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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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Using Factor Analysis to Increase Accuracy of Neural Networks for Predicting Student Test Scores 54

Using Factor Analysis to Increase Accuracy of Neural Network...

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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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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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Detection of Sleep Apnea using Cat Swarm Optimization with TabNet versus CNN Model

Detection of Sleep Apnea using Cat Swarm Optimization with T...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Kujani, T. Arivubrakan, P. Usha, V. Alex David, S. Umamaheswari, R. Sathya, T. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Tamilnadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sri Sairam Engineering College Computer Science and Business Systems Tamilnadu Chennai India

ISBN: (纸本)9798331541170

Sleep apnea is a sleep disease that is often an undetected cause of death. It is generally diagnosed with polysomnography, which can be costly and uncomfortable. To solve this issue, we suggest a website for detecting sleep apnea that uses user-provided data such as the Berlin questionnaire, Epworth Sleepiness Scale (ESS), body measurements, Body Mass Index (BMI), age, and gender. The purpose of this work is to categorize sleep apnea based on the Apnea-Hypopnea Index (AHI) as normal, mild, moderate, or severe. This classification is carried out using a deep learning model, TabNet which suits the best to process the text input. It could be useful for people who feel they have sleep apnea, allowing them to take the essential steps toward a formal diagnosis and therapy. However, this aids in early diagnosis by indicating the stage of the disease, and users are recommended to check with healthcare specialists for a definitive diagnosis. For feature selection, nature-inspired Cat Swarm optimization is applied. The TabNet is trained on a dataset that includes these characteristics as well as the AHI, a common metric for determining the severity of sleep apnea. The algorithm uses these inputs to forecast the likelihood of having sleep apnea: normal, mild, moderate, or severe. The developed TabNet Model is compared with the Convolution Neural Network (CNN) and the results are showcased. To summarize, this approach is a revolutionary technique for sleep apnea detection using Cat Swarm Optimization. It promises to give a more accessible and cost-effective solution for people who feel they have this illness by utilizing user-generated data and deep learning algorithms. © 2024 IEEE.

关键词： Sleep research

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manjula, R. Jagadeesan, S. Ramalingam, Venkadeshan Vetriselvi, T. Johnpeter, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamilnadu Vellore India Information Technology Department University of Technology and Applied Sciences-Shinas Branch Sultanate of Oman Oman Vellore Institute of Technology School of Computer Science and Engineering Department of Iot Tamilnadu Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350313987

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user. © 2023 IEEE.

关键词： Gesture analysis Hand gesture recognition Hand tracking Sign language

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Phase chimera states on nonlocal hyperrings

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Physical Review E 2024年第2期109卷 L022201-L022201页

作者： Riccardo Muolo Thierry Njougouo Lucia Valentina Gambuzza Timoteo Carletti Mattia Frasca Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo 152-8552 Japan Department of Mathematics University of Namur B5000 Namur Belgium naXys Namur Institute for Complex Systems University of Namur B5000 Namur Belgium Faculty of Computer Science University of Namur B5000 Namur Belgium Department of Electrical and Electronic Engineering University of Buea P.O. Box 63 Buea Cameroon Department of Electrical Electronics and Computer Science Engineering University of Catania 95125 Catania Italy Istituto di Analisi dei Sistemi ed Informatica “A. Ruberti” IASI-CNR 00185 Roma Italy

Chimera states are dynamical states where regions of synchronous trajectories coexist with incoherent ones. A significant amount of research has been devoted to studying chimera states in systems of identical oscillators, nonlocally coupled through pairwise interactions. Nevertheless, there is increasing evidence, also supported by available data, that complex systems are composed of multiple units experiencing many-body interactions that can be modeled by using higher-order structures beyond the paradigm of classic pairwise networks. In this work we investigate whether phase chimera states appear in this framework, by focusing on a topology solely involving many-body, nonlocal, and nonregular interactions, hereby named nonlocal d-hyperring, (d+1) being the order of the interactions. We present the theory by using the paradigmatic Stuart-Landau oscillators as node dynamics, and we show that phase chimera states emerge in a variety of structures and with different coupling functions. For comparison, we show that, when higher-order interactions are “flattened” to pairwise ones, the chimera behavior is weaker and more elusive.

关键词： Chimera states Collective behavior in networks Dynamics of networks Pattern formation Synchronization Synchronization transition

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Advanced Machine Learning Algorithms for 1Predictive Analytics in Healthcare to Enhance Patient Outcomes with Data-Driven Insights

Advanced Machine Learning Algorithms for 1Predictive Analyti...

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

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

ISBN: (纸本)9798350388602

This research aims to explore the use of modern complex defensive machine learning algorithms in the provision of predictive analytics for health improvement. Incorporating electronic health records, medical image information, and genomic data the work proposes and assesses multiple machine learning models including deep learning, ensemble learning, and reinforcement learning. The main findings shown very high performance in disease prediction, in chronic illness and in the application of efficiency improvements in hospitals as a whole. Overall, such threats as data quality, data privacy, and model interpretability have been discussed in the research and emphasize the importance of the machine learning approach in healthcare. It is highlighted that further development and integration of the interdisciplinary approaches as well as the use of the transparent and interpretable artificial intelligence and machine learning models can be urgent to increase the acceptance among the healthcare staff. This paper enhances the literature on applying machine learning in healthcare systems adding to the repertoire of knowledge for improved patient care. © 2024 IEEE.

关键词： Electronic health record

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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 1-17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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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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