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Research on Disease Prediction Based on Daily data Analysis of the Elderly Based on Neural Network Algorithms 3

Research on Disease Prediction Based on Daily Data Analysis ...

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3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Lei, Liu Guangdong Open University Guangdong Polytechnic Institute Guangzhou China

ISBN: (纸本)9798350380989

In recent years, with the gradual arrival of an aging society, the health status of the elderly has attracted widespread attention. Against this background, this study aims to explore the potential value of these data for disease prediction by collecting and analyzing daily data of the elderly based on neural network algorithms. First, by using technical means such as smart sensing devices and mobile applications, we collected multi-dimensional data on the elderly's daily life, including but not limited to physiological indicators, activity levels, social interactions, and sleep patterns. These data are considered potential disease predictors because they provide comprehensive information about the physical condition and lifestyle of older adults. Second, we apply the collected data to a neural network algorithm, which has powerful learning and pattern recognition capabilities. By training models, neural networks can learn potential patterns and regularities from large amounts of complex data, thereby providing strong support for disease prediction. We pay special attention to the superiority of neural networks in processing multi-source heterogeneous data to more comprehensively understand the multi-faceted information of the elderly's health status. Through experiments and verification, we evaluate the performance of neural network algorithms in disease prediction in the elderly. This study explores disease prediction based on the daily data of the elderly based on neural network algorithms, aiming to provide scientific basis for establishing a more intelligent and accurate elderly health management system to promote the health and well-being of the elderly. © 2024 IEEE.

关键词： data analysis elderly health neural networks

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Siamese Contrastive Reverse Distillation for Industrial Anomaly Localization 24

Siamese Contrastive Reverse Distillation for Industrial Anom...

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3rd international conference on Frontiers of Artificial Intelligence and machine learning (FAIML)

作者： Liang, Dong Du, Yuji Tan, Shan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China

ISBN: (纸本)9798400709777

The unsupervised anomaly localization based on feature distillation has demonstrated outstanding performance in industrial anomaly localization. It relies on the feature discrepancies between the student network and the teacher network to achieve anomaly localization. In these methods, the student network exclusively learns from the normal features of the teacher network during training, overlooking the explicit constraining of prior anomaly knowledge. This results in uncertainty in the feature discrepancy between the student and teacher for abnormal inputs, leading to a decrease in prediction accuracy. To inject prior anomaly knowledge into the student network during training, this paper proposes Fine-Grained CutPaste (FG-CutPaste) data augmentation strategy and Siamese Contrastive Reverse Distillation Network (SCrd). FG-CutPaste provides pseudo-abnormal samples and corresponding pixel-level pseudo-abnormal labels during the training phase of SCrd. SCrd introduces the Siamese network paradigm along with Contrastive Distillation (CD) loss. The CD loss, utilizing pseudo-abnormal samples and labels, not only reduces the discrepancy of normal features between the student and teacher networks of SCrd but also increases the discrepancy of their abnormal features, achieving explicit constraint on the abnormal features of the student. Experimental results indicate that SCrd achieves outstanding anomaly localization performance, yielding more refined visualizations for anomaly localization.

关键词： anomaly localization contrastive learning knowledge distillation

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Pavement Crack Segmentation using Incomplete Supervision Network 24

Pavement Crack Segmentation using Incomplete Supervision Net...

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3rd international conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2024

作者： Wu, Wenxiu Jin, Yihui Fang, Zhihua Zhang, Jianfeng Fan, Xiaqi Song, Xiaoliang Port and Transportation Management Center Zhejiang Jinhua China Lanxi Transportation Construction Investment Group Co. Ltd. Zhejiang Jinhua China Lanxi Tongda Highway Maintenance Engineering Co. Ltd. Zhejiang Jinhua China Lanxi SHUNDA Road and Bridge Engineering Co. Ltd. Zhejiang Jinhua China

ISBN: (纸本)9798400707308

Challenges such as the lack of baseline pavement disease databases and limited segmentation accuracy and speed impact the performance of pavement disease segmentation and its practical engineering applications. To explore the feasibility of incomplete supervised learning, an experimental study generated inaccurate labels in batches by combining traditional image segmentation algorithms with manual methods, and the Mean Teacher model was tested. For imprecise supervised learning, the test F1 accuracy reached 0.572 after adding imprecise labels to the training set of self-collected data, although external imprecise labels were not feasible. In incomplete supervised learning, the Mean Teacher model showed significant advantages, achieving a test accuracy (MIoU) of 0.796 and an F1 score of 0.596 on self-collected data. Using unlabeled external images can enhance network accuracy in semantic segmentation tasks. With a small number of accurately labeled images, incomplete supervised learning can achieve substantial prediction accuracy. © 2024 Copyright held by the owner/author(s).

关键词： Self-supervised learning

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Predicting Cyber-Attacks and Identifying Perpetrators Using machine learning Techniques 3

Predicting Cyber-Attacks and Identifying Perpetrators Using ...

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3rd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Geerthik, S. Senthil, G.A. Tharagairani, G. Sachin, L. Dinesh Kumar, S. Agni College of Technology Department of Information Technology Chennai India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391565

The new generation of technology has experienced significant growth in recent years and is currently evolving. Instead, as technology advances, the number of crimes also rises. It will result in financial difficulties for the nation and its citizens. Cyberattacks account for the majority of reported attacks. It will target the privacy data of the people and the nation. As a result, it is challenging to determine what kind of attack would be employed, and the length of the inquiry would also rise. Numerous systems have been presented that use different machine-learning techniques to analyze and forecast threats. Consequently, we presented a methodology using machine learning algorithm KNN, SVM, Random Forest, and Gradient Boost Algorithm regression approach for identifying the assault. It determines the type of cyberattack that will be launched and how to foresee it. According to our research, the probability of a cyberattack declines as potential victims' income and educational levels rise. We believe that our suggested model will be helpful to law enforcement cybercrime units in identifying and stopping cyberattacks, increasing their efficacy in thwarting these threats. In conclusion, this study emphasizes how important machine learning is to improving the ability to detect cyberattacks and identify their perpetrators. By employing data-driven insights and advanced analytics, companies can enhance their capacity to anticipate, identify, and mitigate cyber threats, thereby fortifying their defenses in a constantly changing digital landscape. Going forward, sustaining a proactive posture against cyber adversaries and safeguarding the security of our global community that is interconnected depends critically on continuous research and machine learning the result accuracy and efficient has produced KNN is 84%, SVM is 94.6%, and Random Forest is 99.13%. © 2024 IEEE.

关键词： Adversarial machine learning

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Artificial Intelligence Digital Audit System Under machine learning Technology 3

Artificial Intelligence Digital Audit System Under Machine L...

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3rd international conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Liu, Xin Ren, Yi Qi, Guodong Li, Yujing Fan, Rui State Grid Corporation of China Beijing Beijing China Beijing Guodiantong Network Technology Co. Ltd. Beijing China

ISBN: (纸本)9798350376173

The main job of auditing is to evaluate and review, and the huge and complex workload, as well as low tolerance for errors, are the characteristics of this work. However, with the development of technology and the expansion of the market, the workload that auditing needs to face is also constantly increasing. Therefore, this article proposes to promote the digital transformation of auditing based on machine learning (ML) artificial intelligence (AI) to solve this problem. Finally, this article validates the viewpoint of this article through experiments. In the experiment on audit accuracy, when the number of iterations of ML reaches 200, the audit accuracy is already as high as 98.23%, very close to 100%, indicating its obvious effect. In the experiment of work efficiency, the average time spent on evaluating a certain part of the audit system based on AI is 113.5 hours, while compared to the audit system based on big data, which requires 125.9 hours, the difference is also quite significant. Therefore, research has found that the digital audit system assisted by AI based on ML performs well in terms of work efficiency and audit accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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NeuroPCA: Enhancing Alzheimer's disorder Disease Detection through Optimized Feature Reduction and machine learning 3

NeuroPCA: Enhancing Alzheimer's disorder Disease Detection t...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Sagili, Sreenivas Reddy Chidambaranathan, Senthilnathan Nallametti, Nagarjuna Bodele, Harana M. Raja, Laxmi Gayathri, P.G. Rochester Institute of Technology Department of Computer Science United States Virtusa Corporation 20 Corporate Pl South Piscataway United States Cvr College of Engineering Department of Csit Telangana Hyderabad India J D College of Engineering & Management Department of Electronics and Telecommunications Nagpur India Coimbatore India Kongunadu College of Engineering and Technology Department of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350369083

Alzheimer's (memory loss disorder) disorder Disease (AD) poses a significant diagnostic challenge due to its complex etiology and subtle onset of clinical symptoms. The Early and accurate detection will be a crucial for effective management and intervention. The integration of machine learning-Driven (ML) techniques based on neuro imaging and clinical data may offers a promising avenue for enhancing of AD detection. However, the highest dimensionality of such data could be impede the efficacy and accuracy of ML models due to curse of dimensionality and over fitting. This research will introduces the "NeuroPCA System,"a novelist diagnostic framework by employing Principal Components based Analysis (PCA) for features reduction, tailored specifically for Alzheimer's (memory loss disorder) disorder Disease detection. The NeuroPCA System will systematically reduces the dimensionality of input health data (e.g., MRI, PET scans, and genetic information) while by preserving the most informative variance components crucial for distinguish between AD patients and healthy controls. The proposed methodology may encompasses preprocessing of the multi-modal dataset to normalized feature scales, followed by the application of PCA to extracts the principal components that capturing of the maximum variance in the dataset. The reduced feature set will then utilized to train and validate a suite of machine learning-Driven models like Supportive Vector machines (SVM) for classify subjects into AD and non-AD groups. The effectiveness of the NeuroPCA System may evaluated depends on classification accuracy, sensitivity, specificity, and computational efficiency, by using a cross- validated dataset of diagnosed AD patients and a control group. The NeuroPCA System also demonstrates a significant improvement in model performance and computational efficiency, by highlighting the potential of PCA for feature reduction in the context of Alzheimer's (memory loss disorder) disorder Disease detect

关键词： Support vector machines

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Patient Satisfaction Through Interpretable machine learning Approach 3rd

Patient Satisfaction Through Interpretable Machine Learning ...

引用

3rd international conference on machine learning, Internet of Things and Big data, ICMIB 2023

作者： Anandamurugan, S. Jayaprakash, P. Mounika, S. Narendranath, R. Kongu Engineering College Erode India

ISBN: (纸本)9789819939312

In Patient satisfaction, the most important factor in assessing the quality is patient happiness. The happiness key factor impacts the health policy decisions. An individual’s specific health requirements, individualised treatment, and desired health results are of the utmost importance in the period of patient-centered care. Across the past decade, treatment delivery, management, and reimbursement practices have all been impacted by patient satisfaction as a clear insight and quality management of patient experiences. Using machine learning algorithms, the most relevant factors for patient satisfaction are founds. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Deltrux: Insider Threat Detection of End-User Behavior Analysis Using machine learning 3

Deltrux: Insider Threat Detection of End-User Behavior Analy...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Wishvaranga, K.L.S. Gunawardana, M.P.V.A. Perera, M.B.C. Deshan, M.A.P. Abeywardena, Kavinga Yapa Pandithage, Dinithi Sri Lanka Institute of Information Technology Malabe Faculty of Computing Sri Lanka

ISBN: (纸本)9798350354171

In the evolving threat landscape insider threats present a significant challenge within the enterprise environments. Since traditional signature-based analytics approaches focus only on the previously known signature types, it is unable to detect various threats depending on the dynamic activities performed by users. Using a behavioral analysis approach can detect threats by analyzing the dynamic behaviors of end users, and this could also detect zero-day threats. This study introduces a sophisticated, machine learning-based insider threat detection system specifically designed for end-user devices. The system leverages user behavior analytics to identify deviations from normal user activities, focusing on the components such as mouse movements, keystroke dynamics, communication patterns, operating system (OS) calls, and malicious data encryption attempts. The system can dynamically analyze user behaviors to identify insider threats in real-time. Successful detection of Insider threats on each endpoint devices, will pass an alert to a centralized dashboard called 'Deltrux'. © 2024 IEEE.

关键词： Adversarial machine learning

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A Comparison of Deep learning and machine learning Methods for Water Quality Prediction 3

A Comparison of Deep Learning and Machine Learning Methods f...

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Srinivasa Gupta, N. Valarmathi, B. Hiwanj, Ishaan Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798331527549

Predicting water quality is essential to preserving human health and environmental sustainability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accuracies of 62%, 72%, 83%, 69%, 63%, 66%, 71%, 63%, and 64%, respectively, the current techniques utilized were Logistic Regression, Decision Trees, Random Forest Regressor, Extreme Gradient Boosting, Naive Bayes, K-nearest neighbors, Support Vector machine, AdaBoost, and Bagging [9]. This study addresses these challenges by leveraging Adaptive Synthetic Sampling (ADASYN) to balance the dataset and evaluating model performance on datasets of 5,000 and 10,000 entries per class. A robust dataset obtained from Kaggle was used, with five models – Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Random Forest – evaluated and compared. The proposed methods demonstrate significant improvements in accuracy, with XGBoost achieving the highest accuracy of 95.53%, followed by Random Forest at 93.98%. This work underscores the importance of advanced machine learning techniques in addressing the limitations of traditional methods, enhancing accuracy, scalability, and adaptability in water quality prediction. These findings contribute to advancing environmental monitoring and management practices with reliable, data-driven insights. © 2025 IEEE.

关键词： Support vector machines

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Fraud & Anomaly Detection: Using Fine-tuned OCSVM Algorithm and visualization of the enhanced results using machine learning Techniques 3

Fraud & Anomaly Detection: Using Fine-tuned OCSVM Algorithm ...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Garg, Kaushiv Gill, Kanwarpartap Singh Aggarwal, Priyanshi Rawat, Ramesh Singh Banerjee, Deepak Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350381931

Diversion from traditional methods of fraud detection by tackling the issue of insufficient class labels in corporate data is very essential. In contrast, the use of the One-Class Support Vector machine (OCSVM) method is employed for the purpose of unsupervised anomaly detection. This study investigates a dataset including credit card transactions conducted by cardholders from Europe during the month of September in the year 2013. The data indicates a significant discrepancy, with just 0.172% (492 out of 284,807) of the transactions being classified as fraudulent. The dataset used in this research comprises numerical variables that have undergone pre-processing by Principal Component Analysis (PCA). Nevertheless, due to confidentiality concerns, the initial attributes of the dataset have not been revealed or accessed, with the exception of two variables: 'Time', denoting the duration of each transaction in seconds, and 'Amount', indicating the monetary amount associated with each transaction. In precision when there is a lack of class labels, the 'Class' property is used to distinguish between fraudulent transactions and legitimate transactions. Given the significant disparity in the allocation of these categories, it is deemed suitable to use the Area Under the Precision-Recall Curve (AUPRC) as a metric for assessing accuracy. This study presents a new approach to anomaly detection that addresses the issue of data balance and utilises the One-Class Support Vector machine (OCSVM) technology. This study focuses on the difficulties related to the management of datasets that exhibit significant imbalances in the context of fraud detection. Specifically, it addresses the obstacles that arise when class labels are not readily available. © 2024 IEEE.

关键词： Anomaly detection

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