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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"

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Big Data Analysis Of Female Dysmenorrhea Based On Multiple machine learning Methods 4

Big Data Analysis Of Female Dysmenorrhea Based On Multiple M...

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4th international conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Zhang, Xu Deng, Lifeng Song, Wenxiao Xiao, Yahui Shandong University of Science and Technology College of Mathematics and Systems Science Qingdao China China United Network Communications Co. Ltd Qingdao Branch Qingdao China

ISBN: (纸本)9798350373233

Dysmenorrhea is one of the most common gynecological disorders, and when it is severe, it may have many adverse effects on the patient's life and work. In order to assist the treatment of dysmenorrhea and reduce the pain of patients as much as possible, this paper carries out a questionnaire survey on all the females in a university in Qingdao City. Based on the collected data, a variety of machine learning models including association rule, least absolute shrinkage and selection operator (LASSO), decision tree, and random forest are set up to find out the relevant factors affecting dysmenorrhea, and a dysmenorrhea prediction model is set up and optimized. the experimental results show that the various machine learning methods proposed in this paper have good effects on studying the causes of dysmenorrhea and predicting the occurrence of dysmenorrhea. © 2023 IEEE.

关键词： Association rules

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machine learning Based Prediction of Malicious Intrusions in IoT Empowered Cybersecurity application 4

Machine Learning Based Prediction of Malicious Intrusions in...

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4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Upadhyay, Yatharth Tiwari, Damodar Gupta, Shital Sharma, Twinlkle Bansal Institute of Science and Technology Cse Department Bhopal India

ISBN: (纸本)9798350387490

Since the introduction of the Internet of things, the security of the IoT environment has become the most critical concern. Vulnerabilities related to malicious intrusions are major impacts to the IoT systems, impacting the aspects of their integrity, confidentiality and availability. this paper presents an overview of ML methods that may be used to identify and forecast such intrusions, making it easier to prevent future cyber threats. the current paper offers a review of ML based prediction models for intrusion detection for IoT systems. We discuss several ML classification algorithms, feature extraction techniques, databases, performance measures and some of the research issues found in literature. Further, we turn our focus to how ML can further improve cybersecurity tools for IoT contexts and provide suggestions for future research areas to counter new challenges. © 2024 IEEE.

关键词： Adversarial machine learning

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Adaptive Approximation Strategy to Reduce Approximation Error in Reinforcement learning 4

Adaptive Approximation Strategy to Reduce Approximation Erro...

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4th international conference on computer Engineering and application, ICCEA 2023

作者： Li, Min Zhu, William Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350347548

Approximation error exists in the reinforcement learning method which is based on the action-value. In this paper, we propose an adaptive approximation strategy to reduce this error. the motivation is that the approximation way should adapt to the current training. For every period of the training time step, the adaptive approximation strategy tests the current learned policy to judge whether the current approximation is overestimated or underestimated. then according to the result of the judgment, it adjusts the approximation way to adapt the current training by changing the target of action-value to mitigate the error. the experiments illustrate the effectiveness of the proposed strategy. © 2023 IEEE.

关键词： Reinforcement learning

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2024 4th international conference on Consumer Electronics and computer Engineering, ICCECE 2024

2024 4th International Conference on Consumer Electronics an...

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4th international conference on Consumer Electronics and computer Engineering, ICCECE 2024

ISBN: (纸本)9798350384062

the proceedings contain 102 papers. the topics discussed include: identification of schizophrenia and depression based on deep learning and EEG;content-based galaxy image retrieval using convolutional neural networks;spatiotemporal knowledge graph modeling and persistence supported by database;identification of important nodes in the information propagation network based on the artificial intelligence method;raft consensus grouping mechanism based on affinity propagation clustering algorithm;knowledge graph recommendation algorithm combined with PersonalRank sampling;visual perception-driven dynamic adaptive shading;many-objective optimization evolutionary algorithm based on dynamic region partitioning;and research on the application of object detection methods based on multimodal information fusion in digital forensics.

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Academic Performance Analysis of the Students with Feature Retraction Using machine learning Algorithms 4

Academic Performance Analysis of the Students with Feature R...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Chowdhury, Suman Das, Dilip Kumar International University of Business Agriculture and Technology Department of Electrical and Electronic Engineering Dhaka1230 Bangladesh International University of Business Agriculture and Technology Department of Mathematics Dhaka1230 Bangladesh

ISBN: (纸本)9798331540364

this research investigates the application of machine learning algorithms to predict student academic performance based on historical data and current factors. Five machine learning algorithms were employed to analyze thirteen features related to student demographics, academic background, and other relevant factors. the study evaluates the performance of the algorithms using classification metrics, including the classification matrix and confusion matrix. the results demonstrate that after data preprocessing, four out of the five classifiers achieved 100% accuracy, while K-Nearest Neighbors (KNN) achieved 80% accuracy. Random Forest (RF) exhibited a significant improvement from 92% accuracy before preprocessing to 100% accuracy after preprocessing. Chi-square analysis reveals that age and gender features has no significant impact on academic performance. this finding suggests that other factors, such as study habits, motivation, and socioeconomic conditions, may play a more influential role in predicting student success. the research contributes to the understanding of factors influencing student academic performance and provides valuable insights for educators and policymakers. By leveraging machine learning techniques, this study demonstrates the potential for predicting student success and implementing targeted interventions to support student achievement. © 2024 IEEE.

关键词： Adversarial machine learning

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YOLO-based obstacle detection under few-shot learning 4

YOLO-based obstacle detection under few-shot learning

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4th IEEE international conference on Data Science and computer application, ICDSCA 2024

作者： Liu, Chengyu Yang, Yuan School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9798350368239

the obstacle detection industry has been developing rapidly in recent years. Obstacle detection technology has grown by leaps and bounds. Few-shot obstacle detection has a profound significance, which can help us to solve the problem of small samples under high speed conditions in target detection. Due to the limitation of the shooting environment, the sample size about the obstacles is small today. the goal of small sample learning is not for the machine to recognize images in the training set and generalize to the test set;the goal of small sample learning is for the machine to learn to learn on its own. the purpose of learning is for the model to understand the similarities and differences between things and learn to distinguish between them. In this paper, we help with this process by adding a parameter reweighting module. the models will fall into overfitting when a small number of samples are available. In this paper, we propose a small-sample detection method based on improved YOLO-v8. For the problem of small sample size, we improve the generalization ability of the model by adding an attention mechanism at the detection header, and for the problem of model overfitting, we update the model parameters by adding a feature reweighting module to improve the model effect. Finally, the proposed method is validated based on the track area dataset, and the experimental results show that the model can realize the accurate recognition of small sample obstacles. ©2024 IEEE.

关键词： Zero-shot learning

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Recommendation system for anime using machine learning algorithms 4th

Recommendation system for anime using machine learning algor...

引用

4th Annual international conference on Data Science, machine learning and Blockchain Technology, AICDMB 2023

作者： Choudhary, Harsh Raghavendra, S. Ramyashree Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9781032426853

these days, many people watch anime, especially the younger generations. With so many different sorts of programming available, this specialist area of the entertainment industry is drawing an increasing number of people. the word "anime," which was derived from the word "animation," has a growing fan following all over the world. the anime industry has been growing rapidly in recent years, bringing in billions of dollars annually. Significant streaming providers like Netflix and Amazon Prime are paying attention to this industry. Researchers are working hard to apply machine learning algorithms to offer the viewer with the appropriate anime because it is currently a popular trend among the younger generation. the purpose of the research presented in the research article that follows is to further current field reserch. From Kaggle, two datasets have been utilized. the first dataset for an anime comprises 7 columns and 12293 rows. the second rating dataset comprises 3 columns and 7813735 rows. Anime may be rated by users, who can then add it to their finished list. the top 15 matching suggestions and forecasted ratings were then obtained using machine learning approaches (content-based filtering, collaborative filtering, and popularity-based filtering). © 2024 the Author(s).

关键词： Recommender systems

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machine learning and Deep learning Analysis of PCG Data 4

Machine Learning and Deep Learning Analysis of PCG Data

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4th international conference on Artificial Intelligence and Signal Processing

作者： Kotipalli, Khyathi Devi Gayam, Vyshnavi Chandu, Hema Poojitha Abburi, Radha Samanta, Sibendu SRM Univ Dept CSE Amaravati Andhra Pradesh India SRM Univ Dept ECE Amaravati Andhra Pradesh India SRM Univ Dept ECE & CSE Amaravati Andhra Pradesh India

ISBN: (纸本)9798350350661;9798350350654

Cardiovascular diseases are some of the most common diseases today. A new estimate from the World Heart Federation (WHF) states that the number of deaths from cardiovascular diseases (CVD) increased from 12.1 million in 1990 to 20.5 million in the year 2021. In recent years, the field of healthcare has witnessed significant advancements in technology and data analysis techniques. Congenital abnormalities, diseases caused by impaired heart rhythm, vascular occlusion, post-operation arrhythmias, heart attacks and irregularities in heart valves are some of the various cardiovascular diseases. Early recognition of them is very important for obtaining positive results in treatment. One such area of research that holds great promise is the classification of heart sounds using phonocardiography (PCG). Classification of heart sounds has become increasingly important in enhancing diagnostic precision and enhancing patient care. the integration of machine and deep learning into medical diagnostics has emerged as a transformative avenue. machine and Deep learning techniques offer the potential to automate and increase the accuracy of cardiac sound analysis providing healthcare professionals with rapid and constant diagnostics support. this research contributes to efficient cardiac diagnostics, aiding the timely detection of abnormalities and enhancing patient care. It aspires to learn about the value of heart sound classification, the utilization of phonocardiography, and the application of deep learning and machine learning methods for enhancing the accuracy of classification.

关键词： Heart Sound classification machine learning Deep learning Phonocardiography Cardiovascular Diseases MFCCs GFCCs

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An Optimal Real-Time Routing and Clustering Protocol for UAV Networks Based on Security 4

An Optimal Real-Time Routing and Clustering Protocol for UAV...

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4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Sophia, S. Leelipushpam Paulraj, Getzi Jeba Yuvarajan, D. Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

Unmanned Aerial Vehicle (UAV) networks play an important role in different application areas such as military surveillance, emergency services, and infrastructure. However, these networks face significant challenges such as data leakage, unauthorized access, limited processing capacity, and energy constraints. Available techniques such as Elliptic Curve Cryptography (ECC), hash-based techniques, and dynamic clustering algorithms have been proposed to enhance security and operational performance;however, they often encounter challenges concerning scalability, adaptability to real-time changes within the network, and resource-related constraints. this research presents a streamlined authentication framework with the intent to facilitate secure and effective functionalities in turbulent and fluctuating settings. the proposed protocol uses Dijkstra's algorithm for optimal real-time routing, Adaptive Optimization Clustering (AOC) for dynamic resource distribution, and K-means clustering for noise classification. the proposed methodology achieves substantial improvements in power efficiency, latency, and Packet Delivery Rate (PDR) due to reduced computational overhead as well as risks such as data interception and replay attacks. Simulation results conclude that the system suggested here would be secure, scalable, and energy-efficient to be used for applications in demanding scenarios, such as mission-critical scenarios. © 2025 IEEE.

关键词： Emergency services

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Review of machine learning Models in Cyberbullying Detection Problem 4

Review of Machine Learning Models in Cyberbullying Detection...

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

作者： Toktarova, Aigerim Sultan, Daniyar Azhibekova, Zhanar Department of Information Systems International Kazakh-Turkish University Turkistan Kazakhstan Department of Information Systems Al-Farabi Kazakh National University Almaty Kazakhstan Department of Information Communication technologies Kazakh National Medical University Named After S.D.Asfendiyarov Almaty Kazakhstan

ISBN: (纸本)9798350374865

this research paper delves into the intricate domain of cyberbullying detection on social media, addressing the pressing issue of online harassment and its implications. the study encompasses a comprehensive exploration of key aspects, including data collection and preprocessing, feature engineering, machine learning model selection and training, and the application of robust evaluation metrics. the paper underscores the pivotal role of feature engineering in enhancing model performance and highlights the versatility of supervised machine learning techniques such as Support Vector machines, Naïve Bayes, Decision Trees, and others in the context of cyberbullying detection. Furthermore, it elucidates the significance of evaluation metrics like accuracy, precision, recall, F1-score, and AUC-ROC in quantitatively assessing model effectiveness. By providing valuable insights and methodologies, this research contributes to the ongoing efforts to combat cyberbullying, ultimately promoting safer online environments and safeguarding individuals from the pernicious effects of online harassment. © 2024 IEEE.

关键词： Adversarial machine learning

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