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检索条件"任意字段=2022 IEEE International Conference on Current Development in Engineering and Technology, CCET 2022"

共 15319 条记录，以下是41-50 订阅

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Machine Learning Technique of Intrusion Detection System for Vehicular Ad Hoc Networks : An Analysis

Machine Learning Technique of Intrusion Detection System for...

引用

2022 ieee international conference on current development in engineering and technology, ccet 2022

作者： Khadagade, Sanjay Kumar Rai, Avinash Rai, Keerti UIT-RGPV Electronics & Communication Engineering Bhopal India Arka Jain University Electrical & Electronics Engineering Jamshedpur India

ISBN: (纸本)9781665454155

A vehicular ad-hoc network (VANET) is a subset of mobile AD-NET that enables communication between automobiles and surrounding fixed equipment, which is often referred to as roadside equipment. The primary objective of VANET is to provide passenger comfort and safety. Each car will be equipped with a unique electronic gadget that will enable the formation of an ad hoc network and link the passengers. In order to reduce traffic congestion, assure safety, and preserve the environment, intelligent transport systems (ITS) combine computers, communications networks, sensors, location, and automation technologies that gather and create data. ITS also provides users with a variety of services and applications. Intrusion detection is one of the important security problems in today's scenario. A significant number of techniques have been developed to detect and prevent of instruction in the VANET based on machine learning approaches. This paper presents analysis of machine learning technique of intrusion detection system for vehicular ad hoc networks. © 2022 ieee.

关键词： Vehicular ad hoc networks

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Intelligent Localization based on Wavelet Neural Network

Intelligent Localization based on Wavelet Neural Network

引用

2022 ieee international conference on current development in engineering and technology, ccet 2022

作者： Bhalla, Anand Vardhan Mishra, Agya Jabalpur Engineering College Department of Electronics & Telecommunication Madhya Pradesh Jabalpur India

ISBN: (纸本)9781665454155

Radio Frequency identification (RFID) technology is becoming more and more integrated into many facets of life, making it an important field of study. Due to benefits of low power consumption, precise, less delay, etc. the RFID turns into an easy, powerful localization tool. However, present RFID localization techniques lack in accuracy. In this article, Wavelet Neural Network (WNN) based algorithm is proposed for localization using RFID. WNN is used to develop a nonlinear mapping between measured parameters and separation between the tags and readers. The equation-set is constructed based on mapping relationship. Further, use of WNN will optimize the goal function transformed through nonlinear equation set. Results shows that WNN based localization are more effective than the Back Propagation-Support Vector Regression (BP-SVR) algorithm in phrases of localization error. Simulations are conducted to validate the performance of the proposed algorithm and results shows that mean localization error of the proposed algorithm is 0.07m which is 90% superior to BP-SVR algorithm. © 2022 ieee.

关键词： Mapping

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Priority-based Cloud-Fog Architecture for Smart Healthcare Systems

Priority-based Cloud-Fog Architecture for Smart Healthcare S...

引用

2022 ieee international conference on current development in engineering and technology, ccet 2022

作者： Bansal, Sumit Aggarwal, Himanshu Punjabi University Department of Computer Science & Engineering Patiala India

ISBN: (纸本)9781665454155

With the change of lifestyle of society and increased use of IoT based equipment's in the healthcare system, various applications had knocked in modern healthcare facilities and medical data processing at the ground level. So many smart devices like watches, smart bands, mobiles and other wearable sensors are available in market to monitor the health status on regular basis. With the escalation of consumption of these smart devices, large amount of data is being generated which has led to surge in data processing load on data centers. It has posed various challenges such as data traffic, network utilization, power consumption and latency. In this paper, the priority scheduling was compared with non-priority scheduling in cloud and fog systems. The system's performance was tested in terms of energy consumption and network utilization. The results were validated with use of different data sets acquired from sensors and fog devices. iFogSim simulation tool was used for this purpose. The results also demonstrated effect of increasing the number of sensors and fog devices on network utilization and energy consumption in cloud-fog environment. © 2022 ieee.

关键词： Fog computing

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Analysis of Breast Cancer in Early Stage by UsingMachine LearningAlgorithms: A Review

Analysis of Breast Cancer in Early Stage by UsingMachine Lea...

引用

2022 ieee international conference on current development in engineering and technology, ccet 2022

作者： Sandhu, Jasjeet Kaur Kaur, Amandeep Kaushal, Chetna Chitkara University Institute of Engineering and Technology Chitkara University Karnataka India

ISBN: (纸本)9781665454155

The goal of using Machine Learning (ML), a branch of Artificial Intelligence (AI) technology, is to increase the accuracy and efficiency of doctors' work. After skin cancer, breast cancer is the kind of cancer that affects women the most often. Breast cancer is caused by an unchecked multiplication of breast cells. According to the national cancer institute, 287,850 are the Estimated New Cases in 2022, with 15% of all new cancer cases. 43,250 will be the. Estimate deaths in 2022 in the USA. In the United States, female breast cancer accountsfor 15% of all newly diagnosed instances of cancer. An analysis of breast cancer data from 2018 in India revealed 1,62,468 fresh recorded cases and 87,090 deaths from the condition. According to the findings of the most recent research, the state of Kerala has the highest cancer incidence in all of India. This review paper will understand the various stages of Breast cancer, look at datasets and evaluate supervised and unsupervised Machine Learning (ML) algorithms to see which ones are best for early breast cancer prediction made possible by the use of machine learning or deep learning. © 2022 ieee.

关键词： Diseases

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ANN Controller based Single Phase Cascade Thirty-One Level Grid-Tied Inverter for Power Quality Improvement

ANN Controller based Single Phase Cascade Thirty-One Level G...

引用

2022 ieee international conference on current development in engineering and technology, ccet 2022

作者： Jayaraju, Gaddala Rao, Dsnm Kasireddy, Idamakanti Rgukt Srikakulam India Gokaraju Rangaraju Institute of Engineering and Technology Eee Department Hyderabad500090 India Vishnu Institute of Technology Eee Department AP Bhimavaram India

ISBN: (纸本)9781665454155

A novel multilevel inverter configuration design is presented for decrease switches and increase waveform efficiency in a PV-wind system. In binary asymmetric conditions, the proposed design operates to produce a high output voltage level with a limited amount of harmonic distortion. One unipolar sinusoidal reference with fifteen carriers is utilised to generate the switching pulses that are necessary, as well as the appropriate output voltage level. Eight unipolar switches are needed for the suggested design to create the 31-level output voltage level with total harmonic distortion (THD) that fulfils ieee 519's harmonic standard. In a MATLAB simulation, a digital multi-carrier PWM algorithm with pi, fuzzy and ANN controllers is implemented and the proposed scheme is evaluated using the dspic30F2010 controller with an experimental arrangement to validate its procedure. This paper succeeded with the aid of developing a brand new Converter system that reaches the objectives in terms of efficiency and price, by reaching a medium voltage machine with equal value as a low voltage design. © 2022 ieee.

关键词： Harmonic distortion

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Multiple Sources Off-Board Electric Vehicles Charging Station, Different Techniques, Standards and Challenges

Multiple Sources Off-Board Electric Vehicles Charging Statio...

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2022 ieee international conference on current development in engineering and technology, ccet 2022

作者： Pal, Sitaram Kurchania, Anil Shah, Nand Lal Tiwari, Prakash Narayan Soni, Aradhna Tiwari, Kaushal Prasad Rabindranath Tagore University Department of Electrical & Electronics Engineering Bhopal India Rabindranath Tagore University Department of Renewable Energy Bhopal India Sage University Department of Electrical & Electronics Engineering Bhopal India Bits College Department of Electrical and Electronice Engineering Bhopal India

ISBN: (纸本)9781665454155

In recent years, Electric vehicle (EV) hybrid (has grid-connected modes as well as development kit potential) and off-grid (standalone mode) charging systems have seen a rise in research attention. To expand EV adoption globally, the key motivations to offer a smooth infrastructure of charging in rural as well as urban locations where the utility circuit is non-dependable or absent. In this regard, this article examines the off-board and hybrid charging stations' cutting-edge topologies and looks in to each of its many subsystems, including Types, Classification, international Standards, etc. To create a low-cost and effective EV charging station, several components should be optimally connected and run. Additionally, each subsystem is thoroughly investigated to learn about its current condition and technological advancements. They also highlight challenges for both super charger stations. This paper presents an expanded and in-depth discussion of EV charging stations. This Study also discusses various charging technologies, types, architecture, improvements, and difficulties. © 2022 ieee.

关键词： Charging (batteries)

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IoT-Based Battery Monitoring System for Electric Vehicle

IoT-Based Battery Monitoring System for Electric Vehicle

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2022 ieee international conference on current development in engineering and technology, ccet 2022

作者： Lekshmi Sree, B. Sangari, A. Eswaramoorthy, K. Kiranmayee, V. Sheeba, J. Anto Sivamani, D. Narsimha Reddy Engineering College Department of Electrical and Electronics Engineering Secunderabad India St.Martin's Engineering College Department of Electrical and Electronics Engineering Secunderabad India Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9781665454155

One of the most vital and expensive components of electric vehicles is the battery. Of course, the battery is the only source of electricity for an electric vehicle. However, the vehicle's power supply eventually declines, resulting in decreased performance. For battery manufacturers, this is a major concern. In this paper, it is proposed to use IoT approaches to monitor and display the battery performance. Here, the various battery metrics, including voltage, current, and temperature, are tracked, observed, and shown. This alerts the user to prevent the battery from being overcharged or deeply discharged. With the use of various sensors, observation can be carried out. Data on voltage, current, and temperature are sent to a microcontroller unit, which subsequently transmits battery data via the cloud for display. Real-time data of voltage, current, and temperature may be displayed by the monitoring system, and the data can be seen on an Android smartphone and a computer at the same time. As a result, we might be able to improve the battery's efficiency and lifespan. The user interface and results presentation are the two main components of the proposed IoT -based battery monitoring system. According to test results, the system is able to recognize weakened battery performance and notifies the user for further action. © 2022 ieee.

关键词： Microcontrollers

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Remote Patient Monitoring Systems Network Adaptation Scenarios

Remote Patient Monitoring Systems Network Adaptation Scenari...

引用

2022 ieee international conference on current development in engineering and technology, ccet 2022

作者： Khujamatov, Halimjon Akhmedov, Nurshod Lazarev, Amir Arsheen, Sobiya Tashkent University of Information Technologies Dean of the Faculty of Computer Engineering Tashkent Uzbekistan Tashkent University of Information Technologies Data Transmission Networks and Systems Department Tashkent Uzbekistan Maulana Azad National Urdu University Department of Computer Science and Information Technology India

ISBN: (纸本)9781665454155

This article is devoted to the actual topic of studying scenarios for adapting a network infrastructure to a network of remote patient monitoring systems (PCMS). The paper presents an overview and analysis of scenarios for adapting the network infrastructure of monitoring systems. The stages of scenario execution have been studied and simulation models of the RPMS network infrastructure have been developed that implement various adaptation scenarios. A comparative analysis of adaptation scenarios was carried out according to several criteria, such as the speed of request delivery, delivery time, and network load. Simulation models built in the Anylogic environment, a powerful simulator of mass service system, showed the effectiveness of the mesh topology in relation to the ad-hoc topology according to several criteria. The theoretical significance of the work lies in carrying out analytical work on scenarios for adapting the network infrastructure of the RPMS, as well as in studying the network architecture. The practical significance of the work lies in the development of a simulation model that allows you to explore scenarios for adapting the network architecture. © 2022 ieee.

关键词： Network architecture

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Estimation of Electrical Load Reliability for Refrigeration using Photovoltaic System

Estimation of Electrical Load Reliability for Refrigeration ...

引用

2022 ieee international conference on current development in engineering and technology, ccet 2022

作者： Waktola, Ararso Taye Pattanaik, Balachandra Wollega University Department of Electrical and Computer Engineering Ethiopia

ISBN: (纸本)9781665454155

The photovoltaic power system is designed for powering an electrical load of refrigeration using energy from solar radiation. The simulation is done based on the design specification required to run the electrical load of the refrigeration system. The procedure includes the determination of the total load, identification of components required for design, solar data, and design calculation being estimated. The system is designed by considering the component's reliability such as a solar photovoltaic array, storage battery, converter, inverter, and load. For generating 62.7 Kilowatts of power for supplying an electrical load of 200-ton refrigeration, the PV panel of TP300 series of 310 peak power of Tata Solar Panel Manufacturer is used. The monthly average radiation of 5.83 KWh/m2/day of Vadodara town in Ethiopia is being used in the design. The series-connected storage batteries are used for a time when there is no radiation. The generated DC voltage is converted to AC voltage using an inverter for feeding a load of 200-tonne vapor absorption refrigeration system. The designed system is also valuable to implement in remote areas where there is no grid distribution. © 2022 ieee.

关键词： Digital storage

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A Machine Learning-based Automatic Model to Predicting Performance of Students

A Machine Learning-based Automatic Model to Predicting Perfo...

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2022 ieee international conference on current development in engineering and technology, ccet 2022

作者： Garg, Atul Garg, Nidhi Bansal Ghosh, Pinaki Bansal, Ankit Lilhore, Umesh Kumar Simaiya, Sarita Chitkara University Institute of Engineering and Technology Chitkara University Karnataka India School of Advanced Computing Sage University Bhopal India Chandigarh University Department of Computer Science and Engineering Karnataka Mohali India

ISBN: (纸本)9781665454155

Machine learning offers a variety of techniques for use in diverse fields. Massive quantities of student data are produced in educational institutions, and machine-learning techniques are a precious tool for identifying patterns in individuals' behavior. Academicians can learn valuable data concerning the students' expertise and how it relates to their educational assignments by critically examining and evaluating these data. This data serves as the input for cutting-edge techniques and algorithms that can forecast students' academic achievement. This research presents a machine learning-based automatic model for predicting the performance of students. The popular ML methods are Random Forest, K-Neighbors, Extra Trees, Ada-Boost, Decision Tree, Logistic Regression, Gaussian NB, and Bernoulli NB. This research utilizes an online student performance dataset collected from Kaggle. The findings demonstrate that Random forest and decision trees outperform other techniques based on high precision as well as the capacity to interpret the guidelines and structure that were developed for predicting students' educational success. © 2022 ieee.

关键词： Students

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