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 Software Systems

A plethora of negative behavioural activities have recently been found in social media. Incidents such as trolling and hate speech on social media, especially on Twitter, have grown considerably. Therefore, detection of hate speech on Twitter has become an area of interest among many researchers. In this paper, we present a computational framework to (1) examine out the computational challenges behind hate speech detection and (2) generate high performance results. First, we extract features from Twitter data by utilizing a count vectorizer technique. Then, we provide the labeled dataset of constructed features to adopted ensemble methods, including Bagging, AdaBoost, and Random Forest. After training, we classify new tweet examples into one of the two categories, hate speech or non-hate speech. Experimental results show (1) that Random Forest has surpassed other methods by generating 95% using accuracy performance results and (2) word cloud displays the most prominent tweets that are responsible for hateful sentiments.

World University root node

School of Computer Science and Engineering

Vellore Campus

Fulltext

Applied Sciences

Novel Hate Speech Detection Using Word Cloud Visualization and Ensemble Learning Coupled with Count Vectorizer

Classifying protein sequences from biological data has lot of importance in the field of pharmacology. The application of machine learning to find the sequence of amino acids has recently received popularity from various researchers. This chapter proposes a protein sequence classification technique using 1D Convolutional Neural Network (CNN). We also have discussed how NLP algorithms can be used for protein sequencing. We have achieved an accuracy of 85% with the proposed 1D CNN and&nbsp;further improved to 92% after&nbsp;increasing the filter size.

Protein sequence classification using convolutional neural network and natural language processing

The field of genomics is vast and innovation is happening at a rapid pace today. With the availability of lots of medical data and extensive research, the tools at our disposal are sharper than ever. One such tool that has quite a lot of untapped potential is Machine Learning. Machine Learning is the field of computer science that gives computers the ability to understand data and make decisions based on that understanding, in quite a similar way as we humans do. Machine learning has proven to be the next big thing in almost all industries today including medicine. The use of Machine Learning in the field of genomics however, is yet to reach its true momentum. With the help of machine learning, patterns in genetic data can be found that were unknown&nbsp;to us earlier and these patterns can be very useful in making conclusions about diseases and disorders that are inherently genetic in nature. In this work, we have classified the patients based on the two cancer classes, namely acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). &nbsp;

Leukaemia Classification Using Machine Learning and Genomics

In the recent time, enviromental sound classification has received much popularity. This area of research comes under domain of non-speech audio classification. In this work, we have proposed a dilated Convolutional Neural Network approch to classify urban sound. We have carried out feature extraction, data augmentation techniques to carry out our experimental strategy smoothly. We also found out the activation maps of each layers of dilated convolution neural network. An increamental dilation rate has exploited Overall we achieved 84.16% of accuracy from the proposed dilated convolutional method. The gradual increaments of dilation rate has exploited the worse effect of grindding and has lowered down the computational cost. Also, overall classification performance, precision, recall,overall truth and kappa value have been obtained from our proposed method. We have considered 10 fold cross validation for the implementation of the dilated CNN model.

Journal of Intelligent & Fuzzy Systems

Deep convolutional neural network for environmental sound classification via dilation

International Journal of Surgery

Deep learning research should be encouraged for diagnosis and treatment of antibiotic resistance of microbial infections in treatment associated emergencies in hospitals

Fire calamity is one of the worst adversarial events that can happen to the human race. Fire disaster can happen as a manmade disaster or even naturally, and it may cause environmental, social, and financial damages as well. In order to minimalize the unwanted fire calamity, early detection of fire eruptions coupled with immediate and effective response is extremely vital to disaster management systems. The classification of forest fire and non fire images using deep learning techniques has recently received popularity. Detection and prevention of forest fire have lot of significance from the perspective of the forest fire department, specially for the fire and arson investigators. There are shortcomings in the current mechanisms of forest fire detection in terms of accuracy. Hence, we propose a fire detection model using LeNet5 convolutional neural networks (CNN), which can spot fire in outdoor environments by classifying fire and non fire images. L2 regularization is critical technique that manipulates the complexity of the convolutional neural network model. In our work fire images have certain features that decide if the image is fire or non fire.A weight is assigned to every feature. Regularization used to help to reduce the over fitting that used to caused by plenty of weights. Our proposed provides the directiontowards developing a system that detects the early stages of forest fire.This model can further be utilized to prevent the damage caused by the fire. A CNN is a deep learning method, which has been adopted in order to detect the images of fire and non-fire. With the non sparse solution of L2 regularization we have obtained around 87% of train accuracy, 71% of validation accuracy and 70% of test accuracy after running 10 epochs.

Journal of Intelligent \& Fuzzy Systems

L2 regularized deep convolutional neural networks for fire detection

International journal of surgery (London, England)

Deep learning research should be encouraged more and more in different domains of surgery: An open call - Correspondence

Prediction of building energy consumption plays an important role in energy conservation, management, and planning. Continuously improving and enhancing the performance of forecasting models is the key to ensuring the performance sustainability of energy systems. In this connection, the current paper presented a new improved hybrid model of machine learning application for forecasting the cooling load (CL) and the heating load (HL) of residential buildings after studying and analyzing various types of CL and HL forecasting models. The proposed hybrid model, called group support vector regression (GSVR), is a combination of group method of data handling (GMDH) and support vector regression (SVR) models. To forecast CL and HL, this study also made use of base methods such as back-propagation neural network (BPNN), elastic-net regression (ENR), general regression neural network (GRNN), k-nearest neighbors (kNN), partial least squares regression (PLSR), GMDH, and SVR. The technical parameters of the building were utilized as input variables of the forecasting models, and the CL and HL were adopted as the output variables of each network. All models were saved in the form of black box after training and initial testing. Finally, comparative analysis was performed to assess the predictive performance of the suggested model and the well-known basic models. Based on the results, the proposed hybrid method with high correlation coefficient (R) (e.g. R=99.92% for CL forecasting and R=99.99% for HL forecasting) and minimal statistical error values provided the most optimal prediction performance.

IEEE Access

Heating and Cooling Loads Forecasting for Residential Buildings Based on Hybrid Machine Learning Applications: A Comprehensive Review and Comparative Analysis

Much of the hesitation in stock investments is due to apparent volatility about the stock price. Had there been a predictor to accurately predict the final trading price of stocks, it could be an assurance to invest in the Stock Market. Thus we propose, a trustworthy hybrid model by cascading Multivariate Adaptive Regression Splines(MARS) and Deep Neural Network(DNN), to predict closing prices of stock. The high-frequency KOSPI data set has been used and a customized pre-processing algorithm has been applied to clean the data. MARS is then been applied on this clean data and the attributes retained by MARS are passed to a DNN for training. Such application has resulted up to 92% closing price prediction accuracy. Thus, our hybrid model successfully has reduced the dimensional feature without compromising on accuracy as it gave better results than MARS and DNNs individually. Data-Augmentation has also been used to further verify the outcome of this application. Main metrics used for performance evaluation are Correlation(RHO) and R2 value.

Computers and Electrical Engineering

Forecasting stock price by hybrid model of cascading Multivariate Adaptive Regression Splines and Deep Neural Network

Smart agriculture farming is the latest innovative technology in the agriculture field used in most developing countries. But all the places natural resources like water, soil, and quality of the land are not common due to the climate and organic elements presents in their land. For developing agriculture in those places water is the only resource to produce in the farming based on the quality of the water contaminants. Most of the places water quality is at a poor level due to the dissemination of the organics and chemical elements mixed in the water from the industries waste. Rainwater harvesting is also used for the development of agriculture but transforming that water to these places is a challenging task. For that, water management is the important scenario in agriculture to provide water to the plants in a correct proportionality of the contaminants. IoT-based water management is introduced in this research for finding the contaminants mixed in the water level and added nutrition whenever the quality is low. The water level given to the plant plays a vital role in agriculture if it goes high then the crop may be rotten. Nutrition level is not balanced in the water leads to affect the growth of the plant. To overcome this problem face by farmers in agriculture this research work proposed IoT-enabled water management for improving crop health in smart agriculture farming. This research work disseminates the water and nutrition to the plants continuously but the only limited amount will be delivered at a stipulated interval of time.&nbsp;