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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 4138 条记录，以下是291-300 订阅

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A machine learning Approach for Diagnosis of Hepatitis 4

A Machine Learning Approach for Diagnosis of Hepatitis

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Sachdeva, Ravi Kumar Jain, Madhur Bathla, Priyanka Ahuja, Rakesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chandigarh University Punjab Mohali India

ISBN: (纸本)9798350306118

Unbeknownst to themselves, hundreds of millions of individuals worldwide are dealing with a chronic infection. Without exaggeration, chronic hepatitis is one of the most significant medical and social issues in any nation in the world. the authors created an accurate machine-learning (ML) model to increase forecast success rates. the medical information of 155 patients is contained in the publicly accessible hepatitis dataset, which can be found on the UCI machine learning repository. the authors have applied ML classifiers such as Support Vector machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Logistic Regression (LR), and Gaussian Naive Bayes (GNB) to forecast the disease. RF had the highest accuracy, at 100%, of all the ML classification approaches that were used. © 2023 IEEE.

关键词： Logistic regression

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

2023 4th International Conference on Computer Engineering an...

引用

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

ISBN: (纸本)9798350347548

the proceedings contain 175 papers. the topics discussed include: view self-attention network for 3d object recognition;vulnerability assessment for directed weighted complex network by quantifying importance of community nodes;network node importance evaluation based on directed overlapping box coverage algorithm;incremental learning for multi-view 3d shape recognition based on knowledge distillation;multi-armed bandit with time-variant budgets;research on gas outburst prediction model based on improved beluga whale optimization algorithm and temporal convolutional network;two-way prototypical network based on word embedding mixup for few-shot event detection;safety assessment of human with metal implants exposed to magnetic field based on numerical method;topological robustness of supply chain networks under disruptions;research on evaluation strategy of heterogeneous computing chip based on improved common origin grey clustering;joint task offloading, resource allocation and data caching in MEC-assisted vehicular network;dynamic detection method for android terminal malware based on native layer;and search recommendation model based on attention compressed interaction network.

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4th international Informatics and Software Engineering conference - Symposium Program, IISEC 2023

4th International Informatics and Software Engineering Confe...

引用

4th international Informatics and Software Engineering conference, IISEC 2023

ISBN: (纸本)9798350318036

the proceedings contain 57 papers. the topics discussed include: application of ChatGPT in the tourism domain: potential structures and challenges;oblique logistic function for the rank-frequency distribution of letters;innovation in corporate cyber security;a digital twin framework for edge server placement in mobile edge computing;mobile app for kidney patients to provide a comprehensive solution to manage disease;development of a genetic algorithm for vehicle routing problem in military logistics distribution;analyzing customer churn: a comparative study of machine learning models on Pay-TV subscribers in turkey;a study on slice-aware industrial edge applications for next-generation private networks;and a comparison of optimization methods for path finding problem.

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proceedings - ICon EEI 2024: 4th international conference on Electrical Engineering and Informatics: Smart Technology towards a Global Energy Revolution

Proceedings - ICon EEI 2024: 4th International Conference on...

引用

4th international conference on Electrical Engineering and Informatics, ICon EEI 2024

ISBN: (纸本)9798331540791

the proceedings contain 29 papers. the topics discussed include: performance of a TWDM-PON design compensated with dispersion compensation fiber for high data rates;proposing the application of LSTM models to create music;water quality prediction with dilated 1D convolutional networks and comprehensive preprocessing techniques;advanced machine learning techniques for stunting classification using a stacking ensemble approach;smart posture system design for scoliosis patients based on the Internet of things (IoT);usage unified power quality conditioner (UPQC) controller on distribution networks based simulation and analysis;microgrid power flow analysis with variable renewable energy;and securing oil and gas infrastructure: an analysis of emerging cybersecurity threats.

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Deep learning Model Development for an Automatic Healthcare Edge Computing application 5th

Deep Learning Model Development for an Automatic Healthcare ...

引用

5th international conference on Data Science, machine learning and applications

作者： Talal, Hadi Khamis, Ruaa Ali Al-Frady, Laith Middle Tech Univ Baghdad Iraq

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep learning (DL) is assisting academicians and medical professionals in uncovering latent opportunities in data and enhancing the healthcare industry. the edge computing applications like smart healthcare systems where accurate decision-making is required for fast medical treatment. DL in healthcare allows clinicians to correctly analyze any ailment and treat it, resulting in improved medical decisions. We present a unique DL model for the autonomous healthcare edge computing application in this paper. computer Aided Diagnosis (CAD) is an essential requirement of healthcare edge computing where the patient's medical data is used for fast and accurate disease prediction. Propose the DL-based CAD model for automatic disease classification from the input medical images. the model consists of pre-processing, DL-based feature engineering, and classification. Input medical image is first pre-processed for quality improvement and then automatic features are extracted using the pre-trained DL models (ResNet50 and Densenet201). the pre-trained models are improved by performing the feature scaling followed by a separate classification phase. the proposed CAD model is experimentally evaluated using the medical images dataset. the results reveal the efficiency of the proposed model compared to underlying solutions.

关键词： Automatic classification deep learning edge computing features engineering healthcare image processing

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4th international conference on Communication, Computing and Industry 6.0, C216 2023

4th International Conference on Communication, Computing and...

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4th international conference on Communication, Computing and Industry 6.0, C216 2023

ISBN: (纸本)9798350327328

the proceedings contain 65 papers. the topics discussed include: a comparative study between external USB and MIPI CSI for medical imaging and a device driver implementation for endoscope camera using USB;advances in EV charging: powering up batteries in India;data synchronization between MongoDB and Elasticsearch using Monstache in real-time data;in-flight health monitoring of pyro initiator: resistance analysis for actuating devices and igniters;design of bidirectional multilevel converter & T-Type MLI for EV application using HRES generation;advancing alpha-thalassemia carrier screening for better predictions using explainable AI;design & development of improved relay driver using PEM for space application;and event labeling approach for twitter datasets leveraging N-grams, topics, and machine learning algorithms for enhanced event detection.

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Apple leaf scab recognition using CNN and transfer learning 4

Apple leaf scab recognition using CNN and transfer learning

引用

4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Yang, Ziyi Yang, Minchen School of Computer Science University Sains Malaysia Penang 11700 Malaysia

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Apple scab, caused by the pathogen Venturia inaequalis, is one of the most prevalent diseases affecting apple trees, significantly reducing both yield and fruit quality. Early detection and diagnosis are critical for the effective management of this disease, which is typically characterized by the appearance of black lesions on the leaves and fruit. In this study, we developed a deep learning-based system for classifying apple scab using Convolutional Neural Networks (CNN) and transfer learning. the system was trained on a dataset of apple leaf images, employing various data augmentation techniques, such as rotation, flipping, and scaling, to improve the model's robustness. Our proposed model achieved high accuracy in distinguishing between healthy leaves and those affected by apple scab, making it a promising tool for precision agriculture and automated disease monitoring. this research offers a potential solution for reducing dependence on manual labor and enhancing early intervention practices in apple orchards. © 2025 SPIE.

关键词： Fruits

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Ensemble machine learning Approach for Detecting and Predicting Diabetes Mellitus Using Bagging and Stacking 4

Ensemble Machine Learning Approach for Detecting and Predict...

引用

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

作者： Anusha Priyadharshini, C. Arul Kumaran, G. Reva University School Of Computing And Information Technology Karnataka Bangalore India

ISBN: (数字)9798350384628

ISBN: (纸本)9798350384628

the application of ensemble learning techniques more especially, bagging and stacking for better diabetes diagnosis and prediction is explored in this abstract. Due to their precision and individualized approach, advanced machine learning techniques are advantageous for diabetes, a complex metabolic illness. Here Pima Indian diabetes(PID) dataset for research, collected from the UCI machine learning Repository. the dataset contains information about 768 patients and their corresponding nine unique attributes. A novel ensemble machine learning technique for diabetes prediction such as bagging and a meta model is used in stacking to aggregate predictions from several models. A stacked model is created by combining Random Forest and Support vector machines with feature variables like age, body mass index, blood pressure, skin thickness and biochemical indicators. the accuracy of diabetes diagnosis and prediction has improved by bagging and stacking. they make it possible to identify high-risk patients early and to provide individualized therapies. this research has achieved better accuracy with random forest. As a result, the use of bagging and stacking in diabetes research has the potential to completely transform patient treatment by providing accurate risk assessments and individualized management plans. Realizing these methods' full potential in the treatment of diabetes will require further study, which will focus on improving them and addressing the problems they raise. © 2023 IEEE.

关键词： Forecasting

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Harvest Helper: AI-Driven Crop Selection Model 4

Harvest Helper: AI-Driven Crop Selection Model

引用

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

作者： Bardhiya, Himanshu S Pavitra, Mutyala Bagde, Ashutosh Bardhiya, Vansh A Sawangi India Sawangi India Sawangi India

ISBN: (纸本)9798331523923

To maximize agricultural output, this study presents Harvest Helper, an AI-driven crop selection algorithm that makes use of data on market trends, soil health, and environmental variables. Traditional crop selection techniques frequently fall short of keeping up with the complexity of contemporary agriculture as the world's food need grows. the Harvest Helper model uses cutting-edge machine learning algorithms to give farmers customized crop suggestions based on real-time data, increasing yield and resource efficiency. In an extensive analysis including 600 farms in various farming zones, the model showed a 20% average output gain and a 30% decrease in water and fertilizer use. Furthermore, an 85% satisfaction rating with the model's use and performance was reported by farmers in their comments. this study demonstrates how AI has the power to revolutionize farming methods and provide a long-term answer to future food production problems. Recommendations for future study to further enhance and increase the application of the model are presented, along with limitations pertaining data quality and technology accessibility. Accurately predicting crop yields is crucial to resource management and sustainable agriculture. this study examines the effectiveness of three machine learning classifiers on a dataset comprising soil and environmental parameters: Random Forest, Decision Tree, and Logistic Regression. Based on our findings, the Random Forest model performs noticeably better than the other classifiers, suggesting that it might be a reliable tool for crop prediction. © 2025 IEEE.

关键词： Agriculture Artificial Intelligence Crop machine learning Random Forest Recommendations

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Advancements in AI for Depression Detection: A Survey of machine learning and Deep learning applications 4

Advancements in AI for Depression Detection: A Survey of Mac...

引用

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

作者： Subuhi, Afreen Vadlamudi, Muniraju Naidu Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Bowrampet Telangana Hyderabad500090 India

ISBN: (纸本)9798331518981

this survey paper delves into the sophisticated applications of machine learning (ML) and deep learning (DL) techniques in the realm of depression detection. By leveraging a variety of analytical methods, including text analysis, speech analysis, social media monitoring, and image and video analysis, the research highlights the role of advanced algorithms such as Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), Ensemble Methods, Random Forests, Gradient Boosting machines (GBM), and Graph Neural Networks (GNN) in identifying depressive symptoms. the study conducts a thorough review of existing literature, showcasing the efficacy of these techniques while pinpointing critical gaps and challenges, such as data quality and integration with clinical practice. this comprehensive examination underscores the necessity for robust, generalizable models capable of real-world application. Ultimately, the survey aims to demonstrate how ML and DL technologies can significantly enhance early detection and intervention strategies in mental health management, paving the way for improved patient outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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