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

Department of Computational Intelligence

School of Computer Science and Engineering

Vellore Campus

Ad Hoc Networks

Swarm intelligence inspired meta-heuristics for solving multi-constraint QoS path problem in vehicular ad hoc networks

Fine Needle Aspiration Cytology (FNAC) is a quick and minimally invasive technique used to diagnose breast cancer, specifically ductal carcinoma. The incidence of breast cancer (ductal carcinoma) is high among Indian women. Consequently, there is a volume burden on laboratories, primarily regional centers, for diagnosis. The pressure on laboratories and doctors to make a timely and correct diagnosis can be resolved by automating the process to an extent, especially where expertise is limited. Recent advances in Artificial Intelligence techniques on large and complex data have enabled better understanding in the domain of Computer-Aided Diagnosis, which helps both automate and digitize diagnosis. In this study, we have leveraged Convolutional Neural Networks (CNNs) to automate the diagnosis of ductal carcinoma in breast from images produced after FNAC is performed on breast tissue. We created a data set of FNAC images of breast lesions and extracted 1020 Region of Interest (RoI) patches from Giemsa-stained lesions and 631 RoI patches from H&E-stained lesions. The performance of various CNNs was tested on these patches. Three networks performed very well and have the potential to assist doctors in diagnosis. One of them was a light network we built—BFCNet (Breast FNAC Classification Network). It produced the highest average accuracies in the binary classification of Giemsa-stained patches (97.53%) and H&E-stained patches (96.59%). This network fits the data properly and performs well in other parameters. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature.

World University root node

Pattern Analysis and Applications

BFCNet: a CNN for diagnosis of ductal carcinoma in breast from cytology images

Blockchain application and cyber-physical systems play a crucial role to modernize the traditional industrial process, technical procedures, and business models. It uses frame resilient and smart contracts to reduce the complexities of service costs. The blockchain applications associate the key features such as self-verification, and self-integrity to eliminate the role of trusted third parties access. It evolves scientific and industrial progress to reform as Industry 4.0 that uses Artificial Intelligence (AI) to process and extract the significant information of the real-time systems. Moreover, it applies digital analytics to link the data with blockchain and cloud repositories to improve system efficiencies. However, the security and privacy issues are still challenging to investigate the AI techniques and tools. Thus, this paper proposes a privacy-preserving in smart contracts using blockchain and artificial intelligence (PPSC-BCAI) framework that simplifies human interaction, system activities, service alerts, security risks, and fraudulent claims. To analyze the data transaction and sharing, an extreme gradient boosting (XGBoost) is applied. It analyses the transaction service to optimize the network load that reveals whether the transmission rate changes over the connectivity of unreliable networks. © 2021 Elsevier Ltd

Journal of Information Security and Applications

Privacy-preserving in smart contracts using blockchain and artificial intelligence for cyber risk measurements

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Deep-Learning Approach with DeepXplore for Software Defect Severity Level Prediction

2021 Grace Hopper Celebration India, GHCI 2021

YOLO as a Region Proposal Network for Diagnosing Breast Cancer

The discovery of knowledge from large-scale text data or semi-structured data is very difficult. In text mining, useful information is extracted out of such large text corpus which fulfills a user current information need. This process is being exploited by various organizations for quality improvement, business need, and understanding user behavior. The text available in unstructured and semi-structured form can come through sources such as medical, financial, market, scientific, and others documents. Text mining applies quantitative approach to analyze massive amount of textual data and tries to solve information overload problem. The main objective is to review text mining techniques, application areas, and existing issues. © Springer Nature Singapore Pte Ltd 2021.

Lecture Notes in Networks and Systems

Techniques, applications, and issues in mining large-scale text databases

Vast volumes of raw data are generated from the digital world each day. Acquiring useful information and chief features from this data is challenging, and it has become a prime area of current research. Another crucial area is knowledge inferencing. Much research has been carried out in both directions. Swarm intelligence is used for feature selection whereas for knowledge inferencing either fuzzy or rough computing is widely used. Hybridization of intelligent and swarm intelligence techniques are booming recently. In this research work, the authors hybridize both artificial bee colony and rough set. At the initial phase, they employ an artificial bee colony to find the chief features. Further, these main features are analyzed using rough set generating rules. The proposed model indeed helps to diagnose a disease carefully. An empirical analysis is carried out on hepatitis dataset. In addition, a comparative study is also presented. The analysis shows the viability of the proposed model. © 2021 IGI Global. All rights reserved.

Department of Database Systems

Fulltext

International Journal of Healthcare Information Systems and Informatics

Knowledge inferencing using artificial bee colony and rough set for diagnosis of hepatitis disease

Crowdsourcing is treated as open contest for the crowd of people known as workers. All workers can contribute their suggestions and solutions to the platform. Crowdsourcing can connect a large number of people and they can share their knowledge. The amount of unstructured data is increasing now. This is where crowdsourcing can help bigdata by breaking down the data into mini chunks and have the power of crowd to do the organising task. This helps analytic companies to focus on the core aspect of infrastructure and security. It makes sense of the data and not invests resources in organising the data and this distributed environment can be solved intelligently. Here various crowdsourcing techniques in different aspects related to data pre-processing, performance approaches, security issues and applications are analysed. Out of these approaches the most efficient one in each are characterised. The survey helps to analyse the various issues in crowdsourcing and existing proposed solutions for improving the quality of workers. Copyright © 2021 Inderscience Enterprises Ltd.

International Journal of Information Technology and Management

Investigation and analysis on crowdsourcing for improving enterprise QoS

Internet made a big revolution in the real world and thus poses so many challenges to the researchers by generating an enormous amount of data. The data generated contains an enormous amount of unwanted information. Before processing with such a dataset, the important features present in the dataset must be retrieved. The feature selection process is important because the performance of a model built for the purpose of classification, prediction or clustering depends mainly on the number of relevant features present in the dataset. In this proposed work, the real coded genetic algorithm is used to find the important features by considering the fuzzy rough degree of dependency as its fitness function for finding out optimum features for agricultural dataset, iris dataset and Pima Indian diabetes dataset. The experimental results show that the proposed work produces relevant features by maintaining classification accuracy. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

Department of Digital Communications

School of Information Technology and Engineering

A Comparative Study of Genetic Algorithm and Neural Network Computing Techniques over Feature Selection

Breast Cancer (specifically Ductal Carcinoma) is widely diagnosed by Fine Needle Aspiration Cytology (FNAC). Deep Learning techniques like Convolutional Neural Networks (CNNs) can automatically diagnose this condition by processing images captured from FNAC. However, CNNs are trained on manually sampled RoI (Region of Interest) patches or hand-crafted features. Using a Region Proposal Network (RPN) can automate RoI detection and save time and effort. In this study, we have proposed the use of the YOLOv3 network as an RPN and supplemented it with image-based heuristics for RoI patch detection and extraction from cytology images. The extracted patches were used to train 3 CNNs - VGG16, ResNet-50 and Inception-v3 for classification. YOLOv3 identified 164 RoIs in 26 out of 27 images and we achieved 96.6%, 98.8% and 98.9% classification accuracies with VGG16, ResNet-50 and Inception-v3 respectively. © Springer Nature Switzerland AG 2021.