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A Performance Study of Selected Machine Learning Techniques for Predicting Heart Diseases 2nd

A Performance Study of Selected Machine Learning Techniques ...

2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Olorunfemi, Blessing Oluwatobi Ogunde, Adewale Opeoluwa Adeniyi, Abidemi Emmanuel Awotunde, Joseph Bamidele Imoize, Agbotiname Lucky Li, Chun-Ta Faculty of Natural Sciences Department of Computer Science Redeemer’s University Osun Nigeria Department of Computer Science Bowen University Iwo Nigeria Faculty of Information and Communication Sciences Department of Computer Science University of Ilorin Ilorin240003 Nigeria Faculty of Engineering Department of Electrical and Electronics Engineering University of Lagos Lagos Akoka100213 Nigeria Bachelor’s Program of Artificial Intelligence and Information Security Fu Jen Catholic University New Taipei City24206 Taiwan

ISBN: (纸本)9789819777853

Heart Disease remains a leading cause of mortality worldwide. It alarmingly rises at a quick rate, making early heart disease prediction crucial for effective prevention and timely intervention. Heart disease diagnosis is a difficult process that requires technical skills and accuracy to complete. With improvements in technology, computing has lent its voice to simplify the diagnosis of various health problems. Machine learning uses past or existing history to predict future results. Various machine learning techniques have been developed over the years and used in predicting heart diseases with various levels of performance. Identifying the best-suited machine learning technique to use for prediction purposes can be a challenging task. This research work analyses the performance of seven (7) machine learning techniques, comprising AdaBoost Algorithm, KNN, Logistic Regression, Naïve Bayes Classifier, Random Forest, SVM, and XGBoost. The heart disease dataset was downloaded from the UCI repository and analysed using Python programming language in the Jupyter Notebook environment. A comparative analysis of the seven (7) techniques was performed based on Accuracy, Precision, and Recall. From the results obtained, KNN, Random Forest, and XGBoost showed superior performance over the others with an accuracy of 100%, AdaBoost Algorithm followed with an accuracy of 92.2%, SVM followed with an accuracy of 91.71%, Naïve Bayes Classifier followed with an accuracy of 88.29% while Logistic Regression has the least accuracy of 86.34%. KNN, RF, and XGBoost outperformed AdaBoost, SVN, and LR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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ChatGPT the Omniscient? A Guide to Effective Prompting 7

ChatGPT the Omniscient? A Guide to Effective Prompting

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Kovari, Attila Institute of Digital Technology Eszterházy Károly Catholic University Faculty of Computer Science Eger Hungary Institute of Electronics and Communication Systems Obuda University Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Institute of Computer Science University of Dunaujvaros Dunaujvaros Hungary

ISBN: (纸本)9798331531119

The invention of artificial intelligence and natural language processing has revolutionised human-machine interaction, and OpenAI's ChatGPT models are at the forefront of this. GPT-3 and GPT-4 models generate human-like text, answer questions and perform all kinds of language-related tasks. ChatGPT relies heavily on the quality of the requests it receives. This paper addresses the issue of efficient prompting in order to maximise its potential. Different prompting techniques are described in the paper: prompt template design, continuous prompts, few-shot learning, chain of thought prompting and metadata expansion. The conclusions show that learning how to write good prompts is crucial for realizing the full potential of ChatGPT in various natural language processing tasks. © 2024 IEEE.

关键词： Zero-shot learning

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Preserving Performance and Reducing Energy Consumption: A Two-Phase Scheduling Approach in Cloud Computing Environments 9

Preserving Performance and Reducing Energy Consumption: A Tw...

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Joint 9th International Conference on Digital Arts, Media and Technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Thanavanich, Thanawut Kantawong, Krittika Yaibuates, Mayoon Duangtang, Pumipong Siri, Atikhom Sriwanna, Kittakorn Chiang Rai Rajabhat University Faculty of Digital Technology Department of Computer Engineering Chiang Rai Thailand University of Phayao School of Information and Communication Technology Department of Digital Business Phayao Thailand

ISBN: (纸本)9798350318241

Cloud computing has evolved significantly, transitioning from traditional on-demand architectures to decentralized, multi-provider models. This shift is pivotal for enhancing connectivity in the Internet of Things and data-intensive computing. While accessing shared online resources, effective task scheduling is crucial for optimal service performance. However, the challenge persists in balancing resource optimization with potential issues like energy wastage or service performance degradation. This study proposes a two-phase scheduling approach in distributed computing, addressing the balance between performance preservation and energy reduction. The method seamlessly integrates with existing algorithms, employing a prescheduling phase followed by a model to estimate processors, minimizing underutilization. Experimental results highlight its contribution to energy-efficient scheduling, emphasizing a nuanced balance between performance and energy consumption. The approach employs a mathematical model to determine suitable processors, providing versatile options for computational resource providers, aiming for energy-conscious and performance-oriented solutions. © 2024 IEEE.

关键词： Cloud computing

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Human Action Recognition Using Difference of Gaussian and Difference of Wavelet

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Big Data Mining and Analytics 2023年第3期6卷 336-346页

作者： Gopampallikar Vinoda Reddy Kongara Deepika Lakshmanan Malliga Duraivelu Hemanand Chinnadurai Senthilkumar Subburayalu Gopalakrishnan Yousef Farhaoui Department of Computer Science and Engineering(AI&ML) CMR Technical CampusHyderabad 501401India. Department of Information Technology Kakatiya Institute of Technology and ScienceWarangal 506015India. Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women(Autonomous)Hyderabad 500100India. Department of Computer Science and Engineering S.A.Engineering College(Autonomous)Thiruverkadu 600077India. Department of Electronics and Communication Engineering Saveetha Institute of Medical and Technical Sciences(SIMATS)Chennai 602105India. Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India. IDMS Team STI LaboratoryFaculty of Sciences and TechniquesMoulay Ismail University of MeknèsErrachidia 25003Morocco.

Human Action Recognition(HAR)attempts to recognize the human action from images and *** major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments.A novel action descriptor is proposed in this study,based on two independent spatial and spectral *** proposed descriptor uses a Difference of Gaussian(DoG)filter to extract scale-invariant features and a Difference of Wavelet(DoW)filter to extract spectral *** create a composite feature vector for a particular test action picture,the Discriminant of Guassian(DoG)and Difference of Wavelet(DoW)features are *** Discriminant Analysis(LDA),a widely used dimensionality reduction technique,is also used to eliminate duplicate ***,a closest neighbor method is used to classify the *** and UCF 11 datasets were used to run extensive simulations of the suggested strategy,and the accuracy assessed after the simulations were run on Weizmann datasets for five-fold cross validation is shown to perform *** average accuracy of DoG+DoW is observed as 83.6635%while the average accuracy of Discrinanat of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 80.2312%and 77.4215%,*** average accuracy measured after the simulation of proposed methods over UCF 11 action dataset for five-fold cross validation DoG+DoW is observed as 62.5231%while the average accuracy of Difference of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 60.3214%and 58.1247%,*** the above accuracy observations,the accuracy of Weizmann is high compared to the accuracy of UCF 11,hence verifying the effectiveness in the improvisation of recognition accuracy.

关键词： human action recognition difference of Gaussian difference of wavelet linear discriminant analysis Weizmann UCF 11 accuracy

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engineering Framework for Smart Poultry Farming Using Internet of Things

Engineering Framework for Smart Poultry Farming Using Intern...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Manju, K. Dinesh, P.M. Deepa, K. Kathan, Karthik Lakshmi, G. Prasanna Alagarsamy, Manjunathan Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tiruchirapalli India Kongunadu College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Trichy621215 India Sandip University School of Computer Science and Engineering Maharashtra Nashik India K.Ramakrishnan College of Technology Department of Electronics and Communication Engineering Tamil Nadu Trichy621112 India

ISBN: (纸本)9798350390049

Automation is more important now. This article focuses on automating chicken farms using mobile communication devices and wireless sensor networks. Chicken is the world's most popular food because it contains more protein, less fat, and less cholesterol than other poultry. Chicken's nutrition is another benefit. This project utilizes the Internet of Things to monitor and adjust chicken farm environmental conditions. Temperature, moisture, water level, ammonia gas, and waste level were considered. This research aimed to improve chicken farm chick growth. The individual also monitors chicken behavior and feeding. Another usage for the sensor module is water level management and monitoring. If all sensors are connected to the microcontroller, all sensor data may be gathered. After that, a Wi-Fi module uploads the data to a website. A computer or mobile device connected to the internet allows the poultry farm owner to examine its interior surroundings. This informs the owner on agricultural conditions. This system will maintain temperature, humidity, and water level without human intervention using a DC motor, ventilation window, and co-exhaust fan. This will be done without human intervention. Devices will activate as needed when threshold values are attained. Our method allows semi-automated chicken product production and bird behavior monitoring. This allows us to deliver nutritious chicken meat while reducing bird killing. © 2024 IEEE.

关键词： Humidity sensors

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New Design of Intelligent Reflective Surface Based on Metamaterial Surface with Configurable Gain and Direction 2

New Design of Intelligent Reflective Surface Based on Metama...

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2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022

作者： Al-Tag, Abdulaziz A. S. Al-Mahdi, Rayaan M. Al-Hedari, Mohammed B. Al-Ariqi, Buthina A. Al-Hetar, Abdualaziz Taiz University Al-saeed Faculty of Engineering IT Communication and Computer Dep Yemen LIU-Sana'a Yemen

ISBN: (数字)9781665461818

ISBN: (纸本)9781665461818

With the increase in the demand on the data and the increase in the number of users, there was a need to work in high frequencies (THz bands). Which lead us to design reflective surface that work in W-band (80-110 GHz). In this paper, a new Intelligent Reflective Surface (IRS) design based on Metamaterial Surfaces with configurable gain. Dynamic ranges of 3.7 dB for amplitude and 300 degrees for phase are achieved for reflected waves with a steering range of up to 84 for azimuth or elevation. Comparable to ordinary reflecting surfaces that just orient the waves that are reflected. This design provides for good dynamic gain and direction of the reflected wave, as well as horizontal and vertical polarization support. © 2022 IEEE.

关键词： Polarization

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Novel Algorithm for Mobile Robot Path Planning in Constrained Environment

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computers, Materials & Continua 2022年第5期71卷 2697-2719页

作者： Aisha Muhammad Mohammed A.H.Ali Sherzod Turaev Ibrahim Haruna Shanono Fadhl Hujainah Mohd Nashrul Mohd Zubir Muhammad Khairi Faiz Erma Rahayu Mohd Faizal Rawad Abdulghafor Faculty of Manufacturing Engineering Universiti Malaysia Pahang(UMP)Pekan26600Malaysia Department of Mechanical Engineering Faculty of EngineeringUniversity of Malaya50603Kuala LumpurMalaysia Department of Computer Science and Software Engineering College of Information TechnologyUnited Arab Emirates UniversityAl AinUnited Arab Emirates Faculty of Electrical and Electronics Engineering Universiti Malaysia Pahang(UMP)Pekan26600Malaysia Department of Mechatronics Engineering Faculty of TechnologyBayero UniversityKano(BUK)700241Nigeria Computer Science and Engineering Department Chalmers and University of Gothenburg41296GothenburgSweden Department of Artificial Intelligence Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia Department of Computer Science Faculty of Information and Communication TechnologyInternational Islamic University Malaysia53100Kuala LumpurMalaysia

This paper presents a development of a novel path planning algorithm, called Generalized Laser simulator (GLS), for solving the mobilerobot path planning problem in a two-dimensional map with the presence ofconstraints. This approach gives the possibility to find the path for a wheelmobile robot considering some constraints during the robot movement inboth known and unknown environments. The feasible path is determinedbetween the start and goal positions by generating wave of points in alldirection towards the goal point with adhering to constraints. In simulation,the proposed method has been tested in several working environments withdifferent degrees of complexity. The results demonstrated that the proposedmethod is able to generate efficiently an optimal collision-free path. Moreover,the performance of the proposed method was compared with the A-star andlaser simulator (LS) algorithms in terms of path length, computational timeand path smoothness. The results revealed that the proposed method hasshortest path length, less computational time and the best smooth path. Asan average, GLS is faster than A∗ and LS by 7.8 and 5.5 times, respectivelyand presents a path shorter than A∗ and LS by 1.2 and 1.5 times. In orderto verify the performance of the developed method in dealing with constraints, an experimental study was carried out using a Wheeled Mobile Robot(WMR) platform in labs and roads. The experimental work investigates acomplete autonomous WMR path planning in the lab and road environmentsusing a live video streaming. Local maps were built using data from a live video streaming with real-time image processing to detect segments of theanalogous-road in lab or real-road environments. The study shows that theproposed method is able to generate shortest path and best smooth trajectoryfrom start to goal points in comparison with laser simulator.

关键词： Path planning generalized laser simulator wheeled mobile robot global path panning local path planning

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Secure Key Management in 5G Networks Using Elliptic Curve Integrated Encryption Scheme (ECIES) for Low-Power IoT Devices 4

Secure Key Management in 5G Networks Using Elliptic Curve In...

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4th IEEE International Conference on Mobile Networks and Wireless communications, ICMNWC 2024

作者： Chitra Kiran, N. Maindola, Meenakshi Kumara, Bharath Kaliappan, S. Bhatt, Pooja Maranan, Ramya Alliance University Department of Electronics and Communication Engineering Bengaluru India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Ramaiah University of Applied Science Department of Electronics and Communication Engineering Bengaluru India Lovely Professional University Division of Research and Development Phagwara India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Computer Science and Engineering Post Limda India Saveetha School of Engineering Simats Department of Research and Innovation Chennai India

ISBN: (纸本)9798350352931

The rapid proliferation of the Internet of Things (IoT) has led to a surge in cyber threats, demanding the development of robust security frameworks. This paper introduces an advanced threat detection model, leveraging Artificial Intelligence (AI) and Cyber Twin Technologies for enhanced IoT security. The proposed framework integrates a Cyber Twin-a digital replica of physical IoT devices-with real-time data analytics to detect, predict, and respond to sophisticated cyber-attacks. The Cyber Twin continuously monitors IoT networks, identifying abnormal behaviors and enabling the implementation of dynamic security measures through AI-driven intrusion detection systems (IDS). A hybrid deep learning approach combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) is utilized to enhance anomaly detection and threat classification accuracy. The system's ability to simulate various attack scenarios within a digital environment facilitates the development of effective countermeasures while minimizing the impact on actual IoT operations. Experimental results demonstrate significant improvements in detection accuracy, reduced false positives, and enhanced system resilience. The proposed AI and Cyber Twin-based threat detection model sets a new benchmark in safeguarding IoT infrastructures against emerging cyber threats. © 2024 IEEE.

关键词： Network intrusion

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Reinforcement of Medical Image Security Through Steganography and Encryption Techniques 3

Reinforcement of Medical Image Security Through Steganograph...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Khallaf, Fatma El-Shafai, Walid El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. Menoufia University Faculty of Electronic Engineering Department of Electronics and Communication Engineering Menouf32952 Egypt Ahram Canadian University Faculty of Engineering Department of Electrical Engineering Giza Egypt Prince Sultan University Security Engineering Laboratory Department of Computer Science Riyadh11586 Saudi Arabia

ISBN: (纸本)9798350323511

Patients' data is stored in Electronic Health Records (EHRs) for confidentiality purposes. However, the insecure networks of hospitals and clinics puts patient information at risk. To address this ethical issue, secure communication is crucial. The Digital Imaging and communications in Medicine (DICOM) standard governs the transmission of EHRs to maintain data integrity and confidentiality. This paper combines cryptography and steganography techniques to transmit medical information and images across hospital networks, securely. The introduced framework comprises encryption of patients' data using DNA-based Advanced Encryption Standard (AES), hiding the information within images using Least Significant Bit (LSB) embedding, compressing the stego images using Discrete Wavelet Transform (DWT), and reversal of the process at the receiver end. Simulation experiments demonstrate that this framework comprising both encryption and steganography outperforms existing methods in securing the medical data. © 2023 IEEE.

关键词： Cryptography

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Implementation of Cloud Computing and Internet of Things (IoT) by Performance Evaluation 12

Implementation of Cloud Computing and Internet of Things (Io...

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12th International Electrical engineering Congress, iEECON 2024

作者： Sithiyopasakul, Jiran Archevapanich, Tuanjai Sithiyopasakul, Saran Lasakul, Attasit Purahong, Boonchana Benjangkaprasert, Chawalit School Of Engineering King Mongkut's Institute Of Technology Ladkrabang Bangkok Thailand Rajamangala University Of Technology Suvarnabhumi Faculty Of Engineering And Architecture Department Of Electronics And Communication Engineering Nonthaburi Thailand School Of Engineering King Mongkut's Institute Of Technology Ladkrabang Department Of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350383591

The integration of cloud computing and the Internet of Things (IoT) holds transformative potential across diverse industries. Performance assessment is essential to gauge the quality and efficiency of cloud computing and IoT systems. This paper presents a comprehensive performance evaluation of cloud computing and IoT systems, focusing on three major platforms: Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. Experimental results encompass various scenarios, including normal operation, heavy load conditions, IoT applications, and scalability testing. The outcomes reveal distinct performance metrics such as response time, throughput, latency, and reliability for each cloud platform. © 2024 IEEE.

关键词： Internet of things

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