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8th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2020

作者： Anupama, H.s. Jain, Raj v. venkatesh, Revannur Cauvery, N.K. Lingaraju, G.M. Department of Artificial Intelligence and Machine Learning BMS Institute of Technology & Management Bangalore India Department of Computer Science and Engineering R. V. College of Engg Bangalore India Department of Information Science and Engineering R. V. College of Engg Bangalore India Department of Information Science M. S. Ramaiah Institute of Tech. Bangalore India

ISBN: (纸本)9789811557873

Machine learning (ML) is the field that adds intelligence to devices providing them with capabilities to process and identify patterns in data just like human beings do. Programming devices in this manner can help in identifying those patterns which human beings often cannot. Machine learning is based on modelling data mathematically. ML has been gaining a lot of attention in the last few decades, especially in fields of interdisciplinary research. brain–computer Interface (bCI) is an area where Machine Learning Technology is been rapidly using. Also, Machine Learning techniques have to be used so that one can get a better result and more efficiency. Information Transfer Rate is the best way to measure the performance of the signals. The current research is mainly focused on achieving the systems with higher ITR. The focus of the proposed system is to get better and high Information Transfer Rate by merging two different approaches. The approach used in this work is (ssvEP), visually Evoked Potential and (ssAEP) Auditory Evoked Potential by using Hidden Markova Model (HMM). The system which is to be developed checks whether the existing system has such facility if it has, does it provides accuracy which is of a higher rate and can put it in the real-world applications. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to springer Nature singapore Pte Ltd.

关键词： Hidden Markov models

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smart Irrigation system based on the Type of Crop using IoT

Smart Irrigation System based on the Type of Crop using IoT

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International Conference on I-sMAC (IoT in social, Mobile, Analytics and Cloud) (I-sMAC)

作者： U. Kuttalingam Rajesh. M vinod N. Alone Ruthra Ramakrishnan s. Rani Ramya Maranan Department of Computer Science V O.Chidambaram College Thoothukudi Thoothukudi Tamil Nadu India Department of Mechanical Engineering Dr. Ambedkar Institute of Technology Bangalore Karnataka India Computer Engineering Vasantdada Patil Pratishthan's College of Engineering & Visual Arts Mumbai Maharashtra India Department of Civil Engineering St. Joseph's College of Engineering OMR Chennai Tamil Nadu India Department of Commerce Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

In India, agriculture is of utmost importance because it provides the main means of support for a sizeable segment of the people, particularly in rural areas. It ensures food security, boosts the nation's economy, and aids in the country's general socioeconomic development. Irrigation is very important to agriculture and has several important benefits. Irrigation provides crops with a managed and consistent water supply, especially in regions with little or variable rainfall. In spite of how efficient controlled irrigation is, it also necessitates constant human monitoring, which in some situations may seem impossible. This study aims to overcome this limitation by developing a straightforward but effective circuit that can track multiple parameters and determine how much water the plant needs. Three different sensors are used to measure the vitals from the soil and send it to the Arduino board. The user is also allowed to enter the type of plant to pick the perfect value range of the parameters. The Arduino then analyses the values from the sensors and finalizes the amount of water that had to be sent to the plants. The sent data is also shared with the user via the cloud console of the Arduino. The user can also view the flow of the water through the console. This design is tested using three different circumstances for three plant types. The circumstances include normal range, dry range, and extremely dry. The performance of the system is then analyzed to find that the model is able to change the water flow according to the circumstance on its own.

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Pneumonia Detection: An Efficient Approach Using Deep Learning 11

Pneumonia Detection: An Efficient Approach Using Deep Learni...

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11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020

作者： Pant, Ayush Jain, Akshat Nayak, Kiran C Gandhi, Daksh Prasad, b.G. B.M.S. College of Engineering Department of Computer Science Bengaluru Karnataka India

ISBN: (纸本)9781728168517

Pneumonia is one of the largest infectious diseases that cause death in children and elderly people across the globe. Pneumonia impacts all the elderly and young people's families and children everywhere but is most prevalent in sub-saharan Africa and south Asia. In December 2019 Wuhan, a city of China was affected by deadly, gruesome Pneumonia which was declared a pandemic by World Health Organisation. but the reason for the outbreak was not clear to everyone. Later, the doctors identified the disease as a new species of coronavirus, also currently known as COvID-19. The main motivation behind this research was to identify Pneumonia just by using the X-Ray images of the patients. As doctors must do a lot of certain tests to identify if the patient has Pneumonia or not. To solve the cumbersome problem, an ensemble of two deep learning models is developed, to make the work of the doctors simpler. In this paper, a comparison between previously written relevant research papers is done and concluded with an ensembled deep learning model to achieve a remarkable test data accuracy or unseen data accuracy. © 2020 IEEE.

关键词： Coronavirus

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Analysis and Prediction of Wear Characteristics of sustainable Metal Matrix Composites Using Machine Learning with Decision Making Algorithm

Analysis and Prediction of Wear Characteristics of Sustainab...

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smart Electronics and Communication (ICOsEC), International Conference on

作者： vaishali N. Agme Ashish Rushali Pant Mulagundla sridevi A. suman M. vijaya sekhar babu Department of Applied Science Bharati Vidyapeeth College of Engineering Navi Mumbai Maharashtra India Department of CSE Koneru Lakshmaiah Education Foundation (KLEF) - KL Deemed University Vaddeswaram Andhra Pradesh India ME Dehradun R/S Graphic Era Deemed To Be University Dehradun India Department of Computer Science and Engineering CVR College of Engineering Mangalpalli (V) Ibrahimpatnam (M) Telangana India Department of Mathematics Audisankara College of Engineering and Technology Gudur Tirupati Andhra Pradesh India Department of Mechanical Engineering GIET Engineering College Rajamahendravaram Andhra Pradesh India

The goal of this research was to analyse and predict the wear characteristics of sustainable metal matrix composites using machine learning with a decision-making algorithm. The study used Al 7072 alloy with nano-particle siC as an alloying element, which was added in three different compositions (0.3 %, 0.6%, and 0.9 % ) through stir casting. The experiment was based on the Taguchi L27 orthogonal array and an Artificial Neural Network (ANN) was utilised as a machine learning approach to predict the responses with 100% accuracy. The input parameters were composition (C), load (R), and rotation speed (s), while the responses were the specific wear rate (spr) and friction coefficient (Fc), which were optimised using the Taguchi combined optimisation technique. The results showed that the ANN approach was effective in predicting the wear characteristics of the sustainable metal matrix composites and the Taguchi combined optimisation technique improved the wear resistance and friction coefficient. This study provides a valuable contribution to the field of sustainable materials, as the findings can be used to improve the design and production of sustainable metal matrix composites with improved wear characteristics, which can lead to a reduction in energy consumption and environmental impact.

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Physical Layer security for NOMA systems: Requirements, Issues, and Recommendations

arXiv

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arXiv 2023年

作者： Pakravan, saeid Chouinard, Jean-Yves Li, Xingwang Zeng, Ming Hao, Wanming Pham, Quoc-viet Dobre, Octavia A. The Department of Electric and Computer Engineering Laval University Quebec CityQCG1V 0A6 Canada The School of Physics and Electronic Information Engineering Henan Polytechnic University Jiaozuo China The School of Information Engineering Zhengzhou University Zhengzhou450001 China The School of Computer Science and Statistics Trinity College Dublin Dublin Ireland The Faculty of Engineering and Applied Science Memorial University St. John’s Canada

Non-orthogonal multiple access (NOMA) has been viewed as a potential candidate for the upcoming generation of wireless communication systems. Comparing to traditional orthogonal multiple access (OMA), multiplexing users in the same time-frequency resource block can increase the number of served users and improve the efficiency of the systems in terms of spectral efficiency. Nevertheless, from a security viewpoint, when multiple users are utilizing the same time-frequency resource, there may be concerns regarding keeping information confidential. In this context, physical layer security (PLs) has been introduced as a supplement of protection to conventional encryption techniques by making use of the random nature of wireless transmission media for ensuring communication secrecy. The recent years have seen significant interests in PLs being applied to NOMA networks. Numerous scenarios have been investigated to assess the security of NOMA systems, including when active and passive eavesdroppers are present, as well as when these systems are combined with relay and reconfigurable intelligent surfaces (RIs). Additionally, the security of the ambient backscatter (Amb)-NOMA systems are other issues that have lately drawn a lot of attention. In this paper, a thorough analysis of the PLs-assisted NOMA systems research state-of-the-art is presented. In this regard, we begin by outlining the foundations of NOMA and PLs, respectively. Following that, we discuss the PLs performances for NOMA systems in four categories depending on the type of the eavesdropper, the existence of relay, RIs, and Amb systems in different conditions. Finally, a thorough explanation of the most recent PLs-assisted NOMA systems is given. © 2023, CC0.

关键词： Efficiency

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Performance Evaluation of Machine Learning Techniques (MLT) for Heart Disease Prediction

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Computational and Mathematical Methods in Medicine 2023年第1期2023卷 8191261-8191261页

作者： Ansari, Gufran Ahmad bhat, salliah shafi Ansari, Mohd Dilshad Ahmad, sultan Nazeer, Jabeen Eljialy, A.E.M. Department of Computer Science Dr. Vishwanath Karad MIT World Peace University Pune411038 India Department of Computer Applications B.S Abdur Rahman Institute of Science & Technology Chennai600048 India Guru Nanak University Hyderabad501506 India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University P. O. Box. 151 Alkharj11942 Saudi Arabia Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali140413 India Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University P.O.Box. 151 Alkharj11942 Saudi Arabia

The leading cause of death worldwide today is heart disease (HD). The heart is recognised as the second-most significant organ behind the brain. A successful outcome of treatment can be improved by an early diagnosis which can significantly reduce the chance of death in health care. In this paper, we proposed a method to predict heart disease. We used various machine learning algorithms (MLA), namely, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (svM), Naive bayes (Nb), random forest (RF), and decision tree (DT). With the testing data set, we evaluated the model's accuracy in heart disease prediction. When compared to the other five models, the random forest and k-nearest neighbor approaches perform better. With a 99.04% accuracy rate, the k-nearest neighbor algorithm and random forest provide the best match to the data as compared to other algorithms. six feature selection algorithms were used for the performance evaluation matrix. MCC parameters for accuracy, precision, recall, and F measure are used to evaluate models. © 2023 Gufran Ahmad Ansari et al.

关键词： Forecasting

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Detection of Early Fault in Power Electronic Converters through Machine Learning and Data Mining Techniques

Detection of Early Fault in Power Electronic Converters thro...

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Artificial Intelligence and smart systems (ICAIs), International Conference on

作者： Pradeep K v K. Gowrishankar E. sivanantham Katta subba Rao Nagulapati Kiran A. vimal School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering AMET University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering B V Raju Institute of Technology Medak Telangana India Department of Electrical and Electronics Engineering Anil Neerukonda Institute of Technology and Sciences Visakhapatnam Andhra Pradesh India Department of Mechanical Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

The Power electronic system plays a significant role in versatile applications. The power electronic converters are largely used in energy conversion mechanisms. A fault is defined as the abnormal condition of the system that results in various consequences. The important constraints in the modelling of power electronic systems involve losses, Electromagnetic Interference (EMI) and harmonics. This includes the fault detection in the power electronic converters that includes three phase rectifier, d-dc converter and single-phase inverter. These parameter affects the overall efficiency and quality of the system. To overcome the fault in the power electronic converters, the machine learning with data mining techniques is adopted. This helps to predict the early fault and helps to increase the efficiency of the system.

关键词： Electromagnetic interference Rectifiers Machine learning Power system harmonics Harmonic analysis Power electronics Circuit faults

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Hybrid Competitive swarm Optimization Algorithm based scheduling in the Cloud Computing Environment

Hybrid Competitive Swarm Optimization Algorithm Based Schedu...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Gv. shrichandran v s Narayana Tinnaluri J. senthil Murugan T. Meeradevi vijay Kumar Dwivedi s. suma Christal Mary Department of Computer Science & Applications B.Sc -CS Programme SRM Institute of Science and Technology Ramapuram Chennai -89 Department of Computer Science Koneru lakshmaiah education foundation Vaddeswaram Vel Tech High Tech Dr. Rangarajan Dr.Sakunthala Engineering College Avadi Chennai Department of ECE Kongu Engineering College Perundurai Erode V.E.C. Ambikapur Dist Surguja (C.G.) Department of Information Technology Panimalar Engineering College Poonamalle Chennai Tamil Nadu

Cloud computing (CC) platform not only offers resource sharing for users and also provide many on-demand services. business procedures can be managed to utilize the workflow technology on the cloud which signifies most of the problems in employing the resources in an effectual approach because of the dependencies between the tasks. Task scheduling (Ts) in a CC platform applies to the procedure of effectively allocating resources computing to tasks or jobs submitted to the CC environment. In a cloud platform, tasks are separated as smaller subtasks that are applied in parallel on distinct machines. There are many features to consider if the Ts is in CC platform, comprising the kind of tasks like resources cost, resources required, and resources availability. Therefore, this study presents a Hybrid Competitive swarm Optimization Algorithm based Task scheduling (HCsOA-Ts) technique in the CC platform. The presented HCsOA-Ts technique schedules the tasks proficiently in such a way that maximum resource utilization and performance gets accomplished. In the design of HCsOA, Cauchy mutation operator is included in the CsO algorithm and thereby improves the overall performance. In addition, the derivation of the fitness function by the HCsOA-Ts technique undergoes optimal scheduling process and decreases energy usage. The experimental result analysis of the HCsOA-Ts technique is tested using a series of measures. The comprehensive comparison study highlighted the improvements of the HCsOA-Ts technique over recent approaches.

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The Emerging Role of the Knowledge Driven Applications of Wireless Networks for Next Generation Online stream Processing 11

The Emerging Role of the Knowledge Driven Applications of Wi...

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11th International Conference on system Modeling and Advancement in Research Trends, sMART 2022

作者： Gunturu, vijaya Kumari, P. Rajani Chithra, s.M. saikia, bhargabjyoti singh, Rajesh singh, Devesh Pratap SR University Department of ECE Telangana Warangal India D.V.R. and Dr. H.S. Mic College of Technology R. M. K. College of Engineering and Technology Department of Mathematics India Duiet Dibrugarh University Department of ECE Assam India Uttaranchal Institute of Technology Uttaranchal University India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665487320

The present article discusses the use of stream processing to gather data from large-scale WIFI networks. Along with the foundational techniques for deliberate sampling, data collecting, likewise network monitoring in wireless networks, we also examine how understanding extraction may be viewed as an ML problem for applications for large-scale data streaming. We highlight the major This article discusses advancements in large data stream processing methods. We also look more closely at the database collection, edge detection, and methods for machine learning that may be used in the context of WIFI analytics. We discuss challenges, academic research, and the results of wireless network monitoring and stream analysis. Further research is anticipated into other dataflow improvements, such as pattern recognition and optimization algorithms. © 2022 IEEE.

关键词： big data

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A Prediction-Enhanced Physical-to-virtual Twin Connectivity Framework for Human Digital Twin

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Okegbile, samuel D. Cai, Jun Wu, Junjie Chen, Jiayuan Yi, Changyan University of the Fraser Valley School of Computing AbbotsfordBCV2S 7M8 Canada Concordia University Department of Electrical and Computer Engineering MontrealQCH3G 1M8 Canada Department of Electrical and Computer Engineering MontrealQCH3G 1M8 Canada Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Jiangsu Nanjing211106 China

This paper proposes a new secure and privacy-preserving prediction-enhanced solution for reliable physical-to-virtual communications in human digital twin (HDT) systems. With such a prediction-enhanced connectivity (PeHDT) framework, the evolution of any virtual twin (vT) could be triggered in real-time or in advance using the expected state of its physical counterpart. This ensures the continuous maintenance of a true replica of each physical twin (PT), thus relieving the need for timely PT-vT synchronization while the vT-experienced delay is reduced to zero or close to zero. We adopted a secured federated multi-task learning technique to meet the security and privacy constraints of HDT and employed a single server discrete-time batch-service queue framework when characterizing the batching process to reduce the communication burden. Furthermore, we introduced a prediction verification framework to improve the performance of the proposed PeHDT framework. The resulting problem was formulated as a constrained Markov decision process and was solved by introducing a primary-dual deep deterministic policy gradient (DDPG) algorithm. Through a joint investigation of communication, batching and prediction verification schemes, the simulation results show that the proposed PeHDT framework can greatly reduce both the vT-experienced delay and the PT-vT communication time without compromising the specific requirements of HDT. © 2024 IEEE.

关键词： Markov processes

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