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

共 13588 条记录，以下是861-870 订阅

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

2024 4th International Conference on Advancement in Electron...

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

ISBN: (纸本)9798350364729

the proceedings contain 263 papers. the topics discussed include: unleashing the potential: a bibliometric analysis of blockchain applications in education (2016-2024);advancements in sarcasm detection: a review of accurate models for short text data;a comprehensive study on measurements for wearable and implantable antennas;smart cover for motor vehicle with high security system;promoting sustainable choices: the influence of green marketing on consumer decisions;design of low noise amplifier with improved impedance matching network;maximum power extraction using fuzzy logic and analysis of power at different irradiance;diagnostic analysis of implantable bioantenna on human body;exploring research evaluation in social science: a statistical approach;and advancements in deep learning techniques for high-fidelity image inpainting.

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Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax optimization 27

Generalization Bounds of Nonconvex-(Strongly)-Concave Stocha...

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27th international conference on Artificial Intelligence and Statistics (AISTATS)

作者： Zhang, Siqi Hu, Yifan Zhang, Liang He, Niao Johns Hopkins Univ Baltimore MD 21218 USA Ecole Polytech Fed Lausanne Lausanne Switzerland Swiss Fed Inst Technol Zurich Switzerland

this paper studies the generalization performance of algorithms for solving nonconvex(strongly)-concave (NC-SC / NC-C) stochastic minimax optimization measured by the stationarity of primal functions. We first establish algorithm-agnostic generalization bounds via uniform convergence between the empirical minimax problem and the population minimax problem. the sample complexities for achieving.-generalization are (O) over tilde (d kappa(2)epsilon(-2)) and (O) over tilde (d epsilon(-4)) for NC-SC and NC-C settings, respectively, where d is the dimension of the primal variable and. is the condition number. We further study the algorithm-dependent generalization bounds via stability arguments of algorithms. In particular, we introduce a novel stability notion for minimax problems and build a connection between stability and generalization. As a result, we establish algorithm-dependent generalization bounds for stochastic gradient descent ascent (SGDA) and the more general sampling-determined algorithms (SDA).

关键词： Stochastic systems

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Gesture Recognition Technology in Smart Gloves Enhanced by Machine learning 4

Gesture Recognition Technology in Smart Gloves Enhanced by M...

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4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： thangam, S. Kandi, Nithin Charan, Dorai Sai Gosu, Vijay Kumari, J. Jesy Janet Virupaxappa, G. Amrita School of Computing Amrita Vishwa Vidhyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331529635

Gesture recognition systems are changing how people interact with devices, promising natural and intuitive hands-free control in various applications. Based on the ESP32 microcontroller and machine learning techniques, the proposed work is developing a compact, power-efficient system for recognizing hand gestures from data gathered by flex sensors and accelerometers. the entire system fits within memory constraints, as lightweight machine learning models are first trained on a computer using Python and then ideally deployed in optimized versions onto the ESP32. In this respect, the stream of pre-processed sensor data happens to be fed to the ML model for inference, sending back results to the ESP32, which hence triggers specific actions or commands. this fundamentally new approach shows the feasibility of machine learning on resource-constrained platforms, opening the road for wearable technologies that will be more clever and reactive. © 2024 IEEE.

关键词： Accelerometers

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A Review of Machine Vision applications in Aerospace Manufacturing Quality Inspection 4

A Review of Machine Vision Applications in Aerospace Manufac...

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

作者： Deng, Li Liu, Gang Shanghai Univ Engn Sci Sch Mech & Automot Engn Shanghai Peoples R China

ISBN: (纸本)9798350373141;9798350373158

Machine vision is a technology that uses image processing and analysis techniques to acquire and understand image information, enabling the recognition, measurement, and detection of objects. Machine vision is widely used in aerospace manufacturing for automated production, quality inspection, and robot guidance. It can improve the efficiency and quality of aerospace manufacturing, reduce labor costs and risks, promote innovation and optimization, adapt to various inspection needs, and realize intelligent, automated, and digital manufacturing processes. this paper introduces the types of vision technologies, overviews the typical applications of machine vision for quality inspection in aerospace manufacturing, (including component surface inspection, drilling quality inspection, assembly quality inspection, and gluing quality inspection), analyzes the advantages, challenges, and development trends of machine vision for quality inspection in aerospace manufacturing, and looks forward to future research directions to support further research and application.

关键词： Machine vision Aerospace manufacturing Component surface inspection Drilling quality inspection Assembly quality inspection Gluing quality inspection

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Metagenomic Face: Applying Machine learning to Facial Microbiome Data for Pattern Recognition and Disease Classification 4

Metagenomic Face: Applying Machine Learning to Facial Microb...

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4th international conference on Intelligent Technologies, CONIT 2024

作者： Guru Prasad, M.S. Devadas, Raghavendra M Hiremani, Vani Jahan, Afsar Praveen Gujjar, J. Dahiya, Aryan Department of Computer Science and Engg Dehradun India Manipal Institute of Technology Department of Information Technology Bengaluru India Manipal Academy of Higher Education Manipal India University Pune India Department of Computer Applications Dehradun India Faculty of Management Studies Bengaluru India

ISBN: (纸本)9798350349900

this innovative study utilizes machine learning techniques to analyze facial microbiome data, drawing inspiration from the metaphorical representation of microbial information through facial features. Leveraging the Olivetti Faces dataset as a model for facial microbiome data, the research introduces the Metagenomic Face framework. this framework facilitates the translation of microbial information into facial features, enabling the application of machine learning algorithms such as Random Forests or Neural Networks for pattern recognition. the investigation focuses on identifying correlations between facial features and underlying microbial compositions, aiming to assess the feasibility of classifying health and disease states based on these patterns. Preliminary results demonstrate promising outcomes, with precision, recall, and accuracy metrics reaching 0.94, 0.91, and 0.91, respectively, and support for the model encompassing 80 instances. these findings underscore the potential of Metagenomic Face as an innovative approach to microbiome analysis, shedding light on the intricate relationship between facial features and microbial communities. Furthermore, a comparative analysis involving Support Vector Machine, Logistic Regression, and Random Forest classifiers reveals compelling performance metrics. the Support Vector Machine Classifier attained an accuracy rate of 85%, the Logistic Regression Classifier showed an accuracy of 91%, and the Random Forest Classifier displayed a matching accuracy of 91%. these additional insights contribute to a comprehensive understanding of the Metagenomic Face framework's efficacy compared to alternative machine learning approaches, emphasizing its potential in advancing precision medicine through microbiome analysis. © 2024 IEEE.

关键词： Adversarial machine learning

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A Test Case Prioritization Based on Genetic Algorithm With Ant Colony and Reinforcement learning Improvement 47

A Test Case Prioritization Based on Genetic Algorithm With A...

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47th IEEE-Computer-Society Annual international conference on Computers, Software, and applications (COMPSAC)

作者： Yang, Yu Wang, Lu Cha, Na Li, Hua Inner Mongolia Univ Hohhot Inner Mongolia Peoples R China

ISBN: (纸本)9798350326970

In order to improve the efficiency of regression testing in the cloud-network convergence platform, a test case prioritization method based on reinforcement learning and a genetic algorithm is proposed. the classical genetic algorithm of initial population and selection operations are improved by incorporating an ant colony algorithm of solutions to form a part of the starting population in the genetic algorithm. the selection process employs an "elite retention strategy" to avoid the classical genetic algorithm of the problem of getting trapped in locally optimal solutions. the improved algorithm is applied to test the cloud-network convergence platform, and the optimization-seeking abilities of the classical genetic algorithm, the ant colony genetic algorithm, and the reinforcement learning-based ant colony genetic algorithm are compared and analyzed. the findings reveal that the reinforcement learning-based ant colony genetic algorithm outperforms the other two algorithms by finding the best test case for the test case prioritization problem.

关键词： test case prioritization reinforcement learning genetic algorithm ant colony algorithm

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learning optimization Algorithm Combined with Psychology 4

Learning Optimization Algorithm Combined with Psychology

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

作者： Liu, Hui Beijing Information Technology College Beijing China

ISBN: (纸本)9798350333558

learning optimization algorithm is a swarm intelligent optimization algorithm which simulates the teaching process of teachers and the learning process of students. Due to the problems of poor stability, low optimization accuracy and easy to fall into local optimal, a new teaching and learning optimization algorithm combined with psychology theory was proposed. In the improvement of the algorithm, psychological factors of teachers and students are fully considered. In the 'teaching' stage of the original teaching and learning optimization algorithm, a social psychological effect is combined, namely Pygmalion effect, which claims teachers' praise, trust and expectation for students have a kind of energy, which can change the behavior of students to make students better, and to improve the local search ability and convergence of the algorithm. In the 'learning' stage, fully combined with students' psychological learning characteristics, the students are divided into A, B and C based on the average score of the class and the average score of the outstanding students who are higher than the class average. Different behavioral strategies are adopted to adjust the different types of students. And then, 10 Benchmark test functions are used to compare the optimization performance with ABC, TLBO and this algorithm, and compare the learning optimization algorithm (SLBO) combining psychology with the standard SHAP, RNN, XGBoost, K-Modes, Prophet, Levenshtein for a comparative analysis of algorithm performance, so as to verify the search ability of the algorithm, to found that the algorithm has obvious advantages in convergence accuracy, speed and robustness. © 2023 IEEE.

关键词： Students

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An Evaluation of Image Preprocessing in Skin Lesions Detection 3rd

An Evaluation of Image Preprocessing in Skin Lesions Detecti...

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

作者： Silva, Giuliana M. Lazzaretti, Andre E. Monteiro, Fernando C. Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Lab Sustentabilidade & Tecnol Reg Mt SusTEC Campus Santa Apolonia P-5300253 Braganca Portugal Fed Univ Technol BR-80230901 Curitiba Parana Brazil

ISBN: (纸本)9783031530357;9783031530364

this study aims to evaluate the impact of image preprocessing techniques on the performance of Convolutional Neural Networks (CNNs) in the task of skin lesion classification. the study is made on the ISIC 2017 dataset, a widely used resource in skin cancer diagnosis research. thirteen popular CNN models were trained using transfer learning. An ensemble strategy was also employed to generate a final diagnosis based on the classifications of different models. the results indicate that image preprocessing can significantly enhance the performance of CNN models in skin lesion classification tasks. Our best model obtained a balanced accuracy of 0.7879.

关键词： Skin Lesion Classification Convolutional Neural Networks Deep learning Image Preprocessing

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Land cover mapping using Landsat 8 data and supervised learning classifiers in Casablanca region, Morocco 4

Land cover mapping using Landsat 8 data and supervised learn...

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4th international conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Ouchra, Hafsa Belangour, Abdessamad Erraissi, Allae Casablanca Morocco Chouaib Doukkali University Polydisciplinary Faculty of Sidi Bennour El Jadida Morocco

ISBN: (纸本)9798350369786

this study focuses on assessing the effectiveness of various supervised algorithms in classifying Landsat satellite images over the region of Casablanca, Morocco. the region's rapid urbanization and land cover changes make it an ideal case study for evaluating the performance of algorithms in capturing diverse land cover types. the methodology involves data collection in the platform Google Earth Engine (GEE), preprocessing steps, supervised algorithms, and metrics of assessment. the findings provide valuable insights for decision-making processes and sustainable urban development in the region. Overall, this study contributes to the understanding of the most suitable algorithm for Landsat satellite image classification in Casablanca and lays the groundwork for future advancements in remote sensing applications. this paper presents an experimental study of three supervised machine-learning algorithms. these are Support Vector Machine (SVM), Random Forest (RF), Minimum Distance (MD). the objective is to classify the different zones such as water, built-up, cropped, barren, and forest areas within the city of Casablanca. the classification results are evaluated using various accuracy indicators, including Overall Accuracy (OA), Kappa Coefficient, User Accuracy (UA), and Producer Accuracy (PA). the Random Forest (RF) algorithm recorded the best accuracy, reaching 95.42%. In contrast, the Support Vector Machine (SVM) algorithm performed the worst, with an accuracy of just 83%. © 2023 IEEE.

关键词： Landsat

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Machine learning Frameworks for Parking Guidance

Machine Learning Frameworks for Parking Guidance

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2023 IEEE international conference on Communications, Information, Electronic and Energy Systems, CIEES 2023

作者： Neykov, Neyko Stefanova, Svetlana University of Ruse 'Angel Kanchev' Dept. of Computer Science Ruse Bulgaria

ISBN: (纸本)9798350336917

A wide variety of open-source libraries and platforms support the creation of machine learning models that provide ready-to-use implementations of many popular algorithms available under various licenses. Such software frameworks reduce the time and effort of researchers and developers engaged in different projects where there is a need to use specific machine learning techniques. Case-based reasoning (CBR) involved in Parking Guidance Information System (PGIS) uses k-nearest neighbor (kNN) classification as well as several other machine learning methods in the various stages of building the action plan. In this analysis, we explore free toolkit options that can meet the needs of a specific input data system. We also consider additional requirements for efficient indexing and optimization of use cases, aiming to reduce the need for multiple programming languages and platforms and coding algorithms from scratch. © 2023 IEEE.

关键词： Machine learning

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