Computer Science And Technology - Research Publications
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Item Sentiment analysis of Hindi language text: a critical review(Springer, 2023-11-11T00:00:00) Sidhu, Simran; Khurana, Surinder S.; Kumar, Munish; Singh, Parvinder; Bamber, Sukhvinder S.Sentiment analysis involves extracting sentiments from various forms of text, including customer reviews, tweets, blogs, and news clips expressing opinions on diverse subjects, even populist events. The advent of tools supporting regional languages has resulted in a substantial surge of regional language texts. As Hindi ranks fourth in terms of native speakers, the development of sentiment analysis mechanisms for Hindi text becomes crucial. This paper provides a comprehensive review of specific approaches used in Hindi sentiment analysis, encompassing negation handling and the evolution of SentiWordNet for the Hindi Language. Moreover, it offers an overview of available Hindi lexicons and insights into diverse stemmers and morphological analyzers designed for the language. Additionally, the paper conducts an in-depth literature review of various sentiment analysis tasks carried out in Hindi, thereby opening avenues for future research in sentiment analysis and opinion mining in the Hindi language. � 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item Feature Engineering and Ensemble Learning-Based Classification of VPN and Non-VPN-Based Network Traffic over Temporal Features(Springer, 2023-07-29T00:00:00) Abbas, Gazy; Farooq, Umar; Singh, Parvinder; Khurana, Surinder Singh; Singh, ParamjeetWith the rapid advancement in technology, the constant emergence of new applications and services has resulted in a drastic increase in Internet traffic, making it increasingly challenging for network analysts to maintain network security and classify traffic, especially when encrypted or tunneled. To address this issue, the proposed strategy aims to distinguish between regular traffic and traffic tunneled through a virtual private network and characterize traffic from seven different applications. The proposed approach utilizes various ensemble machine learning techniques, which are efficient and accurate and consume minimal computational time for training and prediction compared to conventional machine and deep learning models. These models were applied for both the classification and characterization of network traffic, deriving efficient results. The extreme and light gradient boosting algorithms performed well in multiclass classification, while AdaBoost and Light GBM performed well in binary classification. However, when all the datasets were merged and categorized into two classes and various feature engineering methods were applied, the proposed system achieved an accuracy of more than 99%, with minimal error scores using light GBM with min�max scaling over stratified fivefold, thereby outperforming all existing approaches. This research highlights the efficiency and potential of the proposed model in detecting network traffic. � 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.Item Comparison of Public and Critics Opinion About the Taliban Government Over Afghanistan Through Sentiment Analysis(Springer Science and Business Media Deutschland GmbH, 2023-05-03T00:00:00) Reza, Md Majid; Singh, Satwinder; Kundra, Harish; Reza, Md RashidThe usage of social media has increased exponentially these days. People worldwide are sharing their opinions on different platforms such as Twitter, personal blogs, Facebook, and other similar platforms. Twitter has grown in popularity as a platform for people to express their thoughts and opinions on many different topics. The data from Twitter about the Taliban has been examined in this research work, and various machine learning algorithms have been applied including SVM, LR, and random forest. Text sentiments have been captured via TextBlob. Among the machine learning models applied, SVM outperformed all other models and achieved an accuracy score of around 94% on the tweet dataset and logistic regression outperformed other models with an accuracy score of 83% on the news article dataset. � 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Bug Classification Depend Upon Refactoring Area of Code(Springer, 2023-01-05T00:00:00) Singh, Satwinder; Jalal, Maddassar; Kaur, SharanpreetDue to rapid development in the software industry, various software is being developed, which compromises the software quality. As long as time passes, the software which compromises with the quality starts showing bugs which adversely affect the working of the software system. Sometimes software undergo repeated addition of functionality, so various classes and functions in software systems become bulky and cause smells in code. Code smells also degrade the software quality, increasing the software system's maintenance cost. So these bad smells should be appropriately detected and eliminated from the software system well on time. With the identification of smells in code, one could easily map the refactoring area of code to improve the code. Improving the code will help in preventive maintenance to identify the bugs quickly. To solve this problem, the current study will focus on the five types of code smells: Data Class, God Class, Feature Envy, Refuse Parent Request and Brain method for identification of bugs in the code. The data set used for this study includes the smell extraction and identification from two versions of Eclipse, i.e. Eclipse 3.6 and Eclipse 3.7, which are renowned open soured industry size software systems. Later on, supervised machine learning classifiers j48, Random Forest, and Naive Bayes are used to identify the bugs in code at the class level. The results show that j48 performed well and provides high accuracy for the identification of bugs with the help of bad smells. � 2023, The Institution of Engineers (India).Item Design and implementation of intelligent monitoring system for platform security gate based on wireless communication technology using ML(Springer, 2021-10-30T00:00:00) Li, Chunying; Niu, Hongxia; Shabaz, Mohammad; Kajal, KumariThe platform safety gate is important safety protection equipment in urban rail transit, which makes the rail area relatively independent from the platform waiting area, ensures the safety of passengers, reduces the noise pollution brought by the subway train to the platform, and provides a comfortable waiting environment for passengers. In order to solve the problems of low intelligent degree and single debugging method of railway station safety door equipment data monitoring, a correction algorithm using machine learning based on image grid is proposed. Firstly, based on virtual instrument technology, a set of acoustic signal acquisition and processing systems for sound field visualization is designed and implemented. Then, based on the analysis of requirements, the hardware configuration and system software design are carried out. Finally, the extraction technology of image feature information is adopted, which can reduce the operation time of image target recognition and make the security door control system have real time. The experimental results show that the calibration algorithm is used to calculate the coordinate values of the actual road by using the third-order fitting method. Compared with the coordinate values of the standard grid, the average error of X is 0.0662%, and the average error of Y is 0.0011%. It can not only improve the accuracy of judgment, but also meet the real-time requirements of video monitoring. The system can realize wireless monitoring on the status of platform safety door equipment using machine learning, improve the efficiency of subway operation and the flexibility of station staff maintenance and protection, and ensure the safety and reliability of the platform safety door system. � 2021, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.Item Time series data analysis of stock price movement using machine learning techniques(Springer, 2020) Parray, I.R; Khurana, S.S; Kumar, M; Altalbe, A.A.Stock market also called as equity market is the aggregation of the sellers and buyers. It is concerned with the domain where the shares of various public listed companies are traded. For predicting the growth of economy, stock market acts as an index. Due to the nonlinear nature, the prediction of the stock market becomes a difficult task. But the application of various machine learning techniques has been becoming a powerful source for the prediction. These techniques employ historical data of the stocks for the training of machine learning algorithms and help in predicting their future behavior. The three machine learning algorithms used in this paper are support vector machine, perceptron, and logistic regression, for predicting the next day trend of the stocks. For the experiment, dataset from about fifty stocks of Indian National Stock Exchange�s NIFTY 50 index was taken, by collecting stock data from January 1, 2013, to December 31, 2018, and lastly by the calculation of some technical indicators. It is reported that the average accuracy for the prediction of the trend of fifty stocks obtained by support vector machine is 87.35%, perceptron is 75.88%, and logistic regression is 86.98%. Since the stock data are time series data, another dataset is prepared by reorganizing previous dataset into the supervised learning format which improves the accuracy of the prediction process which reported the results with support vector machine of 89.93%, perceptron of 76.68%, and logistic regression of 89.93%, respectively. � 2020, Springer-Verlag GmbH Germany, part of Springer Nature.Item Investigating the role of code smells in preventive maintenance(University of Tehran, 2019) Reshi J.A.; Singh S.The quest for improving the software quality has given rise to various studies which focus on the enhancement of the quality of software through various processes. Code smells, which are indicators of the software quality have not been put to an extensive study for as to determine their role in the prediction of defects in the software. This study aims to investigate the role of code smells in prediction of non-faulty classes. We examine the Eclipse software with four versions (3.2, 3.3, 3.6, and 3.7) for metrics and smells. Further, different code smells, derived subjectively through iPlasma, are taken into conjugation and three efficient, but subjective models are developed to detect code smells on each of Random Forest, J48 and SVM machine learning algorithms. This model is then used to detect the absence of defects in the four Eclipse versions. The effect of balanced and unbalanced datasets is also examined for these four versions. The results suggest that the code smells can be a valuable feature in discriminating absence of defects in a software.Item TempR: Application of Stricture Dependent Intelligent Classifier for Fast Flux Domain Detection(Modern Education and Computer Science Press, 2016) Chahal, Prabhjot Singh; Khurana, Surinder SinghFast-flux service networks (FFSN) helps the cyber-criminals to hide the servers used for malicious activities behind a wall of proxies (bots). It provides the reliability and detection evasion to a malicious server. FFSN use a large pool of IP addresses for proxies. Detection of FFSN is difficult as few benign technologies like Content distribution networks and round robin DNS have similar working characteristics. Many approaches have been proposed to detect FFSN and fast flux domains. However, due to dynamic behavior of FFSN, these techniques suffer from a significant number of false positives. In this paper, we present a Temporal and Real time detections based approach (TempR) to detect fast flux domains. The features of fast flux domains and benign domains have been collected and classified using intelligent classifiers. Our technique illustrates 96.99% detection accuracy with the recent behavior of fast flux domains.