Identification of ambulance in traffic videos using image processing techniques
Color space thresholding; Multilevel thresholding; Optical Character Recognition (OCR); Region matching for text detection
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.
Journal of Advanced Research in Dynamical and Control Systems, Vol-10, pp. 213-224.
Institute of Advanced Scientific Research, Inc.
Karthik Raj P.R., Department of Computer Science, Christ University, Bengaluru, India; Praveen Kumar P.U., Department of Computer Science, Christ University, Bengaluru, India
Identification of broken characters in degraded documents
Chain code; Heuristic information; Horizontal projection profile; Mean-based thresholding; OCR; Vertical projection profile
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.
International Journal of Intelligent Engineering and Systems, Vol-11, No. 3, pp. 130-137.
https://doi.org/10.22266/IJIES2018.0630.14 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050968301&doi=10.22266%2fIJIES2018.0630.14&partnerID=40&md5=aed79443139d4376c917bc45e98201f7
Ramalingam K., Department of Computer Science, Christ University, Hosur Road, Bengaluru, Karnataka, India; Bhojan R., Department of Computer Science, Christ University, Hosur Road, Bengaluru, Karnataka, India
Identification of Consumer Buying Patterns using KNN in E-Commerce Applications
buying behavior; Consumer behavior analysis; E- Commerce applications; K-Nearest Neighbor; Machine Learning
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.
Yasin W.; Harimoorthy K.; Vetriveeran D.
Proceedings - 2023 3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023, pp. 187-192.
Institute of Electrical and Electronics Engineers Inc.
Yasin W., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Karnataka, Bangalore, India; Harimoorthy K., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Karnataka, Bangalore, India; Vetriveeran D., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Karnataka, Bangalore, India
Identification of coronary artery stenosis based on hybrid segmentation and feature fusion
Coronary artery disease; Keyframe extraction; stenosis; stenosis detection; X-ray angiography
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.
All Open Access; Gold Open Access; Green Open Access
Kavipriya K., Department of Computer Science, Christ Deemed to be University, Bangalore, India; Hiremath M., Department of Computer Science, Christ Deemed to be University, Bangalore, India
Identification of Cyberbullying and Finding Target User's Intention on Public Forums
bag of words; cyberbullying; feature extraction; sentiment analysis; SVM
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.
Krishnasamy L.; Kiruthika M.; Manjunatha Swamy C.; Vigneshwaran T.; Sharon Roji Priya C.; Vijay Anand D.
Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, pp. 2152-2156.
Krishnasamy L., School of Engineering and Technology, Department of CSE, Christ University, Bengaluru, India; Kiruthika M., School of Computing, Department of Computational Intelligence, SRM Institute of Science and Technology, Tamilnadu, Kattankulathur, India; Manjunatha Swamy C., School of Engineering and Technology, Department of CSE, Christ University, Bengaluru, India; Vigneshwaran T., School of Engineering and Technology, Department of CSE, Christ University, Bengaluru, India; Sharon Roji Priya C., School of Engineering and Technology, Department of CSE, Christ University, Bengaluru, India; Vijay Anand D., Kongu Engineering College, Department of Information Technology, Erode, India
Identification of Driver Drowsiness Detection using a Regularized Extreme Learning Machine
Convolutional Neural Network (CNN); Extreme Learning Machine (ELM).; Regularized Extreme Learning Machine (RELM)
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.
Mohan R.; Chalasani S.; Suma Christal Mary S.; Chauhan A.; Parte S.A.; Anusuya S.
Proceedings of the 2023 2nd International Conference on Electronics and Renewable Systems, ICEARS 2023, pp. 1233-1238.
Mohan R., Shri Ram Institute of Technology, Ece, Department, Madya Pradesh, Jabalpur, India; Chalasani S., Eluru Vatluru, Department of Computer Science and Engineering, Andhra Pradesh, India; Suma Christal Mary S., Panimalar Engineering College, Department of Information Technology, Tamilnadu, Chennai, India; Chauhan A., Deemed to Be University, Christ, Department of Life Sciences, Karnataka, Bengaluru, India; Parte S.A., Mits Gwalior, Department of Computer Science and Engineering, Madhya Pradesh, India; Anusuya S., K.Ramakrishnan College of Engineering, Department of Electronics and Communication Engineering, Tamilnadu, India
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.
Krishnan S.; Aruna S.K.; Kanagarathinam K.; Venugopal E.
https://doi.org/10.1155/2023/2556066 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85156220016&doi=10.1155%2f2023%2f2556066&partnerID=40&md5=949b253756a33dfb69fa2287bfe9d74a
Krishnan S., Department of EEE, Mahendra Engineering College (Autonomous), Tamil Nadu, Namakkal, India; Aruna S.K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST, Deemed to Be University, Karnataka, Bangalore, India; Kanagarathinam K., Department of Electrical and Electronics Engineering, GMR Institute of Technology, Andhra Pradesh, Rajam, India; Venugopal E., Department of Electronics and Communication Engineering, School of Electrical Engineering and Computing, Adama Science and Technology University, Adama, Ethiopia
Identification of emission-line stars in transition phase from pre-main sequence to main sequence
Be; Methods: Statistical; Stars: Emission-line; Stars: Evolution; Techniques: Photometric
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.
Bhattacharyya S.; Mathew B.; Banerjee G.; Anusha R.; Paul K.T.; Kartha S.S.
Monthly Notices of the Royal Astronomical Society, Vol-507, No. 3, pp. 3660-3671.
https://doi.org/10.1093/mnras/stab2385 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117047692&doi=10.1093%2fmnras%2fstab2385&partnerID=40&md5=cca1db5c8e51b836edb732869aaec8c2
Bhattacharyya S., Department of Physics and Electronics, CHRIST (Deemed to Be University), Hosur Main Road, Bangalore, 560029, India; Mathew B., Department of Physics and Electronics, CHRIST (Deemed to Be University), Hosur Main Road, Bangalore, 560029, India; Banerjee G., Department of Physics and Electronics, CHRIST (Deemed to Be University), Hosur Main Road, Bangalore, 560029, India; Anusha R., Department of Physics and Electronics, CHRIST (Deemed to Be University), Hosur Main Road, Bangalore, 560029, India; Paul K.T., Department of Physics and Electronics, CHRIST (Deemed to Be University), Hosur Main Road, Bangalore, 560029, India; Kartha S.S., Department of Physics and Electronics, CHRIST (Deemed to Be University), Hosur Main Road, Bangalore, 560029, India
Identification of interstitial lung diseases using deep learning
Classification; Deep learning; Medical image analysis; Neural network
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.
International Journal of Electrical and Computer Engineering, Vol-10, No. 6, pp. 6283-6291.
Institute of Advanced Engineering and Science
https://doi.org/10.11591/IJECE.V10I6.PP6283-6291 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092283268&doi=10.11591%2fIJECE.V10I6.PP6283-6291&partnerID=40&md5=8753b56c556f202486500af7553ccf5e
Raju N., Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, Karnataka, 560029, India; Anita H.B., Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, Karnataka, 560029, India; Augustine P., Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, Karnataka, 560029, India
Identification of language in a cross linguistic environment
Cross linguistic; Multilinguistic; Sentimental analysis
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.
Indonesian Journal of Electrical Engineering and Computer Science, Vol-18, No. 1, pp. 544-548.
https://doi.org/10.11591/ijeecs.v18.i1.pp544-548 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075585761&doi=10.11591%2fijeecs.v18.i1.pp544-548&partnerID=40&md5=613c42f6e25c20331cc14cff1470040b
Thomas M., Regional Research Center, Visvesvaraya Technological University, India; Latha C.A., Department of (CSE), AMC Engineering College, Visvesvaraya Technological University, India; Puthussery A., Department of Science and Humanities, CHRIST (Deemed to be University), India
Identification of misconceptions about corona outbreak using trigrams and weighted TF-IDF model
Coronavirus; Count Vectorizer; COVID-19; K-Nearest Neighbour; Logistic Regression; N-Grams; Nae Bayes; Random Forest; Support Vector Machine; TF-IDF
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.
Journal of Advanced Research in Dynamical and Control Systems, Vol-12, No. 5 Special Issue, pp. 524-533.
https://doi.org/10.5373/JARDCS/V12SP5/20201788 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090681658&doi=10.5373%2fJARDCS%2fV12SP5%2f20201788&partnerID=40&md5=a1b09996ac6631dbadf79f79486f3628
Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Krishnan B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India
Identification of negative comments from positive sentences through data analysis
R & python; Scoring; Sentiment analysis; Social media; Word cloud
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.
International Journal of Mechanical Engineering and Technology, Vol-9, No. 3, pp. 644-654.
Kedia M., Department of Computer Science, Christ University, Bengaluru, India; Das D., Department of Computer Science, Christ University, Bengaluru, India
Identification of new classical Ae stars in the Galaxy using LAMOST DR5
circumstellar matter; stars: emission-line, Be; techniques: photometric; techniques: spectroscopic
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.
Anusha R.; Mathew B.; Shridharan B.; Arun R.; Nidhi S.; Banerjee G.; Kartha S.S.; Paul K.T.; Bhattacharyya S.
Monthly Notices of the Royal Astronomical Society, Vol-501, No. 4, pp. 5927-5937.
https://doi.org/10.1093/mnras/staa3964 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100770941&doi=10.1093%2fmnras%2fstaa3964&partnerID=40&md5=f3bda7eb231e75bc34c8cb0edea4af8e
Anusha R., Department of Physics and Electronics, CHRIST (Deemed to Be University), Hosur Main Road, Bangalore, 560029, India; Mathew B., Department of Physics and Electronics, CHRIST (Deemed to Be University), Hosur Main Road, Bangalore, 560029, India; Shridharan B., Department of Physics and Electronics, CHRIST (Deemed to Be University), Hosur Main Road, Bangalore, 560029, India; Arun R., Department of Physics and Electronics, CHRIST (Deemed to Be University), Hosur Main Road, Bangalore, 560029, India; Nidhi S., Department of Physics and Electronics, CHRIST (Deemed to Be University), Hosur Main Road, Bangalore, 560029, India; Banerjee G., Department of Physics and Electronics, CHRIST (Deemed to Be University), Hosur Main Road, Bangalore, 560029, India; Kartha S.S., Department of Physics and Electronics, CHRIST (Deemed to Be University), Hosur Main Road, Bangalore, 560029, India; Paul K.T., Department of Physics and Electronics, CHRIST (Deemed to Be University), Hosur Main Road, Bangalore, 560029, India; Bhattacharyya S., Department of Physics and Electronics, CHRIST (Deemed to Be University), Hosur Main Road, Bangalore, 560029, India
Identification of Phishing URLs Using Machine Learning Models
Classifier; Machine learning; Phishing; Prediction; XGBoost
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.
Vivek M.; Premjith N.; Johnson A.A.; Maurya A.K.; Diana Jeba Jingle I.
Lecture Notes in Networks and Systems, Vol-865, pp. 209-219.
Springer Science and Business Media Deutschland GmbH
Vivek M., Christ (Deemed to be University), Karnataka, Bengaluru, India; Premjith N., Christ (Deemed to be University), Karnataka, Bengaluru, India; Johnson A.A., Christ (Deemed to be University), Karnataka, Bengaluru, India; Maurya A.K., Christ (Deemed to be University), Karnataka, Bengaluru, India; Diana Jeba Jingle I., Christ (Deemed to be University), Karnataka, Bengaluru, India
Identification of potential ZIKV NS2B-NS3 protease inhibitors from Andrographis paniculata: An insilico approach
Andrographis paniculata; Andrographolide; Molecular docking; Zika virus; ZIKV NS2B-NS3 protease
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.
Thirumoorthy G.; Tarachand S.P.; Nagella P.; Veerappa Lakshmaiah V.
Journal of Biomolecular Structure and Dynamics, Vol-40, No. 21, pp. 11203-11215.
ISSN: 7391102; PubMed ID: 34319220; CODEN: JBSDD
Thirumoorthy G., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Tarachand S.P., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Nagella P., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Veerappa Lakshmaiah V., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, India
Identification of Predominant Genes that Causes Autism Using MLP
ASD; Autism; Autism spectrum disorder; Gene selection; MLP
Autism or autism spectrum disorder (ASD) is a developmental disorder comprising a group of psychiatric conditions originating in childhood that involve serious impairment in different areas. This paper aims to detect the principal genes which cause autism. Those genes are identified using a multi-layer perceptron network with sigmoid as an activation function. The multi-layer perceptron model selected sixteen genes through different feature selection techniques and also identified a combination of genes that caused the disease. From the background study, it is observed that CAPS2 and ANKUB1 are the major disease-causing genes but the accuracy of the model is less. The selected 16 genes along with CAPS2 and ANKUB1 produce more accuracy than the existing model which proved 95% prediction rate. The analysis of the proposed model shows that the combination of the predicted genes along with CAPS2 and ANKUB1 will help to identify autism at an early stage. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Smart Innovation, Systems and Technologies, Vol-282, pp. 269-279.
Joseph A., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Nizar Banu P.K., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India
Identification Of Quality Of Tea Leaves By Using Artificial Intelligence Techniques: A Review
This paper summarizes the outcome of the survey carried out for quality identification of a tea leaf and eventually price prediction. Quality identification can allow to categorizing leaf in different grades, which helps the buyer and seller to acquire suitable quality to their need. Price prediction is an important feature, which can bring certainty at price and farmers can be benefitted more for their good quality. Additionally, if the leaf disease is identified at the initial stage that would also allow farmers to timely resolve the concerned issues and save their corps. In the field of agriculture, this has been always a research area to identify and predict the quality of tea leaves. Various artificial intelligence techniques are hot topics in the field of recognition and their effective combination can not only solve the problem but also enhance recognition accuracy. Therefore, there is an imminent need for a detailed survey on compiling techniques used for the identification of different varieties of tea plants. In this research, we aim to propose a review of the various techniques which can be utilized for determining the quality and price prediction. The Survey is hybrid with a combination of different artificial techniques, which is a suitable approach to target effective tea leaf identification. Further for the classification of tea leaf images, various algorithms can be combined as well to obtain better results and different algorithms can be used for feature extraction based on texture extraction, color extraction, and shape extraction. The Electrochemical Society
ECS Transactions, Vol-107, No. 1, pp. 2053-2071.
https://doi.org/10.1149/10701.2053ecst https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130516574&doi=10.1149%2f10701.2053ecst&partnerID=40&md5=cde4ef08312766e6c2b1638015555e9d
Gaba I., Department of Computer Science, Christ (Deemed to be University), Bangalore, India; Ramamurthy B., Department of Computer Science, Christ (Deemed to be University), Bangalore, India
Identification of Student Programming Patterns through Clickstream Data
clustering; computer programming; educational data mining; regression; student programming patterns
In present educational era, teaching programming to the undergraduates is challenging. For an instructor, focusing on each of the aspect of programming like coding language, logical reasoning, debugging errors, troubleshooting code and problem solving is very daunting task. So, educational researchers are identifying ways to easily identify the student's struggles during programming so that timely assistance can be provided. Using programming platforms or software, a lot of programming data is generated in the form of activity logs or clickstream data. Using machine learning along with data analytics over this programming data can reveal programming patterns of students that may help in early interventions. This study focusses on identifying programming patterns of the students through clustering and groups the students into three major categories namely low performers, strugglers, and high scorers. Further, relevant features like test case success, code compile success and failure, finish test etc. that majorly contribute towards the student programming scores are identified through regression analysis. Through this research, educators can early categorize the students based on their programming patterns and provide timely intervention when necessary, ensuring that no student gets left behind in the fast-paced world of programming education. 2024 IEEE.
Proceedings - International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024, pp. 1153-1158.
Gupta A., Bennett University, Scset, Greater Noida, India; Jindal M., Christ (Deemed to Be University), School of Business and Management, Bangalore, India; Goyal A., Bennett University, Scset, Greater Noida, India
Identification of Superclusters and Their Properties in the Sloan Digital Sky Survey Using the WHL Cluster Catalog
Superclusters are the largest massive structures in the cosmic web, on tens to hundreds of megaparsec scales. They are the largest assembly of galaxy clusters in the Universe. Apart from a few detailed studies of such structures, their evolutionary mechanism is still an open question. In order to address and answer the relevant questions, a statistically significant, large catalog of superclusters covering a wide range of redshifts and sky areas is essential. Here, we present a large catalog of 662 superclusters identified using a modified friends-of-friends algorithm applied on the WHL (Wen-Han-Liu) cluster catalog within a redshift range of 0.05 ? z ? 0.42. We name the most massive supercluster at z ? 0.25 as the Einasto Supercluster. We find that the median mass of superclusters is ?5.8 1015 M ? and the median size ?65 Mpc. We find that the supercluster environment slightly affects the growth of clusters. We compare the properties of the observed superclusters with the mock superclusters extracted from the Horizon Run 4 cosmological simulation. The properties of the superclusters in the mocks and observations are in broad agreement. We find that the density contrast of a supercluster is correlated with its maximum extent with a power-law index, ? ? ?2. The phase-space distribution of mock superclusters shows that, on average, ?90% of part of a supercluster has a gravitational influence on its constituents. We also show the mock halos average number density and peculiar velocity profiles in and around the superclusters. 2023. The Author(s). Published by the American Astronomical Society.
Sankhyayan S.; Bagchi J.; Tempel E.; More S.; Einasto M.; Dabhade P.; Raychaudhury S.; Athreya R.; Heini P.
https://doi.org/10.3847/1538-4357/acfaeb https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178261667&doi=10.3847%2f1538-4357%2facfaeb&partnerID=40&md5=a32dec524e130c36f03988cd0cf5c809
Sankhyayan S., Tartu Observatory, University of Tartu, Observatooriumi 1, Tavere, 61602, Estonia; Bagchi J., Department of Physics and Electronics, CHRIST (Deemed to be University, Bengaluru, 560029, India; Tempel E., Tartu Observatory, University of Tartu, Observatooriumi 1, Tavere, 61602, Estonia, Estonian Academy of Sciences, Kohtu 6, Tallinn, 10130, Estonia; More S., The Inter-University Centre for Astronomy and Astrophysics (IUCAA), S.P. Pune University Campus, Post Bag 4, Pune, 411007, India, Kavli Institute for the Physics and Mathematics of the Universe (IPMU), 5-1-5 Kashiwanoha, Kashiwashi, Chiba, 277-8583, Japan; Einasto M., Tartu Observatory, University of Tartu, Observatooriumi 1, Tavere, 61602, Estonia; Dabhade P., Instituto de Astrofica de Canarias, Calle V Ltea, s/n, San Cristal de La Laguna, E-38205, Spain, Universidad de La Laguna (ULL), Departamento de Astrofisica, Tenerife, E-38206, Spain; Raychaudhury S., The Inter-University Centre for Astronomy and Astrophysics (IUCAA), S.P. Pune University Campus, Post Bag 4, Pune, 411007, India, Department of Physics, Ashoka University, Haryana, Sonipat, 131029, India; Athreya R., Indian Institute of Science Education and Research (IISER), Dr. Homi Bhabha Road, Pune, 411008, India; Heini P., Tuorla Observatory, Department of Physics and Astronomy, University of Turku, Vesilinnantie 5, Turku, 20014, Finland
Identification of the Functional Limitation of Marine Loading or Unloading Arm; A Case Study
Emergency Release System (ERS); Marine Loading or Unloading Arm; QCDC (Quick Connect Disconnect system); sprayer
Marine loading or unloading arms are used to transfer product from tanker vessels that often carries products like petroleum or chemicals from or to the tankers. Cochin Port has dedicated Tanker jetties for handling petroleum with Marine Loading Arms installed for safe handling of cargo. However, my studies in Cochin Port Trust have shown that it has a potential threat to tackle while it is taken for the maintenance process. The case study aids in understanding of the working of marine unloading arm installed in the port and to identify the functional or safety limitations of the existing model installed. This case study also proves that a small change in the design can bring about a big change in the safety of the people working with the equipment. The identified parameters have been studied for providing the necessary alterations of the design which could be implemented on the upcoming project of constructing the marine unloading arm in Cochin Port Trust. To support faster and safety loading/unloading requirement these hydraulically operated marine loading arms are fitted with emergency release couplings and emergency release system. Marine Loading Arms are operated by using the hydraulic system. During maintenance procedure while checking the Emergency Release System (ERS) functionality, accidental release of Emergency release coupling can cause fatality. Hence a fool proof design is suggested with an extra locking arrangement. The studies conducted till now and the reviews conducted contributed in the analysis of the development and validation of the design. A design of a locking machanism for preventing the fatality is created and analysed for suggesting it to the industry so that it could be incorperated in the upcoming project of constructing the marine loading and unloading arm. 2023 American Institute of Physics Inc.. All rights reserved.
Thaliath P.B.; Umesh V.; Shivakumar S.; Sasidhar J.; Darshan S.M.; Sajeev V.A.; Prakash A.
AIP Conference Proceedings, Vol-2764, No. 1
https://doi.org/10.1063/5.0161101 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176772389&doi=10.1063%2f5.0161101&partnerID=40&md5=48cbb57385e3e8f744de44c0eafe8dd0
Thaliath P.B., Department of Mechanical and Automobile Engineering, Christ University Kengeri Campus, Karnataka, Bengaluru, 560074, India; Umesh V., Department of Mechanical and Automobile Engineering, Christ University Kengeri Campus, Karnataka, Bengaluru, 560074, India; Shivakumar S., Department of Mechanical and Automobile Engineering, Christ University Kengeri Campus, Karnataka, Bengaluru, 560074, India; Sasidhar J., Department of Mechanical and Automobile Engineering, Christ University Kengeri Campus, Karnataka, Bengaluru, 560074, India; Darshan S.M., Department of Mechanical and Automobile Engineering, Christ University Kengeri Campus, Karnataka, Bengaluru, 560074, India; Sajeev V.A., Cochin Port Trust, willingdon Island Cochin, Kerala, 682009, India; Prakash A., Cochin Port Trust, willingdon Island Cochin, Kerala, 682009, India