bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design and Fabrication of a Multinomial Model for Machine Interference Problem
Authors: Venkatesa Prabu D. , Meenakshi Priya B. , Priyanka E.B. , Kalaiarassan G. 
Published: February 2021

Abstract
Machining has gotten crucial to the cutting-edge industry. It is utilized legitimately or in a roundabout way in the production of practically all the merchandise and ventures being determined everywhere in the world. This project is mainly carried out in Multi-Function Operating Machine for production industries. It is a multipurpose machine that can be used to perform various operations like drilling (screw fitting, boring, broaching), grinding and lathe operations (facing, step turning, knurling and cutting) of small workpieces very precisely. A lathe is one of the oldest and perhaps most important machine tools ever developed. Hole machining, constituting a considerable percentage of all machining operations, is accomplished, as a rule, be rotary and axial feed motions of the cutting tool or work. The boring cycle is a widely utilized machining activity by which however or daze openings are cut or begun in a workpiece. Penetrating is a machining activity wherein the opening is created or broadened by the utilization of an extraordinary cutting device, called a drill, normally having more than one forefront. Crushing is a machining activity where a multi-edged pivoting grating device called a granulating wheel eliminates abundant material from the workpiece. Granulating of level surfaces called surface crushing. Grinding of flat surfaces called surface grinding.
An Analysis of the Impact of Automation on Supply Chain Performance in Logistics Companies
Authors: Andiyappillai N 
Published: February 2021

Abstract
While many companies currently seek technology for penetration into the sector, precision and efficiency while speeding up the processes at the same time, the performance of the supply chain is confronted by numerous challenges that can hinder its performance. This essay discusses the issues and questions resulting from an automated approach employed by industrial companies. The method thus has a considerable influence on the supply chain’s performance, taking account of internal synchronization. After multiple interviews with two companies, an interactive process was developed. The outcomes of the study were extracted from the variables associated with the exam. We have already seen various statistics that indicate that automation has eliminated manufacturing costs, human bugs and a somewhat time-consuming process for supply chains. Although the quality and effectiveness of employees have improved, cooperation between departments and resources and time taken with each job has also improved.
Design of Miniaturized Antenna Array for Microwave Imaging Application
Authors: Ramkumar V , Balaji A , Chinnammal V , Vanaja S , Malarvizhi C 
Published: February 2021

Abstract
To detect the presence of tumor in the breast, a miniaturized Ultra-wideband (UWB) antenna is simulated with the use of CST microwave studio. In free space, its operating range is about 3.548GHz. The Flame Retardant (FR-4) material is used as a substrate in the design of the microstrip antenna. The antenna has the dimensions of 16 x 20 mm. The improved bandwidth is obtained by making some modifications in the patch and ground plane. To improve the gain and directivity, an array of 2 x 2 elements are created. The improvement in gain and directivity is analyzed for different spacing between the elements like λ/2 and λ. The spacing of λ/2 between the elements created gives the improved directivity and gain.
UWB Sierpinski Fractal Antenna for Breast Tumour Detection Using SAR
Authors: Joselin Jeya Sheela J. , Rosaline S. , Thanga Mugila S , Shanmathi Sri S , Praveen Kumar V. 
Published: February 2021

Abstract
Breast tumour is the most common tumour in the world and one in eight women is likely to develop breast tumours. Survival rate is higher only when the tumour is detected at the early stages. The various methodologies currently available to detect breast tumour are ultrasound, MRI, X-ray mammography etc. But these methods are not cost effective and have other reliability issues. In this paper, tumour is detected by passing microwave signals into the homogeneous breast model by designing an Ultra-Wideband (UWB) Sierpinski fractal micro strip patch antenna (MSA) with reduced ground. SAR (Specific Absorption Rate) calculation helps to find the presence of tumour. The antenna characteristics are studied using CST studio suite 2017 software. Sierpinski fractals and reduced ground are used to achieve UWB and enhance bandwidth. The preferred antenna provides a bandwidth width of 9.71 GHz (2.79 - 12GHz).
Detection & Defensive Approach for Primary User Emulation Attacking Cognitive Radio Network
Authors: Gokulakrishnan K. , Kiruthiga B. , Jayapal N. , Karthi S.P. , Stonier Albert Alexander 
Published: February 2021

Abstract
Cognitive Radio Network (CRN) is the future scope wireless technology that aims to enhance the spectrum utilization. It meets out the increasing demand in spectrum access by all possible wireless applications and services. Security is an important issue but not focused much in CRN. In this research, the detection and defensive approach for Primary User Emulation Attack (PUEA) is presented. In order to secure cognitive radio network against PUEA, two level of detection algorithm is proposed. Energy detection with three threshold value and localization technique is used to detect the attackers. To enhance the Quality of Service (QoS) of the Secondary Users (SU), guard channel is allocated to the secondary user by the fusion center. This process is carried out whenever the communication between the SUs is intervened by the licensed primary user or an attacker.
An Enhanced Anti-phishing Scheme to Detect Phishing Website
Authors: Rajasekar Vani , Premalatha J. , Sathya K. , Dhanesh Raakul S. , Saracevic Muzafer 
Published: February 2021

AbstractPhishing is a network attack in which attacker generates a replica of current legitimate site to contrivance the users into submitting their personal and sensitive information. A good anti-phishing solution should have rapid access time and rapid detection rate, but most of the existing solutions fail to satisfy it. A new method is proposed to prevent phishing attack which evaluates the URL Characteristics and cross check with the white-list. The proposed method has higher access time and higher detection rate than other methods. Whenever the user opens a website for the first time which is not available in the white-list, the algorithm proposed here examines the webpage using URL characteristics. The experimental results depict the accuracy of proposed method having accuracy of 93.8% with less negative rate.
IoT Based Home Automation System using Raspberry Pi 4
Authors: Maragatham T , Balasubramanie P , Vivekanandhan M 
Published: February 2021

Abstract
This paper proposes a propelled home mechanization framework that utilizes an android application to control and screen the home apparatuses. This plan depends on the Internet of Things (IoT).In home computerization, every single home apparatus are organized together and worked without human intercession. In this framework, Raspberry Pi 4 is going to be interfaced with numerous sensors that may evaluate temperature and steaminess, light, energy, etc. Sensors were used to collect information and it would be stored in the data store and an example examination is done on the put away information which tells the client at which time the machines are typically on or off with the goal that they can be naturally controlled with no human intercession by watching the normal use design. The client moreover turns on/off remotely by means of mobile application and web-server.
Categorization of Diabetic Retinopathy using Deep Learning Techniques
Authors: Renukadevi N T , Saraswathi K , Karunakaran S , Anguraj B 
Published: February 2021

Abstract
Diabetic retinopathy is a disease that infects the vision of human eyes suffering from diabetes. It affects the blood vessels of soft tissues at retina, which is located at the backside of the eyes. This disease is evaluated by the physicians based on the retinal images of patients. Detection of the disease initiates human-intensive work for medical practitioners with monetary expenses also. Recent research works have identified that the use of deep learning methods for automatic detection of diabetic retinopathy helps the experts to make quick decision about the patient’s health conditions. In this paper, automated detection of diabetic retinopathy using deep belief networks has been presented which process the retinal images of patients and provides accurate diagnosis of categories of diabetic retinopathy. The proposed method has been trained and tested with Convolutional Neural Networks and Deep Belief Networks. The confidence level of diagnosis is computed and 94.69% with 96.01% are achieved in the detection of Proliferative diabetic retinopathy using CNN and DBN based on the features of data.
Artificial intelligent third umpire runout decision making using image processing
Authors: Nirmala Devi P , Manoj Senthil K , Selvavignesh S 
Published: February 2021

Abstract
Cricket is the most interesting game watched by the people all over the world. The decisions given by the umpire play a vital role in deciding the game winning situation. As human decisions are prone to error, the need of smart technology intervention is mandate. The advances in image processing and computer vision techniques has now paved the way for making effective and correct decisions on all phases of the game and in automatic scoring. This paper proposes a new approach for detecting the run out using stump detection and from the intersection of the bat and the crease line, named Artificial Intelligent third Umpire (AITU). The accuracy of decision making is improved thereby reducing the third umpire work.
A SCALE AND ROTATION INVARIANT FAST IMAGE MINING FOR SHAPES
Authors: Narayana M. , Tamilarasu P. , Karunakaran S. , Subrahmanyam Ch. 
Published: February 2021

Abstract
In recent times, fast content – based image retrieval is required in image mining for shapes, as image database is rising exponentially in size with time. In this paper, a sample, fast and efficient process through Distance Mapping for CBIR is proposed. In distance mapped domain, zero valued points represent the boundary of the object and the rest are assigned the value with the shortest distance to the boundary of the object. All the regional points within the object are defined with positive distance while the background points are given negative distances. The shape signature proposed is obtained from the statistical properties of the distance mapped functional. The features used are the number of grid points within pre-specified narrow band region across the object boundary and their algebraic relationship. It is found that, these features are invariant to sealing and rotations. T his increases the retrieval rate and also the speed of retrieval as no pre-scaling and rotation are required to register the shape. Conventional distance mapping is a time consuming process. This process can be used as coarse level in hierarchical CBIR that shrinks the database size from large set to a small one. This tiny database can further the database can be scrutinized meticulously using the above said methods like wavelets, curve lets etc. For feature extraction to improve the retriveval rate or even in the present work using accurate distance mapping.
Automatic Classification and Accuracy by Deep Learning Using CNN Methods in Lung Chest X-Ray Images
Authors: Thamilarasi V , Roselin R 
Published: February 2021

Abstract
Automatic image segmentation and classification of medical images plays significant role in detection and diagnosis of various pathological process. Normally chest radiography is a basic representation to find many abnormalities present in the chest. Radiology services delayed due to proper detection, segmentation and classification of diseases. Automatic segmentation and classification of medical images improved both pathological and radiological process. In recent days the deep learning with CNN methods provides remarkable successes in medical image diagnosis with in time limit and with minimum cost. The proposed method handles CNN for automatic classification of lung chest x-ray images as normal and up normal. Applying these modern techniques to lung chest x-ray images face more challenges while using small dataset. For testing JSRT dataset used which contains 247 images. Preeminent performance achieved using 180 images of nodule and non-nodule images. This method produce expected classification accuracy with the help of faster computation of CNN within fraction of seconds and attain 86.67% in classification accuracy.
A Survey on metaheuristic nature inspired computations used for Mining of Association Rule, Frequent Itemset and High Utility Itemset
Authors: Logeswaran K , Andal R K S , Ezhilmathi S T , Harshath Khan A , Suresh P , Prasanna Kumar K R 
Published: February 2021

Abstract
Metaheuristics are dilemma-independent methods that are generalizedin a variety of problems. In the real world, various problems are solved using generalized dilemma-independent methods called Metaheuristics Computation. Metaheuristic Nature Inspired Computing (MNIC) is a generalized approach to solve NP-hard problems by taking inspirations from the behavior of mother biological nature and their characteristics. Mining of Association rule, Frequent Itemset and High Utility Itemset are strongly interrelated and developing in the field of Data Mining. Metaheuristic nature inspired computation was widely used for the mining association rules of frequentitemsets and high utility itemsets to address the high computation time and optimal solutions. While various articles have been written, there is no systematic review of contemporary metaheuristic nature inspired approaches used in Association Rule Mining (ARM), Frequent Itemset Mining (FIM) and High Utility Itemset Mining (HUIM). This paper explores recent literature on various metaheuristics nature inspired approaches used for ARM, FIM and HUIM.
A hybrid artificial neural network classifier based on feature selection using binary dragonfly optimization for breast cancer detection
Authors: Parvathavarthini S , Deepa D 
Published: February 2021

Abstract
Medical image analysis has become a challenging task as it contributes to disease diagnosis. Breast cancer has been the prominent reason for death among women. While analysing mammogram images, there is a need for clear differentiation of between benign and malignant tissues. Also, early detection of breast masses lead to prediction of breast cancer at the initial stage and minimizes risk of death. In this work, the image is preprocessed using Median filter and is segmented using Fuzzy C Means clustering. Fuzzy C-Means clustering algorithm helps in extracting the region of interest by allocating pixels with similar characteristics into a single group. A pixel may be present in various clusters with different membership values. The belongingness of a pixel to a cluster is decided by the highest membership value. Then the statistical, texture and shape features are extracted from the image. Since there may be many features that are less relevant for classification process, prominent features are selected with the help of Binary Dragonfly Optimization Algorithm and the selected features are fed into a Feed Forward Neural Network trained with Back Propagation Learning to classify the mass as benign or malignant. Experiments are conducted over 320 images from mini-MIAS database out of which 200 ROIs are used in training and 120 ROIs are used in testing phase. The region of interest from given mammogram images are extracted successfully and classified with an accuracy of 98.75%.
Prediction of Students Performance using Machine learning
Authors: Dhilipan J. , Vijayalakshmi N. , Suriya S. , Christopher Arockiya 
Published: February 2021

Abstract
An enormous measure of computerized information is being produced over a wide assortment in the field of data mining strategies. The creation of student achievement prediction models to predict student performance in academic institutions is a key area of the development of Education Data Mining. A prediction system has been proposed by using their 10th, 12th and previous semester marks. The study is evaluated using Binomial logical regression, Decision tree, and Entropy and KNN classifier. In order to attain their higher score, this framework would assist the student to recognize their final grade and improve their academic conduct.
Fault Diagnosis and Asset Management of Power Transformer Using Adaptive Boost Machine Learning Algorithm
Authors: Balaraman Sujatha , Madavan R. , Vedhanayaki S. , Saroja S. , Srinivasan M. , Stonier Albert Alexander 
Published: February 2021

Abstract
Dissolved Gas Analysis (DGA) data of liquid insulation used to find the incipient faults such as partial discharge, thermal faults of various temperatures, discharge of high and low energy faults, combination of electrical and thermal faults in transformers. The conventional approaches of DGA namely Gas Ratio method, Duval triangle method and the Neural Network seems to be time consuming and sometimes yield erroneous results. In this paper, Adaptive BOOST machine learning algorithm is proposed, which is effective in classifying the transformer incipient faults. The results of proposed algorithm is compared with the results of different other machine learning algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree, Ensembler algorithm for the same set of transformers data. From the comparison, it is evident that ADABOOST machine learning algorithm performs well.
Mitigation and analysis of sub synchronous resonance for DFIG based wind farm using STATCOM controller
Authors: Arockiaraj S. , Karunakaran S. , Tamilarasu P. 
Published: February 2021

Abstract
Large-scale integration in transmission networks has led to several challenges; one of which is the need for increased transmission capacity to transport a bulk amount of power without affecting the voltage. In case of transmitting power to large distances series compensation is used along with Doubly-Fed Induction Generator (DFIG) wind turbine and it is simulated using MATLAB Simulink and the results obtained are validated. DFIG wind turbine is operated at normal operating state. Due to series compensation Sub Synchronous Resonance (SSR) occurs which is quite harmful to the wind generator because of the frequency variation, the gear mechanics of the generator gets damaged. When a larger disturbance occurred in any part of the study system at any bus bar, the effect of SSR would be more harmful. The effect of SSR depends on the compensation percentage we used for series compensation. SSR can be mitigated by using Flexible AC transmission System (FACTS) devices such as STATCOM, SVC, and SSSC. We use STATCOM because of its high stability margin, low switching loss and variable voltage rating. Whenever disturbance occurs in the transmission line then STATCOM comes into action by supplying reactive power and absorbing real power thus maintaining the system stability. The STATCOM controller gives pulse to the STATCOM for respective action such as real power absorption or reactive power injection. Thus the Wind turbine is protected and system stability is maintained.
Review on different control techniques for induction motor drive in electric vehicle
Authors: Marulasiddappa H B , Pushparajesh V 
Published: February 2021

AbstractNow a day Electric vehicles (EV) are called future vehicles in place of internal combustion engines because of their working with pollution free and more efficient. This paper reviews various control strategies of induction motor drives (IMD) for EV applications. Efficiency and performance are the major considerations in selecting control algorithms for induction motor drives. Basically there are scalar control and vector control methods for IMDs. Scalar control technique has drawback of low performance. Conventional direct torque control (DTC) technique is one of the most preferable control technique for controlling torque and flux independently. But due the lower switching frequency in direct torque control leads to more flux ripple and torque ripples and it leads lower performance of induction motor drives.
Development of Optimal Day-Ahead Electricity Pricing Scheme using Real Coded Genetic Algorithm under Demand Response Environment
Authors: Krishna Paramathma M. , Devaraj D. , Agneshidhayaselvi V. , Karuppasamypandian M. 
Published: February 2021

Abstract
Real-time pricing in a Smart Grid scenario allows consumers to move their time-insensitive loads to off-peak hours and maintains the power balance between the demand side and supply side. This scheme has a strong impact on customer behaviour, network operations, and overall control of the power grids. In this proposed work, Real coded Genetic Algorithm (RGA) is used to develop a Day Ahead Real-Time electricity-Pricing (DARTP) model. The established scheme maximizes the benefit of the energy provider without reducing the minimum daily consumption rate, the consumer response to the reported electricity prices, and the constraints of distribution networks. The RGA results demonstrate that the calculated optimal prices bring higher benefits for consumers and energy providers than the posting of market prices directly to consumers on the day ahead. The proposed setup is tested with a 32-node distribution bus system. Simulation results reveal that the deployed methodology will help the participants to shift the peak load time to base load time by receiving optimal DARTP, thereby reducing excessive consumption made during the instance of peak load. The obtained DARTP will be sent to the consumers through the deployment of an Advanced Metering System.
Online estimation of control parameters of FACTS devices for ATC enhancement using artificial neural network
Authors: Karuppasamy Pandiyan M , Agnes Idhayaselvi V , Danalakshmi D , Sheela A 
Published: February 2021

Abstract
The deregulated electricity sector needs an improvement in the Available Transfer Capability (ATC) towards the maintenance of power network at balanced condition and to utilize the system in effective manner. Independent System Operator (ISO) maintains the ancillary services by ensuring the reliability of the power system. One of the major functions of ancillary service provider is to maintain the voltage and power flow at stable level. To improve the ATC, both the line power flow and bus voltage profile have to be modified and it is taken care by the ISO. The major limiting criterion for ATC is bus voltage profile. It is well known that the device Thyristor Controlled Series Compensation TCSC which is one of the Flexible AC Transmission System (FACTS) devices can modify the line flow by adjusting the line reactance and Static VAR compensator (SVC) can improve the bus voltage profile by injecting reactive power to the bus. In this research, an Artificial Neural Network (ANN) based estimation of control parameter of FACTS devices such as TCSC and SVC for ATC enhancement is used. The proposed approach uses two different ANN network to find the different TCSC and SVC control parameters to improve the ATC values without violating its voltage constraints for real time transactions. The ANN algorithms such as Radial Basis Function (RBF) as well as Back Propagation Algorithm (BPA) were used to find the TCSC and SVC Parameters and the results are compared. The proposed methods are demonstrated through Reliability Test System (RTS) of IEEE 24 bus. The simulation output represents the suitability of the anticipated method for Real Time estimation of FACTS devices control parameter settings for ATC Enhancement.