bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Hybrid Binder on Properties of Geopolymer Concrete – State of Art
Authors:   School of Environmental and Construction Technology, Department of Civil Engineering, Kalasalingam Academy of Research and Education, Krishnan Koil, Tamil Nadu, India., R Premkumar , Chokkalingam Ramesh Babu ,   School of Environmental and Construction Technology, Department of Civil Engineering, Kalasalingam Academy of Research and Education, Krishnan Koil, Tamil Nadu, India., Meyyappan PL. ,   School of Environmental and Construction Technology, Department of Civil Engineering, Kalasalingam Academy of Research and Education, Krishnan Koil, Tamil Nadu, India,, Shanmugasundaram M ,   School of Mechanical and Building Sciences, Vellore Institute of Technology, Chennai, Tamil Nadu, India, A Ragasree ,   School of Environmental and Construction Technology, Department of Civil Engineering, Kalasalingam Academy of Research and Education, Krishnan Koil, Tamil Nadu, India
Published: January 2020

Natural pozzolans are colossal wellspring of receptive silica and alumina required for geopolymer amalgamation as an alternative binder for conventional Portland concrete. They are accessible at a relatively minimal effort and produce a low environmental impression through their basic extraction. Following audit paper condenses the mechanical and durability attributes just as the micro structural properties of common pozzolan based geopolymers and their potential as binder. The stretch out of geopolymerization increments with the expansion of curing temperature invigorating higher compressive at early ages with no huge effect on later age strength, in any case, expansion of different cementitious materials and hybrid binder improves the mechanical and durability properties of these geopolymers. This paper displays a succinct audit of different examinations that have demonstrated the use of different modern waste items in the amalgamation of geopolymers.
Effect of Ceramic Aggregate and Fly ash in Normal Strength Concrete
Authors:   Department of Civil Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar District, Tamilnadu, Nagalingam Ganesan
Published: January 2020

In this study, full and partial replacement of stone aggregate by ceramic insulator scrap and partial replacement of cement by fly ash has been done in order to enhance economy in construction. More once, the solution of disposal of wasting from ceramic insulator manufacturing company and thermal power plant is also achieved. Further various mechanical properties of ceramic insulator scrap such as crushing value, impact value, abrasion value, specific gravity, sieve analysis and water absorption has been studied and obtained to make the study fruitful. Concrete of gradeM15 is used to study the compressive strength properties of stone and ceramic aggregates. Replacement of stone aggregates by ceramic aggregates has been done in stages starting from 0% to 100% each stage possessing the variation of 10% and in all the specimens 30% of cement is replaced by 35% of fly ash. Six number of cube samples are cast for each variation and the compressive strength of the same have been obtained at 7, 28 and 56 days of age. Totally 216 cubes are cast and tested for compression. It is found that the optimum percentage at which the stone aggregate can be replaced by ceramic aggregates is 50% and 30% of cement can be replaced by 35% fly ash.
Stereo Vision Based Robot for Remote Monitoring with VR Support
Authors:   Senior Associate Professor, Design and Automation Research Group, SMBS, Vellore Institute of Technology, Chennai, India., Selvakumar A. Arockia , Dean Prof. Dr. Daniel Schilberg ,   Mechatronics and Research, Head of Institute of Robotics and Mechatronics University of Applied Sciences Bochum, Germany., M S Mohamed Fazil ,   Pursuing Master’s, Mechatronics and Robotics, Tandon School of Engineering, New York University.
Published: January 2020

The machine vision systems have been playing a significant role in visual monitoring systems. With the help of stereovision and machine learning, it will be able to mimic human-like visual system and behaviour towards the environment. In this paper, we present a stereo vision based 3-DOF robot which will be used to monitor places from remote using cloud server and internet devices. The 3-DOF robot will transmit human-like head movements, i.e., yaw, pitch, roll and produce 3D stereoscopic video and stream it in Real-time. This video stream is sent to the user through any generic internet devices with VR box support, i.e., smartphones giving the user a First-person real-time 3D experience and transfers the head motion of the user to the robot also in Real-time. The robot will also be able to track moving objects and faces as a target using deep neural networks which enables it to be a standalone monitoring robot. The user will be able to choose specific subjects to monitor in a space. The stereovision enables us to track the depth information of different objects detected and will be used to track human interest objects with its distances and sent to the cloud. A full working prototype is developed which showcases the capabilities of a monitoring system based on stereo vision, robotics, and machine learning.
Experimental and Finite Element Method of Castellated Steel Beam with and without Hexagonal web opening
Authors:   School of Environmental and Construction Technology, Department of Civil Engineering, Kalasalingam Academy of Research and Education, Krishnan Koil, Tamil Nadu, India., R Premkumar , Chokkalingam Ramesh Babu ,   School of Environmental and Construction Technology, Department of Civil Engineering, Kalasalingam Academy of Research and Education, Krishnan Koil, Tamil Nadu, India., Shanmugasundaram M ,   School of Mechanical and Building Sciences, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
Published: January 2020

Castellated beams are made from the conventional I section by the process named as Castellation Process. As of late, broad examination on these castellated steel beams has been directed, including various shapes in web openings. The primary objective of these examination works was to assess and dissect its ideal opening sizes and shapes arrangement. For the most part castellated beam are given hexagonal, round and square formed openings. The finite element investigation is most favored technique for comprehension the flexural strength of castellated beams. In this examination, experimental and numerical displaying approach is utilized for deciding spacing between openings on hexagonal castellated beam. ANSYS software was utilized for the investigation. Opening edge 60o with various opening dispersing 0.15d and 0.25d have beam utilized. Investigation was selected steel beam with and without web openings of ISMB 150 and solid ISMB200 section. Results demonstrated that the castellated bar with hexagonal opening indicated more load carrying capacity and less significant deflection contrasted with solid beam.
Experimental Validation of AMIGO Based PID for a Binary Distillation Column
Authors:   Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India., Yadav Eadala Sarath , Thirunavukkarasu I. ,   Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India., K. Ashutha ,   Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India., K. Ashutha ,   Department of Chemical Engineering, Manipal Institute of Technology,Manipal Academy of Higher Education, Manipal, India.
Published: January 2020

Distillation is highly energy consuming process in industrial application concern. This paper focuses on energy consumption of an actuator through appropriate control design for a binary distillation column. Temperature control of binary distillation column is challenging because of existence of interaction between the variables. Independent variables of the process are fast acting (Reflux flow) and slow acting (Reboiler) with respect to process variables (Tray temperatures). Energy consumption of manipulated variable depends on efficient tuning of controller. AMIGO based PID controller design is implemented in this paper to show the optimal energy utilization of actuator. Performance analysis has been carried out to validate the control structure. It is been analyzed that based on the speed and quality requirement of the process, set of controller values can be obtained and best set of PID can be selected for real time implementation. Results depicts the efficiency of control scheme with performance index.
Investment Casting Queuing Procedure
Authors:   Department of Mathematics, Kalasalingam Academy of Research and Education, Krishnankovil 626126, Tamilnadu., Vanalakshmi R. , Maragathasundari S. ,   Department of Mathematics, Kalasalingam Academy of Research and Education, Krishnankovil 626126, Tamilnadu, Eswar S. Kishore ,   Department of Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankovil 626126, Tamilnadu.
Published: January 2020

Investment casting is a modern procedure dependent on lost wax throwing, which is one of the most seasoned known metal farming methods. Investment casting gets its name from the example being contributed (encompassed) with a headstrong material. The process carried out in Investment casting is well investigated by means of Queuing concept. By the usage of supplementary variable technique, the Queuing issue in investment casting is solved and its corresponding probability generating queue size and all its execution measures are determined. Numerical and graphical study throws eminent recognition for the queuing model under study.
Fabrications of Effluence Free Fire Cracker using Pyrotechnic Chemical Composition
Authors:   Department of Mechanical Engineering, Kalasalingam Academy of research and education, Krishnankoil-626126, India., Manikandan R. , Rajesh S. ,   Department of Mechanical Engineering, Kalasalingam Academy of research and education, Krishnankoil-626126, India.
Published: January 2020

The objective of the study is to increase the safety, health aspects in the workplace and to reduce the environment pollution which occurred during the burning of firecrackers by changing the chemical composition. The normal flash powder chemical composition used in firework industries are potassium nitrate (60%), Aluminum (20%), and sulfur (20%). During the burning of pyrotechnic composition, it releases harmful gases and it leads to health effects to the human being and it pollutes the environment. Similarly, during the preparation of the cracker, workers are exposed by safety hazards. In this work, the mixing ratio of aforementioned chemical powders are changed and thermal analysis, sensitivity analysis like friction, impact and performance test like noise level of firecrackers are carried out for both existing chemical composition and modified chemical composition and it is compared.
Image Dehazing Based on Physical Model and Gray Projection
Authors:   Electronics and Communication Engineering, Rajagiri School of Engineering and Technology, Ernakulam, India., John Joxy , Davies Swapna ,   Electronics and Communication Engineering, Rajagiri School of Engineering and Technology, Ernakulam, India.
Published: January 2020

The climatic scattering and ingestion offer climb to the ordinary marvel of obscurity, which truly impacts the detectable quality of view. Dehazing is the technique used to expel the dimness. In late year, various works have been done to improve the detectable quality of picture taken under horrible climate. The images that are taken under overcast conditions experience the evil impacts of shading contortion and attenuation. The proposed strategy is in light of the Dark Channel Prior speculation and gray projection. The transmission map is resolved using the determined estimation of atmospheric light. It uses box filter to lessen the complexity and to improve the computing speed. This computation can restore image with incredible quality and the speed of image computation is high. The proposed strategy is differentiated with other image enhancement strategies and image restoration techniques. It is likewise exceptionally proficient technique since it can process huge images within less time.
Bianchi Type V Universe and Bulk Viscous Models with Time Dependent Gravitational Constant and Cosmological Constant in General Relativity
Authors:   Dept. of Mathematics, School of Mechanical Engineering, Lovely Professional University, Phagwara, India., Tandon Reena
Published: January 2020

This paper deals with Bulk Viscous Models and Bianchi type- universe in the standard general relativity theory by assuming where constants, is the density. Einstein field equations ( ) have been solved in the presence of a variable gravitational constant as well as a variable cosmological constant together with a bulk viscous fluid. In order to find a deterministic solution of EFEs, a simple form of Hubble parameter being constant when where has been considered here that shows the signature flip from early deceleration to present acceleration. Moreover, the bulk viscous coefficient is allowed to vary with the density as . The physical and geometrical behavior are described in detail for the obtained model. The model obtained here is in good agreement with the present cosmological observations.
Validating the Knowledge Acquisition Process Metrics in Content Management Systems
Authors:   Print Pura Technologies, Charlotte, North Carolina, USA, Jeyasingh Jason , S Justus ,   Associate Professor, School of Computing Science and Engineering, VIT University, Chennai, India.
Published: January 2020

Measuring the processes involved in knowledge engineering for designing and building an intelligent system has taken significant role. Out of the four basic processes involved in knowledge engineering, this paper deals with the knowledge acquisition process and the metrics necessary for measuring the process itself. Three metrics are proposed for the knowledge acquisition process based on the entailment procedures, its length and complexity, and the cohesion and coupling attributes of the collection of knowledge units. These three metrics are formalized based on the Briand’s mathematical properties for validating software metrics. These metrics are indicative in the way it gives an insight on the design and the development of a knowledgebase. In addition to these metrics, newer metrics can also be proposed for knowledge representation and knowledge sharing processes.
Data Mining Technology and Techniques in Medical Science Field Applications
Authors:   Computer Science and Engineering, Vellore Institute of Technology, Vellore, India, Namratha Gonuguntla , P Swarnalatha ,   Computer Science and Engineering,Vellore Institute of Technology, Vellore, India,
Published: January 2020

The electronic and the written records both played an equally significant part in the daily lives of the health department staffs. However, after the globalised version of change in the healthcare department, it has been recorded by the researcher in his examination a portion of the fundamental focuses that demonstrate the genuine advantage of the information and the dangers. Consequently in this report, the analysts will show unmistakably the Data mining procedure and its different focuses. The primary focal point of the investigation is needy upon the Nursing work, and in this manner various health care centres and the nurses will be taken under consideration. The report additionally examines about the data mining subject's experience and how it rose in the therapeutic science field of capacity. The information that are dealt with in the framework has changed into a making care each. It has been noticed that information mining is considered to all computer-based information stockpiling and recovering method. The approach of the digital form of data gathering will also be explained and along with it will also showcase some critical points that are co-related with the findings and analysis. In the end, the researcher will provide its own recommendation for the betterment of the nursing in the medical science field of capacity.
Wavelet Transform Based CT and PET Lung Image Fusion
Authors:   Assistant Professor and Head in Department of Information Technology, Annai Vailankanni Arts and Science College (Affiliated to Bharathidasan University), Thanjavur, India., Devi Dr.M.Chithra
Published: January 2020

The lung cancer reduces the breathing ability and it causes due to uncontrollable cells in the lungs. The fusion of Computed Tomography (CT) and Positron Emission Tomography (PET) has clear information about lungs comparing with individual CT and PET images. The fused image helps to find cancer in a starting stage. In this study, a novel method for fusion of CT and PET lung images using Wavelet Transform (WT) and MaxMin (MM) rule is presented. Initially, the CT and PET lung images are decomposed by WT. The subband coefficients of WT are extracted by using entropy features. Then MM rule is applied, after that the image is reconstructed to form the fused lung image. The performance is evaluated by Reference Image Database to Evaluate therapy Response (RIDER) lung database. Results and discussion shows the performance of Lung Image Fusion (LIF).
Guiding and Navigation for the Blind using Deep Convolutional Neural Network Based Predictive Object Tracking
Authors:   B.Tech, School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu., Alisetti Sai Nikhil , Purushotham Swarnalatha ,   Associate Professor, School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu., Mahtani Lav ,   B.Tech, School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu.
Published: January 2020

Indoor and outdoor Navigation is a tough task for a visually impaired person and they would most of the time require human assistance. Existing solutions to this problem are in the form of smart canes and wearable. Both of which use sensors like on - board proximity and obstacle detection, as well as a haptic or auditory feedback system to warn the user of stationary or incoming obstacles so that they do not collide with any of them as they move. This approach has many drawbacks as it is not yet a stand - alone reliable device for the user to trust when navigating, and when frequently triggered in crowded areas, the feedback system will confuse the user with too many requests resulting in loss of actual information. Our Goal here is to create a Personalized assistant to the user, which they can interact naturally with their voice to mimic the aid of an actual human assistance while they are on the move. It works by using its object detection module with a high reliability training accuracy to detect the boundaries of objects in motion per frame and once the bounding box crosses the threshold accuracy, recognize the object in the box and pass the information to the system core, where it verifies if the information needed to be passed onto the user or not, if yes it passes the converted speech information to the voice interaction model. The voice interaction model is consent-based, it would accept and respond to navigation queries from the user and will intelligently inform them about the obstacle which needs to be avoided. This ensures only the essential information in the form of voice requests is passed onto the user, which they can use to navigate and also interact with the assistant for more information.
Predicting Crisis in Global Trade Network: An Enhanced Decision Tree Based Methods
Authors:   School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India., Marhwal Vashisht , Bamel Piyush ,   School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India., Agarwal Tanay ,   School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.
Published: January 2020

International Trade Relations represent a natural Social Information Network that has been extensively analyzed for various purposes like monitoring the global economy. The aim is to use the Global Trade Network to predict the occurrence of natural disasters or financial crisis based on the fact that the trade relations tax a hit in their patterns. The Global Network compromises of Export-Import Relations between the countries in the form of aWeighted Social Network. Predicting Trade relations help us effectively predict any future crisis and prepare for the same. An analysis of the Global Trade Network would discuss the centrality measures and Degree strengths. Using a list of crises which has occurred in the past and with the help of an efficient Machine Learning Model and Sampling Technique the aim is to improve the accuracy and precision of our prediction and discuss the implications on the network.
Multi-Layered ‘Odd-Even’ Reversible Embedding for Encrypted Images
Authors:   Department of ECE, MVJ College of Engineering, Bangalore India., R Geetha , S Geetha ,   Professor, SCSE, VIT University Chennai, India.
Published: January 2020

Reversible Data Hiding (RDH) has been under research for the past two decades. Recently RDH in encrypted images (RDH-EI) draws more attention among the researchers. Since it is used effectively in privacy protection and cloud computing. In this paper, a RDH-EI using LSB based odd-even embedding technique scheme is proposed. Initially the cover image is encrypted using block and pixel scrambling and bit plane mix ordering. Block and pixel scrambling is done through four random walks/Space Filling Curves (SFCs). Each block and pixel is tested for randomness of information source through each random walk. Finally the secret message is embedded in each block of image based on testing the pixel value and position value in the block using LSB embedding. This method avoids overflow/underflow issues that usually happens in RDH/LSB embedding.
Patient History-driven Framework for Healthcare Analytics
Authors:   SENSE, Vellore Institute of Technology, Chennai, India., R Latha , P Vetrivelan ,   SENSE, Vellore Institute of Technology, Chennai, India.
Published: January 2020

Wireless body are network (WBAN) is evolving more rapidly due to the development of internet of things (IoT). Decision making is the main concern in medical field which leads to optimization. Medical evidences in patient care improve optimization in patient care. Partially observable markov decision process (POMDP) helps in making accurate decisions with the help of observations and past actions in medical field. Hence dynamic decision making makes it possible. In POMDP, the incremental method is designed to incorporate any immediate change and immediately send updates. In this paper, process mining is applied in finding the history of patients who are travelling from one country to another for in search of job or for doing a major clinical operation. Event data is very much important for handling patient’s history. Event data stores the date and time at which the patient gets consultation. Electronic medical records (EMR) are nothing but storage of all the event data of patients visiting the hospital. Event data gives the evidence of patients when they had a consultation with a doctor. Event data is present anywhere. Partially Observable Markov Decision Process for Patient-history and Careflow mining Algorithm for Heuristic Comparison are presented in this paper. Process mining gives a direct relationship by step by step evaluation and improvement of the process. It also exhibits patient care by identifying the execution errors, understanding the process heterogeneity. The online process mining tool, PROM helps finding the history of patient.
Cloud based IoT Applications for Industrial and Home Automation
Authors:   Senior Associate Professor, Design and Automation Research group, School of Mechanical and Building Sciences, Vellore Institute of Technology, Chennai, Tamil Nadu, India., Selvakumar Dr. A. Arockia
Published: January 2020

IoT for Industrial and Home Automation is emerging with a big bang, has huge potential for every field to be used. When there is a need for efficient means to seek IoT interface, a cloud server is what strikes in every design and applications. There are numerous aspects in building a real-time IoT interface, but IoT through cloud can be a source of multiple gains in contrast to its peers such as edge computing [1]. Industrial and Home Automation involve an excellent delivery protocol for an error-free effective transmission in the internet. MQTT protocol is a better option to facilitate the requirements of IoT through its smooth implementation, Quality of Service and data delivery. In today’s world, IoT cloud providers compete to provide reasonable and precise IoT based utilities. Despite extensive engagement of these IoT clouds, we have not initiated standard regularization or few comparative analytical investigations across the research databases. An IoT application calls for diverse resources of a particular cloud and hence it calls for a survey on IoT cloud concerning Latency, interval for subsequent update, user-friendliness, IFTTT compatibility, data handling, processing data, storage limits, servers used and security. An analyses of five of the most eminent clouds (Adafruit IO, Amazon Web Service (AWS), Blynk, Thingspeak and Ubidots) based on the above-described specifications are the factors of motivation for this paper and hence matches the best cloud suited to serve specific purpose and applications.
A modified elephant herd optimization algorithm to solve the single machine scheduling problems
Authors:   Department of Mechanical Engineering, Mepco Schlenk Engineering College, Sivakasi, India., Marichelvam M.K. , Geetha M. ,   Department of Mathematics, Kamaraj College of Engineering & Technology, Virudhunagar, India.
Published: January 2020

This study reports the single machine scheduling problem for finding the minimum the total weighted tardiness. As the problem is non-deterministic polynomial-time hard (NP-hard), the problem might not be answered by the exact solution techniques. Henceforth, different heuristics and meta-heuristics were recommended by different practitioners to tackle the problem. A modified elephant herd optimization algorithm (MEHOA) is investigated in the present work to solve the single machine total weighted tardiness scheduling problem (SMTWTSP). The performance of the anticipated approach is studied with the test instances available in the OR library. The outcomes are compared with several different algorithms offered in the literature and indicate the efficacy of the developed methodology.
Iov Hybrid Architecture – Inter-Vehicular Communication and Data Transmission
Authors:   Electronics and communications Engineering with specialization in IoT and sensors, VIT Vellore, Tamil Nadu, India, Yash Ishan , Talkal Samir Ahmed ,   Information Technology Engineering, VIT Vellore Tamil Nadu, India, Prashant Karapurkar Shivani ,   Electrical and Electronics Engineering, VIT Vellore Tamil Nadu, India
Published: January 2020

The Internet of Vehicle has an Intelligent Vehicular Grid consisting of the sensor platform, which continuously processes the data gathered from the surrounding of the vehicle and feeds it to the infrastructure. This data is further utilized by other vehicles to assist in traffic management, vehicle localization, pollution control and safe navigation. In this paper, we have suggested the ways through which we could attain a high data rate, seamless connectivity, security, improved quality of service and scalability. The first section of the paper consists of the proposed architecture and its benefits, and the latter consists of the scope of improvement with results using sensor dataset its application and challenges. Communication between the infrastructure and other vehicles can create privacy and security violations; hence, we also address ways to ensure the location privacy of the user.
Lung Segmentation and Iterative Weighted Averaging Smoothing Technique on Chest Ct Images
Authors:   Department of Electronics Engineering, Anna University, Chennai, India, Sridevi C. , Kannan Dr. M. ,   Department of Electronics Engineering, Anna University, Chennai, India.
Published: January 2020

Computed Tomography (CT) is one of the most commonly used imaging modalities for tumour detection and diagnosis, due to its high spatial resolution, fast imaging speed and wide availability. Nodules of the lungs and pathological residues with varied diameter can be comfortably viewed by computed tomography and can be categorized as benign or malignant. The key intention of this segmentation and smoothing is to develop an efficient methodology for an automated lung tumour diagnosis. Segmentation is the quantitative pre- processing step used in the chest CT analysis. The iterative weighted averaging technique is used in addressing the issues related to the segmentation and smoothing method employed in this paper. The methodology incorporates the accurate Lung Segmentation, enclosure of Juxtapleural nodules, the proper insertion of the bronchial vessels for enhancing the smoothness of the segmented Lung area. The steps involved in this paper include Image preprocessing by nonlinear anisotropic diffusion filtering, Thorax Extraction, Lung segmentation and iterative weighted averaging technique for smoothing the contours. The main feature in choosing the nonlinear anisotropic diffusion filtering is for properly preserving the regions unaffected with cancer and also to differentiate the artefacts involved due to the subject movements. In the fuzzy c- means clustering algorithm, the lung parenchyma is identified from the thorax region based on the fuzzy membership value and the affected regions are attached. The standard execution time for segmentation process of one dataset is 1.91s, it is the more rapid process than the manual segmentation.
Adaboost Cascade Classifier for Classification and Identification of Wild Animals using Movidius Neural Compute Stick
Authors:   Research Scholar, School of Information Technology and Engineering, VIT University, Vellore, India., Meen S.Divya , Agilandeeswari Dr.L. ,   Associate Professor, School of Information Technology and Engineering, VIT University, Vellore, India.
Published: January 2020

Deep learning has gone deeper and has been utilized in almost all the applications like object recognition, image classification, speech recognition and much more. Most of the real-time applications rely on deep learning for accurate results. But one downside to the deep learning is its demand for GPUs (Graphical Processing Unit) or TPUs (Tensor Processing Units) for faster execution. There was no one-stop-shop hardware and software for deep learning applications, until recently Intel launched the Movidius Neural Compute Stick (NCS). This sleek device provides the power of GPU in a CPU based system. In this work, we have modeled an animal detection system using NCS and AdaBoost classifier powered by Multi-Block Local Binary Pattern (MB-LBP) features. The model has been built upon AlexNet and has achieved an average accuracy of 96.8% and a false rate 2.3% in classifying the animals as wild and non-wild. Furthermore, the model has a speedup of 467% when compared to the execution in the CPU based system.
Obstacle Detector for Blind Peoples
Authors:   Assistant Professor, Department of Information Technology, Kalasalingam Academy of research and education, Krishnankoil, India., Maragatharajan M. , Jegadeeshwaran G. ,   Department of Information Technology, Kalasalingam Academy of research and education, Krishnankoil, India, Aakash R. ,   Department of Information Technology, Kalasalingam Academy of research and education, Krishnankoil, India., Aniruth K. ,   Department of Information Technology, Kalasalingam Academy of research and education, Krishnankoil, India., Sarath A. ,   Department of Information Technology, Kalasalingam Academy of research and education, Krishnankoil, India
Published: January 2020

From the survey of WHO, 39 million people are blind all over the world. It is a quite miserable statement. The peoples who are all suffered from these visual difficulties can use this project to overcome their situations. The ultrasonic sensor used in this project plays a vital role. It detects the object in front of this with a certain range. When object is detected a buzzer sound is given to the user as an indication. While they hear this sound they can know an obstacle in front of them.
Resource Scheduling using Cloud in Chemical & Electro Chemical Coating Application
Authors:   Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Tamilnadu, India., Brintha N.C. , Jappes J.T. Winowlin ,   Department of Mechanical Engineering, Kalasalingam Academy of Research and Education, Tamilnadu, India.
Published: January 2020

Due to the advancements and use of computer based technologies in manufacturing sectors, there has been a drastic change in how manufacturing industries perform their business. Manufacturing tied up with cloud computing technologies can help the customers, suppliers and manufacturers in several ways and hence can maximize profit in production lines. However, even though there are several policies for scheduling workflows in production, effective mapping of tasks with resources is always a challenging issue. If scheduling policies are inappropriate, it will have a negative impact on cost, time and therefore may affect the overall performance of the workflow. This work proposes a Multi-Objective Genetic Algorithm (MOGA) based scheduling model to schedule resources related to chemical and electro chemical coating. The major objective of this work is to reduce makespan, improve resource utilization and also minimize the overall cost of the workflow. This information obtained from the workflow can be used for better decision making when several tasks has to be done in parallel by migrating the tasks to the site of resource availability. The computational results also shows that MOGA performs well in makespan minimization, resource utilization and cost minimization because of its convergence speed and robustness. The analysis results prove that, MOGA can be optimal for estimating the path of where the work can be done such that the makespan and cost is minimized.
Decision Support System for Exemplary Teacher Selection in Elementary School using Topsis Method
Authors:   Department of Information System, STMIK Pringsewu, Lampung, Indonesia, Putri Rara Pramita , Ilayaraja M. ,   School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India., Shankar K. ,   School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India., Hashim Wahidah ,   Institute of Informatics and Computing Energy, Universiti Tenaga Nasional, Malaysia., Maseleno Andino ,   Institute of Informatics and Computing Energy, Universiti Tenaga Nasional, Malaysia., Odarich Irina N. ,   Togliatti State University, Russia.
Published: January 2020

Teacher is one of the most important components owned by school in sustaining life. It is a challenge for the management of educational institutions to be able to provide an appropriate, effective and efficient decision in data management in the competitive business world. It spurs schools to strive harder in improving the quality of the school. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a method that has a concept where the best selected alternative not only has the shortest distance from a ideal positive solution. But also has the longest distance from a ideal negative solution. The criteria used in this system vary, according to the selection of the best teachers provided by the institution. The applicant's score data that has been entered into the system will be calculated using TOPSIS method, by finding the farthest and closest distance from the positive and ideal negative solutions. Applicants with the highest v score will occupy the top of this system. Based on the results of the sample case selection shows that the results of calculations using the same system with manual calculations. This system is able to provide the best teacher recommendations.
Improving Image Steganalyser Performance using Second Order SPAM Features Extracting through Contourlet Transform
Authors:   Department of Information Technology, Kalasalingam Academy of Research and Education, Virudhunagar Dt., Tamil Nadu. India, Subramanian C Bala , Hemalatha J ,   Department of Computer Science and Engineering, Srividya College of Engineering and Technology, Virudhunagar Dt, Tamil Nadu. India., Balakannan S P ,   Department of Information Technology, Kalasalingam Academy of Research and Education, Virudhunagar Dt., Tamil Nadu. India., Geetha S ,   School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu. India.
Published: January 2020

The major challenge posed by feature based blind steganalysers is the scheming of useful image features, which offers true existence of the stego noise rather than the natural noise in the images. Despite hundreds of features being applied in the real time implementation, only low detection accuracy could be achieved. Hence, this paper proposes a new model for detecting the stego image coupled with an examination of the task by applying a two-step process. (a) Extraction of the second order SPAM (Subtractive Pixel Adjacency Matrix) as features and the second order SPAM features of coefficients and co-occurrence matrices of sub band images from the contourlet transform. (b) Implementation of the system, based on an efficient classifier, Support Vector Machine which is capable of providing the higher detection rate than the existing classifers. Full- fledged experimentation with huge database of clean and steganogram images produced from seven steganographic schemes with varying embedding rates, and using five steganalysers were carried out in this study. The study shows that the proposed paradigm enhances the detection accuracy rate substantially and validates its efficiency with its better performance even at low embedding rates.
Automated Framework for Segmenting Skin Lesions using Artificial Bee Colony Optimization with Morphological Reconstruction
Authors:   Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Tamil Nadu, India., Sumathi R. , Venkatesulu M. ,   Department of Information Technology, Kalasalingam Academy of Research and Education, Tamil Nadu, India
Published: January 2020

Nowadays, Many people are affected by skin cancers. Our proposed work designed a framework to extract the skin cancer using artificial bee colony with morphological reconstruction filters, which helps the demonologist to prevent the severity in early stage, Melanoma is the now become a harmful form of skin cancer which leads the skin cells to grow rapidly and form cancerous tumors. We collected various melanoma images from having used samples from public dataset like ISIC archive and a few from clinical datasets. To remove the noise, median filtering is used for preprocessing in the first step, to segment the tumor boundary Artificial bee colony is used and to remove the unwanted pixels using morphological reconstruction filters. Segmentation metrics like precision, recall, accuracy, Mean Square Error, Peak signal to noise ratio and computational time were calculated. Our proposed method yield 97.7% segmentation accuracy when compared with the level set method and Fuzzy C Means clustering techniques
Identification of Pests on Plants Using Clustering and Hybrid Approaches
Authors:   Associate Professor, Department of Computer Science and Engineering from the Arulmigu Kalasalingam College of Engineering, Anna University, Chennai. India., Karkuzhali Dr. S.
Published: January 2020

Enormous agricultural yield is lost each year, because of quick pervasion by pest and insects. A great deal of research is being done worldwide to recognize logical procedures for early discovery/identification of these bio-aggressors. In the past years, a few methodologies dependent on computerization and digital image processing have become known to address this issue. The greater part of the calculations focus on pest identification and location, restricted to a greenhouse environment. Likewise, they include a few complex computations to accomplish the equivalent. In this paper, we developed a unique algorithmic approach to isolate and distinguish pest utilizing clustering and hybrid approaches. The proposed method includes decreased computational complexity and pest detection in green house environment. The whitefly, a bio-aggressor which represents a risk to a huge number of harvests, was picked as the pest of enthusiasm for this paper. The calculation was tried for a few whiteflies influencing various leaves and an accuracy of 96% of whitefly recognition was accomplished.
Digital Image Secure Share Creation Techniques Analysis: A Survey
Authors:   Department of Computer Applications, Kalasalingam Academy of Research and Education, Krishnankoil, India., Parameswari C.Devi , Shankar K. ,   Department of Computer Applications,, Alagappa University, Karaikudi, India.
Published: January 2020

Security is a significant concern in data innovation that managing the web world today. In sharing based technique shares are created, encoded and stored into digital form. This paper discussed the various papers in secret share creation for image security like visual secret share creation, Chinese remainder theorem, Shamir secret sharing and so on. In this design, a secret image is programmed into n shadows of the random model. It is possible to interpret the secret image externally by overlay a certified subset of shadows. These shares are uncovering the secret image to assume to be imprinted on transparencies and in the wake of stacking them to one another. Here more than fifteen papers are analyzed; those papers are gathered from IEEE, Springer, Elsevier and some other Journals. In addition, security measures are analyzed for all image secret sharing procedure.
Finding Best Possible Number of Clusters using K-Means Algorithm
Authors:   Department of Computer Applications, Kalasalingam Academy of Research and Education, Krishnankovil-Virudhunagar , India., Maheswari Dr. K.
Published: January 2020

Customers are assets for business. The companies are investing more for customer relationship management. Retaining customer for long time is a difficult process in today’s trend. On line shopping is also increasing day by day. People are more interested to visit popular web sites and they are spending very less time to choose their products. On line shops are paying more interest to analyze customer preferences, their needs, shopping behaviors through data mining technique. Proper classification is necessary for organizing such data. In this work, Customer with the same buying behavior is grouped based on the features age and salary. K-Means algorithm is applied to form clusters with different K values for original data and normalized data. The within sum of square (wss) is calculated for both the data for different cluster size. The minimum wss is considered to be better which is achieved in normalized data. The validity of cluster is evaluated by elbow, silhouette and gap statistic method to choose the optimal number of clusters. This work is implemented in R software.
Developing an Agricultural Product Price Prediction Model using HADT Algorithm
Authors:   Department of Computer Applications, Kalasalingam Academy of Research and Education, Krishnan koil - 626126, Tamilnadu, India., Rajeswari S. , Suthendran K. ,   Department of Information Technology, Kalasalingam Academy of Research and Education, Krishnan koil - 626126, Tamilnadu, India.
Published: January 2020

Big Data Predictive Analytics and Data mining are emerging recent research field to analyse the agricultural crop price. The applications and techniques of data mining as well as Big Data using agriculture data is considered in this paper. In particular, the farmers are more concern about estimating that how much profit they are about to expect for the chosen crop. As with many other sectors the amount of agriculture data are increasing on a daily source. In this work, agriculture crop price dataset of Virudhunagar District, Tamilnadu, India is considered and for the price prediction model based on data mining decision tree techniques. The main goal is to establish the new predictive model based on Hybrid Association rule-based Decision Tree algorithm (HADT). The outcome for the suggested HADT forecast model is heartening and precise to predict agricultural product prices than other current decision tree models.
Identification of Blood Vessel Clot Region using Fuzzy C Means Clustering Based Artificial Bee Colony Algorithm
Authors:   Department of ICE, Kalasalingam Academy of Research and Education (Kalasalingam University), Srivilliputtur, India., Ramaraj Kottaimalai , Govindaraj Vishnuvarthanan ,   Department of BME, Kalasalingam Academy of Research and Education (Kalasalingam University) Srivilliputtur, India., Murugan Pallikonda Rajasekaran ,   Department of ECE, Kalasalingam Academy of Research and Education (Kalasalingam University) Srivilliputtur, India, Zhang Yudong ,   Department of Informatics, University of Leicester, Leicester, LE1 7RH, UK, Wang Shuihua ,   Department of Mathematics, University of Leicester, Leicester, LE1 7RH, UK., Thiyagarajan Arunprasath ,   Department of ECE, Kalasalingam Academy of Research and Education (Kalasalingam University) Srivilliputtur, India
Published: January 2020

Medical image segmentation results in the multiple fractioning of an input image for a deeper analysis/insight. Localization of objects and detection of boundaries are the core-theme of using segmentation for medical images. It elucidates the process of finding the anatomic structures in medical images. In this paper, we put forth a technique that has Fuzzy C-Means clustering and Artificial Bee Colony (ABC) Optimization has delivered the segmentation of MRA brain image. Artificial Bee Colony (ABC) has been used by many researchers as it is a population-based stochastic approach that has better search-in-space abilities for various optimization problems. The unsupervised clustering FCM has produced candidate outcomes in medical image processing. FCM is mostly preferable for segmenting the soft tissues in brain model, and it provides better output when compared to some of the competitive clustering techniques like KM, EM and KNN. The output of the suggested techniques is verified by using real MRA brain images. The results of Statistical parameters show that our method is notably better compared to other algorithms.