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OPTIMIZED TARGET TRACKING USING HEURISTICS BASE DISPENSED ADVERT ORIENTED ALGORITHM

Microprocessors and Microsystems 2021年

作者： S. Alonshia Dr. P. Sumathy Research Scholar Department of Computer Science and Engineering Bharathidasan University Khajamalai campus Tiruchirappalli- 23 Tamilnadu India Assistant Professor Department of Computer Science and Engineering Bharathidasan University Khajamalai campus Tiruchirappalli- 23 Tamilnadu India

A Wireless Sensor Network based Target-Tracking is an emerging and challenging research area among the researchers. The existing problems to track the targets in some selective sensor nodes must be under continuoussurveillance. To overcome the issues in continuous monitoringof sensor nodes can be optimized by this model concentrates on behavior based node selection dictionary creation and recording. In target-tracking, each sensor is adapted in numerous systems, for example, grounds security, reconnaissance, natural surroundings, and medicinal services checking. Data can be transmitted in a specially appointed multi-bounce structure using the web or by a sign to screen fromsensor coverage area or target and can frame a pervasive system for a few internet providers. In this paper, CENS (Consecutive Edge Node Selection) and HBDAO (Heuristics-base Dispensed-Advert-Oriented) techniques proposed to figure out the trusted and Omni boundary detection of nodes that are in the WSN simultaneously verify the active, passive status and behavior history of each node. This proposed techniques showed better results in the experiments on a simulated environment than the existing edge node detection techniques. This model also contributed tracking and maintain the accuracy, throughout management and latency avoidance in efficient manner.

关键词： Target Tracking Energy Efficient Routing Protocol WSN

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Understanding Hadoop Framework Through Single-Node Cluster Installation

Understanding Hadoop Framework Through Single-Node Cluster I...

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International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC)

作者： V. G. Anisha Gnana Vincy T. Karthija J. Sunil Assistant Professor Department of Computer Science and Engineering V V College of Engineeing Tisaiyanvilai India Assistant Professor Department of Mechanical Engineering V V College of Engineeing Tisaiyanvilai India

ISBN: (纸本)9781538694831

Hadoop is a Java-based open source programming model that becomes a pillar in recent distributed computing by providing massive storage and multiprocessing of data. Hadoop becomes a simple and easy to implement programming model through the usage of commodity hardware. Hadoop open-source became a foundation for massive parallel processing of big data which includes scientific analytics, e-commerce data and sales planning, and processing enormous volumes of sensor from various sensors. Since Hadoop plays vital role in distributed parallel processing of big data, it is good to know the technical details behind the hadoop framework. This paper focus on detailing the necessary steps for the successful implementation of a Hadoop single-node cluster in a individual computer which provide a foundation for the new hadoop users.

关键词： Big Data Java parallel processing public domain software storage management PARALLEL PROCESSING (computerS) big data open source software Store management Java Java programming language Sensors

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Predicting the price range of mobile phones using machine learning techniques

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AIP Conference Proceedings 2021年第1期2387卷

作者： K. S. Kalaivani N. Priyadharshini S. Nivedhashri R. Nandhini 1Assistant Professor Department of Computer Science Engineering Kongu Engineering College Perundurai Erode Tamil Nadu India 2UG Scholar Department of Computer Science Engineering Kongu Engineering College Perundurai Erode Tamil Nadu India

During the purchase of mobile phones, various features like memory, display, battery, camera, etc., are considered. People fail to make correct decisions, due to the non-availability of necessary resources to cross-validate the price. To address this issue, a machine learning model is developed using the data related to the key features of the mobile phone. The developed model is then used to predict the price range of the new mobile phone. Three machine learning algorithms namely Support Vector Machine (SVM), Random Forest Classifier (RFC), Logistic Regression are used to train the model and predict the output as low, medium, high or very high. The dataset used in this study is taken from Kaggle platform. In order to improve the classification accuracy, Chi-Squared based feature selection method is used. Among 21 features available in the dataset, only top 10 features namely RAM, pixel height, battery power, pixel width, mobile weight, internal memory, screen width, talk time, front camera and screen height are selected and used to train the model. Before applying feature selection, the accuracy obtained using SVM, RFC and Logistic Regression is 95%, 83% and 76% respectively. After feature selection, the accuracy of SVM, RFC and Logistic Regression improved to 97%, 87% and 81% respectively. From the experiments conducted, it is found that SVM gave superior performance when compared to other two classifiers.

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Question Answering Systems for Covid-19

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Journal of Physics: Conference Series 2021年第1期2062卷

作者： Poonam Gupta Ruchi Garg Amandeep Kaur Assistant Professor Computer Science and Engineering Department MMEC M.M.(Deemed to be) University Mullana Ambala Haryana India guptapoonam 231 Assistant Professor Electronics and Communication Engineering Department MMEC M.M. (Deemed to be) University Mullana Ambala Haryana India Associate Professor Computer Science and Engineering DepartmentMMEC M.M. (Deemed to be) University Mullana Ambala Haryana India

In the present scenario COVID-19 pandemic has ruined the entire world. This situation motivates the researchers to resolve the query raised by the people around the world in an efficient manner. However, less number of resources available in order to gain the information and knowledge about COVID-19 arises a need to evaluate the existing Question Answering (QA) systems on COVID-19. In this paper, we compare the various QA systems available in order to answer the questions raised by the people like doctors, medical researchers etc. related to corona virus. QA systems process the queries submitted in natural language to find the best relevant answer among all the candidate answers for the COVID-19 related questions. These systems utilize the text mining and information retrieval on COVID-19 literature. This paper describes the survey of QA systems-CovidQA, CAiRE (Center for Artificial Intelligence Research)-COVID system, CO-search semantic search engine, COVIDASK, RECORD (Research Engine for COVID Open Research Dataset) available for COVID-19. All these QA systems are also compared in terms of their significant parameters-like Precision at rank 1 (P@1), Recall at rank 3(R@3), Mean Reciprocal Rank(MRR), F1-Score, Exact Match(EM), Mean Average Precision, Score metric etc.; on which efficiency of these systems relies.

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Development of peanut separator and thruster

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IOP Conference Series: Materials science and engineering 2021年第1期1055卷

作者： M Srinivasan S Nithya Sai Albert Alexander N Prabhakaran C Gokul G Visalaxi Assistant Professor Department of Electrical and Electronics Engineering Kongu Engineering College Erode district-638 060 Tamilnadu India Assistant Professor Department of Electronics and Communication Engineering Kathir College of Engineering Coimbatore district-641 062 Tamilnadu India Student Department of Electrical and Electronics Engineering Kongu Engineering College Erode district-638 060 Tamilnadu India Assistant Professor Department of Electrical and Electronics Engineering Velalar College of Engineering and Technology Erode-638012 India Assistant Professor Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai India

Peanuts have been in existence since the beginning of human life. It has a high carbon content and is rich in oil and protein. Peanut oil extraction is more common in most developing countries. Peanut is a commonly used, edible commodity, and oil is used as a cooking medium. Upon oil extraction, the by-product of the peanut cake often contains 43-65 per cent protein and 6-20 per cent fat, which is usually used as feed for cows and goats. There is currently a shortage of farm workers and an increase in harvesting costs. The factors mentioned above mean that farmers are not interested in growing peanuts. The removal of peanut shells is a time-consuming and costly operation. During harvesting, the temperature is very high, making it impossible for humans to work. Peanut thresher is used to solve these problems by removing peanuts that minimize time and expense. Our project focuses mainly on the design and development of a peanut cutting machine with a 0.5 hp motor. The workers removed the peanut capsules from the harvest at the beginning. The output of this method was very poor and did not meet consumer demand because it was a time-consuming operation. The main part of our project is a rotating drum with nails. They feed the peanuts to the rotating drum. Rotating drum tips isolate the pods from field harvest. There are large peanut harvesters on the market, but small farmers cannot afford these harvesters. Our machine is small, light and inexpensive. Farmers with a small farm may afford to use our machine.

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Performance Evaluation with Multivariable and Multiloop control for a class of square MIMO systems

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IOP Conference Series: Materials science and engineering 2021年第1期1084卷

作者： S Sudhahar C Ganesh Babu D Sharmila Assistant Professor Bannari Amman Institute of Technology Department of Electronics and Instrumentation Engineering Erode Tamilnadu India Professor Professor Bannari Amman Institute of Technology Department of Electronics and Instrumentation Engineering Erode Tamilnadu India Professor Jai Shriram Engineering College Department of Computer Science Engineering Erode Tamilnadu India

Two approaches (centralized and decentralized) for designing a multi-input, multi-output (MIMO) tracing/regulating process are described within article. The vast popular of industrial process control applications are whist focused on multi loop controllers, ignoring the feedback control attainment with advantages of centralized multivariable controllers. Due to their single loop nature, the plant interactions that are merely in use keen on relation in the controller tuning process cannot be suppressed by decentralized controllers. In many situations, therefore it would be beneficial to delimit the detrimental consequences in pairing among inputs and outputs of the closed loop system under certain context. The centralized model predictive controllers (MPC) and decentralized/multi-loop PI controllers are designed. In terms of integral Absolute Error (IAE), Integral Square Error (ISE), and Integral Time-weighted Absolute Error (ITAE), the output of both controllers is then compared. The results of the simulation showed that the MIMO MPC is better than the other suggested control schemes. The projected central controllers minimize interactions superior than the multi loop controllers that have recently been published.

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Credit Card Forgery Identification By Location Using Android Based Monitoring

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IOP Conference Series: Materials science and engineering 2020年第1期993卷

作者： V. M. Jemin P. Vasanthan Assistant Professor Department of Computer Science and Engineering RMK College of Engineering and Technology

In cellular network, the user's location is probably used as an authentication aspect similar to other elements like password, PIN range combined with the use of device that the person consisting of card or a mobile. An encrypted and decrypted key is generated using RSA. The generated values are send to user's cellular as SMS. The user's mobile region is identified by the place of use far flung customer authentication protocol and its integrity is proven by Galileo Navigation satellite known as Local element. We proposed an LRAP based method for secure transactions.

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Object and lane detection for autonomous vehicle using YOLO V3 algorithm

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AIP Conference Proceedings 2021年第1期2387卷

作者： Mohanapriya S. Natesan P. Indhumathi P. Mohanapriya S. T. P. Monisha R. 1Assistant Professor Department of Computer Science and Engineering Kongu Engineering College Perundurai Erode Tamil Nadu India 2Professor and Head Department of Computer Technology Kongu Engineering College Perundurai Erode Tamil Nadu India 3UG Scholar Department of Computer Science and Engineering Kongu Engineering College Perundurai Erode Tamil Nadu India

To detect the objects around an autonomous vehicle is very essential to operate safely. This paper presents to detect and classify the objects for assisting autonomous driving. In autonomous driving systems, the task of object detection itself is one of the most important prerequisites to autonomous navigation. Deep learning one of the computer vision tasks, perform object detection very effectively than compared to earlier methods and this project is to detect the objects like vehicles, persons, traffic lights, etc. In this work, an approach to object detection in deep learning that makes the bounding box for an image to predict is explored. Object detection is the method of detecting the objects present in a given image. Apart from detecting the number of objects present in an image it also specifies in which location that object is present in the image. The objects are detected by means of bounding boxes. In the existing system algorithm like Convolutional Neural Network (CNN) using Resnet-50 were used to detect the objects like vehicles, persons, traffic lights separately. The problem identified here is, in the existing system the camera is fixed in a particular place and it detects objects only if the objects come into the camera frames. It is not detecting both objects and lanes simultaneously when the autonomous vehicle is in motion at any location. To overcome these problems, in the proposed system object detection is performed by mounting camera in front of the moving vehicle. You Only Look Once (YOLO) V3 Algorithm is used for the process of object detection. Compared to earlier detection approaches YOLO V3 shows improvement in detection accuracy. It provides good feature extraction and detection in large-scale. The proposed YOLO al gorithm has better average precision value for detecting all objects than compared to existing CNN using Resnet-50. In addition YOLO V3 algorithm performs lane detection apart from object detection.

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Review for Detecting Smoke and Fire in Forest using Different Technologies

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IOP Conference Series: Materials science and engineering 2020年第1期993卷

作者： Dinesh Komarasamy M. Gokuldhev J Jenita Hermina M Gokulapriya M Manju Assistant Professor Department of Computer Science and Engineering Kongu Engineering College Perundurai Assistant Professor Department of Computer Science and Engineering AMRITA College of Engineering and Technology Nagercoil UG Scholar Department of Computer Science and Engineering Kongu Engineering College Perundurai

Nature has a huge role to maintain the stability in the environment. But, natural disasters damage the environment, affect the life cycle and decline the lifetime of living beings. Nature is destroyed by different disasters namely earthquake, fire, flood, landslide, air pollution and so on. Among these, forest fire is one of the foremost dangerous natural disasters which cause several serious issues like biodiversity loss, global warming, fuel wood loss and air pollution in the environment. Therefore, prediction of fire occurring in forest plays a crucial role to save lots of the environment. Thus, researchers focused on different technologies with different methodologies for predicting the fire that occurred in forest, as early as possible. Moreover, smoke is the focal point for fire, some of the researchers pay their attention on detecting the smoke of the forest using different technologies. Therefore, this paper gives a summary for effectively detecting the smoke and fire in forest.

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Modeling of an Adaptive Energy Based Route Broadcasting Algorithm (AERBA) Using Route Optimization in MANET

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IOP Conference Series: Materials science and engineering 2021年第1期1084卷

作者： S B Manoojkumaar C Poongodi Assistant Professor Department of Computer Science and Engineering J.K.K.Munirajah College of Technology T.N.Palayam Professor & Head Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam

Owed to the uncertain and dynamic behaviour of nodes over Mobile Ad-Hoc Networks (MANETs), the reliable route discovery and data delivery is considered as a challenging factor. With absence of centralized infra-structure and global broadcasting nature, nodes over the distributed environment rely over the neighborhood nodes. For reliable communication, the network needs to broadcast the routing direction to chose the appropriate neighborhood partner to make the functionality effectual during packet relying. Therefore, the evaluation process is determined to be a crucial factor. Various methodologies use the trust-based concepts for choosing the route broadcasting region and neighborhood nodes. However, those methods are identified with certain limitations and the evaluation is performed over single node. The inadequate consideration of these factors leads to the wider range of network model that leads to inappropriate broadcasting. This leads to packet loss, higher energy consumption due to re-transmission of packets. In this work, an Adaptive Energy-based Route Broadcasting Algorithm (AERBA) using Route Optimization (RO) is applied along with the route optimization approach to derive the global solutions. This leads to the trust establishment among the network nodes and pretends to fulfill the constraints related to broadcasting and energy consumption. The anticipated model facilitates appropriate evaluation over the potential nodes with multiple hops for balancing the route broadcasting and energy consumption. Simulation is carried out in MATLAB environment and the results demonstrate lesser energy consumption, higher delivery ration when compared with prevailing approaches.

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