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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281-290 订阅

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the Impact of Factor Analysis-Based Machine learning Forecasting on Conservation Planning 24

The Impact of Factor Analysis-Based Machine Learning Forecas...

引用

4th international conference on Artificial Intelligence, Big Data and algorithms, CAIBDA 2024

作者： Li, Lin . Co. Ltd. Shanxi Taiyuan China

ISBN: (纸本)9798400710247

To scientifically formulate road maintenance strategies and rationally allocate maintenance resources, this study predicts the decay of PCI performance indicators for specific road sections based on a learning effectiveness model and an intelligent optimization algorithm, obtains the pavement condition at each time node during the maintenance planning cycle, and establishes a 10-year high service level road life cycle management and maintenance planning scheme under the condition that maintenance is in a well-funded situation. It was found that the learning model can statistically improve the accuracy of performance prediction, more accurately determine the timing of maintenance and maintenance strategies during the planning period, and thus more rationally utilize maintenance and repair resources. © 2024 Copyright held by the owner/author(s).

关键词： Condition based maintenance

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Hybridization of Equilibrium and Grasshopper optimization algorithms 1

引用

12th international conference on Robotics Vision Signal Processing and Power applications

作者： Dayo, Ebinowen Tusin Mohamad-Saleh, Junita Univ Sains Malaysia Sch Elect & Elect Engn Nibong Tebal 14300 Malaysia Fed Polytech Ile Oluji Ondo Nigeria

ISBN: (数字)9789819990054

ISBN: (纸本)9789819990078;9789819990054;9789819990047

this paper presents a new hybrid version which uses the swarming behaviour of the Grasshopper optimization Algorithm (GOA) while Equilibrium Optimizer (EO) to guide the search agents towards promising regions of the search space. It makes use of the exploration and exploitation abilities of Equilibrium Optimizer to refine the search within the regions. the hybrid approach is based on the utilization of GOA to perform a global search of a wider search space during the initial phase of the algorithm. the result has shown that this process has effectively controlled the balance between exploration and exploitation during the search process and successfully hybridized the two algorithms to outperform the original algorithms.

关键词： optimization Grasshopper Exploration Exploitation Swarm intelligence

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Distributed Frank-Wolfe Algorithm for Constrained Bilevel optimization 18

Distributed Frank-Wolfe Algorithm for Constrained Bilevel Op...

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18th international conference on Control Automation Robotics and Vision

作者： Xiong, Yongyang Liu, Wanquan Wang, Ping You, Keyou Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen 518107 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9798331518509;9798331518493

Bilevel optimization has attracted substantial attentions in recent years due to its wide applications in machine learning. However, most existing algorithms either primarily developed under centralized setting or suffer expensive inner-loop updates for hypergradient estimation. What's worse, the projection operator in constrained scenario may demand prohibitively computational cost, which further necessitates efficient projection-free bilevel optimization algorithms over networks. To fill this gap, we propose a novel single-loop distributed Frank-Wolfe algorithm DBO-FW for constrained bilevel optimization problems by simultaneously leveraging a nested approximation technique and a gradient tracking mechanism to locally estimate the global hypergradient. Moreover, we provide the convergence guarantee for the proposed DBO-FW. Numerical results also validate the efficiency of our algorithm.

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Hybrid Data-Driven and Physical Model for Distribution System State Estimation 4

Hybrid Data-Driven and Physical Model for Distribution Syste...

引用

4th international conference on Smart Grid and Energy Internet, SGEI 2024

作者： Xie, Biao Wang, Zhenyu Lu, Fang Song, Ziwei College of Intelligent Systems Science and Engineering Harbin Engineering University Heilongjiang Province Harbin China

ISBN: (纸本)9798350368314

Realizing accurate Distribution System State Estimation (DSSE) faces challenges including a lack of observability and high complexity of the distribution system. A data-driven approach based on machine learning models may be an option to address this issue, but due to the lack of data labels in practical applications, their application in DSSE is limited. In fact, measurements in distribution systems often contain noise, are damaged or missing, and are unusable. In order to better address these issues, a distribution system state estimation solver is proposed, which is a deep learning model based on Residual Network (ResNet) that study the relationship between measurement values and state variables. A learning method combining traditional machine learning and power flow guidance was proposed to train ResNet, which respects the physical laws of the distribution system by inputting the output of ResNet into the power flow equation. this strategy can learn from noisy measurements, reducing the need for ideal data labels. Experiments on IEEE 33 bus distribution networks have shown that the model proposed in this paper outperforms traditional weighted least squares algorithms in accuracy. ©2024 IEEE.

关键词： Adversarial machine learning

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A Deep learning-Based System for Monitoring Student Behavior and Analyzing learning Situations 4

A Deep Learning-Based System for Monitoring Student Behavior...

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

作者： Chen, Jifeng Shao, Zhengxi Liu, Qingqi Mao, Lei School of Information and Electromechanical Engineering Hunan International Economics University Hunan Changsha China

ISBN: (纸本)9798350359589

this text discusses the real-time monitoring of student behavior in the classroom using deep learning technology. By employing DeepSORT to track student postures and recognize interactive behaviors and utilizing the YOLOv8 model to detect student postures, this study constructs an improved deep learning algorithm to establish a classroom teaching evaluation system. through statistical analysis and real-time monitoring of student classroom states, quantitative assessment criteria have been formulated to accurately evaluate the level of students' concentration. the research results not only provide specific scores for each student's classroom behaviors but also analyze the behavioral characteristics of students and suggest areas for improvement. the study emphasizes the significance of personalized teaching strategies and, based on students' behavior patterns and changes in class, offers tailored behavior correction strategies to enhance the quality of students' attention during lectures. © 2024 IEEE.

关键词： Classroom behavior recognition DeepSORT Student classroom assessment system YOLOv8

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Comparison of Machine learning algorithms for the prediction of Rice Crop Yield in Karnataka 4

Comparison of Machine Learning Algorithms for the prediction...

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4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Senthil, S. Raja, K. Reva University Department Of Computer Applications Karnataka Bengaluru India Reva University Karnataka Bengaluru India

ISBN: (纸本)9798350384628

Increased agricultural market demands have necessitated the development of predictive models to improve crop forecasting in Karnataka. the Govt. of Karnataka provides enriched agriculture data for study to comprehend any key parameters that are influencing crop yield, considering the recent increase in the focus of research on various crops. the main goal of this research was to evaluate the performance of various machine learning algorithms such as logistic regression, random forest, support vector machine, Bayesian ridge regressor and gradient boosting regressor in predicting rice crop yield using a dataset containing numerous factors. this study evaluates the performance of proposed algorithms in terms of Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R2 Score, Precision, Recall, F1-score, Specificity and Accuracy. the Random Forest algorithm outperforms many state-of-the- art crop prediction models, including the Gradient boosting regressor and many others. It also improves efficiency and has a higher R2 score of 0.946 with high precision and accuracy. Ultimately, the goal of this study is to identify the optimal model for improved crop management and yield optimization in the agricultural sector. © 2023 IEEE.

关键词： Crops

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Reinforcement learning for Hyper-Parameter optimization in the context of Capacity Management of SAP Enterprise applications 10

Reinforcement Learning for Hyper-Parameter Optimization in t...

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10th international conference on Control, Decision and Information Technologies, CoDIT 2024

作者： Chernigovskaya, Maria Kharitonov, Andrey Nahhas, Abdulrahman Turowski, Klaus Otto-von-Guericke-University Magdeburg Magdeburg Research and Competence Cluster for Very Large Business Applications Faculty of Computer Science Universitätsplatz 2 Magdeburg39106 Germany

ISBN: (纸本)9798350373974

Capacity management of Enterprise applications (EAs) encompasses critical IT processes that maximize the IT system's performance while minimizing operational costs. Effective management of EAs capacity can be enhanced through precise anomaly detection and workload forecasting. Given sufficient historical monitoring data of EAs, Machine learning (ML) algorithms like Isolation Forest and XGBoost can be applied to address anomaly detection and forecasting tasks. However, the performance of these algorithms can strongly depend on the selected hyper-parameter values. Hence, Hyper-Parameter optimization (HPO) is crucial for successfully adopting ML methods to solve real-world problems. the existing tuning methods like manual tuning, Grid Search, and Random Search often tend to be computationally expensive and time-consuming, making them ineffective for high-dimensional data. On the other hand, recent successes of Deep Reinforcement learning (DRL) in handling various optimization problems have sparked interest in exploring its potential. therefore, this work investigates the adaptation of DRL as a novel HPO approach. We present two implementation scenarios deployed in two real-world use cases and compare the results to the state-of-the-art tuning algorithms. the experiments indicate that the DRL algorithms can be adopted as tuning techniques since they demonstrate consistent policy learning and overall reward maximization. © 2024 IEEE.

关键词： Adversarial machine learning

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Credit Scoring for Loan Applicants Using Machine learning Models 4

Credit Scoring for Loan Applicants Using Machine Learning Mo...

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4th international conference on Intelligent Technologies, CONIT 2024

作者： Krishna, Kaivaram Sivarama Kumar, Bommala Deepak Reddy, Morumpalli Deekshith Saketh, Ch Venugopalan, Manju Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350349900

this study presents a comprehensive framework for credit scoring in loan application processes. the research presents a framework that uses data analysis and machine learning techniques to evaluate creditworthiness, in loan applications. By incorporating cutting-edge algorithms like Naive Bayes, SVM, Decision Tree, Multi Layer Perceptron (MLP), and Gradient Booster (GB) the goal is to revolutionize the decision-making process for loan approvals by using analytics. the model utilizes a dataset that includes payment statements enabling a thorough and randomized credit scoring system for each individual applying for a loan. By leveraging machine learning algorithms, the system examines past payment behaviors to extract patterns and information. these findings are then used to generate a credit score that is further classified as good, bad, or moderate. this approach provides an assessment of an applicant's reliability. To improve the model's accuracy, feature selection techniques and Synthetic Minority Oversampling Techniques (SMOTE) are utilized to compare models' effectiveness, in predicting creditworthiness. the integration of data and machine learning not only enhances the efficiency of loan approval processes but also facilitates more informed financial decisions based on data analysis. On all the machine learning models which were performed, SVM performed best among the five models with an F-measure value of 0.86 incorporating feature selection and SMOTE. © 2024 IEEE.

关键词： Feature Selection

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Machine learning for Real-Time Language Translation: A Server-Side Approach to Overcome Cross-Cultural Communication Challenges 4

Machine Learning for Real-Time Language Translation: A Serve...

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

作者： Elango, S. Mohanaprakash, T.A. Siva, D. Gowtham, A. Sasikumar, A.N. Sunitha, T. Department of Computer Science and Engineering SOET CMR University Bengaluru India Department of Computer Science and Applications SRM Institute of Science and Technology Chennai India Madanapalle Institute of Technology & Science AP India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Artificial Intelligence & Data Science Saveetha Engineering College Chennai India

ISBN: (纸本)9798331515720

the evolution of human civilization has been intrinsically linked to advancements in technology, leading to the development of multiple languages as mediums of communication. However, this linguistic diversity poses significant challenges in cross-cultural communication. Despite numerous efforts and achieving near state-of-the-Art results in language translation, a universal solution remains elusive. this research study proposes an advanced approach to machine translation by utilizing server-side machine learning to enable seamless communication across various languages. By sending GET or POST requests through JQuery, users can efficiently translate text. the process is structured into three key steps: speech recognition (conversion of spoken words to text), translation using machine learning algorithms, and speech synthesis (conversion of translated text to audio). this approach aims to enhance real-Time translation capabilities, offering a more effective and user-friendly solution for overcoming language barriers. © 2024 IEEE.

关键词： Adversarial machine learning

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A Survey of Machine learning Approaches for Parkinson's Disease Prediction 4

A Survey of Machine Learning Approaches for Parkinson's Dise...

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4th international conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Sharma, Kuldeep Sahu, Rakesh Sandhu, Jasminder Kaur Final Year Chandigarh University Department of ME-CSE Punjab Gharuan India Chandigarh University Department of Cse Punjab Gharuan India

ISBN: (纸本)9798350307757

Parkinson's disease, a degenerative disorder of the nervous system, has a significant impact on a substantial population across the globe, leading to gradual deterioration in both, non-motor and motor, capabilities. Timely identification and intervention are of utmost importance in managing the symptoms and enhancing the quality of patients' lifestyle. this paper presents a comprehensive exploration of Parkinson's disease, focusing on its symptoms, diagnosis, machine-learning applications, and predictive models. It delves into various machine learning techniques applied to predict and identify Parkinson's disease, leveraging datasets and algorithms to discern patterns and relationships for early detection and treatment. the study extensively reviews existing literature, highlighting key research papers, methodologies, and findings pertaining to the utilization of machine learning in Parkinson's disease diagnosis and prediction. It covers a range of techniques employed in different studies, such as Random Forest, K-Nearest Neighbor, Convolutional Neural Networks, Support Vector Machine, and others. these papers utilize diverse datasets and algorithms, examining features like vocal characteristics, gait analysis, brain morphological changes, hand movement signals, Electroencephalography signals, and more to diagnose or predict Parkinson's disease. the aim emphasizes the promising potential of machine learning in aiding early detection and personalized treatment plans for Parkinson's disease. © 2024 IEEE.

关键词： Support vector machines

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