Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Department of Computer Applications

Diabetes mellitus is a metabolic illness that causes high blood sugar, which is widely known as diabetes. Insulin is a hormone produced by an organ situated behind the abdomen called the pancreas. This insulin agent moves glucose from your blood into the cells for energy and storage. With diabetic disorder, the body either will not create enough insulin or can’t effectively use the insulin it does create. Untreated high blood glucose or sugar from diabetic disorder will harm the nerves, eyes, kidneys, and different organs of the body. There are different data mining software tools to predict and analyze diabetes. Many attempts have been made by researchers to improve the efficiency of various models. The proposed method is Dimensionality reduction and clustering technique. It gives the highest accuracy for the larger dataset for both balanced and imbalanced datasets. In this paper, large and small datasets have been taken for clustering using K-means approach, Farthest first method, Density based technique, Filtered clustering method and X-means approach. K-means, density based and X-means gives the highest accuracy of 75.64%. For the larger balanced dataset when compared with the smaller balanced dataset.

World University root node

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

Department of Analytics

School of Computer Science and Engineering

Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019)

Detection of Type 2 Diabetes Using Clustering Methods – Balanced and Imbalanced Pima Indian Extended Dataset

This work presents energy, exergy, and thermoeconomic analysis of an advanced Kalina power generation system. In every key component of the proposed system, cost-balance and auxiliary equations are typically developed which is solved efficiently through MATLAB coding. The cost-effectiveness of the entire system is assessed by the determination of thermoeconomic variables for the individual components. The energy analysis of the advanced Kalina cycle system (AKCS) proves marginal improvement to the medium temperature heat recovery Kalina cycle system (KCS). The relative cost difference in HE5, turbine, HE6, and condenser are quite larger than other components of AKCS emphasizing more focus on these components is required. The low values of HE5 (11.77 %) and pump1, P1 (13.33 %) insist that the performance of these two components has to be improved by capital investment into a better and improved design. The energy performance maximizes at operating conditions of 45 bar turbine inlet pressure, 72–74 °C separator inlet temperature, 0.94 ammonia concentration at the condenser and 0.83 separator inlet concentration. The thermoeconomic performance minimizes at operating conditions of 30 bar turbine inlet pressure, 68 °C separator inlet temperature, 0.89 ammonia concentration at the condenser and 0.79 separator inlet concentration. © 2020

Fulltext

Energy Reports

Thermoeconomic investigation on advanced Kalina power generation system

Machine-to-machine communication is now enabled and will rule the world in the future. This is achieved through the Internet and this is called the Internet of Things (IoT) as it enables all the things in the real world to communicate with each other. IoT achieves this by bridging various other software technologies and hardware devices. IoT has flourished in all domains starting with simple home automation to businesses. As everything in this real world is business, enabling IoT in business will be of great help to the enterprises and the decision makers in the field. So here this chapter portrays what an enterprise Internet of Things deals with, its issues, and its applications. Also the importance of business forecast for an enterprise and how it is achieved via various techniques for the business forecast are discussed. For better forecasting results, the advanced machine learning algorithms that can be employed in a different perspective of enterprise IoT and their applications are presented here. This would be a great help for the researchers and practitioners in the field of enterprise IoT. © 2020, Springer Nature Switzerland AG.

Department of Information Technology

EAI/Springer Innovations in Communication and Computing

Advanced Machine Learning for Enterprise IoT Modeling

Managing the source code of the project and other related documents in an organization is a mandatory need, which may ensure clarity in the delivery of the product enhancing the focus of the organization towards its intended product's quality. In this digital era of computing, we have many software configuration management tools to handle various documents, its revisions, versions and so. In this paper, we analyze the importance of various Version Control Systems (VCS) evolved to assist the software development lifecycle of the project, and compare favourite VCS tools in the market based on their features, measure their performance across chosen attribute. Also, we propose a new tool having some of the best features found in our comparison study as well as a few extra attributes that we believe will raise the quality of this new tool. This tools can combat the issues we face with existing tools in the market. © 2020 IEEE.

International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2020

An analysis on Version Control Systems

IoT are prone to vulnerabilities as a result of a lack of centralized management, dynamic topologies, and predefined boundary. There are diverse attacks that affect the performance of IoT network. Flooding attack is a DoS attack that brings down the network by flooding with a huge count of HELLO packets, routed to a destination that does not exist. The main intent of this paper is to develop a novel robust model for detecting and preventing HELLO flooding attacks using optimized deep learning approach. In this proposed research model, the steps like Cluster head selection, k-paths generation, HELLO flooding attack detection and prevention, and optimal shortest path selection are employed. Once after the random cluster head selection and k-paths generation, few Route Discovery Frequency Vectors like Route Discover Time and Inter Route Discovery Time of each node is determined for detecting the HELLO flooding attack. Initially, a threshold function is used to match with the computed Received Signal Strength (RSS) of each node to detect the stranger node. Further, the HELLO flood attack is confirmed by the optimized Deep Belief Network (DBN), which is removed from the network subsequently. Once after securing the network, the shortest route path selection is done optimally by the improved meta-heuristic algorithm. Here, improved Rider Optimization Algorithm (ROA) termed as Bypass-Linked Attacker Update-based ROA (BAU-ROA) is used for performing the optimal DBN as well as optimal shortest path selection. The objective constraints like node trust, distance between the nodes, delay of transmission, and packet loss ratio are considered for performing the optimal shortest path selection. Finally, the experimental evaluation of various performance measures validates the fruitful performance of the proposed model. Based on the analysis, the const function of proposed BAU-ROA is 28.1% superior to D-DHOA, 34.1% superior to DHOA, and 39.2% superior to WOA at 5th iteration. When considering the 10th iteration, the developed BAU-ROA is 12.2% superior to DHOA, 19.3% superior to D-DHOA, 28.1% superior to ROA, and 39.2% superior to WOA. © 2020 Elsevier B.V.

Computer Communications

Prevention of Hello Flood Attack in IoT using combination of Deep Learning with Improved Rider Optimization Algorithm

Role of Edge Analytics in Sustainable Smart City Development

Smart Health Care Development

Production factories have many items produced with various parts to bring out the complete product at the end. Every product that is produced in the factory obtains the company some amount of income. The manufacturing of these products also costs a huge sum for purchase and production. When a situation arises for the company to shut down its manufacturing or when there is some crisis, the industry will not have money to buy the parts to finish the product. The problem of removable pattern mining is to identify the patterns which can be removed to reduce the loss to the factory’s profit under certain conditions. In such situations, the production managers need an alternate to reduce the cost of production without decreasing the profit rate. This paper proposes removable closed patterns to represent and compress the mined removable patterns without much profit reduction. Erasable closed pattern mining algorithm is implemented to mine all the erasable closed patterns by reducing the resources needed for production without affecting the information loss. The removable itemset with one pattern along with their element occurrence pair is taken into consideration. Further the erasable patterns are mined and used for useful extraction of members. Then the erasable closed patterns are mined using the module that checks the closeness of the patterns in the product datasets. © Springer Nature Singapore Pte Ltd 2020.

VIT Unitversity

Advances in Intelligent Systems and Computing Artificial Intelligence and Evolutionary Computations in Engineering Systems

Mining of Removable Closed Patterns in Goods Dataset

Prediction based language models are considered as one of the major concepts in Natural Language processing which gains knowledge from unstructured text data. Extracting insights from sequential data such as biological sequences is an important problem in genomics, proteomics and classifying the secondary structures of protein, helps the researchers in aiding to understand protein functions. This is considered as one of the important preliminaries of Drug development. Traditional techniques such as sequential models, probabilistic techniques and statistical approaches were widely applied in structure prediction which extracts insights from sequence of amino acid. However, handheld feature extraction becomes a tedious task, which eventually leads to less accuracy. Our novel approach creates vectors using word embeddings which is assumed to consider contextual information of amino acids thereby improving the accuracy of secondary structure prediction approach. This is considered as an optimistic solution for secondary structure prediction problem. In this approach a variation of word embeddings - Continuous Bag of Words (CBOW) method is proposed which retains the sequential information of all amino acid in the protein chain. This vector is used as input features of Deep Neural Network classifier and class labels are classified into Helix, Sheet, Coil. We have tested this NLP based approach on GenBank dataset. The infrastructure required for this analysis was leveraged from Google Colab. © 2020 IEEE.

An approach for Protein Secondary Structure prediction using prediction-based language models

Cloud computing is a trending area of information technology (IT). In a cloud environment, the Cloud service provider (CSP) provides all the functionalities to the users or customers in terms of services. With the rapid development of cloud computing, the performance of any cloud environment relies on the quality of services (QoS) at the time of providing the services. A service level agreement (SLA) increases the confidence of the user or customer to use the cloud services in a cloud environment. There should be negotiation between the CSP and users to achieve a strong SLA. Many existing SLA models are already developed. However, these models do not concentrate to maintain the quality in a long-time duration period. To solve this issue, a novel SLA model has been proposed in this article by using Fuzzy logic. Both the theoretical and simulation results show the proficiency of the proposed scheme over the existing schemes in a cloud computing environment. Copyright © 2020, IGI Global.

International Journal of Grid and High Performance Computing

An adaptive service monitoring system in a cloud computing environment

Mahalanobis taguchi system (MTS) is a multi-variate statistical method extensively used for feature selection and binary classification problems. The calculation of orthogonal array and signal-to-noise ratio in MTS makes the algorithm complicated when more number of factors are involved in the classification problem. Also the decision is based on the accuracy of normal and abnormal observations of the dataset. In this paper, a multiclass model using Improved Mahalanobis Taguchi System (IMTS) is proposed based on normal observations and Mahalanobis distance for agriculture development. Twenty-six input factors relevant to crop cultivation have been identified and clustered into six main factors for the development of the model. The multiclass model is developed with the consideration of the relative importance of the factors. An objective function is defined for the classification of three crops, namely paddy, sugarcane and groundnut. The classification results are verified against the results obtained from the agriculture experts working in the field. The proposed classifier provides 100% accuracy, recall, precision and 0% error rate when compared with other traditional classifier models. © 2020 IEEE Computer Society. All rights reserved.