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Prototype-Guided Contrastive Knowledge Graph Representation learning for Diagnosis Prediction 4th

Prototype-Guided Contrastive Knowledge Graph Representation ...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Park, Wooyeol Mulyadi, Ahmad Wisnu Kang, Eunsong Suk, Heung-Il Department of Artificial Intelligence Korea University Seoul Korea Republic of Department of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9789819787012

the medical knowledge graph (KG) constructed from electronic health records (EHR) offers a comprehensive understanding of patients as it facilitates interconnections among medical codes. While KG has been proven to improve diagnostic accuracy, it inevitably contains noisy and incomplete attributes that could compromise the verdicts. Hence, direct utilization of medical KG for downstream clinical tasks may entail inadequacy, even by resorting to proper machine learning methods. Several prior studies have initiated noise filtering methods upon medical KG to relieve such issues by masking its task-irrelevant nodes and relations. However, they focused solely on a patient-wise perspective that feasibly overlooked essential shared traits from a (sub-)population viewpoint. this study proposes a novel prototype-guided predictive model called ProtoCare to predict diagnoses in EHR by unifying prototype and contrastive learning. ProtoCare leverages a set of prototypes to discover latent shared characteristics among groups of patients, while contrastive learning plays the role of noise filtering and KG refinement accounting for both learned patient and selected prototype features. Experimental results on the real-world EHR cohort exhibited that our proposed ProtoCare outperformed several baselines on the diagnosis prediction task. © the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Towards IoT Context data Analysis and Classification 4

Towards IoT Context Data Analysis and Classification

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4th international Multidisciplinary Information Technology and Engineering conference, IMITEC 2024

作者： Bandason, Vivian Sibanda, Khulumani Tamuka, Nyashadzashe University of Fort Hare Department of Computer Science Eastern Cape Alice South Africa Walter Sisulu University Department of Applied Informatics Eastern Cape East London South Africa

ISBN: (纸本)9798350387988

the capability of generating and acquiring data has never been as pronounced as today since the advent of Information Technology in the early 19th century. Big data is a growing trend where data processing has grown so much that it is beyond the ability of commonly used software tools to capture, manage, and analyse this data. the most fundamental challenge for the Big data applications is to explore the large volumes of data and extract useful knowledge. Another recent development furthering the accumulation of data is the Internet of things - wherein the amount of 'things' housing different sensors has multiplied. the Internet of things (IoT) is built on sensors and actuators, which send out large quantities of data about their state and the environment. data processing techniques can be utilized to extract relevant insights from this treasure of data and information that is generated in IoT environments. this research investigates how sensor data from user devices can be used to determine context information about a user. As far as the classification of daily human activities (such as walking, eating, driving and sleeping) is concerned, several studies have proposed traditional computer vision methods. Unlike traditional computer vision methods, which pose mobility limitations, computational complexity, and privacy issues, our approach leverages mobile sensor data for real-time activity recognition. three classification algorithms which are the K-Nearest Neighbours (KNN), Decision Trees, and Support Vector machines (SVM) were evaluated based on their accuracy in classifying human activities. Findings revealed that the KNN was superior to other algorithms in providing a promising approach for context-aware systems in IoT environments. this study highlights the potential of machine learning and sensor data in enhancing user-centric applications through improved context-awareness. © 2024 IEEE.

关键词： data assimilation

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Intelligent Inhalation therapy for Cystic Fibrosis Using IoT and machine learning Solutions 4

Intelligent Inhalation Therapy for Cystic Fibrosis Using IoT...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Ranganathan, Chitra Sabapathy Arul Prasath, A. Nandhini, R. Shanmugam, Kannan Velmurugan, S. Srinivasan, S. Mphasis Corporation Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India School of Computing Science and Engineering Vit Bhopal University Department of Gaming Technology Madhya Pradesh Bhopal India T.J.S. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Optimal patients result in the long-term management of cystic fibrosis (CF) and need personalized treatment strategies due to the hereditary disorder's impact on respiratory problems. One potential approach to individualized CF treatment is intelligent inhalation therapy systems that combine machine learning (ML) algorithms with Internet of things (IoT) sensors. To provide CF patients with a predictive modeling framework that can personalize their inhalation treatment using the Random Forest algorithm. the algorithm is designed to predict individual-level outcomes, including the potential of an exacerbation and the efficacy of therapy, by mining a large and varied dataset that includes patient demographics, lung function measurements, medication adherence records, environmental factors, and reports of symptoms. the Random Forest method was selected due to its capacity to manage diverse data types, detect intricate relationships between variables, and provide insights that can be understood. It shows that the Random Forest model effectively predicts important outcomes for CF care by training, validating, and evaluating it using real-world data from CF patients. Exacerbation risks and patient-specific therapy responses may be better understood by feature significance analysis, allowing for more targeted intervention approaches. the proposed predictive modeling framework will improve the efficiency and effectiveness of inhalation treatment for managing CF. Healthcare practitioners may enhance the management of CF via ML analytics and data collected through the IoT. this allows for delivering personalized treatment regimens, optimizing resource allocation, and better patient outcomes. Challenges include ensuring data accuracy, managing extensive datasets, sustaining real-time monitoring, optimizing algorithms, and ensuring system stability. © 2024 IEEE.

关键词： Personalized medicine

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Description Attribute-Enhanced Spatio-Temporal Zero-Shot Action recognition 4th

Description Attribute-Enhanced Spatio-Temporal Zero-Shot Act...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Kim, Yehna Kim, Ho-Joong Lee, Seong-Whan Department of Artificial Intelligence Korea University Seoul Korea Republic of

ISBN: (纸本)9789819787043

Vision-Language Models (VLMs) have shown remarkable performance in zero-shot action recognition by learning the correlation between video embeddings and class embeddings. However, an issue arises when depending solely on action classes for semantic information due to multi-semantic words - words with multiple meanings. theses words hinder the difficulty of the model to accurately capture the intended concepts of actions. We propose a novel approach which leverages web-crawled descriptions with utilizing a large-language model for the extraction of keywords. this method reduces the reliance on human annotators and avoids the exhaustive manual process of attribute data creation. Moreover, we introduce a spatio-temporal interaction module which focuses on objects and action units to align description attributes with video content. In zero-shot experiment, our model achieves 81.0%, 53.1%, and 68.9% on UCF-101, HMDB-51, and Kinetics-600, respectively, which demonstrates the transferability of our model to downstream tasks. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Zero-shot learning

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Influence of Water Droplet Contamination for Transparency Segmentation 4th

Influence of Water Droplet Contamination for Transparency ...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Knauthe, Volker Weitz, Paul Pöllabauer, thomas Wirth, Tristan Rak, Arne Kuijper, Arjan Fellner, Dieter W. Technical University of Darmstadt Darmstadt Germany Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany CGV Institute Graz University of Technology Graz Austria

ISBN: (纸本)9789819787043

Computer vision techniques are on the rise for industrial applications, like process supervision and autonomous agents, e.g., in the healthcare domain and dangerous environments. While the general usability of these techniques is high, there are still challenging real-world use-cases. Especially transparent structures, which can appear in the form of glass doors, protective casings or everyday objects like glasses, pose a challenge for computer vision methods. this paper evaluates the combination of transparent objects in conjunction with (naturally occurring) contamination through environmental effects like hazing. We introduce a novel publicly available dataset containing 489 images incorporating three grades of water droplet contamination on transparent structures and examine the resulting influence on transparency handling. Our findings show, that contaminated transparent objects are easier to segment and that we are able to distinguish between different severity levels of contamination with a current state-of-the art machine-learning model. this in turn opens up the possibility to enhance computer vision systems regarding resilience against, e.g., datashifts through contaminated protection casings or implement an automated cleaning alert. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Image Domain Translation for Few-Shot learning 27th

Image Domain Translation for Few-Shot Learning

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27th international conference on pattern recognition, ICPR 2024

作者： Chen, Liangyuan He, Zhenan Zhang, Hai College of Computer Science Sichuan University Chengdu China

ISBN: (纸本)9783031781827

Few-shot learning is crucial in machine learning and computer vision. It enables models to recognize new objects with limited labeled data, addressing the challenge of data scarcity and expanding the application of machine learning to domains with scarce data. Previous methods built metric space using labeled data from the base set and then classified queried images from the novel set by finding the nearest class prototype. However, due to the presence of poor-quality data in the novel set, the class prototype often exhibits instability. In response to this challenge, this paper proposes a Semantic Conditional Translation Network for reconstructing stable class prototypes. Specifically, images are first divided into edge domain (i.e., images at the cluster edge) and prototype domain (i.e., images at the cluster center). then, an Enhanced Generative Adversarial Network is introduced to learn the translation from edge toward prototype, where a Non-parametric Classification Regularizer is designed to enlarge the discriminability of the translated samples. Meanwhile, class definitions are exploited as semantics providing precise descriptions and enhancing translation performance. Experimental results demonstrate that the proposed method obtains competitive results on four benchmark datasets. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Leveraging data mining Classifiers for Robust Ddos Attack Detection in Cloud Computing 4

Leveraging Data Mining Classifiers for Robust Ddos Attack De...

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

作者： Garg, Mohan Satish Kumar, R. Mishra, V.A. Kuppuraj, T. Parashar, Komal Senthurya, S. Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - Pg Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Applications Coimbatore641 021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Cse Chennai127 India

ISBN: (纸本)9798331518981

One sort of infiltration in cloud computing environments is the Distributed Denial of Service (DDoS) attack, which covertly inserts malicious data packs into net traffic and has a devastating effect on end users. When it came to protecting the server from several external unauthorised traffics, typical cyber-security solutions fell short. A well-designed intrusion detection system (IDS) is a vital component of any loT design. When developing ML algorithms for intrusion detection, it is necessary to do thorough literature studies to study different machine learning approaches, neural network models, and optimisation algorithms. this will help uncover any gaps or flaws and ultimately lead to the development of more effective algorithms. We create data mining algorithms like C4.5, SVM, and KNN, and test how well they can identify DDoS attacks. Various classifier methods, including CART, Random Forest, and Naive Bayes, are used to compare the created algorithms' performance. While the SVM-based DDoS attack discovery model performed better than all other procedures, it struggled with handling massive datasets, according to the results analysis. An approach to this challenge is the development of Artificial Neural Network (ANN) based approaches for effective intrusion detection. the accuracy of the various approaches shown by KNN, SVM, C4.5 with KNN, and C4.5 with SVM varies when compared. In general, KNN shows respectable accuracy, although SVM is somewhat less accurate. C4.5 integrated with KNN enhances precision. Out of all the approaches that were investigated, the one that sticks out with an astonishing 97% accuracy is the one that is suggested: C4.5 with SVM. © 2024 IEEE.

关键词： Network intrusion

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Zero-shot Automated Class Imbalanced learning 27th

Zero-shot Automated Class Imbalanced Learning

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27th international conference on pattern recognition, ICPR 2024

作者： Wang, Zhaoyang Wang, Shuo School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

ISBN: (纸本)9783031783821

Given a new class imbalanced dataset D and limited computational resources, the challenge arises of selecting promising class imbalanced learning (CIL) pipelines that include resampling methods, classification models, and their corresponding hyperparameters. To address this challenge, we study Zero-shot Automated machine learning and propose a new approach aiming at class imbalanced data, called Zero-shot Automated Class Imbalance learning (ZAutoCIL). ZAutoCIL employs domain-independent meta-learning to develop a zero-shot surrogate model for automated class imbalanced learning. this model aims to recommend effective CIL pipelines for new unseen imbalanced datasets without requiring additional search. Specifically, we meta-train a two-tower model to serve as the surrogate model, adapted from recommender systems, using a pairwise ranking loss on the meta-dataset gained from collecting performance data across a wide range of CIL pipelines and a comprehensive repository of class imbalance datasets. We perform extensive experiments on 100 datasets grouped in 4 parts based on their imbalance ratio. the experimental results demonstrate the efficacy of our approach in automating the recommendation of CIL pipelines given any target imbalanced datasets. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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Optimizing Cancer Forecasting with Cutting-Edge data mining Techniques 4

Optimizing Cancer Forecasting with Cutting-Edge Data Mining ...

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

作者： Pandey, Noopur Nanduri, Sireesha Deshpande, Pallavi Gajendran, P. Dev, Sudhanshu Sivadharshini, B. Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 174103 India Karnataka Bangalore India EnTC Vishwakarma Institute of Information Technology Pune India Karpagam College of Engineering Department of Electrical and Electronics Engineering Coimbatore641032 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of It Chennai127 India

ISBN: (纸本)9798331518981

the needs of the machine learning domain are especially pressing in the medical field. the information as in the medical area is quite complicated, and in order to arrive at an accurate diagnosis, it is necessary to analyse many and diverse forms of data, making these needs all the more important. Because of the high stakes involved, areas like cancer detection need the highest levels of accuracy and dependability in the method of detection. the location of a tumour determines the kind of cancer that has developed. As a result, there is a great deal of variation in the parameters of different cancer types. the lung cancer values for prediction are established by the first contribution. the multi-model framework that this work provides may be utilized to effectively forecast lung cancer. the model's clustering techniques are a hybrid of Decision Tree and K-Means. the prediction process is enhanced by the varying degrees of integration between the two models. An efficient pre-treatment method is included into the model to offer a data purification the system with the basic data being acquired from real-time. Predicting how long the patient will survive levels is done using the decision tree framework, which is a form of regression. the accuracy analysis among the three methods indicates that the suggested method Decision Tree and K-Means based survival prediction (DKSP) markedly surpasses both the Decision Tree (DT) and K-Means techniques. the Decision Tree technique exhibits the lowest level of precision, although K-Means shows an improvement;nonetheless, it still fails to equal the effectiveness of the proposed approach. the DKSP model, achieving an accuracy of 99.23, exhibits an important enhancement, establishing it as the most proficient tool for cancer prediction among those evaluated. © 2024 IEEE.

关键词： Lung cancer

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Effective data Science Leadership Based on Text mining and machine learning Model 4th

Effective Data Science Leadership Based on Text Mining and M...

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4th international conference on Decision Science and Management (ICDSM)

作者： Sun, Yuandong Zhao, Xinyue Univ Calif Los Angeles Los Angeles CA 90095 USA Columbia Univ New York NY 10027 USA

ISBN: (纸本)9789811927683;9789811927676

Leadership becomes an essential trait of a data science team. In this research project, the critical characteristics and difficulties of data science leaders are analyzed from two aspects: the employer's and the employee's expectations. the employer's expectation is studied based on the data from Glassdoor and visualized by word clouds and a bigram chart. the employee's expectation is evaluated by IBM data from Github. In addition, the random forest method is applied in data analysis of the IBM dataset. Excellent team-building skills, monitoring the interactions, responsibility, and seeking opportunities for teammates are characteristics of an outstanding data science leader. And, the difficulties for a leader to overcome are fewer interactions, lack of understanding, and interpersonal conflicts by fluent communication and code of conduct.

关键词： Leadership Random forest method WordClouds Bigram chart

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