Faculty Publications
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Item Optimized Distributed Job Shop Scheduling Using Balanced Job Allocation and Modified Ant Colony Optimization(Springer Science and Business Media Deutschland GmbH, 2022) Vivek, S.; Rakesh, K.; Mohan, B.R.Many challenges are being faced by the manufacturing industry: ensuring profitable growth, reducing costs, increasing productivity, and giving quick responses to customers. To become more productive, reduce transportation costs, and reduce bottleneck on a single factory, industrial companies are shifting from single to distributed systems. Scheduling problems like distributed job shop, distributed flow shop, and distributed process planning are becoming a popular field to study. We try to solve the distributed job shop scheduling problem (DJSP) where the allocation of jobs to different factories needs to be done and additionally, the determination of good operation schedules for each factory. The goal of DJSP is to minimize the makespan over all the factories. To solve this problem, we first use a method of allocating jobs to factories to evenly distribute the workloads among all the factories. Later, we use a bio-inspired algorithm on each factory after the allocations, namely ant colony optimization to get a solution that is close to the most optimal solution. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Petri net model for knowledge-based value chain(2011) Niranjan, U.N.; Itagi, S.; Mohan, B.R.In this paper a novel theoretical model is presented in which the dynamics in a knowledge-based value chain is modeled using Petri nets. From the generic scheme of a knowledge-based value chain, the various components are individually modeled. The theory of Petri nets aptly captures the evolution of knowledge in a system and the process is usually highly interactive in nature. The properties and analysis methods of various classes of Petri nets can be conveniently used to check various constraints while designing the system. © 2011 Springer-Verlag.Item Vulnerability analysis on virtualized environment using FPVA(2013) Jayaraju, L.; Naik, D.; Mohan, B.R.Virtualization is rapidly gaining acceptance as a fundamental building block in enterprise data centers and is the core component of cloud computing platforms. It is most known for improving efficiency and ease of management. While this technology is meant to enhance the security of computer systems, some recent attacks show that virtual machine technology has much vulnerability and becomes exposed to security threats. In this paper, we focus on Vulnerability Analysis on Virtualized Environment using First Principles Vulnerability Analysis (FPVA) methodology. This paper analyses the security of Interrupt Descriptor Table on Xen hypervisor and presents outcome of analysis. This paper aids other enthusiasts in better analysing the Security aspects of Xen system using their own programs for security vulnerabilities. © 2013 IEEE.Item Software aging trend analysis of server virtualized system(IEEE Computer Society, 2014) Mohan, B.R.; Guddeti, G.It is well known that software systems suffer from performance degradation due to resource shrinking and this phenomenon is referred to as Software Aging. Long running software systems tend to show degradation in performance due to exhaustion of operating systems resources, data corruption and numerical error accumulation. The primary objective of the paper is to establish the aging trend in the server virtualized system. It establishes the aging trend by showing that the average response time decreases while total available physical memory decreases. Linear regression model has been used to study the aging trend. © 2014 IEEE.Item Stock price movements classification using machine and deep learning techniques-the case study of indian stock market(Springer Verlag service@springer.de, 2019) Naik, N.; Mohan, B.R.Stock price movements forecasting is an important topic for traders and stock analyst. Timely prediction in stock yields can get more profits and returns. The predicting stock price movement on a daily basis is a difficult task due to more ups and down in the financial market. Therefore, there is a need for a more powerful predictive model to predict the stock prices. Most of the existing work is based on machine learning techniques and considered very few technical indicators to predict the stock prices. In this paper, we have extracted 33 technical indicators based on daily stock price such as open, high, low and close price. This paper addresses the two problems, first is the technical indicator feature selection and identification of the relevant technical indicators by using Boruta feature selection technique. The second is an accurate prediction model for stock price movements. To predict stock price movements we have proposed machine learning techniques and deep learning based model. The performance of the deep learning model is better than the machine learning techniques. The experimental results are significant improves the classification accuracy rate by 5% to 6%. National Stock Exchange, India (NSE) stocks are considered for the experiment. © Springer Nature Switzerland AG 2019.Item Study of stock return predictions using recurrent neural networks with LSTM(Springer Verlag service@springer.de, 2019) Naik, N.; Mohan, B.R.Stock price returns forecasting is challenging task for day traders to yield more returns. In the past, most of the literature was focused on machine learning algorithm to predict the stock returns. In this work, the recurrent neural network (RNN) with long short term memory (LSTM) is studied to forecast future stock returns. It has the ability to keep the memory of historical stock returns in order to forecast future stock return output. RNN with LSTM is used to store recent stock information than old related stock information. We have considered a recurrent dropout in RNN layers to avoid overfitting in the model. To accomplish the task we have calculated stock return based on stock closing prices. These stock returns are given as input to the recurrent neural network. The objective function of the prediction model is to minimize the error in the model. To conduct the experiment, data is collected from the National Stock Exchange, India (NSE). The proposed RNN with LSTM model outperforms compared to an feed forward artificial neural network. © Springer Nature Switzerland AG 2019.Item Optimal Feature Selection of Technical Indicator and Stock Prediction Using Machine Learning Technique(Springer Verlag service@springer.de, 2019) Naik, N.; Mohan, B.R.Short-term trading is a difficult task due to fluctuating demand and supply in the stock market. These demands and supply are reflected in stock prices. The stock prices may be predicted using technical indicators. Most of the existing literature considered the limited technical indicators to measure short-term prices. We have considered 33 different combinations of technical indicators to predict the stock prices. The paper has two objectives, first is the technical indicator feature selection and identification of the relevant technical indicators by using Boruta feature selection technique. The second objective is an accurate prediction model for stocks. To predict stock prices we have proposed ANN (Artificial Neural Network) Regression prediction model and model performance is evaluated using metrics is Mean absolute error (MAE) and Root mean square error (RMSE). The experimental results are better than the existing method by decreasing the error rate in the prediction to 12%. We have used the National Stock Exchange, India (NSE) data for the experiment. © 2019, Springer Nature Singapore Pte Ltd.Item Performance analysis of secondary storage media through file systems benchmarking(Institute of Electrical and Electronics Engineers Inc., 2019) Rakshith, G.; Rozario, R.; Rhevanth, M.; Nikitha, K.M.; Mohan, B.R.Efficient performance of a disk I/O operation involves a multitude of factors such as the type of the disk, I/O scheduling, and the type of the file system used. Due to the various types of file systems available, with each having different structure and logic, properties of speed, flexibility, security, size and more, it becomes imperative to have an objective overview of the merits and demerits of each file system according to the needs of the users. In this work, we present a thorough performance evaluation of ext4, NTFS and Btrfs filesystems along with CFQ, NOOP and Deadline I/O schedulers tested on regular hard disk drives and SSDs. © 2019 IEEE.Item Performance evaluation of dimensionality reduction techniques on high dimensional data(Institute of Electrical and Electronics Engineers Inc., 2019) Vikram, M.; Pavan, R.; Dineshbhai, N.D.; Mohan, B.R.With a large amount of data being generated each day, the task on analyzing and making inferences from data is becoming an increasingly challenging task. One of the major challenges is the curse of dimensionality which is dealt with by using several popular dimensionality reduction techniques such as ICA, PCA, NMF etc. In this work, we make a systematic performance evaluation of the efficiency and effectiveness of various dimensionality reduction techniques. We present a rigorous evaluation of various techniques benchmarked on real-world datasets. This work is intended to assist data science practitioners to select the most suitable dimensionality reduction technique based on the trade-off between the corresponding effectiveness and efficiency. ©2019 IEEE.Item Performance evaluation of topic modeling algorithms for text classification(Institute of Electrical and Electronics Engineers Inc., 2019) Anantharaman, A.; Jadiya, A.; Sai Siri Chandana, C.T.S.; Adikar Bharath, N.V.S.; Mohan, B.R.Text Classification is a paramount task in natural language processing. Topic modeling algorithms have been used with a lot of success for text classification. We evaluate different topic modeling algorithms for two tasks: (1) Short text or sentence classification and (2) Large text or document classification. We give an extensive performance evaluation with the help of a wide range of performance metrics for three topic modeling algorithms on both of these tasks using three publicly available datasets. ©2019 IEEE.