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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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A Review of Pre-processing Techniques for Weed-Plant Detection and Classification in Precision Agriculture 4th

A Review of Pre-processing Techniques for Weed-Plant Detecti...

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4th international conference on Data, Engineering, and applications, IDEA 2022

作者： Sonawane, Sandip Patil, Nitin N. R. C. Patel Institute of Technology Shirpur India

ISBN: (纸本)9789819700363

Precision agriculture has recently gained significant importance in computer vision technologies. Various processes as a part of agricultural production cycle from planting to harvesting can be carried out automatically and effectively by using computer vision. the lack of publicly available image datasets is a major obstacle to the rapid design and assessment of computer vision based applications and also to machine learning algorithms which support these applications. To reduce this bottleneck, numerous image dataset collections have been discovered and made publicly available since 2015. In spite of this development, there is still a need to focus on survey of these datasets. Two most important concerns—choosing the right dataset and knowing how to pre-process and prepare the images in datasets, are considerably challenging task in every application. this review paper gives a thorough analysis of the public image datasets and numerous pre-processing techniques carried out in the field of precision agriculture. this thorough study can lead to development of suitable methods for improved quality and productivity of the crop along with proper weed management. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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MCN: Magnitude-Continuity Network for Video Anomaly Detection under Weak Supervision 4

MCN: Magnitude-Continuity Network for Video Anomaly Detectio...

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4th international conference on machine learning and computer application, ICMLCA 2023

作者： Luan, Xiaohu Zhang, Hong-Min Liangjiang International College Chongqing University of Technology Chongqing4001135 China School of Electrical and Electronic Engineering Chongqing University of Technology Chongqing400054 China

ISBN: (纸本)9798400709449

Video anomaly detection is a complex task that combines anomaly detection and video image processing. Video anomaly detection aims to identify abnormal behavior in video and is widely used in the real world. In existing works, Feature-Magnitude have shown good performance in weakly supervised video anomaly detection. However, Feature Magnitude-based methods is easily affected by the environment, resulting in error detection. Motivated by the fact that the abnormal events in real video are often continuous, we propose a Magnitude-Contiuity Network. In addition, in order to further improve the robustness of existing Feature Magnitude-based methods, the dynamic Feature-Magnitude learning loss function is used. Experimental results on UCF-Crime and ShanghaiTech datasets show that the robustness and performance of the proposed method are significantly improved. © 2023 ACM.

关键词： Anomaly detection

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Research on application of machine learning algorithm in digital economy 3

Research on application of machine learning algorithm in dig...

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3rd international conference on computer Vision and Data Mining, ICCVDM 2022

作者： Diao, Yanhua Wang, Zhentang Li, Qingchen Shandong Youth University of Political Science Shandong Jinan China Chaoyue Technology Co. Ltd. Shandong Jinan China

ISBN: (纸本)9781510661363

this paper mainly uses support vector machine, random forest, logistic regression, and other machine learning algorithms to analyze the core data of listed companies, and then monitor their operating conditions and predict their bankruptcy probability. At the same time, the prediction effects of the three machine learning methods are evaluated by confusion matrix, ROC curve and other methods and indicators, and the best effect of the method is found. By predicting the bankruptcy probability of listed companies this study further supplements and improves the theory of machine learning algorithm on economic quantitative analysis. © COPYRIGHT SPIE.

关键词： Support vector machines

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A Novel Architecture for a Medical Image Recognition System Using Deep learning-Based Multiple Regression Evaluation 4th

A Novel Architecture for a Medical Image Recognition System ...

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4th international conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Parashar, Jyoti Upreti, Kamal Fatima, Iram Gangwar, Divya Saini, Ashok Kumar Vats, Prashant Department of Computer Science & Engineering Dr. Akhilesh Das Gupta Institute of Technology & Management New Delhi India Department of Computer Science Christ University Delhi NCR Campus Ghaziabad India Department of AI & ML Dr. Akhilesh Das Gupta Institute of Technology & Management New Delhi India Department of Management Babu Banarasi Das Institute of Technology & Management New Delhi India Department of Computer Science & Engineering School of Computer Science & Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819751990

Models based on machine learning are optimization models that collect data, assess it, and deliver the reports required by specialists and management to make the best decisions. the application of contemporary machine learning allows the organization to quickly analyze photographs, differentiate voices assist in providing customer service, assess the information that is at hand, and uncover connections to aid in decision-making processes. the results of this investigation use quantitative methodologies to collect data and analyze it using mathematical procedures such as regression modeling as well as analysis of variance. Deep learning techniques applied to digital imaging, particularly in medical treatment, can increase picture quality, aid in modeling, aid in making the best possible diagnosis, and successfully address demands from patients. To analyze the hypothesis, investigators intend to utilize statistical approaches such as descriptive data analysis, regression evaluation, and analysis of variance (ANOVA). the authors employ the purposive sample approach to choose respondents from the healthcare industry. Purpose sampling is a non-probability sampling approach. Researchers collected data from 193 respondents working at hospitals that are privately owned in Southern Asia. As stated by the study, all factors, including efficiently meeting patient needs, have a probability value of under 0.05, indicating that they are statistically noteworthy. Following the study, the coefficient of variance (R squared) is 0.744, or 74.4%. According to the study, there is a high association between better image quality and ML-based digital picture identification systems. the recognition of patterns and the application of artificial intelligence to computerized recognition of pictures also have a close link. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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5th EAI international conference on Emerging Technologies for Developing Countries, AFRICATEK 2022

5th EAI International Conference on Emerging Technologies fo...

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5th EAI international conference on Emerging Technologies for Developing Countries, AFRICATEK 2022

ISBN: (纸本)9783031358821

the proceedings contain 14 papers. the special focus in this conference is on Emerging Technologies for Developing Countries. the topics include: Fourth Industrial Revolution Research Outputs in Africa: A Bibliometric Review;Modelling DDoS Attacks in IoT Networks Using machine learning;towards a Microservice-Based Middleware for a Multi-hazard Early Warning System;indigenous Knowledge Mobile Based application that Quantifies Farmers’ Season Predictions with the Help of Scientific Knowledge;weed Identification in Plant Seedlings Using Convolutional Neural Networks;assessing Institutional Readiness for the Fourth Industrial Revolution: Using learning Analytics to Improve Student Experiences;M-learning During COVID-19: A Systematic Literature Review;Archiving 4.0: Dataset Generation and Facial Recognition of DRC Political Figures Using machine learning;on the machine learning Models to Predict Town-Scale Energy Consumption in Burkina Faso;application of Latent Dirichlet Allocation Topic Model in Identifying 4IR Research Trends;A Conceptual Model for the Digital Inclusion of SMMEs in the Informal Sector in South Africa - the Use of Blockchain Technology to Access Loans;multiple Mobile Robotic Formation Control Based on Differential Flatness.

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A ST-GCN based Motion recognition scheme for upper limb rehabilitation exoskeleton 4

A ST-GCN based Motion recognition scheme for upper limb reha...

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4th international Symposium on Artificial Intelligence for Medicine Science (ISAIMS)

作者： Chang, Xuedong Xia, Kang Wang, Yimin Sun, Han Zhou, Jianfang Hohai Univ Coll Mech & Elect Engn Changzhou Jiangsu Peoples R China Soochow Univ Affiliated Hosp 3 Articular Orthopaed Changzhou 213003 Jiangsu Peoples R China

ISBN: (纸本)9798400708138

Stroke and its related complications such as hemiplegia and disability have placed a huge burden on human society in the 21st century, and different disability leads to personalized needs for rehabilitation and assistance in daily living. To addresses this issue, human-computer interaction (HMI) technology strives to detect and recognize the user's intention, to meet the user's needs through physical responses. In this work, the designed pressure film sensor array on the upper limb rehabilitation exoskeleton combined with our ST-GCN network is used to predict the patient's motion intention. Experimental results show that compared with the conventional method, the ST-GCN model meets the requirements in terms of accuracy, response time and model size, and the proposed method can be used as an optional scheme for the application of rehabilitation exoskeleton.

关键词： ST-GCN Motion intention recognition Human-computer Interaction Deep learning

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Early Prediction of Stroke using machine learning 4

Early Prediction of Stroke using Machine Learning

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Revathy, G. Sesadri, U. theodore, Shaji Justina Princy thilagavathy, J. Senthilvadivu, S. Senthil Murugan, V. SRC Sastra Deemed University Department of CSE Thanjavur India Vardhaman College of Engineering Department of CSE Hyderabad India University of Technology and Applied Sciences Al-MUSANNA Faculty of It - Networking Dept of It Oman Cherran's Arts Science College Department of Cs Kangeyam India Arulmigu Meenakshi Amman College of Engineering Department of It Thiruvannamalai India Srm Institute of Science and Technology Department of Networking and Communications Tamilnadu Kattankulathur603203 India

ISBN: (纸本)9798350300093

A stroke is the outcome of an abrupt cessation of blood flow to a region of the brain. Liable on the fragment of the brain that has been hurt, disability is caused by a loss of blood flow because brain cells gradually perish. In order to forecast stroke and maintain a healthy lifestyle, early symptom detection might be very beneficial. In order to afford a strong substance for the long-term peril prediction of stroke incidence, a quantity of replicas are developed and evaluated using machine learning (ML) in this study. the main contribution of this study is an ensemble, random forest, SVM, and XgBoost method that performs well and is validated by a variety of system of measurement, such as precision, recall, F-measure, and accuracy. According to the results of the experiment, random forest, XgBoost, SVM, and random forest classification have an accuracy of 96%, outperforming the other methods. Last but not least, it is recommended to take a number of preventative steps to lessen the risk of having a stroke, such as quitting smoking and abstaining from alcohol. © 2023 IEEE.

关键词： Support vector machines

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Performance Comparison Analysis of Predicting the Heart Diseases using machine learning Algorithms 4

Performance Comparison Analysis of Predicting the Heart Dise...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Rinku, Dhruva R. Gogula, Sandhya Devi Prathipati, Swarnalatha Ramana Murthy, P.V. Yarava, Rokesh Kumar Kosuri, Uma Devi Cvr College of Engineering Department of Ece Hyderabad India Gitam Gitam School of Technology Dept. of Cse Andhra pardesh Visakhapatnam India Malla Reddy Engineering College Dept. of Cse Hyderabad India Dept. of Cse Guntur India Gokraju Rangaraju Inst. of Engg. & Tech. Department of H & S Hyderabad India

ISBN: (纸本)9798350300093

In the medical field, the process of Heart Disease (HD) prediction process is a challenging task even in the modern digital world. Even though the data generated by the healthcare industries are huge, the data scientists are working tremendously to determine the correlation between the various parameters that causes the HDs. therefore, there exists a need to predict the HDs to safeguard the human kind. the proposed method uses the machine learning (ML) models to predict the HD based on the existing symptoms of the patients. the dataset from the UPI repository is used to evaluate the performance of the proposed models. the various parameters namely precision (p), recall (r), and accuracy (a) are used evaluate the performance measures of the ML models. Observing the results concluded that, the Random Forest model outperformed the other models such as XGBoost, Decision Tree and traditional Neural Network model regarding the prediction accuracy with respect to UCI dataset. © 2023 IEEE.

关键词： Decision trees

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Fault Detection Model Using Measurement Data in Fiber Optic Internet Lines 4

Fault Detection Model Using Measurement Data in Fiber Optic ...

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4th international Informatics and Software Engineering conference, IISEC 2023

作者： Battal, Eray Ozkan, Gozde Savas, Kerem Cakar, Tuna COSKUNOZ Die Machine and Trade Inc. Lean and Digital Transformation Responsible Bursa Turkey Beyes Inc. Ankara Turkey MEF University Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350318036

In this study, a model has been developed to predict potential faults in advance based on performance metrics of various fiber-optic internet lines, as well as alarm (fault data) and performance measurement values from the 5 hours prior to the occurrence of the alarm. Performance metrics that vary over time have been analyzed in a time-series format based on alarm numbers, and anomaly detection methods have been used to label the data for any potential patterns that may occur in the performance metrics specific to the alarm. the labeled data was then fed into a classification model to create a model that enables to detect possible patterns in the relevant performance values for the specific fault type. the best performing model was Random Forest Classifier with accuracy and F1 scores of 0.89 and 0.84 respectively. © 2023 IEEE.

关键词： machine learning

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Detection of Bone Fracture using Prewitt Edge Algorithm and Comparing with Laplacian Algorithm to Increase Accuracy and Sensitivity. 4

Detection of Bone Fracture using Prewitt Edge Algorithm and ...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Nalini, N. Uganya, G. Sathesh, M. Sheela, M.Sahaya Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350300093

the purpose of the research was to is to compare accuracy and specificity in the bone fracture detection using novel modified Prewitt Edge Detection (PED) with Laplacian Edge Detection (LED). Two groups are compared, novel modified Prewitt Edge Detection (PED) (N=10) and Laplacian edge detection (LED) (N=10) the overall sample size was calculated using the G Power software with an alpha of 0.05, enrollment ratio of 0.1, confidence interval of 5%, and power of 80%. Using the SPSS statistical package, an independent sample t-test was used to compare the accuracy and specificity rate. Novel modified Prewitt edge detection (PED) algorithm found to be statistically significant when compared with the Laplacian edge detection (LED) classifier which gives accuracy p= 0.026, and specificity p=0.001(p © 2023 IEEE.

关键词： machine learning

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