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4th IEEE International Conference on Electronic communications, Internet of Things and Big Data, ICEIB 2024

作者： Liu, Xiaoxiao McGrath, Sean Flanagan, Colin Lei, Yiming Zeng, Liaoyuan Electronic and Computer Engineering University of Limerick Limerick Ireland School of Mathematics and Information HeBei University HeBei China Mobile Digital Hospital System School of Electronics Peking University Ministry of Education Engineering Research Center Beijing China University of Electronic Science and Technology of China Department of Information and Communication Engineering Chengdu China

ISBN: (纸本)9798350360721

A comprehensive understanding of physiological data is essential for anesthesiologists to monitor and maintain the health of surgical patients. Understanding anesthetic signals is essential to improve over the past ten decades to comprehend a variety of diseases, ensure the safety of patients, and accelerate their recovery. We identified and summarized four themes of artificial intelligence (AI) research in anesthesia: applications of AI in anesthesiology, signals monitored in different anesthesia stages, commonly monitored signs, and data standardization. The effect of various AI technologies on data analysis was studied, and the types, functions, sensors, characteristics, and intelligent analysis of physiological signals monitored during anesthesia are presented in this article. AI influences anesthesia practices in many ways, including preoperative data evaluation, anesthetic depth monitoring, and postoperative event prediction. © 2024 IEEE.

关键词： Machine learning

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A Comparative Analysis of SMS Spam Detection employing Machine Learning Methods 6

A Comparative Analysis of SMS Spam Detection employing Machi...

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6th International Conference on Computing Methodologies and communication, ICCMC 2022

作者： Aliza, Humaira Yasmin Nagary, Kazi Aahala Ahmed, Eshtiak Puspita, Kazi Mumtahina Rimi, Khadiza Akter Khater, Ankit Faisal, Fahad Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Tampere University Faculty of Information Technology and Communication Sciences Tampere Finland Jadavpur University Department of Computer Science and Engineering India

ISBN: (纸本)9781665410281

In recent times, the increment of mobile phone usage has resulted in a huge number of spam messages. Spammers continuously apply more and more new tricks that cause managing or preventing spam messages a challenging task. The aim of this study is to detect spam message to prevent different cybercrimes as spam messages have become a security threat nowadays. In this paper, studies on SMS spam problems to perform a better accuracy using several different techniques such as Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, Random Forest, Logistic Regression and some more are performed. The result indicated that Support Vector Machine achieved the highest accuracy of 99%, indicating it might be useful as an effective machine learning system for future research. © 2022 IEEE.

关键词： Support vector machines

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A Novel and Accurate Method of Diagnosis via AI and ML for Smart HC system 4

A Novel and Accurate Method of Diagnosis via AI and ML for S...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Elizabeth, J. Ruby Emmanuel, Mathews Aswini, E. Pathak, Shriram Bhuvaneswari, M. Choubey, Abhishek Nehru Institute of Engineering and Technology Department of Computer Science and Business System Tamil Nadu T.M.Palayam India Sathyabhama University of Science and Technology chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering India Ies Insititute of Technology and Management Ies University Department of Mechanical Engineering M.P. Bhopal India Karpagam Academy of Higher Education Department of Biomedical Engineering Coimbatore India Sreenidhi Institute of Science and Technology ghatkesar Hyderabad Yamnampet India

ISBN: (纸本)9798350360165

Recent breakthroughs in the internet of Issues (IoT), computing through the cloud, and the use of AI have changed the standard healthcare system, making a case for smart health care. By combining key technologies including IoT and AI, there is a major improvement in medical services. The merging of IoT and AI offers multiple possibilities within the healthcare industry. This study piece presents a new artificial intelligence- and internet of things convergence-based framework for disease detection in smart health care systems, emphasizing especially on cardiovascular diseases and diabetes. The suggested model includes separate steps, including data collection, preparation, classification, and parameter modification. Utilizing Internet of Things devices like smartwatches along with sensors enables smooth collection of information, while AI methods harness this data for the detection of illnesses. The technique presents a new approach, applying to the Crowd Search Optimizer algorithm-based Flowed Long and Short-Term Memory (CSO-CLSTM) approach to disease detection. CSO is applied to improve both 'weights' while 'bias' factors of the CLSTM framework, while the isolation Fg (iForest) method is implemented to remove outliers. Through testing with health care information, the CSO-LSTM model got excellent accuracy ratings of 96.16% when 97.26% in identifying heart disease while diabetes, respectively. Consequently, the suggested CSO-LSTM model appears as a solid and reliable method of disease evaluation in digital healthcare systems, showing its potential for broad application. © 2024 IEEE.

关键词： Electronic health record

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Anaesthesia Monitoring: From Conventional to AI-Enabled Data Acquisition Methods 20

Anaesthesia Monitoring: From Conventional to AI-Enabled Data...

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20th International Conference on Smart Technologies, EUROCON 2023

作者： Liu, Xiaoxiao McGrath, Sean Flanagan, Colin Lei, Yiming Zeng, Liaoyuan University of Limerick Electronic and Computer Engineering Limerick Ireland School of Electronics Peking University Ministry of Education Engineering Research Center on Mobile Digital Hospital System Beijing China University of Electronic Science and Technology of China Department of Information and Communication Engineering Chengdu China

ISBN: (纸本)9781665463973

It is widely recognized that the timely acquisition and presentation of accurate physiological data is a vital aid to the anaesthesiologist in monitoring and maintaining the well-being of a patient undergoing surgery. Collecting, storing, synchronizing, integrating, evaluating, and retrieving pertinent information from patient data remains very difficult due to limited computer power, the need for specialized software, inconsistency between monitoring devices, and inadequate data storage. This paper introduces the general steps of data acquisition in anaesthesia and summarizes some data acquisition systems. It also introduces some applications of the burgeoning field of artificial intelligence (AI) to data acquisition, illustrating how AI techniques may be used to advantage to assist the anaesthesiologist in monitoring and interpreting such data. © 2023 IEEE.

关键词： Data acquisition

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EEG based stress classification by using difference plots of variational modes and machine learning

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16347-16360页

作者： Salankar, Nilima Qaisar, Saeed Mian School of Computer Science University of Petroleum and Energy Studies Dehradun India Persistent System Limited IT Park Maharashtra Nagpur India Department of Electrical and Computer Engineering EFfat University Jeddah22332 Saudi Arabia Communication and Signal Processing Lab Energy and Technology Research Center Effat University Jeddah22332 Saudi Arabia

The recent trend in healthcare is to use the automated biomedical signals processing for an augmented and precise diagnosis. In this context, an original approach is presented for categorization of stress and non-stress classes by processing the multichannel Electroencephalogram (EEG) signals. The EEG signals are decomposed by using the "Empirical Mode Decomposition" (EMD) and "Variational Mode Decomposition" (VMD), respectively render the "Intrinsic Mode Functions" (IMFs) and Modes. The Second Order Difference Plots (SODPs) are traced for each IMF and Mode. Shapes of SODPs are used to discriminate the two intended categories. The feature space includes areas, Central Tendency Measures (CTMs) and means of SODPs. It is derived from first 7 IMFs and Modes. The identification of stress and non-stress categories is performed by using the robust machine learning algorithms namely, the Support Vector Machine (SVM), Boosting with Random Forest (RF) and Multilayer Perceptron Neural Network (MLPNN). The applicability is tested by using a publicly available EEG signals dataset, recorded during mental arithmetic test. A detailed evaluation is carried out at the subset, individual channel and lobes wise. Noteworthy highest accuracies of subset wise 99.89%, channel wise 98.89% and lobe wise 99.99% are obtained respectively. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Empirical mode decomposition

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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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Mathematical Modelling of Dengue Transmission with Adaptive Fuzzy Backstepping Control and RK-5 Numerical Method

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International Journal of Applied and Computational Mathematics 2025年第3期11卷 1-29页

作者： Vijayalakshmi, G.M. Ariyanatchi, M. Akgül, Ali Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Tamilnadu Avadi 600062 India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Department of Mathematics Art and Science Faculty Siirt University Siirt 56100 Turkey Department of Computer Engineering Biruni University Istanbul Topkapı 34010 Turkey Department of Mathematics Mathematics Research Center Near East University Near East Boulevard Mersin 10 Nicosia 99138 Turkey

The aim of this research is to create an adaptive nonlinear fuzzy backstepping robust controller for a dengue disease outbreak model. We use an SIR-SI compartmental mathematical model to analyse the diffusion mechanism of dengue disease. A nonlinear adaptive controller regulates prevention strategies for susceptible human beings and enhances hospitalization-based treatment for the infected people. Besides, the spraying of chemical insecticides will reduce the infected mosquito population. Adaptation laws are used to derive fuzzy system parameters, enhancing the system's adaptability. Then we construct a suitable Lyapunov function, which guarantees convergence, robustness, and stability for a closed-loop system. Finally, we plot all the system's control variables graphically and calculate their numerical approximations using the Runge–Kutta technique of fifth order. These simulations demonstrate that the proposed adaptive controllers can effectively control the dengue disease and ensure the stability of the system. Hence, we conclude that adaptive backstepping control measures can protect more people from dengue infection. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2025.

关键词： Adaptive back stepping Dengue model Fuzzy system Lyapunov function Runge–Kutta method

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TDA-SSNN: Efficient Hybrid Top-Down Attention Siamese Sparrow Neural Network for Automatic Drone Navigation 4

TDA-SSNN: Efficient Hybrid Top-Down Attention Siamese Sparro...

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4th International Conference on Sustainable Expert systems, ICSES 2024

作者： Aamer, Najmuddin Bhadula, Shuchi Mahashree, P. Patil, Harshal Gurpur, Shashikala Verma, Apury Bharat Ratna Indira Gandhi College of Engineering Department of Computer Science and Engineering Maharashtra Solapur410003 India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun248002 India School of Electronics and Communication Engineering Rev A University Karnataka Bangalore560064 India School of Computer Science & Engineering Iilm University Uttar Pradesh Greater Noida201 306 India Viman Nagar Maharashtra Pune411014 India Parul University Faculty of Management studies P.O.Limda Ta.Waghodia Gujarat Vadodara391760 India

ISBN: (纸本)9798331540364

Unmanned aerial vehicles (UAVs) are increasingly employed in diverse applications such as military operations, delivery services, traffic and architectural monitoring, and aerial surveillance. These drones often encounter challenges in navigating complex and dynamic environments, including irregular object shapes, occlusions, and varying contrasts with backgrounds. To overcome these obstacles, this study presents a novel hybrid approach that integrates Top-Down Attention with a Siamese Sparrow Neural Network (TDA-SSNN) for efficient and data-driven drone navigation. The methodology begins with sourcing input images from publicly available datasets, including those from Udacity's project. To enhance image quality, the Robust Generalized Maximum Blake-Zisserman Total Correntropy (RMB-ZTC) adaptive filter is employed for preprocessing. Feature selection is performed using a novel Gorilla Troops Optimizer with Artificial Hummingbird Optimization (GTO-AMO). For automatic drone navigation, the TDA-SSNN is utilized, enhanced by an advanced Sparrow Search Algorithm to optimize the performance of the Top-Down Attention-based Siamese Neural Network. The proposed approach achieves a remarkable accuracy of 99.6%. © 2024 IEEE.

关键词： Drones

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Hybrid UHF Quad Antenna for Indoor Use

Hybrid UHF Quad Antenna for Indoor Use

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International Conference on Smart systems and Technologies (SST)

作者： Tomislav Barić Hrvoje Glavaš Vanja Mandrić Department of Electromechanical Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Power Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Communication Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9798350386394

ISBN: (纸本)9798350386400

Amateur ultra-high frequency (UHF) directional antennas intended for indoor use are typically used in confined spaces. Also, their position and orientation often have to be changed, therefore they are exposed to frequent mechanical stress. For this reason, it is preferable that this type of antenna is lightweight, compact, with a mechanically reinforced (strong) construction. Also, it is desirable that the antenna has the best possible electrical and electromagnetic characteristics. This paper presents one of the ways in which the mechanical, electrical and electromagnetic requirements for amateur radio UHF directional indoor antenna can be simultaneously met. In the article, a relatively simple construction of the antenna is proposed. The antenna consists of two vertically stacked hybrid antennas from the Quad antenna family. The paper also presents an interesting choice of materials which ensures a mechanically reinforced (strong) construction and lightweight of the antenna. Numerical calculations and optimizations were carried out using 4nec2 software. Bandwidth and voltage standing wave ratio were determined by measurement.

关键词： Antenna measurements Coaxial cables Voltage measurement Aluminum UHF antennas Soldering Electromagnetics Junctions Antennas Stress

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Dual Radial-Resonant Wide Beamwidth Circular Sector Microstrip Patch Antennas

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Chinese Journal of Electronics 2023年第4期32卷 710-719页

作者： MAO Xiaohui LU Wenjun JI Feiyan XING Xiuqiong ZHU Lei Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau

In this article, a design approach to a radial-resonant wide beamwidth circular sector patch antenna is advanced. As properly evolved from a U-shaped d ipole, a prototype magnetic dipole can be fit in the radial direction of a circular sector patch radiator, with its length set as the positive odd-integer multiples of onequarter wavelength. In this way, multiple TM0m（m = 1,2,...） modes resonant circular sector patch antenna with short-circuited circumference and widened E-plane beamwidth can be realized by proper excitation and perturbations. Prototype antennas are then designed and fabricated to validate the design approach. Experimental results reveal that the E-plane beamwidth of a dual-resonant antenna fabricated on air/Teflon substrate can be effectively broadened to 128°/120°, with an impedance bandwidth of 17.4%/7.1%, respectively. In both cases, the antenna heights are strictly limited to no more than 0.03-guided wavelength. It is evidently validated that the proposed approach can effectively enhance the operational bandwidth and beamwidth of a microstrip patch antenna while maintaining its inherent low profile merit.

关键词： Geometry Patch antennas Perturbation methods Prototypes Magnetic resonance Microstrip antennas Bandwidth

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