Department Of Computer Science And Technology

Permanent URI for this communityhttps://kr.cup.edu.in/handle/32116/79

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Author: search.filters.author.Singh, Satwinder

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    Pharmacophore derived 3D-QSAR, molecular docking, and simulation studies of quinoxaline derivatives as ALR2 inhibitors
    (Taylor and Francis Ltd., 2023-09-12T00:00:00) Singh, Yogesh; Kumar, Niraj; Kulkarni, Swanand; Singh, Satwinder; Thareja, Suresh
    Aldose Reductase 2 (ALR2), a key enzyme of the polyol pathway, plays a crucial role in the pathogenesis of diabetic complications. Quinoxaline scaffold-based compounds have been identified as potential ALR2 inhibitors for the management of diabetic complications. In the present work, molecular dynamic simulation studies in conjugation with pharmacophore mapping and atom-based 3D-QSAR were performed on a dataset of 99 molecules in comparison with Epalrestat (reference) to mark the desirable structural features of quinoxaline analogs to generate a probable template for designing novel and effective ALR2 inhibitors. The most potent compound 81 was subjected to MD simulation studies and found to be stable, with better interactions with the binding pocket as compared to Epalrestat. The MM-GBSA and MM-PBSA calculations showed that compound 81 possessed binding free energies of ?35.96 and ?4.92 kcal/mol, respectively. Atom-based 3D-QSAR yielded various pharmacophoric features with excellent statistical measures, such as correlation coefficient (R 2 value), F-value (Fischer ratio), Q 2 value (cross-validated correlation coefficient), and Pearson�s R-value for training and test sets. Furthermore, the pharmacophore mapping provided a five-point hypothesis (AADRR) and docking analysis revealed the active ligand-binding orientations on the active site�s amino acid residues TYR 48, HIE 110, TRP 111, and TRP 219. The results of this study will help in designing potent inhibitors of ALR2 for the management of diabetic complications. Communicated by Ramaswamy H. Sarma. � 2023 Informa UK Limited, trading as Taylor & Francis Group.
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    Advances and challenges in thyroid cancer: The interplay of genetic modulators, targeted therapies, and AI-driven approaches
    (Elsevier Inc., 2023-09-20T00:00:00) Bhattacharya, Srinjan; Mahato, Rahul Kumar; Singh, Satwinder; Bhatti, Gurjit Kaur; Mastana, Sarabjit Singh; Bhatti, Jasvinder Singh
    Thyroid cancer continues to exhibit a rising incidence globally, predominantly affecting women. Despite stable mortality rates, the unique characteristics of thyroid carcinoma warrant a distinct approach. Differentiated thyroid cancer, comprising most cases, is effectively managed through standard treatments such as thyroidectomy and radioiodine therapy. However, rarer variants, including anaplastic thyroid carcinoma, necessitate specialized interventions, often employing targeted therapies. Although these drugs focus on symptom management, they are not curative. This review delves into the fundamental modulators of thyroid cancers, encompassing genetic, epigenetic, and non-coding RNA factors while exploring their intricate interplay and influence. Epigenetic modifications directly affect the expression of causal genes, while long non-coding RNAs impact the function and expression of micro-RNAs, culminating in tumorigenesis. Additionally, this article provides a concise overview of the advantages and disadvantages associated with pharmacological and non-pharmacological therapeutic interventions in thyroid cancer. Furthermore, with technological advancements, integrating modern software and computing into healthcare and medical practices has become increasingly prevalent. Artificial intelligence and machine learning techniques hold the potential to predict treatment outcomes, analyze data, and develop personalized therapeutic approaches catering to patient specificity. In thyroid cancer, cutting-edge machine learning and deep learning technologies analyze factors such as ultrasonography results for tumor textures and biopsy samples from fine needle aspirations, paving the way for a more accurate and effective therapeutic landscape in the near future. � 2023 The Author(s)
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    Hate Speech and Offensive Language Detection in Twitter Data Using Machine Learning Classifiers
    (Springer Science and Business Media Deutschland GmbH, 2023-05-03T00:00:00) Shah, Seyed Muzaffar Ahmad; Singh, Satwinder
    Social media is rapidly growing in popularity and has its advantages and disadvantages. Users posting their daily updates and opinions on social media may inadvertently hurt the feelings of others. Detecting hate speech and harmful information on social media is critical these days, lest it led to calamity. In this research, machine learning classifiers such as Na�ve Bayes, support vector machines, logistic regression, and pre-trained models BERT and RoBERTa, developed by Google and Facebook, respectively, are used to detect hate speech and offensive content from Twitter data on a newly created dataset that included tweets and articles/blogs. The sentiments were obtained using the VADER sentiment analyzer. The results depicted that the pre-trained classifiers outperformed the machine learning classifiers utilized in this study. An accuracy score of 96% and 93% was scored by BERT and RoBERTa, respectively, on the tweet dataset, whereas on a dataset of articles/blogs, accuracy of 97% and 98%, respectively, was achieved by both the classifiers outperforming other classifiers used in this work. Further, it can also be depicted that neutral content is shared more in articles/blogs, hate content is mostly shared equally in both the tweets and article/blogs, whereas offensive content is shared higher in tweets than articles/blogs. � 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Analyze Dark Web and Security Threats
    (Springer Science and Business Media Deutschland GmbH, 2023-05-03T00:00:00) Ansh, Samar; Singh, Satwinder
    The deepest area of data storage where data mining and data management are not possible without the Tor (network) Policy is known as the dark web. The dark web is a paradise for government and private sponsored cybercriminals. In another word, the dark web is known as the underworld of the Internet used for sponsored and organized cybercrime. Tor network at the entry relay/guard user source IP replaced with local IP (i.e., 10.0.2.15) by default and every user machine ID (IP) recognize as local IP (10.0.2.15). A single source IP allocated for each user without collision makes the user an anomaly or invisible over the Internet. Tor browser works similar to VPN by default as a function to hide the source IP, but the advantage is Tor network�s volunteer devices are used as a tunnel to establish communication and offer freedom from surveillance of user activity. Tor browser offers a circuit (IP Route) for user activity, where the circuit allows available Tor IP at the exit relay for the user. The dark web uses the same IP at entry relay around the world, but at exit relay, IP is different and available based on country. In a dark web network, data transfer as an encapsulation of packet/massage is placed after three-layer of different encryption. Proposed six different machine-learning classifiers (Logistic Regression, Random Forest, Gradient Boosting, Ada Boosts, K-Nearest Neighbors, Decision Tree) used to the optimal solution and proceed to analyze security threats perform in the Dark web based on the communication protocol and user activity as data flow and active state. � 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    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 Rashid
    The 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.
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    A systematic literature review on phishing website detection techniques
    (King Saud bin Abdulaziz University, 2023-01-11T00:00:00) Safi, Asadullah; Singh, Satwinder
    Phishing is a fraud attempt in which an attacker acts as a trusted person or entity to obtain sensitive information from an internet user. In this Systematic Literature Survey (SLR), different phishing detection approaches, namely Lists Based, Visual Similarity, Heuristic, Machine Learning, and Deep Learning based techniques, are studied and compared. For this purpose, several algorithms, data sets, and techniques for phishing website detection are revealed with the proposed research questions. A systematic Literature survey was conducted on 80 scientific papers published in the last five years in research journals, conferences, leading workshops, the thesis of researchers, book chapters, and from high-rank websites. The work carried out in this study is an update in the previous systematic literature surveys with more focus on the latest trends in phishing detection techniques. This study enhances readers' understanding of different types of phishing website detection techniques, the data sets used, and the comparative performance of algorithms used. Machine Learning techniques have been applied the most, i.e., 57 as per studies, according to the SLR. In addition, the survey revealed that while gathering the data sets, researchers primarily accessed two sources: 53 studies accessed the PhishTank website (53 for the phishing data set) and 29 studies used Alexa's website for downloading legitimate data sets. Also, as per the literature survey, most studies used Machine Learning techniques; 31 used Random Forest Classifier. Finally, as per different studies, Convolution Neural Network (CNN) achieved the highest Accuracy, 99.98%, for detecting phishing websites. � 2023 The Author(s)
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    Improving the Quality of Open Source Software
    (wiley, 2023-02-08T00:00:00) Kaur, Sharanpreet; Singh, Satwinder
    This study aims at development of generating metrics based code smells prediction to improve the software quality assurance by working at preventive maintenance level. In order to do so, Refactoring is the best solution for identification of smelly areas in the code to reveal the portions which demands patching. It not only increases the life of code but eventually increases the quality of software in long run, where versions of a software are launched one after the other. The empirical model development considered Deep learning based neural network technique for establishing the association between code smells and metrics in the source code of Eclipse which is a Java based application contributing efficiently on the open source platform. A statistical analysis was pre applied on the set of code smells and metrics for finding the connection between the both. Later on, Multi Layer Perceptron model development on four versions of Eclipse has been made. Subsequently Area Under Curve (ROC) has been generated for class & method level code smells. The value of ROC in predicting code smells pointed towards the fact that Neural Network Multi Layer Perceptron model perform fair to good in determining the presence of code smells based on software metrics in Eclipse. Therefore from the results obtained it is concluded that smelly classes are predicted efficiently by software metric based code smells prediction model. The present study will be beneficial to the software development community to locate the refactoring areas and providing resources for testing. The results of empirical study also guide the development community by providing information relative to code smells and its types. The aim of this study is to provide statistical proof of linkage between metrics and code smells. The software metric based prediction � 2023 Scrivener Publishing LLC.
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    Object Oriented Metrics Based Empirical Model for Predicting �Code Smells� in Open Source Software
    (Springer, 2023-01-03T00:00:00) Kaur, Sharanpreet; Singh, Satwinder
    Refactoring is a technique which involves the change in internal layout of software system while keeping external features same as before. It aims at identification of smelly areas in code in order to apply patch. The presence of these smelly areas in code is also called �Code Smells� generally known as �Bad Smells.� Under the present study, the statistical relationship between software metrics, code smells and faults is investigated in open source systems. Eight versions of two open source projects � Eclipse and Tomcat are investigated in order to identify the effect on classes subject to code smells and faults than other classes. It was observed that in almost all releases of Tomcat, a good perceptiveness was revealed by area under ROC curve. While an excellent perceptiveness was collected by class level code smells for Eclipse, whereas a fair to good discrimination is generated by method level code smells by area under ROC curve. The results obtained from the study showed that software metrics model for smelly classes is helpful in predicting classes. � 2023, The Institution of Engineers (India).
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    Bug Classification Depend Upon Refactoring Area of Code
    (Springer, 2023-01-05T00:00:00) Singh, Satwinder; Jalal, Maddassar; Kaur, Sharanpreet
    Due 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).
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    Targeting mitochondrial bioenergetics as a promising therapeutic strategy in metabolic and neurodegenerative diseases
    (Elsevier B.V., 2022-05-11T00:00:00) Bhatti, Gurjit Kaur; Gupta, Anshika; Pahwa, Paras; Khullar, Naina; Singh, Satwinder; Navik, Umashanker; Kumar, Shashank; Mastana, Sarabjit Singh; Reddy, Arubala P.; Reddy, P. Hemachandra; Bhatti, Jasvinder Singh
    Mitochondria are the organelles that generate energy for the cells and act as biosynthetic and bioenergetic factories, vital for normal cell functioning and human health. Mitochondrial bioenergetics is considered an important measure to assess the pathogenesis of various diseases. Dysfunctional mitochondria affect or cause several conditions involving the most energy-intensive organs, including the brain, muscles, heart, and liver. This dysfunction may be attributed to an alteration in mitochondrial enzymes, increased oxidative stress, impairment of electron transport chain and oxidative phosphorylation, or mutations in mitochondrial DNA that leads to the pathophysiology of various pathological conditions, including neurological and metabolic disorders. The drugs or compounds targeting mitochondria are considered more effective and safer for treating these diseases. In this review, we make an effort to concise the available literature on mitochondrial bioenergetics in various conditions and the therapeutic potential of various drugs/compounds targeting mitochondrial bioenergetics in metabolic and neurodegenerative diseases. � 2022 Chang Gung University