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

Alzheimer’s disease is the most dreadful. Despite the various Alzheimer’s treatments available nowadays, the survival rate of Alzheimer’s patients is very much low. Detection of Alzheimer’s disease in an earlier stage is one of the most important things to reduce the death rate. Magnetic Resonance Imaging (MRI) is an important tool in medical informatics and clinical diagnosis. This MRI image helps to diagnose and detect Alzheimer’s disease at advanced stages. There are several approaches implemented to discover Alzheimer’s by MRI data. Features are extracted discriminately and combinations of different classification techniques for classification is implemented in the proposed work. In this paper, the curvelet-based transform technique is utilized for extracting the features. The AdaBoost classifier is utilized for combining multiple weak classifiers into one strong classifier to improve the accuracy of the result. To improve the efficiency of the result, AdaBoost classifier with SVM was used to obtain better results than existing ones. The proposed model with 95.66% accuracy predicts the AD from the brain MRI images at an earlier stage. This accuracy value is far better than other classifiers like Decision Tree and Random Forest. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

World University root node

Computer Science

School of Computer Science and Engineering

Chennai Campus

Advances in Intelligent Systems and Computing

Machine learning based diagnosis of alzheimer’s disease

In data mining, mining high utility itemset (HUI) is one among the recent thrust area that receives several approaches for solving it in an effective manner. In the past decade, addressing optimization problems using evolutionary algorithms are an unavoidable strategy due to its convergence towards optimal solution within the stipulated time. The results of evolutionary algorithms on various optimization problems are far effective when compared to the exhaustive approaches with respect to computational time. The problem with HUI is discovering a set of items from a transactional database that possess high level of utility when compared with other distinctive sets. This problem becomes harder while addressing the count of items in the database while its higher and computational time to solve this problem using exhaustive search becomes exponential as proposition of items in transaction database increases. In this paper, an optimization model based on the biological behaviour of grey wolf is proposed; the model namely grey wolf optimization algorithm is used to solve HUI using five different Boolean operations. The proposed model is evaluated using standard performance metrics over synthetic datasets and real-world datasets. The proposed model results are then compared with recent HUIM models to show the significance. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

Soft Computing

High utility itemset mining: a Boolean operators-based modified grey wolf optimization algorithm

Recent researches in cloud discusses about the application response testing, performance testing, security testing and many more, but still there is a lack of researches addressing issues like resource utilization and user interactions in cloud SaaS testing. The load on the cloud, SaaS instance keeps varying dynamically with respect to time, it is difficult to find the exact load at a particular interval of time. One does not know where to look for the solution and where to start, this made SaaS instances non deterministic in nature. In order to find a solution for such non deterministic problems, we make use of Genetic Algorithm which is considered as a good solution for non-deterministic problems.We determine the optimized resources that a cloud instance, would need to manage the dynamic load at all times. To address the resource utilization of a group of users in Multi-Tenant Architecture (MTA), we adopt Genetic Algorithm which uses a popular technique, called neighborhood search and instance ranking policy. The basic concept of this paper is to explore the neighbors of an existing solution, that is considered as the solutions which can be obtained with a specific operation on the base population. In addition to that,this paper discusses about the ranking of all the available population and select the most highly ranked one. Instance ranking policies are aimed at minimizing the number of nodes in use or maximize the resources available to each node in an instance

International Journal of Engineering and Advanced Technology

Performance Testing in A Multi Tenant Cloud Architecture using Genetic Algorithm

In today's Business world, big data projects play an important role in increasing the profit of the business. Success of the big data project lies on the complete understanding of the customer requirement. A big data project not only gives the solution for day to day functionality of the system but also gives a solution to improve the business. Hence understanding the requirement is very important. Most of the big data projects are based on the object oriented approach. Most of the software industry relies on UML use case model to specify, analyze and design the big data project. The difference between other project and big data project is data characteristics. The three V characters, volume, velocity and variety play major role in the design decision of big data project. The existing UML use case diagram does not have a facility to represent the big data V characteristics. In this paper we are proposing big data use case diagram, suitable for specifying big data characteristics. The research result shows that big data use case diagram improves the understandability for the requirements of the big data project. © 2019 American Scientific Publishers.

Journal of Computational and Theoretical Nanoscience

Improving big data projects requirement understandability by using big data use case diagram

Every software development organization strives for customer satisfaction. It is universally accepted that the success of software development lies in the clear understanding of the client requirements. During requirement elicitation and analysis stage, the system analyst identifies the functional and non-functional requirements from the customer. Security, usability, reliability, performance, scalability and supportability are the significant quality attributes of a software system. These quality attributes are also referred as non-functional requirements. Only a few functional and quality attributes requirement help to identify and shape the software architecture. A software system’s architecture is the set of prime design decisions made about the system. If the requirement influences the architectural design decision then, it is referred as Architecturally Significant Requirement (ASR). Identifying and specifying all the possible ASR are important tasks in the requirement elicitation and analysis stage.In this research, general problems that are faced while capturing and specifying ASR in requirement elicitation and analysis is studied. Among the different requirement elicitation techniques, use case diagram has been identified and enhanced to solve the problem of capturing and specifying ASR during the requirement elicitation and analysis phase

Fulltext

Asian Journal of Pharmaceutical and Clinical Research

EVALUATION OF CAPTURING ARCHITECTURALLY SIGNIFICANT REQUIREMENTS

Deep learning methods are a great machine learning technique which is mostly used in artificial neural networks for pattern recognition. This project is to identify the Whales from under water Bioacoustics network using an efficient algorithm and data model, so that location of the whales can be send to the Ships travelling in the same region in order to avoid collision with the whale or disturbing their natural habitat as much as possible. This paper shows application of unsupervised machine learning techniques with help of deep belief network and manual feature extraction model for better results.

VIT Unitversity

DETECTION OF WHALES USING DEEP LEARNING METHODS AND NEURAL NETWORKS

Customer involvement and lack of expressive communication mechanism are the major problems in capturing Architecturally Significant Requirements (ASR) in requirement elicitation and analysis stage in the software development. In our earlier work we have introduced Architect Use Case Diagram (AUCD) an enhanced version of use case diagram, which is useful to specify functional and quality attribute requirements in one diagram. Gamification is an emerging technique used in the software industry to improve the user’s commitment, inspiration and performance. In this paper we proposed gamification using AUCD, a new methodology for requirement elicitation and analysis. The combination of gamification and AUCD motivates the users to participate in the requirement elicitation and analysis actively, which helps the analyst to capture more architecturally significant requirements. The proposed gamification using AUCD method is applied in different projects for validating the effectiveness. The research result shows that gaming using AUCD improves user participation in requirement elicitation. © Springer International Publishing Switzerland 2016.

Proceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC – 16’) Smart Innovation, Systems and Technologies

Improving User Participation in Requirement Elicitation and Analysis by Applying Gamification Using Architect’s Use Case Diagram

Department of English

School of Social Sciences & Languages

Vellore Campus

Asian Journal of Chemistry

Synthesis, Characterization, DNA Cleavage, Antimicrobial and Catalytic Studies of Tridentate (N3) Schiff Base Ligand and its Complexes with Transition Metal Ions

Today’sSoftware Industries’ mostpreferred Modeling Language for a Software Development is Unified Modeling Language [UML] 6, 9. System Analyst uses theUML Use Case Diagram [UCD] for representing functional requirements of the system in Requirement Analysis Stage1. The UCD givesonly the summary of the System. The Use Case Specification (UCS) document elaborates the full story of the system10. Quality attributes Requirement does not fit into UCD and UCShence it is captured in supplementary documents10. Some of the requirements from functional and nonfunctional requirementsinfluence the architecture of the system and these arestatedas Architecturally Significant Requirement [ASR]11. Use Case diagram provides an effective communication medium for customer interaction. Several interactions between the System Analyst and customer arerequired infinalizing theContract1. Customer involvement and gathering the complete requirement are two important factors for any successful software project1. Most of the customers actively participate indescribing the functional requirements but they show less interest in describing Quality attributes and other nonfunctional requirements12. Omitted important ASR will affect the Quality of the System12. Hence improving Customer involvement in the Requirement stage is necessary. The goal of this paper is to analyzeUML Use Case Diagram’sweakness while capturing ASR and intern propose an enhanced UCD to capture ASR in a simple and effective way and we name it as Architect’s Use case diagram [AUCD]. AUCD introduces Sub flow Connector, Security flow Connector, Alternative flow connector, Scalability indicator and Performance indicator to specify ASR. A Case study demonstrates the usage of Architect’s Use Case Diagram and the results shows AUCD is helpful for capturing ASR during the requirement analysis stage and is also useful for effective communication with customer. © Research India Publications.