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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4179 条记录，以下是921-930 订阅

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A Comparative Study of machine learning Methods for Intrusion Detection 10

A Comparative Study of Machine Learning Methods for Intrusio...

引用

10th international conference on Electrical and Electronics Engineering, ICEEE 2023

作者： Sibai, Fadi N. Asaduzzaman, Abu Sibai, Ahmad Gulf University for Science and Technology Electrical & Computer Engineering Dept. Mishref Kuwait Wichita State University Electrical & Computer Engineering Dept. WichitaKS United States University of South Florida Computer Science Dept. TampaFL United States

ISBN: (纸本)9798350304299

In this work, we applied 8 machine learning (ML) techniques to detect intrusions, namely, neural networks, kNN, SVM, random forest, trees, AdaBoost, naive Bayes, and stochastic gradient descent SGD. Using the NSL-KDD data set, these ML techniques were trained and tested to correctly classify the network and operating system records of this dataset into one of 24 possible attacks. the performances of these ML methods were analyzed and compared, with the random forest method performing at the top. To the best of our knowledge, this is the first work on investigating more than 4 ML classifiers on this data set in one single work and using the same set of tools. © 2023 IEEE.

关键词： Intrusion detection

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Adaptive Path Planning Using Gaussian Process Regression: A Reinforcement learning Approach 4

Adaptive Path Planning Using Gaussian Process Regression: A ...

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4th international conference on Signal Processing and computer Science, SPCS 2023

作者： Zhao, Lanqing Computer Science University of South China Hengyang421000 China

ISBN: (纸本)9781510672505

In recent years, the development of underwater and sub-ice wireless sensor networks has been promoted by advances in wireless sensor network technology. A key issue to improve the efficiency and performance of underwater robot networks is adaptive trajectory planning. this paper studies adaptive trajectory planning of multiple AUVs in the underwater environment for estimating the water parametric field of interest, using a reinforcement learning system where a fixed access point (AP) on the ice serves as a communication gateway. the water parameter field is modeled as a Gaussian process (GP) with unknown hyperparameters, and a field hyperparameter estimation method based on the maximum likelihood method is employed for more accurate estimation. Compared to the traditional Monte Carlo (MC) algorithm, the proposed algorithm demonstrates superior performance in terms of resource consumption and exploration rate. © 2023 SPIE.

关键词： Reinforcement learning

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Deep learning approach for weather classification using pre-trained convolutional neural networks 4

Deep learning approach for weather classification using pre-...

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4th international conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Tarwani, Harit Patel, Shivang Goel, Parth Anand Changa388421 India Anand Changa388421 India

machine learning has had significant uses in weather prediction, and is especially useful in areas of agriculture and livestock, and outdoor vision. While various studies have been done on weather forecasting, limited work has yet been undertaken on its classification. Automating weather classification can free and save human resources. While simultaneously enhancing decision-making in the fields discussed. As such, the researchers have implemented transfer learning on different convolutional neural network (CNN) models, like ResNet50V2, EfficientNetB5, and EfficientNetV2S, for classification across 11 fields. the models were fine-tuned to improve performance and trained on a dataset that included diverse conditions like snow, lightning, and haze. Among the considered models, EfficientNetV2S showed the best accuracy (92.35%). this outcome indicates the effective application of machine learning, mainly transfer learning, in the future for weather classification and thus, its subsequent uses. ©2025 the Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Multi Model Approach to Evaluate and Enhance Student - Teacher Interactions 5

Multi Model Approach to Evaluate and Enhance Student - Teach...

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5th international conference on Advancements in Computing, ICAC 2023

作者： Daksith, Nisal Wanaguru, Sathsara Kolonne, Uresha Dolawatta, Amaya Abeywardhana, Lakmini Kasthurirathna, Dharshana Sri Lanka Institute of Inoformation Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Inoformation Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Advancements in artificial intelligence (AI) and machine learning (ML) have enabled the development of tools to address issues in Virtual Classrooms. this research focuses on utilizing AI and ML to monitor student behavior in virtual classrooms by analyzing facial expressions, background noise, and keystroke patterns. An AI based approach is proposed to analyze real-time webcam data, detecting patterns of inattention, and notifying teachers accordingly. the application also monitors background noise levels, identifying non-conducive learning environments, and analyzes keystroke patterns to detect unrelated activities. the research draws from academic literature to evaluate the effectiveness of the application while considering ethical implications such as potential bias and student privacy protection. Overall, this study contributes to the existing literature on AI and ML-based applications in education, demonstrating how these technologies can revolutionize student behavior monitoring, leading to enhanced outcomes for virtual classroom learning. © 2023 IEEE.

关键词： machine learning

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Deep learning-Based Classification of Rice Varieties Using Image Analysis: A Comparative Study of Neural Network Architectures 4

Deep Learning-Based Classification of Rice Varieties Using I...

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4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Santhiya, S. Sharmila, C. Jayadharshini, P. Sree Varshini, B. Sarankumar, T. Rathika, C. Kongu Engineering College Department of Artificial Intelligence Erode India Srm Institute of Science and Technology Department of Cse Tiruchirappalli India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518981

Rice is quite rich in genetic variation that has led to thousands of varieties each with unique patterns, shapes, and colors. the work of the research uses image analysis together with machine learning approaches to classify five types of rice: Arborio, Basmati, Ipsala, Yasemin, and Karacadag. the paper contains a data set of 75,000 images and an advanced architecture for a neural network that will improve quality grading of rice and help push agricultural application computer vision technology forward. the authors describe a deep learning framework capable of classifying various types of rice by using agricultural data from different collections of rice images. the model is demonstrated through experiments on activities related to image classification. the evaluation performance measures used were accuracy, precision, and recall. the experiments gave a peak accuracy of 97.59% on the dataset in question. these results underscore the fact that deep learning models can augment both the accuracy and efficiency of skin cancer diagnosis while emphasizing the necessity to utilize data from an extensive and diverse population for the training of the models. Furthermore, the higher effectiveness of AlexNet architecture compared with LeNet architecture suggests that more complex and deeper convolutional neural network architectures would perhaps be appropriate for cancer prediction. © 2024 IEEE.

关键词： Convolutional neural networks

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A Face Recognition conference Attendance System Based on Deep learning 4

A Face Recognition Conference Attendance System Based on Dee...

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4th international Symposium on computer Engineering and Intelligent Communications, ISCEIC 2023

作者： Cui, Shuguang Wang, Haixia Guo, Jun Liu, Jing Hunan Automotive Engineering Vocational College Zhuzhou China

ISBN: (纸本)9798350306323

Face recognition based on deep learning is an important application of artificial intelligence. For meeting check-in and other scenes that need to take into account both recognition accuracy and response time. the conference sign in system developed in this paper is based on clmtrackr library to realize pure front-end live detection, and based on RetinaFace algorithm to realize face position detection. the optimized FaceNet algorithm realizes the extraction of 128 dimensional feature vectors of the face. In view of the problem that the original Inception of FaceNet is easy to cause the feature map to be too thick, the improved MobilenetV1 model is used to improve the speed of face recognition. the data set test results in LFW show that the average recognition speed is increased to 0.003 seconds per frame, which is 0.001 milliseconds less than that of the ordinary model per frame, and the performance is improved by 25.0%. At the same time, the accuracy can reach 97.3%. © 2023 IEEE.

关键词： Face recognition

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SplitFed Resilience to Packet Loss: Where to Split, that is the Question 26th

SplitFed Resilience to Packet Loss: Where to Split, that is ...

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26th international conference on Medical Image Computing and computer-Assisted Intervention (MICCAI) / 8th ISIC Workshop / 1st Care-AI Workshop / 1st MedAGI Workshop / 4th DeCaF Workshop

作者： Shiranthika, Chamani Kafshgari, Zahra Hafezi Saeedi, Parvaneh Bajic, Ivan V. Simon Fraser Univ Sch Engn Sci Burnaby BC Canada

ISBN: (纸本)9783031474002;9783031474019

Decentralized machine learning has broadened its scope recently with the invention of Federated learning (FL), Split learning (SL), and their hybrids like Split Federated learning (SplitFed or SFL). the goal of SFL is to reduce the computational power required by each client in FL and parallelize SL while maintaining privacy. this paper investigates the robustness of SFL against packet loss on communication links. the performance of various SFL aggregation strategies is examined by splitting the model at two points - shallow split and deep split - and testing whether the split point makes a statistically significant difference to the accuracy of the final model. Experiments are carried out on a segmentation model for human embryo images and indicate the statistically significant advantage of a deeper split point.

关键词： SplitFed learning packet loss human embryo image segmentation

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Power System Fault Classification and Prediction Based on Intelligent Algorithms 4

Power System Fault Classification and Prediction Based on In...

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4th international conference on Advances in Electrical Engineering and computer applications, AEECA 2023

作者： Hu, Hongbin Wang, Yuqiang Han, Junfei Zhong, Ming Zhang, Yifan An, Haijun Inner Mongolia Power Research Institute Inner Mongolia Enterprise Key Laboratory of Smart Grid Simulation of Electrical Power System Hohhot China Inner Mongolia Tianda Tiancai Information Technology Co. Ltd. Hohhot China

ISBN: (纸本)9798350308082

Power system fault classification and prediction based on intelligent algorithms is a method that utilizes machine learning and data mining techniques to classify and predict fault conditions in power systems. this method can effectively prevent and avoid the occurrence of faults in the power system by collecting a large amount of power system data and using intelligent algorithms for processing to predict the type and location of faults that may occur in the power system, thereby improving the reliability and stability of the power system. this paper mainly applies the method of algorithm comparison and experiment to analyze the correlation of power system fault classification and prediction. Experimental data show that the recall rate and accuracy of the adaptive moment estimation algorithm mentioned in this article are both above 0.9, ranking first. © 2023 IEEE.

关键词： machine learning

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Prediction of Length of Stay after Total Hip Arthroplasty Based on XGBoost Algorithm 4

Prediction of Length of Stay after Total Hip Arthroplasty Ba...

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4th international conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Zhang, Ran Jiang, Xian The Affiliated Hospital of Southwest Medical University Department of Anesthesiology Luzhou China

ISBN: (纸本)9798350342710

Total hip arthroplasty is an effective treatment for hip joint disease, but the length of stay (LOS) after surgery is an important indicator that affects patient recovery and medical expenses. therefore, accurate prediction of LOS after total hip arthroplasty is of great significance for patient care and reasonable allocation of hospital resources. In order to overcome the subjectivity and uncertainty of traditional clinical experience prediction and the limitation of weak generalization ability of linear regression models, this study models and predicts the LOS after total hip arthroplasty based on the XGBoost algorithm, and conducts detailed data analysis and model evaluation. the results show that the XGBoost algorithm exhibits better predictive performance than traditional methods, with an average prediction error of 2.03 days, which can objectively and accurately evaluate patient conditions and predict LOS. © 2023 IEEE.

关键词： machine learning

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An Enhancement of Reinforcement learning by Scheduling with learning Effects 18

An Enhancement of Reinforcement Learning by Scheduling with ...

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18th conference on computer Science and Intelligence Systems, FedCSIS 2023

作者： Rudek, Radoslaw General Tadeusz Kósciuszko Military University of Land Forces Czajkowskiego 109 Wroclaw51-147 Poland

ISBN: (纸本)9788396744784

this paper present results, which reveal that approaches obtained for scheduling problems with learning effects can be successfully used to improve the quality of machine learning methods. It is illustrated by modelling some aspects of Q-learning agents as scheduling problems with the learning effect, and constructing sequencing and dispatching algorithms, which take into account the existence of learning. their application to determine the sequence of tasks processed by Q-learning agents can visibly speed up their convergence to an optimal strategy. Furthermore, we show that a dispatch of tasks according to the longest processing time algorithm for parallel computing can be replaced by a more efficient procedure, if agents can learn. the numerical analysis reveals that our approach is efficient, robust and only marginally dependents on a learning model and an accurate approximation of task processing times. © 2023 Polish Information Processing Society.

关键词： Reinforcement learning

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