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 Software and Systems Engineering

In today’s modern world security is of the prime concern and there is a need for a system that can safeguard an individual’s privacy and help for secure transfer of data across a network. As there is a need for disclosing the identity of a person to distinguish if he is an authorized user or not we develop a system that can recognize the identity of a person based on his biometric features. In the past years the need to protect the data from illegitimate users has always been a great dilemma. So this paper proposes to encrypt the biometrics of a person using a XOR function and detecting the user to maintain confidentiality in the secure data by HAAR classifiers.From the extracted details, a database is created for known users. This biometric database is more secure as these data are encrypted and un-modifiable for each individual. The system is developed in python with Opencv environment.

School of Information Technology and Engineering

Vellore Campus

World University root node

Journal of Advanced Research in Dynamical and Control Systems

An approach for facial recognition using deep learning

A 15-step pre-processing procedure is proposed to improve the accuracy of sentiment mining of depression related posts in the tweets. Perhaps, for the first time, converting emoticons in the depression related tweets into text form is proposed during the pre-processing stage. In this paper, Term Frequency-Inverse Document Frequency with n-grams is used for feature extraction. Sentiment analysis conducted on a dataset consisting of 1.6 million depression related tweets using the proposed pre-processing module with feature extraction using Term Frequency-Inverse Document Frequency with n-grams and Logistic Regression (LR) for classification resulted in 81% of accuracy in detecting depression related tweets. © Springer Nature Switzerland AG 2020.

Department of Manufacturing Engineering

School of Mechanical Engineering

Lecture Notes on Data Engineering and Communications Technologies

Detection of Depression Related Posts in Tweets Using Classification Methods – A Comparative Analysis

Production factories have many items produced with various parts to bring out the complete product at the end. Every product that is produced in the factory obtains the company some amount of income. The manufacturing of these products also costs a huge sum for purchase and production. When a situation arises for the company to shut down its manufacturing or when there is some crisis, the industry will not have money to buy the parts to finish the product. The problem of removable pattern mining is to identify the patterns which can be removed to reduce the loss to the factory’s profit under certain conditions. In such situations, the production managers need an alternate to reduce the cost of production without decreasing the profit rate. This paper proposes removable closed patterns to represent and compress the mined removable patterns without much profit reduction. Erasable closed pattern mining algorithm is implemented to mine all the erasable closed patterns by reducing the resources needed for production without affecting the information loss. The removable itemset with one pattern along with their element occurrence pair is taken into consideration. Further the erasable patterns are mined and used for useful extraction of members. Then the erasable closed patterns are mined using the module that checks the closeness of the patterns in the product datasets. © Springer Nature Singapore Pte Ltd 2020.

Department of Computer Applications

VIT Unitversity

Advances in Intelligent Systems and Computing Artificial Intelligence and Evolutionary Computations in Engineering Systems

Mining of Removable Closed Patterns in Goods Dataset

Advances in Intelligent Systems and Computing Intelligent Systems Design and Applications

Analysis on Improving the Performance of Machine Learning Models Using Feature Selection Technique

Traditional technique for determining the soil texture and other soil properties is performed in laboratory which is a time consuming task. In this paper, machine learning algorithms are deployed to classify the soil texture and its properties without any intervention of laboratory equipment using the satellite images recorded by Landsat 8. These images are used to extract the terrain properties of the region which is integrated with weather data for the specific region and the vegetation index which are the major factors affecting the soil condition. A major aim of this paper is to design a robust technique for extracting, transforming Landsat images to numerical data and pre-processing the data for classifying the soil property. Minimum Noise Fraction (MNF) is utilized to segregate and remove noise from the Landsat images for subsequent processing. A significant amount of noise is present in the raw data which affects the accuracy of the analysis. Terrain features are extracted after noise removal from the MNF transformed images and merged with the weather data, and vegetation index for a period of time and then classified using voting classifier of the ensemble modeling or analysis of the soil texture of the region. The voting is performed by integrating the results of logistic regression, support vector machine and decision tree. With this study, the consolidated dependence of the soil texture on the environmental factors is analyzed and a cross validation accuracy of 94.44% is obtained. © 2020, Semnan University, Center of Excellence in Nonlinear Analysis and Applications. All rights reserved.

International Journal of Nonlinear Analysis and Applications

Terrain mapping of landsat8 images using mnf and classifying soil properties using ensemble modelling

Software Defined Networking, or commonly called as SDN, is a fairly novel concept which makes use of the current conventional networking trends to provide the users a much flexible and customizable networking service. This networking concept puts forth the capability of centrally managing the network. One of the most prevalent concerns regarding the development of a Software Defined Networking System is network attacks. Network attacks may be easy to identify at times, but not always. The main task of an intrusion detection system is to detect and identify various threats and further action may include administration of prevention and mitigation strategies. Though this area of software defined networking is a widely researched topic, nowadays it is hard to detect most of the network attacks. A user may specify certain rules and actions to be taken in the case of a given set of network attacks, but it becomes impossible for man to predict the formation of new attacks. Thus, writing rules manually becomes a tedious task. The main objective of this paper is to attempt to automate rule generation for detection, prevention, and mitigation of the effects caused by every possible network attack. An attempt is being made to associate intrusion detection systems with Genetic Algorithm in order to predict the possibility of formation of new attacks, and to automate the rule generation for such predictive cases. © 2020 IEEE.

International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2020

Intrusion Detection in Software Defined Networking using Genetic Algorithm

Population has become a great challenge for many developing countries. Dumping of wastes became a major problem. An overflown canister is a burden to humans as it impacts their health and resulting in a bad odour. The cleaning process should be made easy thereby reducing human efforts and cost. In this paper 'INTELLIGENT TRASHCAN MONITORING SYSTEM' we confer an idea of intelligent trash supervising where the level of trash is measured using an ultrasonic sensor and led lights is used to indicate the level. The data is collected through microcontrollers. Once it is full a quick notification is sent to municipal officers. © 2020 IEEE.

Department of Smart Computing

Intelligent Trashcan Monitoring System Using Iot

Movie recommendation system offers a mechanism to allocate the user to attain the famous film by getting an opinion from similar users or past rating by user. This produces recommender systems has a crucial part of website and e-commerce application. The primary objective of the system to prefer a recommender system by data clustering and computational intelligence. We proposed a hybrid model collaborative movie recommendation system that performs with a combination of K-means clustering with ant colony optimisation technique (ACO-KM) that has employed in movie dataset. The proposed system compared with existing works, and its efforts have been analysed. The evaluation process of movie recommendation system that offers improved result from ACO-KM collaborative movie recommender system based on precision, recall, mean square error (MSE), and accuracy. By comparison of speed (in seconds) of various approaches in Movielens dataset, our approach gives best result 42.24 compared with existing one 53.22. The outcome of this experiment from Movielens dataset that offers scalability and efficiency in a recommendation by decreasing cold start issues. Copyright © 2020 Inderscience Enterprises Ltd.

International Journal of Internet Technology and Secured Transactions

A hybrid model collaborative movie recommendation system using K-means clustering with ant colony optimisation

In this digital era, all the information takes the form of various media during communication. This leads to the need for more concern on the security measures so that the data being transmitted is resilient to various malicious attacks. In this paper, a modified line map algorithm is proposed and it is considered as a chaotic map to scramble the image. The resultant chaotic Line map has been employed to permute the image pixels, whereas diffusion is performed by XOR operation to completely encrypt the image. The robustness of the image against attacks has been increased by repeated permutation-diffusion round for ‘n’ number of times. Histogram analysis, Number of pixels change rate and correlation analysis has been performed to analyze the robustness of the proposed technique. Simulation results proves that this methodology can be implemented in real time applications. © 2020, Springer Nature Switzerland AG.

Department of Information Technology

Image Encryption Using New Chaotic Map Algorithm

Generally, the risks associated with malicious threats are increasing for the Internet of Things (IoT) and its related applications due to dependency on the Internet and the minimal resource availability of IoT devices. Thus, anomaly-based intrusion detection models for IoT networks are vital. Distinct detection methodologies need to be developed for the Industrial Internet of Things (IIoT) network as threat detection is a significant expectation of stakeholders. Machine learning approaches are considered to be evolving techniques that learn with experience, and such approaches have resulted in superior performance in various applications, such as pattern recognition, outlier analysis, and speech recognition. Traditional techniques and tools are not adequate to secure IIoT networks due to the use of various protocols in industrial systems and restricted possibilities of upgradation. In this paper, the objective is to develop a two-phase anomaly detection model to enhance the reliability of an IIoT network. In the first phase, SVM and Naïve Bayes, are integrated using an ensemble blending technique. K-fold cross-validation is performed while training the data with different training and testing ratios to obtain optimized training and test sets. Ensemble blending uses a random forest technique to predict class labels. An Artificial Neural Network (ANN) classifier that uses the Adam optimizer to achieve better accuracy is also used for prediction. In the second phase, both the ANN and random forest results are fed to the model’s classification unit, and the highest accuracy value is considered the final result. The proposed model is tested on standard IoT attack datasets, such as WUSTL_IIOT-2018, N_BaIoT, and Bot_IoT. The highest accuracy obtained is 99%. A comparative analysis of the proposed model using state-of-the-art ensemble techniques is performed to demonstrate the superiority of the results. The results also demonstrate that the proposed model outperforms traditional techniques and thus improves the reliability of an IIoT network. © 2021 Tech Science Press. All rights reserved.