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

共 13584 条记录，以下是711-720 订阅

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Impact of Hyper Parameter optimization Over Performance Improvement of CNN Models 5

Impact of Hyper Parameter Optimization Over Performance Impr...

引用

5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Ganesh Kumar, M. Karthiga Devi, K. Nesarani, A. Department of Computer Applications Krishnankoil India Department of Computer Science and Engineering Andra Pradesh India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

With the use of Convolutional neural Networks (CNN) in medical image processing, researchers focus on improving the accuracy of CNN model in numerous ways. One such concepts booms with optimizing the hyper-parameters of CNN models. Discovering the optimal value of hyper-parameters was a time consuming task and available optimizing techniques fails to provide the boundary of optimal hyper-parameter values. this research article was concerned with optimizing the hyper-parameter during transfer learning focusing the impact of optimal hyper-parameter value and optimization methods on CNN models. In this research we focused on selection of optimal hyper-parameter value and performed a comparative analysis of the optimization methods. three optimization algorithms such as Grid Search, Random Search and Bayesian optimization were used and evaluated with accuracy metrics. It was observed that Bayesian optimization yields best CNN model with accuracy of 85.4% over other algorithms. © 2024 IEEE.

关键词： Convolutional neural networks

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Dynamic Spectrum Fusion: An Adaptive learning Approach for Hybrid NOMA/OMA in Evolving Wireless Networks 4

Dynamic Spectrum Fusion: An Adaptive Learning Approach for H...

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4th international conference on Advanced Research in Computing, ICARC 2024

作者： Perera, Geethaka S. Senanayake, Dimantha Y. Basnayake, Vishaka Jayakody, Dushantha N.K. School of Engineering CTR Sri Lanka Lusofona University COPELABS Portugal

ISBN: (纸本)9798350384864

this study introduces an advanced adaptive learning algorithm to optimize the choice between Non-Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA) in wireless networks. Our approach utilizes a dual-node simulation environment to evaluate the performance of a novel hybrid NOMA-OMA scheme, especially under varying conditions of user mobility. A key introduction of our research is the implementation of a versatile, data-driven scoring system, designed to maximize system performance across diverse applications. To improve the decision-making process, we incorporated a range of machine learning techniques;Random Forest, Gradient Boosting Machine (GBM), and Multilayer Perceptron. this comprehensive approach, blending deep learning and traditional algorithms, is pivotal in determining the most effective Multiple Access (MA) scheme in various spatial scenarios. the integration of reinforcement learning techniques enables our system to dynamically adapt to changing conditions, facilitating real-time decision-making. the result is a dynamic and adaptive Hybrid NOMA/OMA scheme, demonstrating a significant improvement over traditional systems. © 2024 IEEE.

关键词： Wireless networks

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Research on Heterogeneity Problem of Federated learning Based on Knowledge Distillation-Translate 4

Research on Heterogeneity Problem of Federated Learning Base...

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4th international conference on New Energy and Power Engineering, ICNEPE 2024

作者： Liu, Xunjie Wang, Haotian Guo, Binghui Wang, Yutian Gao, Haiwei Beihang University School of Mathematical Sciences Center for Future Blockchain and Privacy Computing Beijing China Beihang University School of Software Beijing China Beihang University Institute of Artificial Intelligence Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing China Beijing Wuzi University School of Economics Beijing China Beijing Jiaotong University School of Mechanical Electronic and Control Engineering Beijing China

ISBN: (纸本)9798331516086

With rising computing power, data-driven AI algorithms have become essential in fields like image processing, with significant applications in electricity inspection image detection. However, competition and privacy concerns prevent data sharing between electricity companies, limiting the development of smart inspection technologies. Federated learning offers a solution, enabling companies to collaboratively train a global model without sharing raw data, thus enhancing model performance and data efficiency. When data distribution is inconsistent (non-IID), traditional federated learning faces challenges that affect training efficiency and accuracy. To address this, we propose FedTSD, a federated learning algorithm designed for electricity inspection tasks. By dynamically exchanging teacher-student roles between the global and local models, FedTSD uses a central server to transfer additional knowledge, boosting generalization and reducing overfitting. Experiments on CIFAR-10 and SVHN datasets show that FedTSD outperforms traditional methods, achieving higher accuracy and smoother training, with faster convergence, which is crucial for efficient electricity inspections. © 2024 IEEE.

关键词： Federated learning

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the Intersection of AI, ML, and Industry: A Review of Emerging Trends in Real-World applications 4

The Intersection of AI, ML, and Industry: A Review of Emergi...

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

作者： Joshi, Ankush Kumar, Vikash Chauhan, Naman Kumar, Anuj Singh, Ritik Kumar Coer University Computer Science and Application Roorkee India

ISBN: (纸本)9798350364729

Machine learning (ML) and artificial intelligence (AI) are transforming a number of industries, spurring creativity, and improving productivity. this review article examines the new developments at the nexus of industry, artificial intelligence, and machine learning. It offers a thorough rundown of current developments and real-world uses. We demonstrate how artificial intelligence (AI) and machine learning (ML) are being incorporated into industrial processes to solve complex problems and enhance operational workflows by looking at important industries including healthcare, banking, manufacturing, retail, and transportation. We highlight the revolutionary power of these technologies, talk about the difficulties and moral issues, and suggest future paths for study and development through in-depth case studies and practical examples. © 2024 IEEE.

关键词： Smart manufacturing

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Research on the Construction of Smart Grid Digital System Based on Big Data Cloud Computing Model optimization Algorithm 4

Research on the Construction of Smart Grid Digital System Ba...

引用

4th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Li, Zhipeng Liu, Shaobo Zhang, Jianfeng Wang, Jun Shen, Shuo State Grid Gansu Electric Power Company Lanzhou China State Grid Gansu Electric Power Company Digital Division Lanzhou China

ISBN: (纸本)9798350359589

An intelligent power business development method based on digital service information is proposed. At the same time, a complete data mining framework is constructed. Parameters such as distance of adjacent pixels, similarity degree of carrier components and average amplitude were determined by coding and parsing. then the digital business information resources are extracted. the mining method of data cloud in power system based on time series is established. the correlation degree of each information characteristic of middle station is analyzed based on classification and characteristic identification of power system middle station. Experiments show that the algorithm can effectively improve the distribution of data in the middle station in every minute. the standard deviation of the algorithm is reduced and the mining effect is improved. © 2024 IEEE.

关键词： artificial intelligence digital system optimization algorithm smart grid

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An Empirical Study of the Impact of Test Strategies on Online optimization for Ensemble-learning Defect Prediction 40

An Empirical Study of the Impact of Test Strategies on Onlin...

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40th international conference on Software Maintenance and Evolution

作者： Hamamoto, Kensei Tsunoda, Masateru Tahir, Amjed Bennin, Kwabena Ebo Monden, Akito Toda, Koji Nakasai, Keitaro Matsumoto, Kenichi Kindai Univ Higashiosaka Osaka Japan Massey Univ Palmerston North New Zealand Wageningen UR Wageningen Netherlands Okayama Univ Okayama Japan Fukuoka Inst Technol Fukuoka Japan OMU Coll Technol Osaka Japan NAIST Ikoma Japan

ISBN: (纸本)9798350395693;9798350395686

Ensemble learning methods have been used to enhance the reliability of defect prediction models. However, there is an inconclusive stability of a single method attaining the highest accuracy among various software projects. this work aims to improve the performance of ensemble-learning defect prediction among such projects by helping select the highest accuracy ensemble methods. We employ bandit algorithms (BA), an online optimization method, to select the highest-accuracy ensemble method. Each software module is tested sequentially, and bandit algorithms utilize the test outcomes of the modules to evaluate the performance of the ensemble learning methods. the test strategy followed might impact the testing effort and prediction accuracy when applying online optimization. Hence, we analyzed the test order's influence on BA's performance. In our experiment, we used six popular defect prediction datasets, four ensemble learning methods such as bagging, and three test strategies such as testing positive-prediction modules first (PF). Our results show that when BA is applied with PF, the prediction accuracy improved on average, and the number of found defects increased by 7% on a minimum of five out of six datasets (although with a slight increase in the testing effort by about 4% from ordinal ensemble learning). Hence, BA with PF strategy is the most effective to attain the highest prediction accuracy using ensemble methods on various projects.

关键词： fault prediction multi-armed bandit problem overlooking risk-based testing

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Performance optimization and Model Research of Machine learning in Speech Recognition System 6

Performance Optimization and Model Research of Machine Learn...

引用

6th international conference on Artificial Intelligence and Computer applications, ICAICA 2024

作者： Shang, Wenwen Xi'an FANYI University Shaanxi Xi'an710105 China

ISBN: (纸本)9798350377255

this paper proposes an improved method based on machine learning, which combines the deep neural network (DNN) architecture and speech enhancement technology to significantly improve the recognition accuracy and robustness of the system. In traditional speech recognition models, complex noise environments often lead to inaccurate feature extraction and poor model training effects, thereby reducing the practical application value of the system. To solve the above problems, this paper introduces adaptive noise reduction algorithms and time-frequency domain enhancement technologies in speech preprocessing to optimize the quality of input signals. In addition, through the improved DNN model structure, the ability to model the spatiotemporal features of speech signals is enhanced. At the same time, the dynamic learning rate strategy and gradient optimization algorithm are adopted to improve the convergence efficiency and generalization performance of the model. Experiments verify the superior performance of the improved model in multi-noise scenarios. Compared with traditional methods, the speech recognition accuracy of the improved model is improved by 12.5%, indicating that it has stronger robustness under noise interference. the processing delay of the system is reduced by an average of 15.3%, which greatly improves the real-time performance. © 2024 IEEE.

关键词： Frequency domain analysis

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4th international Informatics and Software Engineering conference - Symposium Program, IISEC 2023

4th International Informatics and Software Engineering Confe...

引用

4th international Informatics and Software Engineering conference, IISEC 2023

ISBN: (纸本)9798350318036

the proceedings contain 57 papers. the topics discussed include: application of ChatGPT in the tourism domain: potential structures and challenges;oblique logistic function for the rank-frequency distribution of letters;innovation in corporate cyber security;a digital twin framework for edge server placement in mobile edge computing;mobile app for kidney patients to provide a comprehensive solution to manage disease;development of a genetic algorithm for vehicle routing problem in military logistics distribution;analyzing customer churn: a comparative study of machine learning models on Pay-TV subscribers in turkey;a study on slice-aware industrial edge applications for next-generation private networks;and a comparison of optimization methods for path finding problem.

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An Approach to Radiotherapy Treatment Planning based on Machine learning algorithms 4

An Approach to Radiotherapy Treatment Planning based on Mach...

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4th international conference on Expert Clouds and applications, ICOECA 2024

作者： Divya, Komati Bhargavi, C.H. Abirami, M. Arjun, Ponnaganti Padmaja, Pabbathi Dhanwanth, Batini Panimalar Institute of Technology Department of Computer Science and Engineering Chennai India Department of Computer Science and Engineering Chennai India St.Peter's Institute Of Higher Education And Research Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350385793

Customizing radiation treatments for each patient is a formidable obstacle in the fight against cancer. Because they rely on human intervention and generalization, traditional methods often provide less-than-ideal results and unwanted side effects. Utilizing patient-specific anatomical and tumor-specific data, this research analyzes the revolutionary possibilities of machine learning for planning radiation therapy. the goal of this research work is to forecast radiation doses, identify tumor boundaries, and reduce the exposure to healthy tissue using Deep learning (DL), Support Vector Machine (SVM) and Random Forest (RF) models. Radiation planners must prioritize data heterogeneity, model interpretability, and clinical validation if machine learning has to be widely used. Improved patient outcomes and quality of life may be achieved via the use of Machine learning (ML) to improve the radiation efficacy and accuracy, as shown in this research study that synthesizes existing research. © 2024 IEEE.

关键词： Radiotherapy

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A Unified Framework for Recommend Medicine Review Categories through Bi-LSTM and Supervised learning 4

A Unified Framework for Recommend Medicine Review Categories...

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4th international conference on Expert Clouds and applications, ICOECA 2024

作者： Kumar, Anuj Shekhar, Shashi GLA University Department of Computer Engineering & Applications Uttar Pradesh Mathura India

ISBN: (纸本)9798350385793

the number of users and daily tweets on Twitter are significant. the medicine review categories system is designed to leverage the vast amount of patient-generated data to create a reliable and effective medication recommendation framework. the proposed system employs Deep learning and Machine learning techniques, specifically utilizing a deep learning technology known as Bi-Directional Long Short-Term Memory (Bi-LSTM) and machine learning algorithms Decision Tree, Random Forest, and Naive Bayes to evaluate textual data effectively. the primary objective of our system is to identify appropriate categories of input text /review based on patient reviews and defined categories in the dataset. In the dataset, we found two columns where one is a review and another is categories like treatment and medication, prevention, symptoms causes, news, and others where we will find the exact categories of our input review. We evaluate the system's performance using various metrics, including Fl Score, precision, recall, and accuracy, ensuring its robustness and efficacy. Bi LSTM models provided an accuracy of 95%, the Decision Tree models provided an accuracy of 94 %., the Random Forest models resulted in an accuracy of 92%., and the Naive Bayes models provided an accuracy of 84%. this comprehensive approach aims to provide medical experts with a reliable tool for predicting and ultimately contributing to more informed and personalized healthcare decisions. © 2024 IEEE.

关键词： Contrastive learning

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