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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151-160 订阅

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Flower Pollination Algorithm-Optimized Deep CNN Features for Almond(Prunus dulcis) Classification 4

Flower Pollination Algorithm-Optimized Deep CNN Features for...

引用

4th international conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Yurdakul, Mustafa Atabas, Irfan Tasdemir, Sakir Kirikkale University Engineering Faculty Computer Engineering Dept Kırıkkale Turkey

ISBN: (纸本)9798350349856

Almond is a nut rich in essential nutrients. In addition to being a food, it is also used in cosmetics and the pharmaceutical industry. the market value of almonds is determined according to the quality of the almonds. Manually determining the quality of almonds by humans is a prone to error, time-consuming, and tiring process. In this study, For this reasons, well-known twelve pre-trained CNNs were used to classify almonds as normal and damaged. then, the most successful model was used as a feature extractor, and the features were classified with various machine learning algorithms. In addition to all these, features were selected by using the FPA algorithm, and the classification process was carried out. Experimental results showed that the use of CNNs as feature extractors and classification with machine learning algorithms can provide better results than the classical softmax structure. In addition, the proposed FPA-based feature extraction increases the classification performance. © 2024 IEEE.

关键词： optimization

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Enhanced Bayesian Optimized Support Vector Machine (BO-SVM) Classification and Prediction of Heart Disease 4

Enhanced Bayesian Optimized Support Vector Machine (BO-SVM) ...

引用

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

作者： Chairmadurai, P. Kavitha, P. Kamalakkannan, S. Pallavaram Department of Computer Science Chennai India Pallavaram Department of Computer Applications Chennai India Pallavaram Department of Information Technology Chennai India

ISBN: (纸本)9798331523923

Medical data analysis is increasingly used to plan, improve research techniques, and explain diagnoses. the allocation of medical resources is based on the prevalence of specific pathologies. Artificial intelligence (AI) offers robust software solutions to analyze existing data and provide optimal predictions of medical outcomes. the dataset for heart disease prediction consists missing values in important features like cholesterol, ECG results and lifestyle factors. this research focuses to solve the issues in the traditional machine learning algorithm and designing a system model incorporating multiple data processing algorithms to classify heart disease. the classification process allows for the generation of a predictive model from training and testing datasets. these datasets are processed using an algorithm for classification to create a new model that can analyze detailed data, potentially with these same classes, by combining computational techniques with mathematical tools. this study uses optimization strategies to improve this model's performance and maximize prediction outcomes. this study aims to provide a framework to forecast cardiac problems based on significant risk using various classifier techniques, including the Bayesian Optimized SVM, to variables. the UCI repository is the source of the dataset utilized in this investigation. Linear Discriminant Analysis is used for feature extraction to decrease dimensionality and enhance the model's capacity to identify relevant patterns in the data. To generate the results as a precision, Recall, f1 score and accuracy. © 2025 IEEE.

关键词： Support vector machines

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Modeling and Cost optimization for M/M/1/K Queue with Dual-Phase Service and Repair Under threshold Recovery 1

引用

4th international conference on optimization learning algorithms and applications

作者： Nidhi Sanga, Sudeep Singh SV Natl Inst Technol Dept Math Surat 395007 Gujarat India

ISBN: (数字)9783031774324

ISBN: (纸本)9783031774317;9783031774324

In this paper, we investigate an M/M/1/K queue with dual-phase service and repair under threshold recovery. this system is characterized by a single server that provides customers with two consecutive phases of service. Furthermore, the system may experience the unpredictability of sudden server breakdowns, necessitating repair. A dual-phase repair process is implemented to address server failures, but repair begins only when the number of customers in the system reaches a certain threshold value of Q (1 <= Q <= K). To thoroughly understand the system, we mathematically construct steady-state equations for the model and then use a recursive method to solve them, thereby yielding steady-state probabilities. We also derive several performance measures, with the primary objective of decision-makers being to maximize profit while minimizing the inconvenience caused by service delays for customers. In pursuit of this goal, we construct a cost function that incorporates the threshold parameter and the service rate as decision variables and define an optimization problem with the aim of attaining the optimal cost. A population-based particle swarm optimization (PSO) is implemented to minimize the cost by identifying the optimal threshold parameter and service rate. After developing a model, the objective of the study is twofold: Firstly, to conduct a thorough analysis of system performance using measures such as system size and waiting time, providing decision-makers with valuable insights into operational efficiency and customer satisfaction levels. Secondly, the study aims to minimize the cost function by using PSO, thereby optimizing operational processes to achieve the most cost-effective solution while maintaining high service quality.

关键词： Finite queue Dual-phase service Server breakdown Dual-phase repair threshold recovery Cost optimization PSO

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A Neural Network-Based Approach to Identifying Wrinkles and Recommending Cosmetic Products 1

引用

4th international conference on optimization learning algorithms and applications

作者： Tonello, Guilherme de M. Varanda Pereira, Maria Joao Alves, Paulo Ortoncelli, Andre Roberto Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Lab Sutentabilidade & Tecnol Regioes Montanha Sus Campus Santa Apolonia Braganca Portugal Univ Tecnol Fed Parana Coordenacao Engn Software Campus Dois Vizinhos Curitiba Parana Brazil

ISBN: (数字)9783031774263

ISBN: (纸本)9783031774256;9783031774263

Skincare has become a constant demand among the population, who are increasingly concerned about their health. Furthermore, environmental issues arouse the interest of the masses in natural and sustainable products. this project proposes an approach for recommending thermal-based products based on a set of information provided by the user, combined with the results of computer vision algorithms (to identify the age and occurrence of wrinkles on the user's forehead). A list of recommended products is generated Based on the profile determined for the user. To predict wrinkles, for each facial image sent by the user, we apply a pre-processing step that segments and prepares the region of interest, which a CNN will process. As a CNN, we used the VGG16 architecture trained using a transfer learning and fine-tuning strategy, which improved the results obtained, reaching an accuracy of 92% in classifying wrinkles. An algorithm provided by the Deepface tool is used to predict the user's age, based on the sent picture. Which is crossed with the user's information to determine a level of aging in order to improve the quality of the recommended products.

关键词： Deep learning Wrinkle Detection Recommendation System Skin Care

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A Tutorial on Quantum Computing and Deep learning with Quantum Deep Neural Networks 4

A Tutorial on Quantum Computing and Deep Learning with Quant...

引用

4th international Mobile, Intelligent, and Ubiquitous Computing conference

作者： Mohsen, Saeed Elnashar, Gamal A. Abdel-Aziz, M. Al Madinah Higher Inst Engn & Technol Elect & Commun Engn Dept Giza 12947 Egypt King Salman Int Univ KSIU Fac Comp Sci & Engn Dept Artificial Intelligence Engn South Sinai 46511 Egypt Ain Shams Univ Fac Comp & Informat Sci Dept Basic Sci Cairo 11566 Egypt

ISBN: (纸本)9798350367782;9798350367775

Quantum Computing (QC) technology and Deep learning (DL) science have garnered significant attention for their potential to revolutionize computation. this paper introduces the basic concepts and terminologies of QC and DL to provide an understanding of these fields. In the context of QC, we explain key concepts such as qubits, quantum superposition (QS), quantum gates (QG), quantum measurement (QM), quantum circuits, quantum algorithms (QA), and different quantum computing applications. Regarding the DL, we present also the main concepts about various types of learning, DL models, hyperparameters of DL models, and DL applications. Also, this paper highlights the main differences between quantum deep learning (QDL) and classical deep learning (CDL). Additionally, we describe the required sequential steps to construct a quantum deep neural network (QDNN) model. this paper presents a valuable reference for beginners and researchers to a quick understanding of QC and DL and seeking to know about the latest developments in these rapidly advancing fields.

关键词： Quantum computing deep learning algorithms quantum gates qubit applications QDNN model hyperparameters

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Mobile Ad Hoc Networks: A Public Adoption and Risk Management Model Leveraging the Axiom Methodology and Machine learning optimization 4

Mobile Ad Hoc Networks: A Public Adoption and Risk Managemen...

引用

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

作者： Aqui, Jedidiah Hosein, Michael University of The West Indies Department of Computing and Information Technology St. Augustine Trinidad and Tobago

ISBN: (纸本)9798350352931

A Mobile Ad Hoc Network (MANET) is a decentralized wireless network of mobile nodes that self-configure without fixed infrastructure. Initially deployed for military and emergency scenarios, recent research highlights MANETs' potential as an alternative to traditional Internet Service Providers (ISPs), particularly during crises like the COVID-19 pandemic. this paper advances the MANET Axiom Risk Methodology, refining its risk assessment framework with machine learning optimization and dynamic weighting mechanisms. By leveraging Decision Tree and Naive Bayes algorithms, the optimized methodology achieves a 20% improvement in risk assessment accuracy and a 30% reduction in computational overhead, enabling real-time operation in low-resource environments. the resulting system combines robust risk profiling with an intuitive interface, empowering users to evaluate MANET connections confidently. this approach enhances security, scalability, and accessibility, positioning MANETs as a resilient alternative for public adoption during critical network disruptions. © 2024 IEEE.

关键词： Decision trees

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Leveraging the Power of Deep learning, Machine learning, Artificial Intelligence, and Big Data in Finance and Education 4

Leveraging the Power of Deep Learning, Machine Learning, Art...

引用

4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Swapna, A. Vemula, Hari Leela Amarnath, B. Sasidhar, B. Mahatma Gandhi Institute of Technology Department of Cse Telangana Hyderabad India Mahatma Gandhi Institute of Technology Faculty of Management Science Dept. of M&h Telangana Hyderabad India

ISBN: (纸本)9798350387490

this study delves into the transformative role of Deep learning, Machine learning, Artificial Intelligence, and Big Data in revolutionizing finance and education. Within the financial sector, AI-powered algorithms are reshaping trading practices, portfolio management, and customer interactions. the utilization of big data further strengthens financial institutions' competitive edge through data-driven decision-making. In education, the integration of these technologies offers personalized learning paths, intelligent tutoring systems, and real-time student performance analysis. the study showcases successful applications and the resulting benefits in both domains, highlighting the significance of leveraging these technologies for enhanced outcomes in finance and education. © 2024 IEEE.

关键词： Decentralized finance

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Simulated Annealing optimization-based Reliability Analysis of Grey Markov Process 24

Simulated Annealing Optimization-based Reliability Analysis ...

引用

4th international conference on Internet of things and Machine learning

作者： Tan, Chenglong He, Guimei Chai, Xiaolong Guangdong Univ Finance & Econ Sch Stat & Math Guangzhou Peoples R China Guangdong Univ Finance & Econ Inst Artificial Intelligence & Deep Learning Guangzhou Peoples R China

ISBN: (纸本)9798400710353

A modified Grey Markov prediction model was developed by combining traditional Grey Models with Markov modified prediction models, and further optimizing the weights of intermediate end-points in the Markov model using simulated annealing algorithm. this method provides a solution to the problem of poor fitting performance of the original Grey prediction model when dealing with volatility data, and improves the accuracy of prediction. through empirical analysis of the total fishery output value (in billions of yuan) of Hubei Province from 2002 to 2022, the reliability test results show that the Grey Markov model optimized by simulated annealing exhibits better predictive reliability compared to traditional Grey models and standard Grey Markov models.

关键词： WGMM model Reliability Analysis Simulated Annealing optimization

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Proximal Policy optimization with Entropy Regularization 4

Proximal Policy Optimization with Entropy Regularization

引用

4th international conference on Computer, Control and Robotics (ICCCR)

作者： Shen, Yuqing Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350373141;9798350373158

this study provides a revision to the Proximal Policy optimization (PPO) algorithm, primarily aimed at improving the stability of PPO during the training process while maintaining a balance between exploration and exploitation. Recognizing the inherent challenge of achieving this balance in a complex environment, the proposed method adopts an entropy regularization technique similar to the one used in the Asynchronous Advantage Actor-Critic (A3C) algorithm. the main purpose of this design is to encourage exploration in the early stages, preventing the agent from prematurely converging to a sub-optimal policy. Detailed theoretical explanations of how the entropy term improves the robustness of the learning trajectory will be provided. Experimental results demonstrate that the revised PPO not only maintains the original strengths of the PPO algorithm, but also shows significant improvement in the stability of the training process. this work contributes to the ongoing research in reinforcement learning and offers a promising direction for future research on the adoption of PPO in environments with complicated dynamics.

关键词： reinforcement learning policy gradient entropy regularization

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Diagnosing Diabetes Using Binary Whale optimization Algorithm-Based Feature Selection 4

Diagnosing Diabetes Using Binary Whale Optimization Algorith...

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4th IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic Control and Computer Engineering, MI-STA 2024

作者： Ali, Marium Khairalla Lawgali, Ahmed University of Benghazi Computer Science Facutly of Information Technology Benghazi Libya

ISBN: (纸本)9798350372632

Early diagnosis of diabetes is critical for prompt treatment that can prevent the disease's progression to potentially lethal complications. Due to factors such as high blood pressure, genetic diabetes, and modern lifestyles that include inactivity, diabetes is one of the most common and severe diseases in the world. Using machine learning to assist in the creation of quick and precise diagnoses is one endeavor being made in the health sector. Feature selection approaches are a crucial step towards achieving high classification accuracy since they improve the efficiency of learning algorithms. In order to detect diabetes disease, the features from the Pima Indians Diabetes (PID) dataset were selected using a binary whale optimization algorithm (BWOA). After that, the classification is applied by the Gaussian process classifier (GPC) and linear discriminant analysis (LDA) independently. the proposed BWOA-GPC system obtained the highest classification accuracy of up to 85.94%. © 2024 IEEE.

关键词： Blood pressure

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