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

Institute for Industry and International Programme

Convolutional neural network (CNN) is one of the deep structured algorithms widely applied to analyze the ability to visualize and extract the hidden texture features of image datasets. The study aims to automatically extract the self-learned features using an end-to-end learning CNN and compares the results with the conventional state-of-art and traditional computer-aided diagnosis system’s performance. The architecture consists of eight layers: one input layer, three convolutional layers and three sub-sampling layers intercepted with batch normalization, ReLu and max-pooling for salient feature extraction, and one fully connected layer that uses softmax function connected to 3 neurons as output layer, classifying an input image into one of three classes categorized as nodules ≥ 3 mm as benign (low malignancy nodules), malignant (high malignancy nodules), and nodules < 3 mm and non-nodules ≥ 3 mm combined as non-cancerous. For the input layer, lung nodule CT images are acquired from the Lung Image Database Consortium public repository having 1018 cases. Images are pre-processed to uniquely segment the nodule region of interest (NROI) in correspondence to four radiologists’ annotations and markings describing the coordinates and ground-truth values. A two-dimensional set of re-sampled images of size 52 × 52 pixels with random translation, rotation, and scaling corresponding to the NROI are generated as input samples. In addition, generative adversarial networks (GANs) are employed to generate additional images with similar characteristics as pulmonary nodules. CNNs are trained using images generated by GAN and are fine-tuned with actual input samples to differentiate and classify the lung nodules based on the classification strategy. The pre-trained and fine-tuned process upon the trained network’s architecture results in aggregate probability scores for nodule detection reducing false positives. A total of 5188 images with an augmented image data store are used to enhance the performance of the network in the study generating high sensitivity scores with good true positives. Our proposed CNN achieved the classification accuracy of 93.9%, an average specificity of 93%, and an average sensitivity of 93.4% with reduced false positives and evaluated the area under the receiver operating characteristic curve with the highest observed value of 0.934 using the GAN generated images. © 2020, Springer-Verlag London Ltd., part of Springer Nature.

World University root node

Vellore Campus

Neural Computing and Applications

ROI-based feature learning for efficient true positive prediction using convolutional neural network for lung cancer diagnosis

The proposed work aims to solve data sparsity problem in the recommendation system. It handles two-level pre-processing techniques to reduce the data size at the item level. Additional resources like items genre, tag, and time are added to learn and analyse the behaviour of the user preferences in-depth. The advantage of the proposed method is to recommend the item, based on user interest pattern and avoid recommending the outdated items. User information are grouped based on similar item genre and tag feature. This effectively handle overlapping conditions that exist on item's genre, as it has more than one genre at initial level. Further, based on time, it analyses the user non-static interest. Overall it reduces the dimensions which is an initial way to prepare data, to analyse hidden pattern. To enhance the performance, the proposed method utilized Apache's spark Mllib FP-Growth and association rule mining approach in a distributed environment. To reduce the computation cost of constructing tree in FP-Growth, the candidate data set is stored in matrix form. The experiments were conducted using MovieLens data set. The observed results shows that the proposed method achieves 4% increase in accuracy when compared to earlier methods. © 2020 The Authors

Fulltext

Journal of King Saud University - Computer and Information Sciences

An improved hidden behavioral pattern mining approach to enhance the performance of recommendation system in a big data environment

Statistical mediation and moderation analysis are well-known throughout the behavioral studies. Gradually, these techniques are being incorporated in the contour of the study of mediated moderation or moderated mediation. An entire child approach towards education focuses awareness on the community, emotional, psychological, corporal as well as cognitive progress of students. At its central part such a arrival to observe the principle of schooling as developing future citizens and providing the foundation for every child to carry out their future. It has five creed based upon child growth, which emphasizes the approach and affirms that all child in both school and in society earns to be healthy, safe, engaged, supported and challenged. This structure has been worn as the gallows in the expansion of a choice of school upgrading processes that make certain that the approach is incorporated and classified into the progression and strategy of the school, locality, and society. The outline does not seek out to dissociate itself from intellectual increase but it does seek out to enlarge what comprise academic advance in the 21st century and intends to relocate concentration on all traits essential for enlightening and communal accomplishment. Parallel mediation and moderation model is applied for the study. © IAEME Publication

Department of Mathematics

School of Advanced Sciences

International Journal of Advanced Research in Engineering and Technology

Mediation and moderation model on whole child education

Indoor tracking has evolved with various methods. The most popular method is using signal strength measuring techniques like triangulation, trilateration and fingerprinting, etc. Generally, these methods use the internal sensors of the smartphone. All these techniques require an adequate number of access point signals. The estimated positioning accuracy depends on the number of signals received at any point and precision of its signal (Wi-Fi radio waves) strength. In a practical environment, the received signal strength indicator (RSSI) of the access point is hindered by obstacles or blocks in the direct path or Line of sight. Such access points become an anomaly in the calculation of position. By detecting the anomaly access points and neglecting it during the computation of an indoor position will improve the accuracy of the positioning system. The proposed method, Practical Hindrance Avoidance in an Indoor Positioning System (PHA-IPS), eliminate the anomaly nodes while estimating the position, so then enhances the accuracy. Open Access. © 2020 B. Bonthu and Subaji M, published by De Gruyter.

Department of Information Security

School of Computer Science and Engineering

Open Computer Science

An effective algorithm to overcome the practical hindrance for Wi-Fi based indoor positioning system

A variable that roots between the dependent and independent variables is a mediator. The fundamental roles in data analysis necessitate various variables mainly when the new variables are inflated among reliant and self-sufficient variables. Several areas especially in psychology, industry, edification, science, production area, etc mediation analysis is obliged that desire regression analysis. For many studies mediation analysis is feasible. In addition to it, using fuzzy mediation theory whenever the variables are not plainly distinct at times it is much more logical to articulate the data. For instance, the variables using fuzzy numbers are superior to articulate than using real numbers (crisp numbers). Some models of unclear mediation study using membership grade functions on behavioral science have been proposed in this study with few examples. The entire, straight and not direct effects are found out using psychological data for the mediator and confounding variable to exist. Also the standard error and confidence intervals are estimated. © 2020 Alpha Publishers. All rights reserved.

Journal of Green Engineering

Fuzzy optimization of mediation on behavioral study using membership grade functions

Most of the traditional recommendation systems are based on user ratings. Here, users provide the ratings towards the product after use or experiencing it. Accordingly, the user item transactional database is constructed for recommendation. The rating based collaborative filtering method is well known method for recommendation system. This system leads to data sparsity problem as the user is unaware of other similar items. Web cataloguing service such as tags plays a significant role to analyse the user's perception towards a particular product. Some system use tags as additional resource to reduce the data sparsity issue. But these systems require lot of specific details related to the tags. Existing system either focuses on ratings or tags based recommendation to enhance the accuracy. So these systems suffer from data sparsity and efficiency problem that leads to ineffective recommendations accuracy. To address the above said issues, this paper proposed hybrid recommendation system (Iter-ALS Iterative Alternate Least Square) to enhance the recommendation accuracy by integrating rating and emotion tags. The rating score reveals overall perception of the item and emotion tags reflects user's feelings. In the absence of emotional tags, scores found in rating is assumed as positive or negative emotional tag score. Lexicon based semantic analysis on emotion tags value is adopted to represent the exclusive value of tag. Unified value is represented into Iter-ALS model to reduce the sparsity problem. In addition, this method handles opinion bias between ratings and tags. Experiments were tested and verified using a benchmark project of MovieLens dataset. Initially this model was tested with different sparsity levels varied between 0%-100 percent and the results obtained from the experiments shows the proposed method outperforms with baseline methods. Further tests were conducted to authenticate how it handles opinion bias by users before recommending the item. The proposed method is more capable to be adopted in many real world applications. © 2020 World Scientific Publishing Company.

International Journal of Information Technology and Decision Making

Integrating Sentiment Analysis on Hybrid Collaborative Filtering Method in a Big Data Environment

Learning Management System (LMS) is the new normal among top institutions in India. Faculties at technical universities in India have started using LMS for various purposes to implement blended learning. LMS like Schoology, Moodle and Google classroom used for many academic activities by academicians across India. Some universities in India have built their own LMS for exclusive online courses. LMS can be used effectively to instruct, share materials, share information, conduct assessments and evaluate digitally by faculties in their classrooms. This paper is an attempt to explore the possibilities of using Google classroom to practice blended learning in English courses. © 2020, World Academy of Research in Science and Engineering. All rights reserved.

Department of Digital Communications

School of Information Technology and Engineering

International Journal of Emerging Trends in Engineering Research

Practicing blended learning at engineering institutions using google classroom to teach english

Accurately identifying the exact boundary region of the pulmonary nodules in lung cancer images are the most challenging tasks in the Computer Aided Diagnosing schemes (CADx). Detecting the boundaries from different nodule structures is crucial due to the presence of similar visualization characteristics between the nodules and its surroundings. The study proposed an approach for pulmonary nodule region of interest (NROI) detection and segmentation using Computed Tomography (CT) lung images. Lung nodule CT images are acquired from the Lung Image Database Consortium (LIDC) public repository having 1018 cases. In this paper, a methodology for automated tumor grading of pulmonary lung nodules is proposed using Convolutional Neural Network (CNN). The salient features of benign and malignant nodules from different nodule structures are automatically self-learned and classified based on the classification strategy. The stages involved in the methodology are: 1) Pre-processing the image datasets using discrete wavelet transforms (DWT). 2) NROI segmentation. 3) NROI Feature extraction using CNN. 4) Nodule classification. CNN are trained with self-learned extracted features from NROI and are further classified as benign or malignant. Analyzing and segregating these extracted features plays a vital role in the correct classification of malignancy levels. The methodology is compared with conventional state-of-art methods and traditional hand-crafted methods. A total of 710 pulmonary nodules are used in the study, with 258 benign samples and 452 malignant samples. A consistent behavior was observed using CNN with reduced low false positives and a classification accuracy of 96.5%, sensitivity of 96%, specificity of 96.55% and standard Receiver operating characteristic (ROC) curve with the highest value of 0.969 was recorded. © 2020 - IOS Press and the authors. All rights reserved.

Intelligent Decision Technologies

Deep learning the features maps for automated tumor grading of lung nodule structures using convolutional neural networks

Identifying the exact pulmonary nodule boundaries in computed tomography (CT) images are crucial tasks to computer-aided detection systems (CADx). Segregation of CT images as benign, malignant and non-cancerous is essential for early detection of lung cancers to improve survival rates. In this paper, a methodology for automated tumor stage classification of pulmonary lung nodules is proposed using an end-to-end learning Deep Convolutional Neural Network (DCNN). The images used in the study were acquired from the Lung Image Database Consortium and Infectious Disease Research Institute (LIDC-IDRI) public repository comprising of 1018 cases. Lung CT images with candidate nodules are segmented into a 52&nbsp;×&nbsp;52&nbsp;pixel nodule region of interest (NROI) rectangle based on four radiologists’ annotations and markings with ground truth (GT) values. The approach aims in analyzing and extracting the self-learned salient features from the NROI consisting of differently structured nodules. DCNN are trained with NROI samples and are further classified according to the tumor patterns as non-cancerous, benign or malignant samples. Data augmentation and dropouts are used to avoid overfitting. The algorithm was compared with the state of art methods and traditional hand-crafted features like the statistical, texture and morphological behavior of lung CT images. A consistent improvement in the performance of the DCNN was observed using nodule grouped dataset and the classification accuracy of 97.8%, the specificity of 97.2%, the sensitivity of 97.1%, and area under the receiver operating characteristic curve (AUC) score of 0.9956 was achieved with reduced low false positives.