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检索条件"任意字段=4th Conference on Learning and Intelligent Optimization"

共 11565 条记录，以下是591-600 订阅

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Experimental Study and optimization of Energy Consumption During end Face Turning of AISI 4140 4

Experimental Study and Optimization of Energy Consumption Du...

引用

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

作者： Fatima Zohra, El Abdelaoui Jabri, Abdelouahhab Barkany, Abdellah El Boharb, Ali Moujibi, Nabil Sidi Mohammed Ben Abdellah University Mechanical Engineering Laboratory Faculty Of Sciences And Techniques Fez Morocco Sidi Mohammed Ben Abdellah University Laboratory Of Intelligent Systems Georesources And Renewable Energies Faculty Of Sciences And Techniques Fez Morocco

ISBN: (纸本)9798350309508

this study focuses on energy consumption during 'end face' turning operation of AISI 4140 steel under dry conditions. An experimental study was conducted to establish the energy consumption model based on the specific machine parameters, considering crucial machining parameters such as feed rate (f), cutting speed (v), and depth of cut (ap). this study led to a formula using multiple regression model which represents energy consumption of a machine lathe. Subsequently, the Genetic Algorithm (GA) optimization technique parameters were calibrated using the Taguchi design with L16 orthogonal array table. With the goal of minimizing energy consumption, this optimization approach was performed using tournament selection. this integrated approach between experimental studies and optimization provides a reliable means to continuously improve manufacturing processes, thereby contributing to a more economy use of energy. © 2024 IEEE.

关键词： Energy utilization

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Beyond Traditional Methods: Evaluating Advanced Machine learning Models for Superior Fraud Detection 4

Beyond Traditional Methods: Evaluating Advanced Machine Lear...

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4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Singh, Agampreet Gill, Kanwarpartap Singh Kumar, Mukesh Rawat, Ruchira Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Uttarakhand Dehradun248002 India Graphic Era Deemed to be University Uttarakhand Dehradun248002 India

ISBN: (纸本)9798331529635

the banking industry is increasingly concerned about credit card fraud due to its potential risks to both consumers and financial institutions. In recent years, machine learning techniques have proven effective in detecting fraudulent transactions. this study compares the performance of two wellknown machine learning methods such as XGBoost and Artificial Neural Networks (ANNs) in identifying credit card fraud. We assess the accuracy, precision, recall, and F1 score of these methods using a publicly available credit card transaction dataset. Additionally, the study examines the computational efficiency and scalability of ANNs and XGBoost to determine their suitability for real-time fraud detection systems. ANNs achieve the highest accuracy at 96.9%, surpassing all five methods evaluated, while XGBoost, with an accuracy of 92.7%, outperforms all other classifiers. these results provide valuable insights for financial institutions looking to implement or improve fraud detection systems, highlighting the strengths and limitations of each approach. this research contributes to ongoing efforts to leverage advanced machine learning techniques to combat credit card fraud and enhance financial security. © 2024 IEEE.

关键词： Adversarial machine learning

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A Study on Ensemble Classification Method Based on Accuracy and Confusion Entropy 4

A Study on Ensemble Classification Method Based on Accuracy ...

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4th International conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Guo, GuangYuan Wang, ShuQin Tianjin Normal University College of Computer and Information Engineering Tianjin300384 China

ISBN: (纸本)9798331534622

this paper proposes an ensemble classification method integrating accuracy and confusion entropy to address the limitations of existing ensemble learning approaches that overly rely on single evaluation metrics, particularly in scenarios involving class imbalance and high-dimensional data. the method employs a dynamic weighting mechanism to balance the importance of accuracy and confusion entropy, while Bayesian optimization is used to determine optimal parameters, enabling the effective selection and integration of classifiers. Experimental results demonstrate that the proposed method outperforms traditional approaches on multiple UCI datasets in terms of accuracy,F1 score, AUC, and confusion entropy, showcasing exceptional robustness and adaptability in complex tasks. © 2024 IEEE.

关键词： Contrastive learning

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Optimizing Microclimate Comfort in Smart Constructions Using an intelligent Fuzzy-PID Temperature Regulation System 4

Optimizing Microclimate Comfort in Smart Constructions Using...

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4th IEEE International conference on Smart Information Systems and Technologies, SIST 2024

作者： Abdrakhmanov, Rustam Alchinbayeva, Oichagul International University of Tourism and Hospitality Turkestan Kazakhstan M. Auezov South Kazakhstan University Shymkent Kazakhstan

ISBN: (纸本)9798350374865

this research paper investigates the implementation and performance of an smart Fuzzy-PID Temperature Control approach within the context of optimizing the microclimate in intelligent buildings. the study delves into the intricate dynamics of this innovative control system, emphasizing the critical role of precise parameter tuning and the incorporation of fuzzy logic for adaptive PID control. the research encompasses the development and application of a comprehensive fuzzy rule base, encompassing fuzzification, fuzzy inference, and defuzzification processes to facilitate dynamic parameter adaptation. through meticulous simulations and comparative analyses with conventional control systems, the study reveals the superior performance of the proposed controller in maintaining a stable and comfortable indoor environment. Temperature stability, precision, and narrow fluctuations are hallmarks of its efficacy, contributing to enhanced comfort of residents and energy saving. the results presented in this paper highlight the promising potential of the intelligent Fuzzy-PID Temperature Control approach in advancing the management of intelligent buildings, aligning with contemporary objectives of sustainability and resource optimization. this research underscores the significance of integrating intelligent control systems for improved indoor microclimate regulation and lays the foundation for further exploration and practical implementations in the realm of smart building management. © 2024 IEEE.

关键词： Adaptive control systems

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Route optimization for Urban Last-Mile Delivery: Truck vs. Drone Performance 4th

Route Optimization for Urban Last-Mile Delivery: Truck vs. D...

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4th International conference on optimization learning Algorithms and Applications

作者： Silva, A. S. Berger, Guido S. Mendes, Joao Brito, thadeu Lima, Jose Gomes, H. T. Pereira, Ana I. Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI P-5300253 Braganca Portugal Inst Politecnico Braganca Ctr Invest Montanha CIMO P-5300253 Braganca Portugal Inst Politecn Braganca Lab Assoc Sustentabilidade Tecnol Regioes Montanh P-5300253 Braganca Portugal

ISBN: (纸本)9783031774256;9783031774263

In urban environments, last-mile item delivery relies heavily on trucks, causing issues like noise pollution and traffic congestion. Unmanned Aerial Vehicles (UAVs) offer a promising solution to these challenges. this study compares the effectiveness of delivery using trucks versus drones. Two customer datasets, one clustered and one random, were used for testing. Route optimization involved four deterministic and four non-deterministic algorithms. the performance of these algorithms, considering the total distance traveled, was evaluated across different datasets and vehicle types. the top two algorithms were further assessed for environmental impact and cost efficiency. Battery consumption along the routes was also analyzed to gauge operational feasibility.

关键词： Vehicle Routing Problem UAVs Guided Local Search

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Investigating Learners' Interaction Patterns in Synchronous and Asynchronous Online Discussions and their Relationship with learning Performance 4

Investigating Learners' Interaction Patterns in Synchronous ...

引用

4th International conference on Educational Technology, ICET 2024

作者： Zhao, Yuping Chai, Huanyou Wang, Jixin Central China Normal University Faculty of Artificial Intelligence in Education Wuhan China

ISBN: (纸本)9798350376944

Learner interactions are necessary for academic success and can be implemented synchronously or asynchronously in online education. Taking an online course from a university in Central China as an example, this study adopted social network analysis and k-means clustering methods to quantify and cluster learners' interaction patterns and further explore their relationship with learning performance. Based on the interaction patterns observed, learners were classified into four clusters: "double active", "double negative", "synchronous active", and "asynchronous active". In addition, learners in the "double negative"cluster had the lowest level of learning performance. © 2024 IEEE.

关键词： Federated learning

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An Active learning based Latency Prediction Approach for Neural Network Architecture 4

An Active Learning based Latency Prediction Approach for Neu...

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4th International conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Ji, Yong Wei, Daikun Shi, Leiyang Liu, Peng Li, Cheng Yu, Jun Cai, Xu Qingdao Power Supply Company State Grid Shandong Electric Power Company Qingdao China Co. Ltd Baoding China Wuhan University School of Computer Science Wuhan China

ISBN: (纸本)9798350394375

the multi-objective Neural Architecture Search (NAS) automates the process of neural network architecture design. It also evaluates and balances the accuracy and performance during the design process. the latency is used as a performance optimization object by many multi-objective NAS methods. A latency evaluation approach based on a latency prediction model can solve the efficiency problem of latency evaluation, but building a latency dataset of a certain scale for training the latency prediction model requires high computational costs. this paper uses multilayer perceptron (MLP) as the latency prediction model, takes an iterative selection of architectures to collect latency, trains the latency prediction model and proposes an active learning latency prediction model construction approach based on the performance of the latency prediction model to automatically stop collecting a large amount of latency data. It can avoid the high time cost problem. In the experiments, compared with the control group, using the active learning process to construct a latency prediction model for FPGA devices can save about 94% of the latency collection cost and achieve almost the same evaluation performance. © 2024 IEEE.

关键词： Iterative methods

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Enhancing Coverage in Wireless Sensor Networks Using Machine learning Techniques 4

Enhancing Coverage in Wireless Sensor Networks Using Machine...

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4th IEEE Interdisciplinary conference on Electrics and Computer (INTCEC)

作者： Priyadarshi, Rahul Vikram, Raj Huang, ZeKun Yang, Tiansheng Rathore, Rajkumar Singh Siksha O Anusandhan Deemed Univ Fac Engn & Technol ITER Bhubaneswar 752054 India CV Raman Global Univ Dept Comp Sci & Engn Bhubaneswar 752054 India Shanxi Univ 92 Wucheng Rd Taiyuan Shanxi Peoples R China Univ South Wales Llantwit Rd Pontypridd CF37 1DL M Glam Wales Cardiff Metropolitan Univ Cardiff Sch Technol Llandaff CampusWestern Ave Cardiff CF5 2QS Wales

ISBN: (纸本)9798350349467;9798350349450

Wireless Sensor Networks (WSNs) are essential for tracking environmental and physical variables in a range of applications. One of the biggest challenges in WSN deployment is still achieving excellent coverage while preserving energy. In this work, we provide a unique machine learning-based method to improve WSN coverage efficiency. Our technique minimizes energy consumption and delivers better coverage by dynamically modifying sensor node placement tactics depending on current environmental data. the results of the experiments confirm the efficacy of the suggested methodology and underscore its practicability for implementation in many applications that need extensive coverage in ever-changing settings.

关键词： Wireless Sensor Networks Coverage optimization Machine learning Dynamic Deployment Energy Efficiency

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Using optimization Algorithms to Improve the Classification Of Diseases On Rice Leaf Of EfficientNet-B4 Model 23

Using Optimization Algorithms to Improve The Classification ...

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8th International conference on intelligent Information Technology (ICIIT)

作者： Quynh, Anh Nguyen Quoc, Khang Nguyen Ngoc, Hoang Tran Quach, Luyl-Da FPT Univ Can Tho Vietnam

ISBN: (纸本)9781450399616

Rice is an important food source, so increasing rice yields is essential to disease management. Much related research has performed classification and disease detection on rice leaf using machine learning models. However, this study aims to synthesize data to evaluate rice leaf diseases through collected data and contribute new data sets. this data set uses optimization algorithms (RMSprop and Adam) combined with the EfficientNet-B4 model with learning rates of 0.01 and 0.001. the research showed that the optimal algorithm combined with the EfficientNet-B4 model gave high results of 93% (F1-Score) and an accuracy of 89%. the research results show the influence of optimal parameters on the models and find the most optimal parameter results.

关键词： EfficientNet-B4 transfer learning rice diseases rice leaf optimal algorithm

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A Study of optimizations in Rainfall Prediction using Machine learning Models 4

A Study of Optimizations in Rainfall Prediction using Machin...

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4th International conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Sinha, Chaitanya Kumar Sharma, Udit Maragatharajan, M. Manikandan, J. VIT Bhopal University School of Computing Science and Engineering Madhya Pradesh India

ISBN: (纸本)9798331515720

Rainfall prediction is essential for many industries, such as agriculture, water resource management, and disaster relief. Recently, there has been a lot of interest in machine learning techniques to increase the accuracy of rainfall forecasts. To improve rainfall prediction models' accuracy, we provide a hybrid strategy in this study that combines conventional algorithms with cutting-edge optimization techniques. We utilize the XG Boost, Random Forest, and Linear Regression algorithms as baseline models. these are then combined with the Crow Search Algorithm (CSA) optimization technique. Crow collective behavior inspired the nature-inspired metaheuristic algorithm, CSA, which has demonstrated promising results in solving complicated problems. this study intends to improve the baseline models' performance by dynamically adjusting their parameters through the use of CSA. the proposed approach incorporates CSA with the chosen machine learning algorithms, which adaptively modify parameters to increase prediction accuracy. We validate the suggested approach with historical rainfall data and conduct extensive experimentation to show its efficacy. © 2024 IEEE.

关键词： Prediction models

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