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.

&nbsp;

&nbsp;

VIT University

Education institutions like Schools, colleges, and universities in India are currently based on traditional learning methods and follow the conventional setting of face-to-face interaction/lectures in a classroom. Most of the academic sector started unified learning, still most of them struct with old steps. The unexpected Plague of a deadly infection called COVID-19 caused by (SARS-Cov-2) trembled the whole world. The WHO announced it as a disease outbreak. This circumstance challenged the whole education system worldwide and compelled educators to change to an online mode immediately. Many educational organizations that were earlier unwilling to change their traditional didactic practice had no choice but to move exclusively to online teaching–learning. This article provides an elaborate discussion about the education sector's impact during a disease outbreak in India. It offers a detailed discussion regarding how India adopts the e-learning approach in this critical situation. Further, it describes how to cope with the challenges related to e-learning. © 2021, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.

World University root node

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

Department of Computer Applications

Fulltext

International Journal of Systems Assurance Engineering and Management

Adoption of E-Learning during Lockdown in India

Decision-making has been one of the challenging tasks in solving real-world problems. Decision-making mimics human activity which in turn has noteworthy impacts. Several researches have proved that the quality of decisions has been improved by developing computer-based technologies that aid and extend the human capabilities. Decision support systems (DSS), which augment in making proper decisions, to an extent do sometimes lack intelligence. The recent advances in the field of artificial intelligence (AI) have made this possible in a wide range of multifarious applications. AI has the capability of mimicking human decision-making and has also shown that they are capable of assisting and improving human decision-making in real-time complex environments. Embedding intelligence into the DSS using the AI capabilities has been progressing, thus forming the intelligent support systems for decision-making (ISSDM). These support systems have been used in simplifying the decision-making process in several areas, namely, healthcare, cybersecurity, finance, marketing, and commerce. This chapter reviews the role of artificial intelligence tools such as artificial neural networks, fuzzy logic, bio-inspired algorithms, and intelligent agents in the decision-making process. © Springer Nature Switzerland AG 2020.

Department of Technology Management

School of Mechanical Engineering

Internet of Things for Industry 4.0 EAI/Springer Innovations in Communication and Computing

Decision Making Models Through AI for Internet of Things

Everything begins from a single origin with a unique point in space and time. This is the spark of innovation that fuels the most amazing breakthroughs. It is the passion for discovery that unveils the genesis of all that exists in the universe. Today the power of AI helps computer achieve superhuman capabilities in image recognition and lets scientists save our precious resources by analyzing in 1 month what used to take 10 years. Everyday devices translate even the most complex languages from voice into text and images into words helping the visually impaired recognize an old friend or letting a visually impaired woman read to her child for the first time. Autonomous vehicles give us the freedom to reimagine our city streets where there are no streets, to help the lost to find their way home. We see robots teach themselves to perform simple tasks. We even watch them all as they take their first step. And today a 2500-year-old game meets its match as a computer competes with one of the greatest human champions of all time and wins. This is a collective imagination fuelled by forward-looking technologies and the beginning of the most amazing discoveries yet to come. There are many applications of deep learning. Machine learning provides us an incredible set of tools. If there is a difficult problem in hand, we need not find an algorithm for it, it finds out by itself what is important about the problem and tries to solve it on its own. In many problem domains they perform better than human experts. One of its applications is toxicity detection for different chemical structures by means of deep learning. It is so efficient that it could find the toxic property of the chemicals. Next one is mitosis detection from large images. Mitosis means that certain nuclei are undergoing different transformations that are quite harmful and quite difficult to detect. The technique called convolution neural networks outperforms professional radiologists at their own task. Learning algorithm can read a website and find out whether fake content is discussed there as real content or not. Because of the imperfection of the 3D scanning procedures, 3D scanned furniture is too noisy to use as they are. However, there are techniques to look at these really noisy models and try to figure out how they should look by learning the symmetries and other properties of real furniture. This algorithm can also do an excellent job at predicting how different fluids behave in time and the attire for expected to be super useful in physical simulation in the following years. Another application where a computer algorithm is called deep Q learning plays pong against itself and eventually achieves expertise.

Machine Intelligence and Automation: Deep Learning Concepts Aiding Industrial Applications

Machine learning is the intriguing area of research that spreads across all domains helping in providing quality decisions. Demographic have more influence in social happenings along with various personal and social factors. Suicide analysis is one such issue to be handled with great concern that will provide precautionary based on situations. Suicide prediction can be carried on using data mining that can be used to predict the suicide earlier so that it can be prevented. Suicide is an action resulting in death performed by themselves. Common factors that influence the rate of suicides are cause, method of suicide, year, gender, educational qualification, social status. For this clustering technique in datamining that falls under unsupervised provides great platform. Silhouette score is used for mapping the number of cluster to get the good clustering. Various plots like box plot, scatter plot and so on helps to provide greater insight. Based on analysis the required remedial could be arrived. © BEIESP.

International Journal of Engineering and Advanced Technology

K-means and hierarchical based clustering in suicide analysis

Machine learning is the worldwide recent research technique for various systems as they are intelligent enough to find the solution for classification and prediction problems. The proposed work is about a hybrid genetic fuzzy algorithm that performs an optimal search as well as classification upon uncertain data. The data which is uncertain is suitable for fuzzy classifiers to predict the disease. The hybrid genetic fuzzy system applied on the attributes selects relevant attributes. The selected attributes are fed into the fuzzy classifier. The fuzzy rules are again generated using genetic algorithms. This algorithm is applied on three of the important and bench marking data sets taken from the UCI machine learning repository. The heart disease, Wisconsin breast cancer and Pima Indian diabetes datasets produce classification accuracy as 89.65%, 99.5% and 88.93% respectively. In this article there is a comparative study on few of the feature selection and feature reduction techniques.

International Journal of Engineering & Technology

Comparative study on dimensionality reduction for disease diagnosis using fuzzy classifier

Clustering of text documents is an important technique for enhancing automated learning. Matching is the technique used in order to relate or match the various set of related documents. Clustering groups a set of documents which are similar and dissimilar for unsupervised learning where the user has learning materials which are from raw data which requires further classification. Established feature extraction strategies intend to change over the representation of the major dimensional data set into a lower-dimensional informational collection by anticipating process through mathematical changes. The concept of feature clustering is to aggregate the features into clusters with a high level of pair wise semantic relations. Each cluster is dealt as a single new feature, and, hence, feature dimensionality can be radically lessened. HAC, K-Means, TF/IDF-weighted vectors and cosine similarities is used for the various vectors of data and is applied to text in a direct way to optimize the vectors. © 2006-2018 Asian Research Publishing Network (ARPN).

ARPN Journal of Engineering and Applied Sciences

Analysing effective methodologies used for text clustering using weighted algorithms

There is a severe issue where every one of clients' estimations of dread of losing control of their own information can change into a colossal check to the wide assembling of cloud associations during estimating of construction data. The materials used for building is retrieved with specific application. The data related to planning process, includes policies of land pricing, plan development, the planning environment and the procedures. Tending to this issue, this application gives a flexible association interface that can be utilized to store and recover any kind of records, at whatever point, from wherever for resolving. Here client's records which has data related to the construction as well as the criteria for urban planning is stored and will be secured in a memory zone called Bucket Repository (BR). From this the client can trade as single or diverse reports and client can download single record from the BR. A settled whole space will be given to all clients. Once the space is totally utilized by the client, he/she can't utilize this application any more. This cloud facilitator will ensure that the entire client's information which is moved is protected in the cloud and open from wherever and at whatever time.But without web connection client can't get to his/her records from that compartment. There are different rivals around there who are doing creative work on new stages and user experience. © IAEME Publication.

Department of Smart Computing

International Journal of Civil Engineering and Technology

Estimating construction data using an authenticated application for Urban planning

Background/Objectives: The need for digital learning is rapidly increasing with the effective granularity of learning objects. Retrieval of learning materials from smart environment helps the learner to have a personalized experience using classification algorithms. Methods/Statistical Analysis: The digital learning objects are stored in a local repository in the smart architecture. The framework involves mobile agents which play a vital role in predicting the intuitive nature of the learner which helps the learner to get the targeted content. Content retrieval is based on the document summarisation and word classification which is proposed using classification algorithms like content similarity algorithm. Effective personalization is obtained by better accuracy in terms of precision and recall. Findings: Document summarization is done using classification algorithm initially based on the type of document retrieved by the learner. The word classification based on the retrieved document is further processed with a pre processing method which is used in the domain ontology. The similarity of the topical terms retrieved helps in classifying the document and presenting the content to the learner for more personalized experience. Further these contents which are stored in a smart framework include mobile agents which help in providing synchronous communication between the users working on similar problems at the same time and gives uninterrupted content to the user based on the query even while the user is moving in different locations. The content similarity algorithm provides better accuracy of retrieved data thereby providing the exact document requested by the learner. The samples of the data classification provide the expected the result of the retrieved data which involve document summarization based on the word classification. The results have proved that the learner has a better intuitive experience with the targeted document wherein the learner gets better personalization. Applications/Improvements: More intelligent agents can be involved for learner as well as author to make the system more personalized and provide better collaboration. The classification algorithms along with clustering of documents obtained after summarization can yield better accuracy for large data sets.

Indian Journal of Science and Technology

Analysis of Document Summarization and Word Classification in a Smart Environment

Objectives: Some researches on wireless sensor systems have concentrated on low information rate applications, for example, natural surroundings checking. Interestingly, late years have seen the rise of high information rate applications, for example, on-going auxiliary wellbeing observing and preventive hardware support. For example, a basic wellbeing observing framework may need to test the increasing speed of every sensor at rates as high as 550 Hz. Method: The high rates can be identified by bringing about high system stack when countless are conveyed for highly granular checking using Conflict Free Transmission Scheduling (CFTS). In addition, the framework may have profoundly factor workload because of ecological changes. For instance, a catastrophic effect may trigger countless inquiries with a specific end goal to survey any potential harm to the structure. Subsequently, a key test is to give a high throughput inquiry benefit that can gather information from huge systems and adjust to workload changes. Conclusion: As the large amount on the greatest question rate is accomplished under CFTS which is of handy significance it can be utilized to forestall and organize over-burden. © 2016, International Journal of Pharmacy and Technology. All rights reserved.

International Journal of Pharmacy and Technology

An approach to handle multiple queries using conflict free transmission scheduling

Record de duplication aims at identifying the objects which are potentially replicated in a data repository. Though the concept exists it still continues to receive a significant amount of attention from the database community and researchers due to the intrinsic difficulty in producing a redundant free repository,especially in the context of large datasets. In the case of large scale de duplication,the blocking and classification phases typically rely on the user to configure or tune the process. For instance,the classification phase usually requires a manually tagged set of data. However,selecting and labelling for a defined set is a very costly task which is often restricted to expert users. Some active approaches have been proposed to address this problem by selecting the information associated pairs. © 2016,International Journal of Pharmacy and Technology. All rights reserved.

Analyzing a selection strategy for data de-duplication in large datasets

An emerging aspect in learning environment is to effectively retrieve learning materials from learning object repositories. An extension of the IEEE LOM standard with a domain ontology and automatic annotation for effective retrieval of learning objects is proposed. To make best use of learning objects outside the Learning Management System, we associate the learning materials with metadata pedagogic attributes such as prerequisite, type, difficulty level, importance, roles and significance. Automatic annotation of the learning object repository is developed in a semantic oriented approach. The techniques such as specific indexing for parsing the documents and using standard classification algorithms for automatic extraction with concept identification and significance for effective personalization is developed using domain ontology. The usage of Word Net based processing and the identification of learning objects based on the concept for text and media learning objects has proved effective using the domain ontology. The experimental results have proved better precision and recall using the combination of methods for effective personalization. The developed evaluating methods correlate with the scores of the topics and generally outperform the traditional term frequency inverse document frequency (TF-IDF) method. The proposed specific indexing method obtains the highest precision and recall of all. © IDOSI Publications, 2013.

World Applied Sciences Journal

Effective retrieval of text and media learning objects using automatic annotation

Learning Objects (LO) have been used for designing e-learning paradigms. Granularity of learning objects is related with the concept of reusability. The reused learning objects are henceforth used for effective personalization. Personalization based on the learner's intuitiveness is preferred using preference based and correlation based algorithms. Based on the user preference and learning style with domain ontology, the effective personalization can be attained. The performance measures and evaluation aids in deciding the learner's effective personalized experience of the system. © 2005 - 2012 JATIT & LLS.