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Classification and Segmentation of Apple Diseases Based on Deep learning 24

Classification and Segmentation of Apple Diseases Based on D...

引用

16th international conference on machine learning and Computing (ICMLC)

作者： Huang, Huanzhou Wang, Zhuowei Zhao, Genping Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou Peoples R China

ISBN: (纸本)9798400709234

Aiming to address the issue of low accuracy in existing algorithms due to the limited scale of specific apple disease datasets and complex background information, a deep learning method integrating apple disease classification and segmentation is proposed. Initially, the method employs the CycleGAN network for data enhancement to combat overfitting problems in deep learning. Secondly, it constructs a deep residual disease classification network embedded in the convolutional block attention module. In essence, the ResNet50(*) module extracts fine-grained visual features and incorporates both the channel attention mechanism and spatial attention mechanism to focus on two critical features, thereby enhancing the disease classification performance of the model across various scenarios. Lastly, a hierarchical semantic segmentation model is introduced to achieve a more accurate segmentation effect through segmented segmentation. Experimental results demonstrate that the proposed method achieves an accuracy of 97.82% on the apple disease classification task, while obtaining MPA and MIoU scores of 94.85% and 91.75%, respectively, on the segmentation task.

关键词： Apple disease classification and segmentation Deep learning Convolutional block attention module Deep residual network Hierarchical semantic segmentation

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Chronic Kidney Disease Prediction Using machine learning Algorithms

Chronic Kidney Disease Prediction Using Machine Learning Alg...

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2024 international conference on Advances in Modern Age Technologies for Health and Engineering science, AMATHE 2024

作者： Nagamani, T. Kavya, R. Lavanya, D. Miruthula, S. Kongu Engineering College Department Of Computer Science And Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350371567

Early detection and characterisation play a crucial role in effectively managing and treating chronic illness. Given the increasing prevalence of chronic kidney disease (CKD), the healthcare system faces a significant burden, as CKD raises the risk of developing end-stage renal disease and related morbidity and mortality. The timely identification of CKD is vital to saving numerous lives. This study uses a feature selection strategy alongside various machine learning (ML) algorithms to develop a diagnostic system for detecting CKD. The study utilised approximately 400 clinical records of CKD patients sourced from the UCI machine learning repository. To refine the datasets for the predictive model, various data preparation techniques were applied, including feature encoding, label encoding, data cleaning, ambiguity removal, handling missing values, and feature selection. Ultimately, the Extra Trees classifier outperformed the other two machine learning models, exhibiting the capability to diagnose chronic kidney disease (CKD) with an impressive 98% accuracy rate and a low 2% true negative rate, all without any data leakage. © 2024 IEEE.

关键词： Decision trees

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Enhancing Cervical Cancer Prediction: A Comparative Analysis of machine learning Algorithms and Development of a Novel Screening Tool 25

Enhancing Cervical Cancer Prediction: A Comparative Analysis...

引用

25th IEEE international conference on Information Reuse and Integration for data science (IEEE IRI)

作者： Edafetanure-Ibeh, Faith Tobore Harrisburg Univ Sci & Technol Data Sci PhD Harrisburg PA 17101 USA

ISBN: (纸本)9798350351194;9798350351187

The early discovery of cervical cancer is crucial for efficient treatment and increased survival rates, making it a severe public health concern [1]. This study uses a consistent dataset to compare various machine-learning methods for cervical cancer prediction. We utilized a variety of machine learning techniques, including Random Forest, Naive Bayes, Support Vector machine (SVM) with a linear kernel, K-Nearest Neighbors (KNN), Logistic Regression, and Extreme Gradient Boosting (XGBoost), to identify and forecast the risk of cervical cancer. Based on the accuracy, precision, recall, F1-score, and confusion matrices, the effectiveness of these algorithms was assessed [2]. The most appropriate model for this application is XGBoost, which fared better than other models in recall and F1-score, even if more conventional methods, such as Random Forest and KNN, showed excellent overall accuracy. The study results imply that XGBoost has excellent potential for creating an efficient cervical cancer screening tool due to its balance of sensitivity and precision. The model is then integrated into a web-based application and an interactive chatbot designed to facilitate early detection and assessment of cervical cancer risks.

关键词： Cervical Cancer machine learning Predictive Modeling XGBoost Classification Healthcare Analytics

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Leveraging machine learning to Predict Obesity Trends: Insights from Lifestyle and Biometric data

Leveraging Machine Learning to Predict Obesity Trends: Insig...

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2025 IEEE international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Adapala, Rishi Manikanta Kancharla, Yaswanth Shaik, Baba Ameer Jyotsna, C. Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Bangalore India

ISBN: (纸本)9798331512965

This is increasingly important as global obesity rates continue to rise: the causes need to be understood, and the ability to predict trends determined. In this study, we use machine learning techniques to predict obesity trends given lifestyle and biometric data from a large obesity dataset. Our methodology starts with data preprocessing that includes transforming categorical variables into numerical variables and standardizes the numerical variables into such a form so that machine learning algos can work with them properly. Supervised machine learning models, such as tree based methods, neural networks and various ensemble techniques are applied, and evaluated. For determining the most effective predictive approach, these models are evaluated by several performance metrics such as accuracy, precision, recall, and F1 score;the best model is chosen based on these metrics. In addition, the study includes explainable AI technique (SHAP - SHapley Additive exPlanations;LIME - Local Interpretable Model-Agnostic Explanations) to make the results more interpretable. The results from these methods provide insight into how different lifestyle and biometric factors contribute to the prediction of obesity;such as physical activity, diet patterns, and body mass index. The results from this work demonstrate not only how machine learning can improve prediction accuracy but also how these methods can provide actionable insights into the many contributing factors to obesity. © 2025 IEEE.

关键词： Self-supervised learning

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Environment Classification Method Using Autoencoder to Select Appropriate Crowd Model for Robot Simulation* 20

Environment Classification Method Using Autoencoder to Selec...

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IEEE 20th international conference on Automation science and Engineering (CASE)

作者： Nakazawa, Saki Kato, Yuka Tokyo Womans Christian Univ Grad Sch Sci Suginami Ku Tokyo 1678585 Japan

ISBN: (纸本)9798350358513;9798350358520

Various crowd models developed in the field of crowd simulation have been used to learn action policies of reinforcement learning for the navigation of autonomous mobile robots. However, there is no single crowd model that can be applied to all environments because crowd movement is highly dependent on the environment. In this paper, we propose a method for selecting an appropriate crowd model for each environment by classifying the spatio-temporal data to be simulated into multiple categories. For that, we extract features related to crowd movement and geographic shape for use in the robot simulation. Specifically, we generate image data to visualize pedestrian movement trends, perform feature extraction using an autoencoder, and classify the results into several categories. Through evaluation experiments using pedestrian movement trajectory datasets, this paper shows that visually similar feature images are classified into the same categories.

关键词： Adversarial machine learning

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Unmasking Frame Duplication: Comprehensive Approach to Detection in Digital Video Using machine learning Algorithms and Signal Processing Techniques

Unmasking Frame Duplication: Comprehensive Approach to Detec...

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2024 international conference on Advances in data Engineering and Intelligent Computing Systems, ADICS 2024

作者： Praddipraj, N.N. Praveen Kumar, B. Sreeji, S. Sathyabama Institute Of Science And Technology Dept. Of Computer Science And Engineering Tamilnadu Chennai600119 India

ISBN: (纸本)9798350364828

Our research proposes a comprehensive approach to identify duplicate frames in digital videos. It integrates machine learning and signal processing techniques for effective identification. The process begins with pre-processing, enhancing video quality through temporal filtering and noise reduction. Next, feature extraction captures statistical and structural information from each frame. Deep neural networks and other machine learning methods are used to classify frames as original or duplicated using these features. To enhance detection accuracy, signal processing methods like block-based and key point-based approaches are applied. Block-based methods divide frames into segments for region comparison, while key point-based methods identify and match key points between frames. Our method's effectiveness and robustness are demonstrated through experiments on various digital videos. This combination of machine learning and signal processing offers a comprehensive solution to uncover frame duplication, ensuring video content's integrity and authenticity. © 2024 IEEE.

关键词： learning algorithms

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Remote Monitoring and Control of Agricultural Systems Using IoT and machine learning 3

Remote Monitoring and Control of Agricultural Systems Using ...

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international conference on Advances in Computing, Communication and applied Informatics (ACCAI)

作者： Raj, G. Bhupal Mohan, Chinnem Rama Karthik, A. Nagpal, Amandeep Laxmi, Ms Vijay Asha, V SRUniv Sch Agr Warangal Telangana India Narayana Engn Coll Dept Comp Sci & Engn Nellore 524004 Andhra Pradesh India Inst Aeronaut Engn Hyderabad India Lovely Profess Univ Phagwara India Maharishi Markandeshwar Univ Ambala India New Horizon Coll Engn Dept Comp Applicat Bangalore Karnataka India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

Cutting-edge technology is essential to improve resource use, output, and farming methods to meet climate change and other needs. This research uses IoT and machine learning to remotely track and operate agricultural *** Internet of Things (IoT) lets several sensors strategically placed around the farm collect data simultaneously. These sensors assess temperature, humidity, and soil wetness, which determine crop health. The data is wirelessly delivered from a central location to a designated area for processing and *** learning is used to interpret data. These algorithms can improve irrigation schedules, predict food yields, diagnose diseases, and offer insect control alternatives. machine learning (ML) models improve by learning from data and adapting to environmental *** system allows remote farming monitoring using actuators and automated tools. An easy-to-use interface on desktops or mobile devices allows agricultural experts utilise machine learning models to regulate insecticides, modify irrigation water, and start harvesting *** proposed technology improves farming productivity, durability, resource efficiency, and cost. It encourages farmers to make sensible decisions and manage their resources to better address environmental *** conclude, machine learning and the Internet of Things could considerably improve farming system remote monitoring and control. The development of new technologies in this field is crucial to its long-term success and ability to provide for future generations.

关键词： Remote Monitoring Control Systems Agricultural IoT Precision Agriculture machine learning Sensor Networks data Analytics

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Degree of Difference in Clinical data and Imaging Based on machine learning and Complex Network 24

Degree of Difference in Clinical Data and Imaging Based on M...

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9th international conference on machine learning Technologies (ICMLT)

作者： Kong, Guanqing Li, Xiuxu Wu, Chuanfu Zhang, Lanhua Linyi Peoples Hosp Ctr Hlth Data Sci Linyi 276000 Shandong Peoples R China Shandong First Med Univ & Shandong Acad Med Sci Sch Med Informat & Engn Tai An 271016 Shandong Peoples R China

ISBN: (纸本)9798400716379

Clinical patients should have corresponding clinical indicators to characterize the disease. Multimodal data and physiological indicators provide a basis for patient diagnosis and assessment, but small sample data pose statistical difficulties. In order to better support the clinical conclusions, from a data-driven perspective, using machine learning algorithms, we explored the support of physiological indicator data for multimodal data in the case of insufficient samples, and according to the results of the model, it is shown that the data-driven results can better support the final conclusions, and therefore, integrating the multimodal data and the clinical indicators can better provide the diagnosis and assessment conclusions for the clinical patients.

关键词： data driven Multimodal imaging Complex network machine learning

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A survey on predictive maintenance for manufacturing using data mining techniques 4th

A survey on predictive maintenance for manufacturing using d...

引用

4th international conference on Computational Methods in science and Technology, ICCMST 2024

作者： Mathew, Albin Kaur, Savleen Department of Computer Science Lovely Professional University Punjab India

ISBN: (纸本)9781032911571

This paper delves into the significance of predictive maintenance, a technique leveraging intelligent sensors for efficient data collection and evaluation to prevent problems and accidents. A data-focused predictive maintenance system has been established for manufacturing production lines, facilitating the identification of potential issues before they occur. machine learning plays a pivotal role in predicting the precise moment when industrial equipment requires repair, ensuring quick problem-solving and adaptive decisionmaking. The paper’s goal is to conduct a comprehensive literature analysis of data Mining and machine learning methods tailored for predictive maintenance, emphasizing those currently under examination in this domain, and showcasing the practical applications of the most advanced machine learning techniques. This review provides a unique and in-depth exploration of the evolving landscape of data Mining and machine learning methods within the context of predictive maintenance in manufacturing. Beyond a mere survey, it showcases the potential of cutting-edge machine learning methodologies to revolutionize predictive maintenance practices. Ultimately, this review weaves together the threads of technological innovation and practical utility, providing readers with a comprehensive roadmap to navigate the future of predictive maintenance in manufacturing. © 2025 the Author(s).

关键词： Predictive maintenance

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A NOVEL APPROACH OF data science INCORPORATING PHYSICAL KNOWLEDGE FOR EARLY STUCK PIPE DETECTION 43

A NOVEL APPROACH OF DATA SCIENCE INCORPORATING PHYSICAL KNOW...

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ASME 43rd international conference on Ocean, Offshore and Arctic Engineering (OMAE)

作者： Inoue, Tomoya Kaneko, Tatsuya Nakagawa, Yujin Wada, Ryota Abe, Shungo Yasutake, Gota JAMSTEC Yokosuka Kanagawa Japan Univ Tokyo Chiba Japan JOGMEC Tokyo Japan JAPEX Tokyo Japan

ISBN: (纸本)9780791887868

Early detection of stuck pipes during drilling operations is crucial and challenging. Some of the existing studies on the stuck pipe detection have adopted supervised machine learning approaches that employ datasets for "stuck" and "normal". However, for early detection before the occurrence of stuck pipe, the application of ordinal binary classification in supervised machine learning has presented several elemental concerns, such as limited stuck pipe data, and lack of an exact "stuck sign" before occurrence. Our previous studies proposed unsupervised machine learning approaches using data on the normal activities with long short-term memories and autoencoder, the mixture probability model, and graph attention model, and presented the possibility of predicting a stuck pipe. On the other hand, there observed false positives responding the operation of drilling equipment or changes in drilling data. We introduce a data science approach, including machine learning, incorporating physical knowledge to overcome frequent false positives. This study initially introduces the stuck pipe predictions using unsupervised machine learning approaches, and a typical false positive case. Subsequently, we present machine learning integrating physical knowledge and demonstrate early stuck pipe detection using field data containing stuck pipe events. The results show significant suppression of false positives. Our machine learning approach is expected to contribute to considerable reduction of nonproductive time in drilling operations, potentially preventing well abandonment.

关键词： Physical knowledge stuck pipe detection data science

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