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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Brown, Michael Scott Momtaz, Maliha Mai, Aaron Modi, Dhruvil University of Maryland Baltimore County Information Systems Department BaltimoreMD United States Hood College Computer Science and It Department FrederickMD United States Texas A&m College Station Computer Science and Engineering Department College StationTX United States University of Maryland Baltimore County Department of Computer Science and Electrical Engineering BaltimoreMD United States

ISBN: (纸本)9798350351507

This work is a full research-to-practice paper that describes a predictive method to improve the prediction of student test scores. Predicting student test scores is difficult. However, doing so can improve education greatly by improving advising, scheduling, tutoring assignment and other educational processes. This research extends previous research by using a domain space reduction technique to improve accuracy. Factor Analysis is used to reduce the number of domain attributes for improving the accuracy of a Neural Network to predict student test scores. In this research datasets for Mathematics and Language of high school student test scores were used. Test scores were predicted using a Neural Network computing the Mean Absolute Error as a measurement of accuracy. The datasets have 30 domain attributes each. Factor Analysis was used to reduce the domain size from between 1 to 29, each time using it to train the Neural Network. Because the Mean Absolute Error may vary depending upon which records in the dataset are used for training versus testing, 50 trials of each dataset size were executed producing an Average Mean Absolute Error for each domain size. A statistical test was used to show statistical significance between the Neural Network without Factor Analysis and the Neural Network with varying domain sizes using Factor Analysis. Results were very promising and correspond to previous research that used Principal Component Analysis. Numerous domain sizes had significantly better Average Mean Absolute Errors than the accuracy of the Neural Network without Factor Analysis. This research shows that reducing the domain size using Factor Analysis can greatly improve the accuracy of Neural Networks when predicting student test scores. The best improvements occurred when domain sizes were very small ranging from 2 to 6. Domain reduction techniques, such as Factor Analysis, have been shown to improve predictive models for student test score prediction. Future research

关键词： Support vector machines

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ECG Signal Feature Extraction and SVM Classifier Based Cardiac Arrhythmia Detection 2

ECG Signal Feature Extraction and SVM Classifier Based Cardi...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Venkatesan, C. Thamaraimanalan, T. Ramkumar, M. Sivaramakrishnan, A. Marimuthu, M. HKBK College of Engineering Department of Electronics and Communication Engineering Bangalore India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Knowledge Institute of Technology Department of Computer Science and Business Systems Salem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh India Sona College of Technology Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350397635

In a variety of medical applications, electrocardiogram (ECG) records are essential for the identification of cardiovascular disorders. Cardiovascular issues such cardiac arrhythmias and coronary heart disease (CHD) are among the most prevalent and can result in cardiac arrest or sudden cardiac death. This study employs ECG signal preprocessing and feature extraction to identify cardiac arrhythmias and evaluate CHD risk. This research stresses the use of a Support Vector Machine (SVM) classifier for cardiac arrhythmia identification after ECG signal preprocessing. The preprocessed ECG signal is then subjected to arrhythmic beat classification to find anomalies. Extracted R-peaks from the ECG signal are divided into normal and arrhythmic risk subjects using the SVM classification-based approach for abnormality identification. When compared to other similar classifiers, the K-Nearest Neighbor (KNN) classifier offers the highest classification accuracy of 97.5%. © 2023 IEEE.

关键词： Electrocardiograms

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Use of IoT sensor devices for efficient management of healthcare systems: a review

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Discover Internet of Things 2024年第1期4卷 8页

作者： Gopichand, G. Sarath, T. Dumka, Ankur Goyal, Himanshu Rai Singh, Rajesh Gehlot, Anita Gupta, Lovi Raj Thakur, Amit Kumar Priyadarshi, Neeraj Twala, Bhekisipho School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science Women Institute of Technology VMSBUTU Uttarakhand Dehradun India Department of CSE Graphic Era Deemed to Be University Uttarakhand Dehradun India Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Lovely Professional University Punjab Phagwara Jalandhar144001 India Department of Electrical Engineering JIS College of Engineering Kolkata741235 India Digital Transformation Portfolio Tshwane University of Technology Staatsartillerie Rd Pretoria West Pretoria0183 South Africa

With an aging population and an increase in chronic sickness, healthcare systems are under increasing pressure. Internet of Things (IoT) technology has recently garnered a lot of interest for its ability to alleviate this burden. The IoT is a network of interconnected wireless digital devices that can gather, transmit, and store data automatically, without the need for human or computer intervention. In order to better anticipate health problems, diagnose, treat, and monitor patients in and out of the hospital, the IoT offers numerous advantages that could streamline and improve health care delivery. As a matter of urgency, decision-makers and heads of state around the world are enacting policies to provide healthcare services through the use of technology. Thanks to recent advancements in the IoT, healthcare has been able to evolve at a rapid pace. Research on Internet IoT uses in healthcare and newly developed emergency equipment are summarized in this article. © The Author(s) 2024.

关键词： Internet of things

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Graded Classification of Liver Cirrhosis Using Machine Learning Algorithms on a Highly Unbalanced Dataset 1st

Graded Classification of Liver Cirrhosis Using Machine Learn...

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1st International Conference on Human-Centric Smart Computing, ICHCSC 2022

作者： Sengupta, Diganta Mondal, Subhash De, Anish Kumar Singh, Yash Raj Department of Computer Science and Engineering Meghnad Saha Institute of Technology Kolkata700150 India Department of Computer Science and Business Systems Meghnad Saha Institute of Technology Kolkata700150 India

ISBN: (纸本)9789811954023

Liver cirrhosis is the fibrosis of liver caused by a long-term damage of the organ. This study classifies the disease in four classes based on a highly unbalanced dataset having 18 features and a data count of 6800 with labeled data of 465, 1507, 1322, and 3506 for the four classes using machine learning (ML) algorithms. Twelve ML algorithms have been deployed for the classification purpose which reflected the highest accuracy of 68.21% for the Histogram Gradient Boost Classifier. For further improvement of the accuracy, hyper-parameter tuning was done on all the ML algorithms which fetched the highest accuracy of 77.97% for the Gradient Boost Classifier (GBC). Further improvement of accuracy was observed with stacking model which furnished an accuracy of 84.24%. The stacked model comprised of the GBC as the meta-learner, and K-Nearest Neighbor (KNN), Xtreme Gradient Boost algorithm (XGB), Support Vector Machine (SVM), and the Light Gradient Boost Machine (LGBM) as the base-learners. To the best of our knowledge, this is the first attempt for graded classification of liver using all the ML algorithms, including hyper-parameter tuned and stacked models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Poster Abstract: Enhancing Human Motion Sensing with synthesized Millimeter-Waves 23

Poster Abstract: Enhancing Human Motion Sensing with synthes...

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23rd ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2024

作者： Zhang, Xiaotong Wang, Kun Li, Zhenjiang Zhang, Jin City University of Hong Kong Department of Computer Science Hong Kong Hong Kong Southern University of Science and Technology Res. Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Shenzhen China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350362015

This poster introduces SynMotion, a novel mmWave-based human motion sensing system addressing the scarcity of training datasets. By synthesizing mmWave signals using existing vision-based human motion datasets, this system overcomes the challenge of collecting and labeling mmWave data, facilitating wider adoption of mmWave technology for applications like activity recognition, skeleton tracking and radar placement recommendation. © 2024 IEEE.

关键词： Millimeter waves

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A Survey of Cyber Attacks on Cyber Physical systems:Recent Advances and Challenges

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 784-800页

作者： Wenli Duo MengChu Zhou Abdullah Abusorrah Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China IEEE Department of Electrical and Computer Engineering K.A.CARE Energy Research and Innovation Center Department of Electrical and Computer Engineering K.A.CARE Energy Research and Innovation Center and Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz University

A cyber physical system(CPS)is a complex system that integrates sensing,computation,control and networking into physical processes and objects over *** plays a key role in modern industry since it connects physical and cyber *** order to meet ever-changing industrial requirements,its structures and functions are constantly ***,new security issues have arisen.A ubiquitous problem is the fact that cyber attacks can cause significant damage to industrial systems,and thus has gained increasing attention from researchers and *** paper presents a survey of state-of-the-art results of cyber attacks on cyber physical ***,as typical system models are employed to study these systems,time-driven and event-driven systems are ***,recent advances on three types of attacks,i.e.,those on availability,integrity,and confidentiality are *** particular,the detailed studies on availability and integrity attacks are introduced from the perspective of attackers and ***,both attack and defense strategies are discussed based on different system *** challenges and open issues are indicated to guide future research and inspire the further exploration of this increasingly important area.

关键词： Attack detection attack strategy cyber attack cyber physical system(CPS) secure control

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Binary Classification of Thyroid Using Comprehensive Set of Machine Learning Algorithms 7th

Binary Classification of Thyroid Using Comprehensive Set of ...

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7th International Conference on Emerging Applications of Information Technology, EAIT 2022

作者： Sengupta, Diganta Mondal, Subhash Raj, Aman Anand, Ankit Department of Computer Science and Engineering Meghnad Saha Institute of Technology Kolkata700150 India Department of Computer Science and Business Systems Meghnad Saha Institute of Technology Kolkata700150 India

ISBN: (纸本)9789811951909

Binary classification of Thyroid has been observed in several manuscripts in literature. This study also classifies the disease using twelve machine learning algorithms. The results are compared with existing proposals and found to exhibit better performance in terms of the standard machine learning algorithm evaluation parameters. The result improvements are due to the data preprocessing techniques that have been applied in the study. The results that are presented in this paper are void of any tuned machine learning model. Only the basic implementations have been done on the preprocessed data. Random Forest presented the best results amongst all the participants, including literary counterparts with an accuracy of 99.14%, coupled with an F1-Score of 0.991. We have implemented the machine learning models on a resource constraint dataset containing only 3772 instances, and an initial feature vector containing 29 features. Data preprocessing minimized the initial feature vector to a reduced feature vector containing 15 features. We claim this study can form a benchmark for binary classification of thyroid using machine learning models, as our results exhibit implementation on twelve machine learning models as well as improvements in the performances. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Securing Multi-Tenant Cloud Environments with Fully Homomorphically Encrypted Secure Multiparty Computation

Securing Multi-Tenant Cloud Environments with Fully Homomorp...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Ponmalar, A. Pandiarajan, Rajkumar Sudha, I. Ramesh, P.S. Jagannathan, J. R.M.K. Engineering College Tamilnadu Chennai India School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering 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 Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India

ISBN: (纸本)9798350388602

The aim of this work is to examine the application of FHE-SMPC as a viable security model for protecting the tenant isolation in multi-tenant cloud model. In multi-tenant environments, where various customers use the same cloud system, controlling the data leakage and protecting privacy is a major issue. Actual safeguards fail to meet the needs of data protection during processing, therefore, the introduction of highly elaborate encryption processes must happen. This research work focuses on the use of FHE technique and SMPC such that data can undergo processing while the plaintext remains unknown to any party. A kind of approach is based on the acquisition of synthetic and real data sets, system-level architecture, and cryptographic algorithms using open-source libraries with cloud service providers. The results of such implementation indicate that despite the fact that the FHE-SMPC system has a considerable computational overhead in comparison with classical schemes it is very secure and protected against different types of cryptographic attacks. The problem of scalability is still an open issue, but the complex is quite efficient with various data types that is why it can be used in various cloud applications. This paper offers a pertinent solution for security of data that is housed in multitenant environment and will go along way in creating fertile ground for research and innovation in secure clouding. © 2024 IEEE.

关键词： Cloud security

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Multi-task Collaborative Perception Algorithm Based on Consensus Perception

Multi-task Collaborative Perception Algorithm Based on Conse...

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2024 IEEE International Conference on Unmanned systems, ICUS 2024

作者： Luo, Fuyu Yu, Yunbo Guo, Xiaoye Systems Engineering Research Institute China State Shipbuilding Corporation Beijing China Department of Computer Science Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350384185

This Multi-agent collaborative perception is a hot topic in the current field of machine learning. Through communication and sharing of perceived scene information between agents, this approach can tackle issues such as occlusion and sparse observations present in single-agent observations. Existing studies have shown that localization errors are a significant factor affecting the accuracy of perception results. Furthermore, most prior work has focused solely on the performance of algorithms in individual tasks without considering multi-task scenarios. In this paper, a multitask collaborative perception method based on consensus perception is proposed. It integrates local perception information from each intelligent agent to form a global perception consensus, which is then used to correct the spatial feature maps of the agents, alleviating the impact of localization errors on perception results. Additionally, the use of a feature selection module based on spatial confidence maps and a multi-scale attention feature fusion module optimizes the performance of the collaborative perception process. By setting up multiple decoders in parallel, a single model is able to handle diverse tasks. Experimental results demonstrate that the proposed consensus-based collaborative perception method effectively mitigates the negative impact of localization errors on perception performance, and the algorithm can achieve good perception results across multiple tasks. © 2024 IEEE.

关键词： Feature Selection

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Closer through commonality: Enhancing hypergraph contrastive learning with shared groups

Closer through commonality: Enhancing hypergraph contrastive...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Roh, Daeyoung Han, Donghee Kim, Daehee Han, Keejun Yi, Mun Kaist Graduate School of Data Science Korea Republic of Hansung University School of Computer Engineering Korea Republic of Kaist Department of Industrial and Systems Engineering Korea Republic of

ISBN: (纸本)9798350362480

Hypergraphs provide a superior modeling frame-work for representing complex multidimensional relationships in the context of real-world interactions that often occur in groups, overcoming the limitations of traditional homogeneous graphs. However, there have been few studies on hypergraph-based contrastive learning, and existing graph-based contrastive learning methods have not been able to fully exploit the high-order correlation information in hypergraphs. Here, we propose a Hypergraph Fine-grained contrastive learning (HyFi) method designed to exploit the complex high-dimensional information inherent in hypergraphs. While avoiding traditional graph augmentation methods that corrupt the hypergraph topology, the proposed method provides a simple and efficient learning augmentation function by adding noise to node features. Furthermore, we expands beyond the traditional dichotomous relationship between positive and negative samples in contrastive learning by introducing a new relationship of weak positives. It demonstrates the importance of fine-graining positive samples in contrastive learning. Therefore, HyFi is able to produce high-quality embeddings, and outperforms both supervised and unsupervised baselines in average rank on node classification across 10 datasets. Our approach effectively exploits high-dimensional hypergraph information, shows significant improvement over existing graph-based contrastive learning methods, and is efficient in terms of training speed and GPU memory cost. The source code is available at https://***/Noverse0/***. © 2024 IEEE.

关键词： Contrastive Learning

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