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International Conference on Computing and Networking Technology (ICCNT)

作者： M Arunika S Saranya S Charulekha S Kabilarajan G Kesavan Department of Computer Science and Engineering Dr. N. G. P. Institute of Technology Coimbatore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Explainable Artificial Intelligence (XAI) has emerged as a critical research area to address the opacity and lack of interpretability associated with complex machine learning models. The increasing use of AI across society underscores the urgency for accountable and transparent decision-making to safeguard societal well-being. This paper provides a current landscape of XAI, discussing key challenges, methodologies, and implications. It reviews existing XAI techniques including feature importance analysis, surrogate models, and rule-based explanations. It also addresses their limitations and potential biases. The role of human-centric design in developing interpretable AI systems is emphasized, emphasizing the importance of involving end-users in the design process to ensure that explanations align with human mental models.

关键词： Surveys Analytical models machine learning algorithms Explainable AI Reviews Computational modeling Decision making Cognitive science

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A Comprehensive Evaluation of Ensemble machine learning algorithms for Sepsis Prediction: A Comparative Study

A Comprehensive Evaluation of Ensemble Machine Learning Algo...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Narinder Kaur Arvinder Kaur Computer Science & Engineering Chandigarh University Gharuan Mohali India UITHM Chandigarh University Gharuan Mohali India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Sepsisis still a big problem in health care, and it can be hard to spot and treat on time. Traditional ways of spotting sepsis aren't always good enough to make a quick diagnosis, so new ways are being looked into. Since it becomes a big health issue, hospital expenditures is increasing with time span. machine learning, branch of Artificial Intelligence, has become a useful way to find sepsis. This study focuses on detecting sepsis using several machine learning models. There are six different models used, including logistic regression(LR), K-Nearest Neighbors (KNN), Random Forest (Random Forest), LGboost, Adaboost and XGBoost. The performance of the models is assessed using various evaluation metrics and comparison has been made. The experimental results demonstrate that the Random Forest, LGBoost and XGBoost model are the best imputation approach for predicting the disease's early stage with an accuracy of 77.5%,74.2% and 74.2% respectively. These are suitable for machine learning applications and is especially useful when dealing with complex and high-dimensional data-sets.

关键词： Measurement machine learning algorithms Hospitals Nearest neighbor methods Predictive models Sepsis Imputation Internet of Things Random forests Logistics

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Comparison of Novel machine learning algorithms for Predicting Chronic Renal Disease

Comparison of Novel Machine Learning Algorithms for Predicti...

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Computational Intelligence for Green and Sustainable Technologies (ICCIGST), International Conference on

作者： G B Hima Bindu Dinesh Valluru Harshita Vachhani M. Sunil Kumar D. Ganesh P. Neelima Department of of CSE School of Technology The Apollo University Chittoor Andhra Pradesh India Department of IT MLRITM Engineering College Hyderabad Telangana India Dept.of Computer Science Pratibha College of Commerce and Computer Studies Chinchwad Pune Maharshtra School of Computing Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College(Autonomous) Tirupati Andhra Pradesh India Department of CSE School of Engineering & Technology SPMVV Tiruapti

ISBN: (数字)9798350381887

ISBN: (纸本)9798350381894

Chronic Renal Disease (CRD) is a serious sickness that affects the entire world and is the primary cause of mortality for many other illnesses. Due to the lack of visible symptoms at the beginning of CRD, people usually neglect the illness. Early CRD diagnosis enables individuals to receive prompt therapy to slow the course of their illness. Because it quickly and accurately identifies potentials, model-based machine learning can help doctors accomplish this aim. In this paper, an ML strategy for CRD forecasting. The UCI machine learning repository provided the CRD dataset, which has a high number of uncertain data values. To address unknown values or unknown data, KNN estimation was employed, which picks multiple instances that contain the most comparable measures to manage the undetermined data value for every insufficient sample. Missing data is prevalent in real therapy situations, therefore individuals may skip particular measurements for a variety of reasons. The technique is generated using five machine learning algorithms (SVM, Logistic Regression, k-nearest neighbor, Random Forest, and Neural Network). Random forest outscored the other models of machine learning regarding accuracy.

关键词： Support vector machines machine learning algorithms Accuracy Medical treatment Nearest neighbor methods Predictive models Prediction algorithms Random forests Diseases Logistics

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Heart Disease Prediction Using machine learning algorithms

Heart Disease Prediction Using Machine Learning Algorithms

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Knowledge Engineering and Communication Systems (ICKECS), International Conference on

作者： Poreddy AmoghVarshith Dontamsetti Harika Paluri Priyanka S Sujit Kumar D. Haritha Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

This study investigates the efficacy of various machine intelligence algorithms in predicting congestive heart failure risk using a comprehensive dataset. The algorithms studied include Decision Trees, Random Forests, Support Vector machines, Logistic Regression, K Nearest Neighbors, and Convolutional Neural Networks (CNN). Results reveal varying levels of effectiveness, with Random Forests demonstrating notable accuracy and reliability in predicting risk. CNNs excel in capturing complex relationships within the dataset, occasionally surpassing Random Forests in accuracy. Support Vector machines and Logistic Regression also exhibit commendable performance in assessing coronary thrombosis risk.

关键词： Heart Support vector machines Logistic regression machine learning algorithms Accuracy Nearest neighbor methods Prediction algorithms

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Multi-Sensor Approach for Cobalt Exploration in Asturias (Spain) Using machine learning algorithms

Multi-Sensor Approach for Cobalt Exploration in Asturias (Sp...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Morgana Carvalho Antonio Azzalini Joana Cardoso-Fernandes Patrícia Santos Alexandre Lima Ana Cláudia Teodoro Department of Geosciences Environment and Spatial Planning Faculty of Sciences University of Porto Porto Portugal ICT (Institute of Earth Sciences) – Porto Pole (Portugal)

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This study explores dimensionality reduction techniques, namely, PCA (Principal Component Analysis) and ICA (Independent Component Analysis), to condense Earth Observation (EO) data obtained from Landsat 9 and PRISMA satellites to detect alteration zones related to Cobalt (Co) mineralization in the Áramo mine, situated in Asturias, Spain, by employing Support Vector machine (SVM) machine learning (ML) algorithm. The ICA-based models exhibit slightly better performance than PCA-based ones, particularly in delineating alteration zones in the Landsat 9 image, showing promising results in distinguishing alteration zones from host rocks, demonstrating the viability of these techniques applied to mineral exploration. However, the results show the need for refined field data collection methodologies to enhance prediction accuracy for more robust results, in the scope of the HORIZON Europe S34I project (https://***/).

关键词： Support vector machines Dimensionality reduction Training machine learning algorithms Accuracy Satellites Landsat

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Investigating the Feasibility of Encryption as a Risk Mitigation Strategy for machine learning algorithms in Medical Applications

Investigating the Feasibility of Encryption as a Risk Mitiga...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Vishakha Joseph Shiv Shankar Shankar Gadug Sudhamsu Department of Management ARKA JAIN University Jamshedpur Jharkhand India Department of Mathematics Vivekananda Global University Jaipur India Department of Computer Science and Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In current years, the usage of system learning algorithms for clinical purposes has become more and more ubiquitous, and the privacy and protection of affected person records have emerged as more vital topics of debate. As such, it is vital to explore diverse threat mitigation techniques. In this summary, we inspect the possibility of the usage of encryption as a danger mitigation strategy for gadget-mastering algorithms in medical programs. We first overview the specific forms of encryption to be had, after which we examine the feasibility of the use of encryption to guard patient records in a system-learning context. We examine the overall performance effects of encrypting data before the application of a machine studying the set of rules, in addition to capacity vulnerable factors that encryption by me might not be able to shield towards. Sooner or later, we speak about the quality practices for enforcing and the usage of encryption for medical programs regarding device getting-to-know algorithms. The outcomes of this analysis show that even as encryption is a feasible threat mitigation approach, quality practices nonetheless want to be set up for its implementation.

关键词： Privacy Biomedical equipment machine learning algorithms Accuracy Prevention and mitigation Medical services Information leakage Encryption Protection Risk mitigation

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Cloud-Enabled Mobile App Architecture for Water Quality Monitoring Utilizing machine learning algorithms

Cloud-Enabled Mobile App Architecture for Water Quality Moni...

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Opportunity Research Scholars Symposium (ORSS)

作者： Samhitha Challagundla Subbarao Mahimna Patel Ahyoung Lee Hoseon Lee Department of Computer Science Kennesaw State University Marietta GA USA Department of Electrical and Computer Engineering Kennesaw State University Marietta GA USA

ISBN: (数字)9798350390698

ISBN: (纸本)9798350390704

This study presents an innovative framework for a water quality monitoring system, emphasizing the integration of cloud-enabled mobile applications with advanced machine learning (ML) algorithms. In our approach, sensors transmit real-time data to a cloud-based platform via a central gateway, facilitating dynamic data storage and accessibility through a dedicated mobile application. The system incorporates cutting-edge ML algorithms for data analysis and prediction, enhancing accuracy and effectiveness. Leveraging cloud computing infrastructure ensures seamless scalability and accessibility. The experimental setup includes the design and implementation of the mobile application, featuring functionalities for data visualization, user interaction, and real-time alerts. This framework demonstrates the potential of cloud-enabled mobile applications in augmenting ML algorithms for various applications, notably in water quality monitoring and environmental management.

关键词： Cloud computing machine learning algorithms Heuristic algorithms Water quality Computer architecture Prediction algorithms Real-time systems Mobile applications Sensors Monitoring

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Untargeted metabolomic analysis of honey mixtures: Discrimination opportunities based on ATR-FTIR data and machine learning algorithms

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MICROCHEMICAL JOURNAL 2023年第1期188卷

作者： Berghian-Grosan, Camelia Hategan, Ariana Raluca David, Maria Magdas, Dana Alina Natl Inst Res & Dev Isotop & Mol Technol 67-103 Donat St Cluj-napoca 400293 Cluj Romania

Honey adulteration issues represent an important concern at distinct societal levels (i.e. producers, consumers, and state authorities) because honey represents one of the most falsified food commodities in the world. Adulterations can be more or less subtle and, as a consequence, these practices can be easy or very difficult to detect. One of the subtlest types of adulteration is represented by the detection of honey mixture, when honey is wrongly labelled as monovarietal. During the last few years, it was demonstrated that a refinement of the analytical results can be achieved by the employment of artificial intelligence in the development of food and beverages recognition models. In this light, our study proposes a new approach for the detection of colza honey addition to acacia one and the identification of the presence of sunflower honey in linden samples. For this purpose, the association between ATR-FTIR spectroscopy and machine learning algorithms was applied for recognition models development. Based on these models, it was possible to detect the mixture of colza-acacia mixture with an accuracy of 94.4%, while the blend of linden and sunflower honey was possible to be identi-fied with a 90.7% accuracy.

关键词： ATR-FTIR spectroscopy Vibrational honey markers Classification models machine learning algorithms

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Anomaly Detection in Electronic Health Records Across Hospital Networks: Integrating machine learning With Graph algorithms

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2025年第5期29卷 3723-3735页

作者： Niu, Haoran Omitaomu, Olufemi A. Langston, Michael A. Grady, Stephen K. Olama, Mohammed Ozmen, Ozgur Klasky, Hilda B. Laurio, Angela Ward, Merry Nebeker, Jonathan Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN 37830 USA Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA US Dept Vet Affairs Washington DC 20001 USA

In a large hospital system, a network of hospitals relies on electronic health records (EHRs) to make informed decisions regarding their patients in various clinical domains. Consequently, the dependability of the health information technology (HIT) systems responsible for collecting EHR data is of utmost importance for patient safety. Recently, novel methods and tools aimed at identifying anomalies in EHR data to bolster the reliability of HIT systems have been introduced. However, these existing methods and tools primarily concentrate on individual hospitals, which limits our understanding of system-wide anomalous events and their potential impact on patient safety across multiple hospitals. In this article, we introduce a new approach to detecting anomalies in EHR data within a network of hospitals. This is achieved by combining advanced machine learning techniques with graph algorithms to create a tool capable of swiftly identifying and responding to deviations. Our proposed approach employs a combination of five machine learning models, harnessing the unique strengths of each model to provide a more robust detection system. The detected anomalies are then represented as graphs, allowing us to recognize patterns across the hospital network. This aids in identifying anomalies that span multiple medical facilities, potentially indicating broader system-level risks. Extensive real-world testing of our approach demonstrated its ability to offer actionable insights compared to existing methods. Additionally, its scalable design ensures seamless integration into existing HIT infrastructures.

关键词： Anomaly detection Hospitals machine learning algorithms machine learning Principal component analysis Wavelet analysis Monitoring Correlation Clustering algorithms Solid modeling Health information technology system-level anomaly detection high-dimensional time-series data machine learning graph algorithms

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machine learning algorithms in classifying TCM tongue features in diabetes mellitus and symptoms of gastric disease

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EUROPEAN JOURNAL OF INTEGRATIVE MEDICINE 2021年 43卷 101288-101288页

作者： Fan, Shangyong Chen, Bin Zhang, Xirui Hu, Xiaojuan Bao, Lingshan Yang, Xiaodong Liu, Zhaobang Yu, Yingcong Yangzhou Univ Dept Coll Informat Engn Yangzhou 22500 Jiangsu Peoples R China Shanghai Univ Tradit Chinese Med Shanghai Innovat Ctr TCM Hlth Serv Shanghai 201203 Peoples R China Wenzhou Med Univ Wenzhou Peoples Hosp Dept Gastroenterol Wenzhou 3 Clin Inst Wenzhou 325000 Peoples R China Chinese Acad Sci Suzhou Inst Biomed Engn & Technol Dept Med Imaging Suzhou 215163 Peoples R China

Introduction: Tongue diagnosis in Traditional Chinese Medicine (TCM) is used in clinical practice for disease diagnosis but this may be perceived as being too subjective. Objective technological approaches can lead to more accurate diagnosis and have become a focus for research. The aim of this study was to explore different tongue features in people with diabetes, gastric symptoms using images collected using digital tongue imaging. Methods: machine learning algorithms were used to classify diabetes mellitus(DM) patients and healthy participants, and patients exhibiting two different TCM gastric disease symptoms (liver-stomach disharmony and spleen-stomach deficiency). Multi-type feature extraction and selection from 466 tongue images was conducted. In the feature extraction stage, texture features and four TCM tongue features were identified: constitution color, coating color, cracks, and plumpness and slenderness. In the classification stage, two different classification algorithms were employed, Random Forest and Support Vector machine, to classify DM and TCM gastric disease symptoms. Results: Regarding the classification results, the area under the receiver operating characteristic curve (AUC) reached 97.9% +/- 0.1%, with maximum sensitivity and specificity of 93.5% +/- 0.6% and 95.0% +/- 0.8%, for DM patients and healthy volunteers respectively. For the classification of TCM gastric disease symptoms, the highest AUC was only 69.4% +/- 2.6%, with a sensitivity and specificity of 65.2% +/- 2.9% and 63.7% +/- 2.1%, respectively. Conclusions: Experiments shows that the TCM tongue features (color, cracks, plumpness and slenderness) and texture features have good performance for disease diagnosis. However, classification of DM was significantly better than for TCM gastric symptoms. The RF classification model was also significantly better than the SVM classifier model.

关键词： Tongue diagnosis Traditional Chinese Medicine Diabetes mellitus TCM Syndrome Gastric disease machine learning algorithms

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