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

共 13504 条记录，以下是511-520 订阅

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Automated Software Testing Using Machine learning: A Systematic Mapping Study 10

Automated Software Testing Using Machine Learning: A Systema...

引用

10th international conference on optimization and applications, ICOA 2024

作者： Ahammad, Abdellatif El Bajta, Manal Radgui, Maryam National Institute of Statistics and Applied Economics SI2M Laboratory Rabat Morocco

ISBN: (纸本)9798350387353

In today's digital world, software quality assurance is a crucial part of the Software Development Life Cycle (SDLC), and automated testing is essential to this effort. this study investigates how machine learning (ML) can augment automated testing by analyzing research from 2006 onward to determine its possible uses, prevalent techniques, and related advantages and disadvantages. Our findings reveal a growing interest in leveraging ML for testing, particularly in tasks like test case generation and user interface validation. Common ML techniques such as symbolic AI, evolutionary algorithms, and deep learning are emerging as the primary methods. ML holds promise for accelerating testing processes, enhancing accuracy, and improving adaptability. However, challenges such as sourcing high-quality training data, understanding complex ML models, and integrating ML with existing tools persist. this study illuminates the transformative potential of ML in testing and provides valuable insights for guiding future research in this dynamic field. © 2024 IEEE.

关键词： Adversarial machine learning

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An Efficient and Security Federated learning for Data Heterogeneity 4

An Efficient and Security Federated Learning for Data Hetero...

引用

4th international conference on Information Communication and Software Engineering (ICICSE)

作者： Gao, Junchen Ning, Zhenhu Cui, Meili Xing, Shuaikun Beijing Univ Technol Fac Informat Technol Beijing Peoples R China

ISBN: (纸本)9798350351705;9798350351699

the statistical heterogeneity problem in federated learning significantly constrains its performance. Current research predominantly focuses on personalizing local models. However, excessive emphasis on local data compromises the effective utilization of global information, leading to optimal performance only in highly heterogeneous local datasets. To address this limitation, this paper proposes a novel approach called Federated learning with Local Intermit Initiation (FedLII). FedLII alternates between personalization and "client distribution" periodically. the "client distribution" facilitates cross-client data-level optimization by disseminating local models to other clients for training. Consequently, this method enhances overall model quality while maintaining personalization, ensuring exceptional robustness and security across varying degrees of heterogeneity. the paper provides a theoretical analysis of FedLII's advantages and compares it with several exemplary models, such as FedProx and APPLE, using three benchmark datasets: Cifar10/100 and AG news. Experimental results demonstrate that FedLII exhibits broader applicability and superior performance across datasets with varying degrees of heterogeneity.

关键词： Information security Federated learning Statistical heterogeneity

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optimization & Reinforcement of Solar Irradiation Strength of Solar Panel through K-Means Clustering 4

Optimization & Reinforcement of Solar Irradiation Strength o...

引用

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

作者： Ullah, Hameed Li, Liping Khan, Abdul Haseeb Hassan Anhui University Department of Electronic and Information Engineering Hefei China USPCAS-E Uet Peshawar Department of Renewable Energy Engineering Peshawar Pakistan

ISBN: (纸本)9798350359589

the maximum working capacity of photovoltaic cells cannot be maximized because of temperature variations and hence not able to generate ideal electricity if the temperature is less than 25 Celsius and greater than 60 Celsius. In this paper, optimization and reinforcement of solar irradiation is done through K-means clustering. Hot and cold spots in a solar panel can be identified with this technique. the experiment was conducted using a two-axis solar tracking system, and through simulation results, temperature variations were minimized, which resulted in maximum power generation. © 2024 IEEE.

关键词： K-means clustering Renewable energy Solar irradiations

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Application of Machine learning to Increase the Efficiency of the Global Search Algorithm for Solving Multicriterial Problems 4th

Application of Machine Learning to Increase the Efficienc...

引用

4th international conference on Numerical Computations: theory and algorithms, NUMTA 2023

作者： Barkalov, Konstantin Grishagin, Vladimir A. Kozinov, Evgeny Lobachevsky State University of Nizhni Novgorod Nizhni Novgorod Russia

ISBN: (纸本)9783031812408

Decision making models described as problems of multicriterial optimization are very complicated for investigation, because the property of criteria contradictoriness leads to the notion of the solution as the set of non-dominated parameters (Pareto set). the complexity of these problems increases significantly in the case where criteria are multiextremal. In the paper a novel algorithm for multicriterial black-box optimization with multiextremal criteria is considered. this algorithm applies ideas of complexity reduction when the initial multicriterial problem is reduced to a set of univariate scalar subproblems on the base of Peano mapping and maximum convolution. For solving univariate problems an information-statistical global optimization algorithm with guaranteed convergence to global optimum is used. As a core of novelty, this algorithm includes in its computational scheme a machine learning procedure with combination of accumulating the information of solved subproblems that allows one to significantly accelerate building the Pareto set. Effectiveness of the proposed approach is estimated in the representative computational experiment on test sets of multicriterial problems with multiextremal criteria for different dimensions, in comparison with several nature-inspired multiobjective optimization algorithms. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： optimization algorithms

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Advancements and applications of Artificial Intelligence in Various Sectors: From Internet of Vehicles to 6G and Healthcare 4

Advancements and Applications of Artificial Intelligence in ...

引用

4th international conference on Pervasive Computing and Social Networking (ICPCSN)

作者： Mittal, Amit Pandey, Harsh Arsh, Mohammad Soni, Mukesh Goyal, Himanshu Rai Sharma, Somesh Graph Era Hill Univ Dept Allied Sci Bhimtal Campus Bhimtal Uttarakhand India Graph Era Hill Univ Dept CSE Bhimtal Campus Bhimtal Uttarakhand India Dr DY Patil Vidyapeeth Pune Maharashtra India Dr DY Patil Sch Sci & Technol Pune Maharashtra India Graph Era Deemed Univ Dept Compter Sci & Engn Dehra Dun Uttarakhand India Graph Era Hill Univ SOM Bhimtal Campus Bhimtal India

ISBN: (纸本)9798350386356;9798350386349

this research study explores the diverse application of Artificial Intelligence (AI) across various areas, it transforms impact on technological advancement. this survey begins by investing in AI's role in enhancing the efficiency of the Internet of Vehicles through real-time Data collection and edge computing addressing challenges in optimizing edge service. the paper goes into AI's critical contribution to medical diagnosis, disease detection and treatment highlighting applications in breast cancer detection and medical image analysis. the concept of human-AI collaboration is introduced, using the integration of human intelligence with AI to create a hybrid intelligence system. the paper further discusses an ice role in the COVID-19 pandemic from diagnosis to treatment utilizing deep learning techniques and extreme learning machines. this study extends to large-scale, power grid simulation analysis, where AI including deep learning and reinforcement learning significantly improves efficiency and accuracy. the integration of AI into robotics explains the role of teaching, assistant robots and vision-based assessment systems. the concept of smart farming is introduced, showcasing its potential to revolutionize agriculture production. Focusing on maximum PowerPoint tracking techniques for the improvement of efficiency. this study explores applying the artificial immune system of multi-objective optimization with decision-making and introduces the beneficial perturbation network for overcoming continued learning challenges in an Artificial Neural Network (ANN). Satellite communication and the vision of a 6G network are discussed in the integration of AI, IOT and 5G. In the context of the next generation, Smart credit is highlighted in this study and finally concluded by discussing about the challenges in Wireless Sensor Networks (WSN) and the increasing importance of AI.

关键词： Artificial Intelligence Medical Diagnosis Smart Farming Communication Education

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Transfer learning-based foundation pit deformation prediction 4

Transfer learning-based foundation pit deformation predictio...

引用

4th international conference on Internet of things and Smart City, IoTSC 2024

作者： Wang, Yao Zhang, Huanhu Sha, Feng Lu, Lisi Limited Zhejiang Hangzhou310000 China

ISBN: (纸本)9781510681767

With the rapid urbanization in China, the construction of high-rise residential buildings has become increasingly vital. However, the increasing scale and depth of substructures in tall buildings have posed challenges in the excavation of deep foundation pits. the excavation of such deep foundation pits demands comprehensive monitoring and management to ensure structural stability and safety. this study aims to address the issue of foundation pit deformation prediction using machine learning *** mechanics models and numerical analysis methods have demonstrated substantial errors in this context. therefore, this paper reviews the applications of Gaussian processes, genetic algorithm-optimized BP neural networks, and Long Short-Term Memory (LSTM) networks in foundation pit deformation prediction. However, these methods exhibit limitations, such as the stationary assumption in Gaussian processes, high computational costs associated with genetic algorithms, and LSTM's limited performance with small datasets. To mitigate the challenges posed by limited monitoring data, this study introduces a transfer learning strategy based on the CNN-LSTM-Attention model. Validation results from practical case studies suggest that this strategy effectively enhances the accuracy of foundation pit deformation prediction, especially when dealing with limited data. © 2024 SPIE.

关键词： Tall buildings

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Enhanced Personality Profiling: Unveiling MBTI Traits through Machine learning Analysis 11

Enhanced Personality Profiling: Unveiling MBTI Traits Throug...

引用

11th international conference on Reliability, Infocom Technologies and optimization, ICRITO 2024

作者： Kandpal, Harshit Gupta, Deepa Bathla, Ruchika Amity Institute of Information and Technology Amity University Noida Delhi India

ISBN: (纸本)9798350350357

this machine learning paper analyzes the MBTI personality dataset, comprising 16 personality types. It employs a structured approach, including Exploratory Data Analysis (EDA), data grouping, visualization, preprocessing, feature engineering, and model training with both 60-40 and improved 70-30 splits. the study compares the performance of various algorithms and concludes with a robust verification process. the insights gained contribute to understanding the interplay between personality traits and associated features, with potential applications in personality assessment and human resources. © 2024 IEEE.

关键词： Data visualization

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Enhancing Crop Health in Smart Greenhouse through IoT-Based Data optimization and Deep learning algorithms 4

Enhancing Crop Health in Smart Greenhouse Through IoT-Based ...

引用

4th international conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2024

作者： Slimani, Hicham El Mhamdi, Jamal Jilbab, Abdelilah Mohammed V University In Rabat Rabat Morocco

ISBN: (纸本)9798350309508

Integrating convolutional neural networks (CNNs) with the Internet of things (IoT) is paramount in agriculture, particularly greenhouses. By leveraging IoT capabilities, operators can collect agro-environmental information inside the greenhouse based on installed sensor nodes. this data-driven approach minimizes water, fertilizer, and energy waste. Simul-taneously, CNNs enhance the monitoring systems by facilitating early detection and classification of crop diseases. Our research proposes a comprehensive solution: an online technology platform for intelligent greenhouses based on IoT and CNNs. this platform effectively collects environmental and physical variables and detects diseases in real time using image-based analysis. the results of our study demonstrate that the system architecture is a reliable IoT platform, leading to significant energy savings. Moreover, the disease identification accuracy and classification process achieved an impressive rate of over 98 %, ensuring the system's efficacy in identifying and categorizing diseases. Additionally, the system exhibits a recall rate of over 90 %, indicating its ability to identify and recall crop disease instances accurately. © 2024 IEEE.

关键词： Greenhouses

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Proceedings of the 2023 4th international conference on Machine learning and Computer Application, ICMLCA 2023

Proceedings of the 2023 4th International Conference on Mach...

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4th international conference on Machine learning and Computer Application, ICMLCA 2023

ISBN: (纸本)9798400709449

the proceedings contain 182 papers. the topics discussed include: combinatorial model based on extreme learning machine for network traffic prediction;survey on Chinese word segmentation;research on rock powder coating ratio based on artificial neural network;optimization of maritime intelligence facilities based on big data algorithms;instance discrimination for improving Chinese text classification;failure prediction of bolted composite connections using continuum damage model;prediction study of slope displacement using CGA-BP neural network;research on the knowledge embedding vector method based on TF-IDF and auto-encoder;MCN: magnitude-continuity network for video anomaly detection under weak supervision;and research on network security situation assessment technology based on correlation analysis.

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A Machine learning Plus-Features Based Approach for Optimal Asset Allocation 23

A Machine Learning Plus-Features Based Approach for Optimal ...

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4th ACM international conference on AI in Finance (ICAIF)

作者： Ramirez, Domingo Suarez, Fernando Pena, Jose-Manuel Larre, Omar Cifuentes, Arturo Fintual Adm Gen Fondos SA Santiago Chile CLAPES UC Santiago Chile

ISBN: (纸本)9798400702402

Within the asset management industry, the portfolio selection problem stands as one of the most important challenges. the approaches that experts have taken to address this problem have ranged from intuition-based decisions to sophisticated optimization frameworks. In this paper, we introduce a novel portfolio optimization methodology for investors with a medium- to long-term horizon focusing on public markets via index funds. Our method marries a Markowitz-inspired framework with a Conditional-Value-at-Risk (CVaR) constraint, and employs synthetic data generated through a Modified Conditional Generative Adversarial Network (CTGAN) approach that incorporates contextual information, specifically, the U.S. Treasury yield curve. the synthetic data generation algorithm captures the essential structure of the training dataset, while the CVaR-based optimization leads to portfolios with very good out-of-sample performance. this study emphasizes the merits of integrating contextual information and demonstrates the generative networks superiority over traditional approaches based solely on historical data. this innovative approach lays the groundwork for further advancements in asset allocation strategies fueled by synthetic data generating processes.

关键词： asset allocation portfolio optimization synthetic data machine learning features contextual information GAN CTGAN neural networks

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