campaigns, personalizing customer interactions, and enabling sustainable growth. By integrating machine learning and marketing analytics, organizations can capitalize on unparalleled opportunities to gain a competitive advantage in today's data-driven scenario, translating consumer insights into actionable strategies with unprecedented accuracy and efficacy. © 2024 IEEE.

关键词： Adversarial machine learning

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Machine learning-Based Ship Energy Efficiency algorithms 4

Machine Learning-Based Ship Energy Efficiency Algorithms

引用

4th international conference on Energy, Power and Electrical Engineering, EPEE 2024

作者： Zhang, Defu Chen, Mengqin Zhu, Wei Lv, Hongbin Tianjin University of Technology Maritime College Tianjin China Tianjin University of Technology Paper Union and other carbon ship technology research centers Tianjin China Yingkou AtoN Division Northern Navigation Service Center Liaoning Yingkou China Co. Ltd Tianjin China

ISBN: (纸本)9798331518066

this paper establishes fuel consumption prediction models using the Random Forest algorithm (RF) and Deep Neural Network (DNN) algorithm, with the models' performance evaluated based on Mean Square Error (MSE) and the coefficient of determination (R2). On this basis, a speed optimization model is developed using a DQN algorithm to dynamically adjust speed for minimizing fuel consumption. the evaluation of the model is conducted through the accumulation of reward values. the results show that the fuel consumption prediction model built using the RF algorithm achieves an MSE of 0.00064 and an R2 of 0.99781. the model constructed with the DNN algorithm yields an MSE of 0.00516 and an R2 of 0.99799. Comparatively, the RF algorithm slightly outperforms the DNN. Finally, the speed optimization model developed using the DQN algorithm learns the optimal policy through interactions with the environment to maximize the expected accumulated reward, ultimately enhancing energy efficiency by optimizing speed in the given environmental data. © 2024 IEEE.

关键词： Deep Neural Networks Q-learning Ship Energy Efficiency Speed optimization

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Enhancing Cloud Performance with AI-Driven Load Balancing and optimization algorithms 4

Enhancing Cloud Performance with AI-Driven Load Balancing an...

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4th international conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Singhal, Amit Kumar Goel, Pawan Garg, Devesh Sharma, Chhaya Raj Kumar Goel Institute of Technology Department of Cse U.P. Ghaziabad India Rkgit Department of Cse UP Ghaziabad India

ISBN: (纸本)9798350364729

Cloud computing has become a crucial technology for handling large-scale data and delivering scalable, on-demand services across various industries. As cloud environments continue to grow in complexity, efficient load balancing has emerged as a critical factor in maintaining performance and minimizing resource wastage. In this paper, we address the challenge of dynamic load balancing in cloud systems, with a focus on leveraging AI-driven optimization algorithms to enhance overall system performance. Although traditional algorithms like round-robin and heuristic-based methods have been widely studied, they face limitations in scalability, adaptability, and energy efficiency, leaving many challenges unresolved. Our research proposes a novel AI-driven load balancing framework that integrates machine learning models with optimization algorithms to dynamically adjust workloads in real-time, optimizing resource utilization and reducing latency. this innovative approach represents the first of its kind in applying AI for real-time decision-making in cloud load balancing, providing a more adaptive and efficient solution. Experimental results demonstrate that our framework outperforms existing methods by achieving up to 25% improvement in response time and 30% reduction in resource overhead. this first-of-its-kind solution shows significant potential for future cloud computing advancements, offering a more intelligent and responsive method for managing cloud resources effectively. © 2024 IEEE.

关键词： Cloud platforms

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Enhanced Heart Disease Prediction through Optimized Ensemble Random Forest Model 4

Enhanced Heart Disease Prediction Through Optimized Ensemble...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Devi, A. Raj, T. Nirmal Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya Department of Computer Science and Applications Kancheepuram India

ISBN: (纸本)9798331540364

Nowadays, the necessity for precise early detection of heart disease is critical with the conventional methods. However, challenges such as handling imbalanced datasets, ensuring model interpretability, and avoiding overfitting persist in predictive models. this paper assesses the efficacy of various machine learning algorithms by analyzing a vast dataset of cardiovascular health indicators. the approaches employed consist of classifiers such as Logistic Regression, K- Nearest Neighbors, Support Vector Machines, Naive Bayes, Decision Trees, Random Forests, Gradient Boosting, Stochastic Gradient Descent, and AdaBoost. Finally, these algorithms will be evaluated using performance indicators such as F1-score, ROC curves, DET curves, accuracy, precision, recall, and recall. the results indicated that the Decision Tree and Random Forest classifiers are the most adequate, each achieving an accuracy of 98.54% and near-perfect AUC scores of 0.99 and 1.00, respectively. this comparative analysis highlights the superiority of ensemble learning techniques in making robust predictions, thereby significantly contributing to the early detection and management of heart disease. © 2024 IEEE.

关键词： Adversarial machine learning

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