M.K. Nallakaruppan

Bio: M.K. Nallakaruppan is an academic researcher from VIT University. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 3, co-authored 7 publications receiving 23 citations.

Quick fix for obstacles emerging in management recruitment measure using IOT-based candidate selection

Abstract: In this paper, decision-making tool is developed for the identification of the optimal aspirant for the recruitment procedure. It is developed using the IoT-based smart sensor architecture and Visual Studio programming language. The ideal candidates are determined by using the objective function, which is extracted from the sequence of the pairwise comparison performed using the combination of MCDM algorithms. Later, after the pairwise comparison, the process is extended to the next step, that is to allocate ranking for the best suitable candidate. This makes the tool more feasible and accurate. To identify the ranking of the finest aspirant, the combination of two MCDM technologies is used that is TOPSIS and GRA. In TOPSIS, mainly two artificial alternatives are used that is positive ideal alternative and negative ideal alternative. The Grey relational grade reduced by Grey theory (Tsai et al. in Int J 11:45–53, 2003) will be used to make an integrated and authentic evaluation system for identifying who is the best aspirant among all the candidates applied for the job of a professor.

Location Aware Climate Sensing and Real Time Data Analysis

Abstract: In any country tourism and development plays a vital role in uplifting the economy and every country it's willing to invest very huge money for the development and the enhancement of the tourism. This work aims at building an interactive web service that is deployed in such a way that it collects information about the best climates around the places and builds up an application that extracts, processes and analyses the data to auto communicate through mailing and other means to the people who are interested in travelling these places. Data collected for this purpose through the sources that supply the data regarding on the climate. Later this data is mathematically analysed with time series with regression model for the formal verification of the data samples involved in the work. The data is further analysed R programing model to compare the fitting efficiency of the central tendencies on mean and the variance of the temperature data o during the analysis. Post analysis process deploys a system that generates the report and communicates these details further to intimate the user regarding the change of climate.

Analysis of CPU Scheduling Algorithms for Cloud Computing

Abstract: Cloud computing refers to the advancement of distributed computing which takes the computational aspects of data processing into high-power centralized data centers over networks. It refers to the use of a centralized pool of resources which are allocated to a large number of customers on a pay-as-you-go model. This creates the need of scheduling algorithms which allow us to decide which job process, amongst the ones received will be allocated resources first for execution. Job scheduling is one of the major areas of research nowadays in the field of cloud computing as it helps not only in proper utilization of resources but also in avoidance of deadlock within the cloud infrastructure. There exist a large number of scheduling algorithms such as First Come First Serve Scheduling Algorithm, Generalized Priority Algorithm, Round Robin Scheduling Algorithm and Least Slack Time Scheduling Algorithm. This paper proposes a mechanism to implement each of these and then compute their average waiting times and average turnaround times for a common test case is more efficient one for cloud environment.

Smart Water Resource Management Using Artificial Intelligence—A Review

Abstract: Water management is one of the crucial topics discussed in most of the international forums. Water harvesting and recycling are the major requirements to meet the global upcoming demand of the water crisis, which is prevalent. To achieve this, we need more emphasis on water management techniques that are applied across various categories of the applications. Keeping in mind the population density index, there is a dire need to implement intelligent water management mechanisms for effective distribution, conservation and to maintain the water quality standards for various purposes. The prescribed work discusses about few major areas of applications that are required for efficient water management. Those are recent trends in wastewater recycle, water distribution, rainwater harvesting and irrigation management using various Artificial Intelligence (AI) models. The data acquired for these applications are purely unique and also differs by type. Hence, there is a dire need to use a model or algorithm that can be applied to provide solutions across all these applications. Artificial Intelligence (AI) and Deep Learning (DL) techniques along with the Internet of things (IoT) framework can facilitate in designing a smart water management system for sustainable water usage from natural resources. This work surveys various water management techniques and the use of AI/DL along with the IoT network and case studies, sample statistical analysis to develop an efficient water management framework.

Iterative Approach for Frequent Set Mining Using Hadoop Over Cloud Environment

Abstract: Cloud computing initially gained popularity as it offered an alternative for handling the ever-growing size of data. One of the main advantages of Cloud computing is parallel processing of data, which causes the effect of pooling the resources of various systems. The proposed project aims to implement the feature for the purpose of data mining and will use the Apriori Algorithm to demonstrate the results. Hadoop platform will be utilized for this project. The system will receive a dataset and redistribute it to the nodes of the cloud. Here, Apriori algorithm will be applied upon the sections of the dataset and the results will then be combined to obtain the frequent itemsets in the global data. Using the frequent item sets, rule mining will be achieved.

A Recent Survey on Internet of Things (IoT) Communication Protocols

Abstract: Internet of Things (IoT) consists of sensors embed with physical objects that are connected to the Internet and able to establish the communication between them without human intervene applications are industry, transportation, healthcare, robotics, smart agriculture, etc. The communication technology plays a crucial role in IoT to transfer the data from one place to another place through Internet. This paper presents various communication protocols, namely Zigbee, Bluetooth, Near Field Communication (NFC), LoRA, etc. Later, it provides the difference between different communication protocols. Finally, the overall discussion about the communication protocols in IoT.

Applied General Statistics.

Abstract: Applied General Statistics-Frederick Emory Croxton 1939 \"This book is intended for the use of readers who are interested in the understanding of statistical methods, and in their application in various fields, especially the social sciences. Consequently, the illustrative material has been drawn mainly from the fields of economics, sociology, and business, but occasionally from others. The arrangement of the topics treated in this volume is about the same as in the authors' earlier book, Practical Business Statistics. The present text, however, does not stress business applications of statistical methods, but does present a greatly amplified treatment of analytical methods. The extensive discussion of the description and analysis of statistical data and of the making of statistical inferences will, we hope, make it useful to a wide group of teachers emphasizing various aspects of statistics\"--Preface. (PsycINFO Database Record (c) 2010 APA, all rights reserved).

Significance of machine learning in healthcare: Features, pillars and applications

Abstract: Machine Learning (ML) applications are making a considerable impact on healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the speed and accuracy of physicians' work. Countries are currently dealing with an overburdened healthcare system with a shortage of skilled physicians, where AI provides a big hope. The healthcare data can be used gainfully to identify the optimal trial sample, collect more data points, assess ongoing data from trial participants, and eliminate data-based errors. ML-based techniques assist in detecting early indicators of an epidemic or pandemic. This algorithm examines satellite data, news and social media reports, and even video sources to determine whether the sickness will become out of control. Using ML for healthcare can open up a world of possibilities in this field. It frees up healthcare providers' time to focus on patient care rather than searching or entering information. This paper studies ML and its need in healthcare, and then it discusses the associated features and appropriate pillars of ML for healthcare structure. Finally, it identified and discussed the significant applications of ML for healthcare. The applications of this technology in healthcare operations can be tremendously advantageous to the organisation. ML-based tools are used to provide various treatment alternatives and individualised treatments and improve the overall efficiency of hospitals and healthcare systems while lowering the cost of care. Shortly, ML will impact both physicians and hospitals. It will be crucial in developing clinical decision support, illness detection, and personalised treatment approaches to provide the best potential outcomes.

IoT and Its Impact on the Electronics Market: A Powerful Decision Support System for Helping Customers in Choosing the Best Product

Abstract: Many companies have observed the significant benefits they can get via using internet. Since then, large companies have been able to develop business transactions with customers at anytime, anywhere, and in relation to anything, so that we now need a more comprehensive concept than the internet. This concept is the Internet of Things (IoT). IoT will influence decision making style in various phases of selling, buying and marketing process. Therefore, every individual and company should know precisely what IoT is, and how and why they should incorporate it in their operations. This motivated us to propose a smart system based on IoT to help companies and marketers make a powerful marketing strategy via utilizing obtained data from IoT devices. Not only this, but the proposed system can also solve the problems which face companies and customers in online shopping. Since there are different types of the same product, and also different criteria for purchasing which can be different between individuals, customers will need a decision support system to recommend them with the best selection. This motivates us to also propose a neutrsophic technique to deal with unclear and conflicting information which exists usually in the purchasing process. Therefore, the smart system and neutrosophic technique is considered as a comprehensive system which links between customers, companies, marketers to achieve satisfaction for each of them.

Fuzzy Multicriteria Analysis for Performance Evaluation of Internet-of-Things-Based Supply Chains

Abstract: This paper presents a fuzzy multicriteria analysis model for evaluating the performance of Internet of Things (IoT)-based supply chains. The inherent uncertainty and imprecision of the performance evaluation process was handled by using intuitionistic fuzzy numbers. A new fuzzy multicriteria group decision making algorithm based on the technique ordered preference by similarity to the ideal solution (TOPSIS) approach, and the concept of similarity measures was developed for determining the overall performance of each alternative. The advantage of the proposed fuzzy multicriteria analysis model is that it can overcome the limitations of the existing approaches in an intuitionistic fuzzy environment. The fuzzy multicriteria group decision-making model provides organizations with the ability to evaluate the performance of their IoT-based supply chains for improving their competitiveness. An example is presented to highlight the usefulness of the proposed model for tackling a real world IoT performance evaluation problem.