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Identification of potential ZIKV NS2B-NS3 protease inhibitors from Andrographis paniculata: An insilico approach
Andrographis paniculata is a widely used medicinal plant for treating a variety of human infections. The plant's bioactives have been shown to have a variety of biological activities in various studies, including potential antiviral, anticancer, and anti-inflammatory effects in a variety of experimental models. The present investigation identifies a potent antiviral compound from the phytochemicals of Andrographis paniculata against Zika virus using computational docking simulation. The ZIKV NS2B-NS3 protease, which is involved in viral replication, has been considered as a promising target for Zika virus drug development. The bioactives from Andrographis paniculata, along with standard drugs as control were screened for their binding energy using AutoDock 4.2 against the viral protein. Based on the higher binding affinity the phytocompounds Bisandrographolide A (-11.7), Andrographolide (-10.2) and Andrographiside (-9.7) have convenient interactions at the binding site of target protein (ZIKV NS2B-NS3 protease) in comparison with the control drug. In addition, using insilico tools, the selected high-scoring molecules were analysed for pharmacological properties such as ADME (Absorption, Distribution, Metabolism, and Excretion profile) and toxicity. Andrographolide was reported to have strong pharmacodynamics properties and target accuracy based on the Lipinski rule and lower binding energy. The selected bioactives showed lower AMES toxicity and has potent antiviral activity against zika virus targets. Further, MD simulation studies validated Bisandrographolide A & Andrographolide as a potential hit compound by exhibiting good binding with the target protein. The compounds exhibited good hydrogen bonds with ZIKV NS2B-NS3 protease. As a result, bioactives from the medicinal plant Andrographis paniculata can be studied in vitro and in vivo to develop an antiviral phytopharmaceutical for the successful treatment of zika virus. Communicated by Ramaswamy H. Sarma. 2021 Informa UK Limited, trading as Taylor & Francis Group. -
Identification of Phishing URLs Using Machine Learning Models
In this study, we provide a machine learning-based method for identifying phishing URLs. Sixteen features, including Have IP, Have At, URL Length, URL Depth, Non-standard double slash, HTTPS domain, Shortened URL, Hyphen Count, DNS Record, Domain age, Domain active, iFrame, Mouse Over, Right click, Web Forwards, and Label, were extracted from the 600,000 URLs we gathered as a dataset of legitimate and phishing URLs. We then used this dataset to train a variety of machine learning models. These included standalone models such Naive Bayes, Logistic Regression, Decision Trees, and K-Nearest Neighbors (KNN). We also used ensemble models likeHard Voting, XGBoost, Random Forests, and AdaBoost. Finally, we used deep learning models such as Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN).On evaluation of performance metrics like accuracy, precision, recall, train time and prediction time it was found that XGBoost provides the best performance across all categories. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Identification of new classical Ae stars in the Galaxy using LAMOST DR5
We report the first systematic study to identify and characterize a sample of classical Ae stars in the Galaxy. The spectra of these stars were retrieved from the A-star catalogue using the Large sky Area Multi-Object fibre Spectroscopic Telescope (LAMOST) survey. We identified the emission-line stars in this catalogue from which 159 are confirmed as classical Ae stars. This increases the sample of known classical Ae stars by about nine times from the previously identified 21 stars. The evolutionary phase of classical Ae stars in this study is confirmed from the relatively small mid- and far-infrared excess and from their location in the optical colour-magnitude diagram. We estimated the spectral type using MILES spectral templates and identified classical Ae stars beyond A3, for the first time. The prominent emission lines in the spectra within the wavelength range 3700-9000 are identified and compared with the features present in classical Be stars. The H ? emission strength of the stars in our sample show a steady decrease from late-B type to Ae stars, suggesting that the disc size may be dependent on the spectral type. Interestingly, we noticed emission lines of Fe ii, O i, and Paschen series in the spectrum of some classical Ae stars. These lines are supposed to fade out by late B-type and should not be present in Ae stars. Further studies, including spectra with better resolution, is needed to correlate these results with the rotation rates of classical Ae stars. 2021 2020 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Identification of negative comments from positive sentences through data analysis
Social media has become a part, where people say what they think. It has made remarkable condition for individuals to impart their thoughts to the world. More consumers are writing their reviews which help people to make decisions about the quality and whether they should purchase the product. With respect to distinguishing perspectives out of this immense pool of conclusions, it turns into a laborious task and doing it physically is in every practical sense impossible. When we want to purchase things the best way to choose the finest product is to rely upon the opinions of others who already purchased those items. Sentiment analysis is utilized to choose whether the author's view is positive, negative, or neutral towards a specific item. This paper provides a review of apple mobile phone where we find polarity of a product based on scoring. We also worked on identifying negative comments in a positive sentence. We found the count of different polarity of words from overall positive feedbacks and stored the negative words so that we can identify which feature of the product is not acceptable and should be work with. We are representing our final result using wordcloud where we can detect which features has flaws. IAEME Publication. -
Identification of misconceptions about corona outbreak using trigrams and weighted TF-IDF model
Misconceptions of a particular issue like health, diseases, politics, government policies, epidemics and pandemics have been a social issue for a number of years, particularly after the advent of social media, and often spread faster than true truth. The engagement with social media like Twitter being one of the most prominent news outlets continuing is a major source of information today, particularly the information distributed around the network. In this paper, the efficacy of Misconception Detection System was tested on Corona Pandemic Dataset extracted from Twitter posts. A Trigram and a weighted TF-IDF Model followed by a supervised classifier were used for categorizing the dataset into two classes: one with misconceptions about COVID-19 virus and the other comprising correct and authenticated information. Trigrams were more reliable as the functional words related to coronavirus appeared more frequently in the corpus created. The proposed system using a combination of trigrams and weighted TF-IDF gave relevant and a normalized score leading to an efficient creation of vector space model and this has yielded good performance results when compared with traditional approaches using Bag of Words and Count Vectorizer technique where the vector space model was created only through word count. 2020, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Identification of language in a cross linguistic environment
World has become very small due to software internationationalism. Applications of machine translations are increasing day by day. Using multiple languages in the social media text is a developing trend. Availability of fonts in the native language enhanced the usage of native text in internet communications. Usage of transliterations of language has become quite common. In Indian scenario current generations are familiar to talk in native language but not to read and write in the native language, hence they started using English representation of native language in textual messages. This paper describes the identification of the transliterated text in cross lingual environment. In this paper a Neural network model identifies the prominent language in the text and hence the same can be used to identify the meaning of the text in the concerned language. The model is based upon Recurrent Neural Networks that found to be the most efficient in machine translations. Language identification can serve as a base for many applications in multi linguistic environment. Currently the South Indian Languages Malayalam, Tamil are identified from given text. An algorithmic approach of Stop words-based model is depicted in this paper. Model can be also enhanced to address all the Indian Languages that are in use. Copyright 2020 Institute of Advanced Engineering and Science. All rights reserved. -
Identification of interstitial lung diseases using deep learning
The advanced medical imaging provides various advantages to both the patients and the healthcare providers. Medical Imaging truly helps the doctor to determine the inconveniences in a human body and empowers them to make better choices. Deep learning has an important role in the medical field especially for medical image analysis today. It is an advanced technique in the machine learning concept which can be used to get efficient output than using any other previous techniques. In the anticipated work deep learning is used to find the presence of interstitial lung diseases (ILD) by analyzing high-resolution computed tomography (HRCT) images and identifying the ILD category. The efficiency of the diagnosis of ILD through clinical history is less than 20%. Currently, an open chest biopsy is the best way of confirming the presence of ILD. HRCT images can be used effectively to avoid open chest biopsy and improve accuracy. In this proposed work multi-label classification is done for 17 different categories of ILD. The average accuracy of 95% is obtained by extracting features with the help of a convolutional neural network (CNN) architecture called SmallerVGGNet. 2020 Institute of Advanced Engineering and Science. All rights reserved. -
Identification of emission-line stars in transition phase from pre-main sequence to main sequence
Pre-main-sequence (PMS) stars evolve into main-sequence (MS) phase over a period of time. Interestingly, we found a scarcity of studies in existing literature that examine and attempt to better understand the stars in PMS to MS transition phase. The purpose of this study is to detect such rare stars, which we named as 'transition phase' (TP) candidates-stars evolving from the PMS to the MS phase. We identified 98 TP candidates using photometric analysis of a sample of 2167 classical Be (CBe) and 225 Herbig Ae/Be (HAeBe) stars. This identification is done by analysing the near-and mid-infrared excess and their location in the optical colour-magnitude diagram. The age and mass of 58 of these TP candidates are determined to be between 0.1-5 Myr and 2-10.5 M?, respectively. The TP candidates are found to possess rotational velocity and colour excess values in between CBe and HAeBe stars, which is reconfirmed by generating a set of synthetic samples using the machine learning approach. 2021 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Identification of Dry Bean Varieties Based on Multiple Attributes Using CatBoost Machine Learning Algorithm
Dry beans are the most widely grown edible legume crop worldwide, with high genetic diversity. Crop production is strongly influenced by seed quality. So, seed classification is important for both marketing and production because it helps build sustainable farming systems. The major contribution of this research is to develop a multiclass classification model using machine learning (ML) algorithms to classify the seven varieties of dry beans. The balanced dataset was created using the random undersampling method to avoid classification bias of ML algorithms towards the majority group caused by the unbalanced multiclass dataset. The dataset from the UCI ML repository is utilised for developing the multiclass classification model, and the dataset includes the features of seven distinct varieties of dried beans. To address the skewness of the dataset, a Box-Cox transformation (BCT) was performed on the dataset's attributes. The 22 ML classification algorithms have been applied to the balanced and preprocessed dataset to identify the best ML algorithm. The ML algorithm results have been validated with a 10-fold cross-validation approach, and during validation, the CatBoost ML algorithm achieved the highest overall mean accuracy of 93.8 percent, with a range of 92.05 percent to 95.35 percent. 2023 S. Krishnan et al. -
Identification of Driver Drowsiness Detection using a Regularized Extreme Learning Machine
In the field of accident avoidance systems, figuring out how to keep drivers from getting sleepy is a major challenge. The only way to prevent dozing off behind the wheel is to have a system in place that can accurately detect when a driver's attention has drifted and then alert and revive them. This paper presents a method for detection that makes use of image processing software to examine video camera stills of the driver's face. Driver inattention is measured by how much the eyes are open or closed. This paper introduces Regularized Extreme Learning Machine, a novel approach based on the structural risk reduction principle and weighted least squares, which is applied following preprocessing, binarization, and noise removal. Generalization performance was significantly improved in most cases using the proposed algorithm without requiring additional training time. This approach outperforms both the CNN and ELM models, with an accuracy of around 99% being achieved. 2023 IEEE. -
Identification of Cyberbullying and Finding Target User's Intention on Public Forums
Numerous cybercriminals are active in the online realm, carrying out cyber-crimes according to predetermined and preplanned agendas. Cyberbullying, which was formerly limited to physical limits, has now expanded online as a result of technology advancements. One type of cyberbullying is denigration or insult. The cyberbullying cases are in exponential rise in social media as per the reports of Computer Emergency Team by Sri Lanka. Insulting words are changeable in dynamic and the same terminology may have numerous meanings depending on the context. Bullying cannot be defined just because a statement comprises such a term. As a result, when classifying comments, standard keyword detecting approaches are insufficient. Other languages also may have dealt with this issue by utilizing lexical databases like WordNet, which might give synonyms as well as homonyms for words. Because no adequate lexical database mainly for the English language has been built, recognizing a word like bullying is difficult. As a result, employed rules to solve the problem. Facebook comments containing profanity were gathered, outliers were eliminated, and the remaining messages were pre-processed. Five feature extraction rules were employed to assess insult in the text. Following that, used the Support Vector Machine (SVM) technique. Using an F1-score of 85%, the findings demonstrate that when compared to existing works, SVM performs better. The focus on English language cyberbully identification, which has never been addressed earlier, distinguishes this study. 2023 IEEE. -
Identification of coronary artery stenosis based on hybrid segmentation and feature fusion
Coronary artery disease has been the utmost mutual heart disease in the past decades. Various research is going on to prevent this disease. Obstructive CAD occurs when one or more of the coronary arteries which supply blood to myocardium are narrowed owing to plaque build-up on the arteries inner walls, causing stenosis. The fundamental task required for the interpretation of coronary angiography is identification and quantification of severity of stenosis within the coronary circulation. Medical experts use X-ray coronary angiography to identify blood vessel/artery stenosis. Due to the artefact, the image has less clarity and it will be challenging for the medical expert to find the stenosis in the coronary artery. The solution to the problem a computational framework is proposed to segment the artery and spot the location of stenosis in the artery. Here the author presented an automatic method to detect stenosis from the X-ray angiogram image. A unified Computational method of Jerman, Level-set, fine-tuning the artery structure, is developed to extract the segmented artery features and detect the arterys stenosis. The current experimental outcomes illustrate that this computational method achieves average specificity, sensitivity, Accuracy, precision and F-scores of 95%, 97.5%, 98%, 97.5% and 97.5%, respectively. 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Identification of Consumer Buying Patterns using KNN in E-Commerce Applications
In recent days, with the advancement of technologies, people use electronic medium to carry out their businesses. E-commerce is a process of allowing people to buy and sell products online using electronic medium. E-commerce has a wide range of customer base as well. The data generated through transaction helps the enterprises to develop the marketing strategy. The growth of this e-commerce application depends on several factors. Some of the factors are follows 1) Customer demand, 2) Analyzing buying pattern of the users, 3) Customer retention, 4) dynamic pricing etc. It is very difficult to analyze the buying pattern of customers as there is a wide range of customer base in the online platform. To overcome this problem, this research study discusses about the challenges and issues in e-commerce applications, also identifies and analyses the buying patterns of customer using various machine learning techniques. From the implementation it is identified that, KNN algorithm performed well while comparing it with various other machine learning algorithms. Performances of these algorithms have been analyzed using various matrices. For analyzing, the model is tested using e-commerce dataset (Amazon dataset downloaded from Kaggle.com). From the analysis it found that KNN algorithm computes and predicts better compared to other machine learning algorithms either Nae Bayes, or Random Forest, or Logistic Regression etc. 2023 IEEE. -
Identification of broken characters in degraded documents
Optical Character Recognition (OCR) deals with the recognition of characters in a text document. Steps like Preprocessing, Segmentation and Recognition are embedded in the OCR machine. When a document is scanned it will be taken into OCR and will recognize the characters. But noisy scanning of documents, low-quality printed documents and thresholding error leads to the generation of broken characters. When these documents are given as inputs into OCR, the recognition becomes a tedious process since the broken characters are misunderstood by the OCR machine. So the broken characters have to be identified and segmented separately. This work aims to enhance the degraded documents with broken characters using image processing techniques. For identifying or recognizing the broken character from the image various techniques like vertical projection profile, horizontal projection profile, chain code, mean based thresholding are used. The lines from the document are separated using line segmentation. Separate characters are extracted using Vertical Projection Profile and Horizontal Projection Profile. The character is identified using chain coding. The broken characters are found from them using Mean-based Thresholding and is merged using Heuristic information. The proposed method achieves an accuracy of 92.88% and also performs well for color image documents as well as black and white image documents also because of the effective preprocessing. 2018 Intelligent Network and Systems Society. -
Identification of ambulance in traffic videos using image processing techniques
Traffic congestion is one of the commonly faced problems in the Urban areas. To eliminate these problems, there is a need for an Intelligent Transportation System (ITS) that proposes an efficient method to reduce the traffic problems and introduces the priority system for the Emergency vehicles. This paper proposes two frameworks that identify ambulance in traffic videos based on features such as color, siren and text. Frames are extracted from videos to employ methods like multilevel thresholding and region matching. Multilevel thresholding is used for segmenting the ambulance from the other occurring vehicles based on the white color. Region matching for text detection method is employed in the segmented vehicle. Color space thresholding is used for the detection of siren based on red or blue color feature. Optical character recognition (OCR) is employed to extract the text in the frame. Word comparison and Matching detects the ambulance text based on the outcome of OCR. The performance of Framework 1 and Framework 2 are evaluated based on Word accuracy and from the experimental results it is observed that Framework 2 is better from 75% word accuracy. 2018, Institute of Advanced Scientific Research, Inc. All Rights reserved. -
Identification and structure-activity relationship studies of small molecule inhibitors of the human cathepsin D
Cathepsin D, an aspartyl protease, is an attractive therapeutic target for various diseases, primarily cancer and osteoarthritis. However, despite several small molecule cathepsin D inhibitors being developed, that are highly potent, most of them show poor microsomal stability, which in turn limits their clinical translation. Herein, we describe the design, optimization and evaluation of a series of novel non-peptidic acylguanidine based small molecule inhibitors of cathepsin D. Optimization of our hit compound 1a (IC50 = 29 nM) led to the highly potent mono sulphonamide analogue 4b (IC50 = 4 nM), however with poor microsomal stability (HLM: 177 and MLM: 177 ?l/min/mg). To further improve the microsomal stability while retaining the potency, we carried out an extensive structureactivity relationship screen which led to the identification of our optimised lead 24e (IC50 = 45 nM), with an improved microsomal stability (HLM: 59.1 and MLM: 86.8 ?l/min/mg). Our efforts reveal that 24e could be a good starting point or potential candidate for further preclinical studies against diseases where Cathepsin D plays an important role. 2020 Elsevier Ltd -
Identification and standardization of counsellor competencies for masters level counsellor education programs in India
Counselling psychology programs in India have been criticized for being ‘poor replicas of concepts that have originated in western cultures’. The lack of Indian models has been quoted as a drawback indicating that trainees are not necessarily competent to provide effective counselling services. The present study aimed at identifying and standardizing competenciesfor post graduate counsellor training in India based on local needs.The study employed a mixed methods design with four phases. In the first phase, a list of key occupational tasks were drawn up through a systematic review of literature and interviews with three expert practitioners. The second phase was the development of a counsellor competency list which outlined the various competencies required to fulfil the key occupational tasks determined in the previous stage. Seventy one competencies were identified and the list was then given for expert validation. In the third phase, the competency list was given to 75 practicing counsellors across India who rated the competencies on a 5-point likert scale, based on its importance for post graduate counsellor trainees. In the final stage the prioritized competencies were analyzed using a concept development approach to identify core competencies required for master level counselling psychology trainees. The resulting core competencies were three foundational competency domains which included ethical practice, personal and professional development and cultural sensitivity. -
Identification and standardization of counsellor competencies for master level counsellor education programs in india
Counselling psychology programs in India have been criticized for being poor replicas of newlineconcepts that have originated in western cultures . The lack of Indian models has been quoted as a drawback indicating that trainees are not necessarily competent to provide effective counselling services. The present study aimed at identifying and standardizing competencies for post graduate counsellor training in India based on local needs.The study employed a mixed methods design with four phases. In the first phase, a list of key occupational tasks were drawn up through a systematic review of literature and interviews with three expert practitioners. The second phase was the development of a counsellor competency list which outlined the various competencies required to fulfil the key occupational tasks determined in the previous stage. Seventy one competencies were identified and the list was then given for newlineexpert validation. In the third phase, the competency list was given to 75 practicing newlinecounsellors across India who rated the competencies on a 5-point likert scale, based on its importance for post graduate counsellor trainees. In the final stage the prioritized competencies were analyzed using a concept development approach to identify core competencies required for master level counselling psychology trainees. The resulting core competencies were three foundational competency domains which included ethical practice, personal and professional development and cultural sensitivity. There were also three newlinefunctional competency domains which included the counselling process, the supervision newlineprocess and the promotion of counselling in India. Specific competencies under each domain were also listed along with behavioral indicators for the same. Thus the core competencies that counsellor trainees must develop to provide an effective service were identified and this has implications for training and practice. -
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Idealised Bilinear Moment-Curvature Curves of Reinforced Masonry (RM) Walls
In this paper, an analytical investigation of the axial loadflexural strength interaction of reinforced masonry walls is carried. The curvature ductility of masonry walls is evaluated for walls with different modes of reinforcement configurations under different levels of axial loads. An analytical expression for evaluating the curvature ductility of masonry walls at varying axial loads is proposed in this paper. Value of curvature ductility obtained from the proposed expression is compared with existing methods. Results indicate the proposed model can be used to determine the ductility of reinforced masonry walls. 2020, Springer Nature Singapore Pte Ltd.