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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Knaup, Jacob W. Tsiotras, Panagiotis Georgia Institute of Technology School of Interactive Computing College of Computing and the Institute for Robotics and Intelligent Machines AtlantaGA30332-0250 United States Georgia Institute of Technology School of Aerospace Engineering The Institute for Robotics and Intelligent Machines AtlantaGA30332-0150 United States

ISBN: (纸本)9798350316339

This work examines the optimal covariance steering problem for systems subject to unknown parameters that enter multiplicatively with the state and control, in addition to additive disturbances. In contrast to existing works, the unknown parameters are modeled as random variables and are estimated online. A dual control problem is formulated in which the effect of the planned control policy on the parameter estimates is modeled and optimized for. The parameter estimates are then used to modify the pre-computed control policy online in an adaptive control fashion. Finally, the proposed approach is demonstrated in a vehicle control example with closed-loop parameter identification. © 2024 IEEE.

关键词： Steering

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

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IEEE Transactions on Mobile computing 2025年第7期24卷 5731-5748页

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen the College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore the School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore the School of Automation Guangdong University of Technology Guangzhou510641 China the Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

—Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Markov processes

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Prediction of Level of Damage of Brain Due To Tumor Via Integrated CNN Technique 5

Prediction of Level of Damage of Brain Due To Tumor Via Inte...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Agrawal, Vivek Kaswan, Kuldeep Singh Kumar, Sanjay Galgotias College of Engineering and Technology Department of Information Technology U.P Greater Noida India Galgotias University School of Computing Science and Engineering U.P Greater Noida India Galgotia College of Engineering and Technology Department of Computer Science Engineering U.P Greater Noida India

ISBN: (纸本)9798350383522

Image transform is the individual of ultimate questioning and intelligent field at the period. Tumor is an uneven expansion of container cruel intellect. The lump may be classification as favorable (non-malignant) and diseased (malignant). Earlier stage of swelling is previously in time or order discovered concerning matter through test of figure by doctors and it is a opportunity absorbing task and occasionally gives inaccurate consequence. Today different register forms are secondhand in healing field that supports a fast and correct result. Magnetic Resonance Images (MRI) is ultimate usual image method for resolving within makeup of human bulk. The MRI is secondhand even in disease of most harsh affliction of healing wisdom like mind tumors. The intellect carcinoma discovery process comprise figure prepare methods includes four stages. Image pre-handle, concept separation, feature ancestry, and eventually categorization. There is various existent of methods are handy for intellect carcinoma separation and categorization to discover the intelligence swelling. There are many methods handy presents a study of existent methods for intellect swelling discovery and their benefits and restraints. To overcome these disadvantages, intend a Convolution Neural Network (CNN) located classifier. CNN located classifier used to equate the prepared and test dossier, from this take high-quality result. © 2024 IEEE.

关键词： Brain Tumor Detection CNN Image Preprocessing Roduction

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Web-Based Mental Health Predicting System Using K-Nearest Neighbors and XGBoost Algorithms 8th

Web-Based Mental Health Predicting System Using K-Nearest Ne...

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8th International Visual Informatics Conference, IVIC 2023

作者： Zulkefli, Nurul Farhanaa Diah, Norizan Mat Ismail, Azlan Hanum, Haslizatul Fairuz Mohamed Ibrahim, Zaidah Arif, Yunifa Miftachul School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia Informatics Engineering Faculty of Science and Technology Universitas Islam Negeri Maulana Malik Ibrahim Malang Indonesia

ISBN: (纸本)9789819973385

Problems with mental health are common presently and have been a worry for a long time. Mental health problems, like anxiety, depression, and panic attacks, can be caused by numerous things. Therefore, recognising the start of mental disease is becoming increasingly crucial to maintaining a good life balance. This study uses machine learning to identify any possible mental health disorders in an individual to attain this goal. The investigation employed supervised machine learning to predict mental health status, namely K-Nearest Neighbors (KNN) and XGBoost, with performance evaluation criteria including accuracy, precision, recall, and F1 score. When these two algorithms were compared, it was discovered that XGBoost produced a more effective prediction model, which was then employed to develop a web-based mental health prediction system. The web-based method creates a questionnaire for mental health issues. Based on the user’s responses to the questions, the system will predict his or her mental health status as normal, depression, anxiety, stress, loneliness, or regularity. Every component of the system, including buttons and forms, has been successfully tested using functionality tests. Moreover, the system’s advantages, weaknesses, and future study directions are identified. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Nearest neighbor search

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Optimizing Heart Disease Prediction with Feature Sensitivity and Risk Reduction Strategies Using Machine Learning Classifiers 2024

Optimizing Heart Disease Prediction with Feature Sensitivity...

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16th International Conference on Contemporary computing, IC3 2024

作者： Saranya, G. Tyagi, Amit Kumar Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Fashion Technology National Institute of Fashion Technology New Delhi India

ISBN: (纸本)9798400709722

Coronary heart disease (CHD) is associated with a loss of coronary artery blood supply to the heart. The risk of coronary cardiac disease is decreased in physical activity in epidemiologic studies. Researches have shown that regular exercise and the risk of heart disease have an inverse correlation. Sedentary people have the chance of CHD about twice as high-intensity workouts. Among persons with and without CHD, low physical activity levels have been reported to be significant predictors of all-cause and cardiovascular mortality. Evaluating a heart disease risk prediction model using machine learning classifier by considering a feature, number of hours of exercise per week and average calories burnt per day reduces the risk of developing heart disease. The optimum exercise level to stop CHD, however, is uncertain. Increased levels of exercise increase the risk of heart disease, if the patient increases in age. Exercise, both decrease and increase the risk of heart disease. Assessment of the risk of heart disease is very crucial based on effect of just exercise. Data Stratification is one solution where for a given age group, exercise decreases the risk of developing the disease. Various classifiers are used to assess the model. SVM with RBF kernel, KNN and Random Forest gives better accuracy compared to other classifiers. © 2024 Copyright held by the owner/author(s).

关键词： Risk assessment

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Heart Disease Forecast: A Comparative Analysis of Recurrent Neural Network and Long Short Term Memory 3

Heart Disease Forecast: A Comparative Analysis of Recurrent ...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Althaph, Bollapalli Challa, Nagendra Panini Nagaraju, Jajam Prasanna, Kamepalli S. L. VIT-AP University India Tirumala Engineering College Department of Information Technology Narasaraopet India Amrita Vishwa Vidyapeetham Department of School of Computing Amaravati India

ISBN: (纸本)9798331518592

Currently, the healthcare industry faces numerous significant challenges, being the need to improve the prognosis accuracy for cardiac illnesses. Using Long Short-Term Memory technique during this test to contribute to the research and development of an intelligent system that can predict cardiac illness. This paper presents a comparison of the RNN and LSTM techniques for forecasting cardiac disease. This comparison prioritizes accuracy and simultaneously considers other prognostic factors. After analysis this research concludes that LSTM algorithm is the most preferable method when compared to the RNN methodology. © 2024 IEEE.

关键词： Electronic health record

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Wide and fault diameter in Kneser graphs for enhanced fault tolerance in parallel computing

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International Journal of Networking and Virtual Organisations 2024年第3期31卷 169-190页

作者： Sundara Rajan, R. Kirithiga Nandini, G. Lin, Yuqing Reji, Remi Mariam Department of Mathematics Hindustan Institute of Technology and Science Chennai603 103 India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai603 103 India School of Electrical Engineering and Computing The University of Newcastle CallaghanNSW2308 Australia Department of Mathematics Christian College Kerala Chengannur689 122 India

A system’s fault tolerance is its capacity to function even if one or more of its components fail. Implementing a fault-tolerant network becomes an important criterion for reliable computing. Reliability measures play a significant part in recognising the role of faulty and non-faulty processors in a parallel computing system. Parallel computing is used primarily for saving time, solving big problems, and doing multiple tasks at once at the same time. Various reliability measures have been introduced to evaluate a network’s fault-tolerance capability. We have measured the wide diameter and fault diameter of the Kneser graphs in this study. Also, we have verified the fault diameter obtained using an experimental study. Further, we have described some applications of wide diameter and fault diameter in parallel and distributed computing. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Fault tolerant computer systems

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UWB Radar Signal Kick Detection for Tailgate Unlocking Based on Spatio-Temporal Network 2

UWB Radar Signal Kick Detection for Tailgate Unlocking Based...

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2nd IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2024

作者： Wang, Yuhan Yan, Shuang Fu, Yiling Shangguan, Tianyun Sun, Yuxuan Sun, Lin Hangzhou City University School of Computer and Computing Science Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China Westlake University School of Engineering Hangzhou China

ISBN: (纸本)9798350307733

Inductive tailgate systems have significantly enhanced user convenience in vehicle operations. However, the performance of current systems can be hindered by environmental factors, leading to reduced recognition accuracy and durability over time. To address these issues, we propose a kick gesture recognition method based on ultra-wideband (UWB) signals, implemented through a spatial-temporal neural network. Our method starts with employing a Time-Range-Doppler method to extract kick gesture sequences that contain temporal and spatial features from preprocessed data. These sequences are then fed into a network containing both Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) cells to predict the final results, where the CNN section is used to learn spatial features and the RNN section is used to learn temporal features. To verify the effectiveness of our approach, we conducted experiments in three typical parking environments: underground garages, overgrown lawns, and sloped terrains, collecting data from 12 participants for model training and testing. Experimental results demonstrated the feasibility of using UWB signals for wireless kick gesture recognition and confirmed the high accuracy of our proposed method. © 2024 IEEE.

关键词： Gesture recognition

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Security of Medical Multimodality Images for Telemedicine Using PDESNet Encryption Standard 2

Security of Medical Multimodality Images for Telemedicine Us...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Madhuri, P. Shyamala Kumar, D. J. Nagendra Amutha, B. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Tamilnadu Chennai603203 India Vishnu Institute of Technology Department of Information Technology Andhra Pradesh Bhimavaram534 202 India

ISBN: (纸本)9798350308846

The telemedicine approach is a famous medical assistance service in worldwide, in this process diagnosis records like Scan reports, X-ray, CT and all other radiology reports analysis has been done with doctors. Many times, sending and receiving of these images from one point to many points, due to this security has been reduced. The multimodality images are major key diagnosis elements to detect disease of patients, these multimodality images security also been reduced due to low encryption techniques. In this work an advanced cryptographic encryption technique i.e Parallel Data encryption Standard (PDESNet) algorithm. The PDES is a modern novel algorithm can provide security to multimodal images from transmission to storage part. Finally summarize the proposed algorithm with existed models in terms of PSNR, BER, NC, MSE, accuracy and recall. © 2023 IEEE.

关键词： encryption multimodal images security Telemedicine

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Weighted Average Ensembled Probability Pipeline of Pre-trained CNN Models for Hand Gesture Classification 15

Weighted Average Ensembled Probability Pipeline of Pre-train...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Gunti, Rahul Rohan Reddy, B. Swathi, Gunti Sharath Kumar Reddy, G. Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai601103 India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350370249

The automatic detection of sign language from hand gesture images is fundamental for effective human-computer interaction, especially for individuals with hearing and speech disorders. Achieving accurate detection and classification of sign language gestures is paramount. However, accurately recognizing these gestures frequently presents challenges due to dull backgrounds, variations in gesture orientation, and fluctuations in lighting conditions. Previous approaches tried to overcome these challenges but still the model performance lacks recognition accuracy and visual quality. To address this need for accuracy, we proposed a Weighted Average Ensembled Probability Pipeline composed of EfficientNetB7, ResNet-50, and Xception models. Leveraging the Extensive Hand Gesture Recognition Database (HGRD), comprising 20,000 images, our individual models achieved notable accuracies: 96.498 % for EfficientNetB7, 93.2 % for ResNet-50, and 90.175 % for Xception. Through ensemble learning, we integrate these models to attain an impressive overall accuracy of 98.95 %. This research contributes to robust gesture recognition system and underscores the significance of ensemble techniques in enhancing performance for Gesture recognition systems. © 2024 IEEE.

关键词： Gesture recognition

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