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Root cause analysis of COVID-19 cases by enhanced text mining process
The main focus of this research is to find the reasons behind the fresh cases of COVID-19 from the publics perception for data specific to India. The analysis is done using machine learning approaches and validating the inferences with medical professionals. The data processing and analysis is accomplished in three steps. First, the dimensionality of the vector space model (VSM) is reduced with improvised feature engineering (FE) process by using a weighted term frequency-inverse document frequency (TF-IDF) and forward scan trigrams (FST) followed by removal of weak features using feature hashing technique. In the second step, an enhanced K-means clustering algorithm is used for grouping, based on the public posts from Twitter. In the last step, latent dirichlet allocation (LDA) is applied for discovering the trigram topics relevant to the reasons behind the increase of fresh COVID-19 cases. The enhanced K-means clustering improved Dunn index value by 18.11% when compared with the traditional K-means method. By incorporating improvised two-step FE process, LDA model improved by 14% in terms of coherence score and by 19% and 15% when compared with latent semantic analysis (LSA) and hierarchical dirichlet process (HDP) respectively thereby resulting in 14 root causes for spike in the disease. 2022 Institute of Advanced Engineering and Science. All rights reserved. -
A two-stepped feature engineering process for topic modeling using batchwise LDA with stochastic variational inference model
Online ratings and customer feedback on hotel booking websites support the decision-making process of the customer as the reviews provide a deeper understanding about all aspects of a hotel. Consequently, review and rating analyses are of great interest to consumers and hotel owners for the hotel related social media services. The key challenge, however, is to make the wide variety of information accessible in a simple, fast and relevant way and the solution is Topic Modelling and Opinion Mining. Common approaches like Latent Semantic Analysis (LSA) and Hierarchical Dirichlet Process (HDP) have order affects. If the input dataset is shuffled then different topics are generated leading to misleading results. To overcome this, a two-stepped feature engineering process is used: first step is to use a TF-IDF with modified trigrams calculation followed by the second step in removing weak features from the corpus thereby reducing the dimensionality of the Vector Space Model (SVM) for efficient Topic Modeling and sentiment analysis of the considered corpus. Sentiment score is calculated using VADER tool and Topic Modeling is done with Batch Wise Latent Dirichlet Allocation (LDA) using Stochastic Variational Inference (SVI) model. The modified trigrams included calculation of probabilities of words not only in the backward direction but also the probability calculation of the next two words of the target word thereby retaining its context information. The proposed method using Batchwise LDA with SVI along with two-stepped feature engineering process considerably improved its performance when compared to LSA and HDP models due to the fact of identifying hidden and relevant topics in terms of their optimized posterior distribution in hotel reviews dataset. The Batchwise LDA with SVI improved its performance by 3% in terms of its coherence values by using two-stepped feature engineering process and by 9% and 4% increase when compared with LSA and HDP models respectively. 2020, Intelligent Network and Systems Society. -
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. -
Self-supervised learning based anomaly detection in online social media
Online Social Media (OSM) produce enormous data related to the human behaviours based on their interactions. One such data is the opinions expressed and posted for any specific issue addressed in the OSM. Majority of the opinions posted would be categorized as positive, negative and neutral. The lighter group's opinions are termed anomalous as it is not conforming the regular opinions posted by other users. Though, lot of conventional classification and clustering based learning algorithms works well under supervised and un-supervised environment, due to the inherent ambiguity in the tweeted data, anomaly detection poses a bigger challenge in text mining. Though the data is un-supervised, for the learning purpose it is treated as Supervised Learning by assigning class labels for the training data. This paper attempts to give an insight into various anomalies of OSM and identify behavioural anomalies for a Twitter Dataset on user's opinions on demonetization policy in India. Through Self-Supervised learning, it is observed that 86% of the user's opinions did agree to the demonetization policy and the remaining have posted negative opinions for the policy implemented. 2020, Intelligent Network and Systems Society. -
A facile, green synthesis of carbon quantum dots from Polyalthia longifolia and its application for the selective detection of cadmium /
Dyes and Pigments, Vol.210, ISSN No: 0143-7208.
Carbon quantum dots (CQDs) has received world-wide recognition for their outstanding physicochemical properties that have the ability to substitute the semiconductor quantum dots. Herein, we have developed a strategy to determine the presence of Cd<sup>2+</sup> using CQDs as a fluorescence probe. The CQDs were synthesized from the leaves of <em>Polyalthia longifolia</em> (a natural source) through a one-step hydrothermal method. The CQDs obtained from <em>Polyalthia longifolia</em> (p-CQDs) was characterized using XRD, TEM, FTIR, Raman Spectroscopy, XPS Studies, UV–Visible spectroscopy and PL Spectroscopy. The p-CQDs displayed bright red fluorescence under the UV light, with good water solubility, and appreciable photostability and a quantum yield of 22%. The p-CQDs had a quasi-spherical morphology with an average particle size of 3.33 nm. -
Investigation of corrosion behavior of Cenosphere reinforced iron based composite coatings
In the present study cenopshere was reinforced with FeCrNiC (Metco 42C) as matrix material and prepared four different feedstock powders such as FeCrNiC+0%Cenosphere, FeCrNiC+5%Cenosphere, FeCrNiC+10%Cenosphere and FeCrNiC+15%Cenosphere were coated by plasma spray technique on T22 substrate. Evaluation of the substrate and coatings potential under salt spray test was performed. Dense fog of 5% NaCl salt water was used to create a corrosive atmosphere within the chamber. The salt water's pH was kept constant at 6.57. The materials that underwent corrosion were examined using X-ray diffraction (XRD), and scanning electron microscopy (SEM). The FeCrNiC+15%Cenosphere and FeCrNiC+10%Cenosphere coatings exhibited reduced weight loss during a 168-h corrosion test compared to the FeCrNiC+5%Cenosphere, FeCrNiC coatings, and substrate. The excellent chemical stability and corrosion resistance of Cr23C6, SiO2, NiO, and Cr2O particles contribute to gradually avoid the formation of red rust on Fe-based coated samples with exposure approaches to 52 and 130 h. 2024 The Authors -
Sibling Bereavement Among Young Indian Adults
This qualitative study explores the bereavement experiences of 12 surviving siblings in India, focusing on familial, societal, and cultural influences. Six themes emerged: The Demanding Familial Role, Isolation That Accompanies the Grief, Damaging Impact of Society, Positive Role of Friends and Family, Support Systems, and Continuing Bonds. Participants often felt the burden of supporting their parents, leading to personal grief suppression and isolation, exacerbated by societal stigmas. Conversely, empathetic friends, supportive extended family, and professional resources like therapy provided crucial coping mechanisms. Continuing bonds with the deceased offered comfort and connection. The study highlights the need for comprehensive support systems tailored to cultural and societal contexts. It emphasizes the importance of public awareness and education to foster a supportive response to bereavement. Further research with larger, more diverse samples is recommended. The Author(s) 2024. -
The complexities of home and belonging in the Gulf-Malayalee experience: a close reading of Salim Ahameds Pathemari (2015)
This paper explores the interaction between home, belonging, and migration by closely reading Salim Ahameds 2015 Malayalam film, Pathemari. The paper briefly traces migration history from Kerala to the Gulf and its impact on Keralas housing boom, influencing its socioeconomic and cultural landscape. Through this, the paper examines how Gulf Malayalees navigate the multifaceted and contested concept of home despite being physically and emotionally displacedthe paradox of belonging and unbelonging, in their attempts to secure a material home while working as blue-collared Malayalee migrants in the Gulf. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Diverse Morphologies of Nb2O5 Nanomaterials: A Comparative Study for the Growth Optimization of Elongated Spiky Nb2O5 and Carbon Nanosphere Composite
Controlled synthesis and design of nanomaterials with intricate morphologies and active phases offer new prospects in harnessing their unique chemical and physical properties for various applications. Herein, a facile and efficient hydrothermal approach is reported for obtaining various complex Nb2O5 nanostructures, including thin sheets, thick flakes, spiky and elongated spiky sea urchin morphologies using urotropin as a growth-directing and hydrolyzing agent in various mixed and pure solvents. The detailed structural and chemical composition, surface morphology and crystallinity of as-synthesized Nb2O5 nanostructures are presented. The urotropin concentration, reaction time, and water-ethanol solvent mixture play a critical role for obtaining the elongated spiky sea urchin morphologies. The spiky Nb2O5 structures show a pseudohexagonal phase with less urotropin content, while thin sheets are obtained with a higher urotropin concentration and are primarily amorphous. These structures undergo transformation in their crystal phase and morphologies during calcination at higher temperatures revealing the active role of urotropin in stabilizing them. A composite of spiky sea urchin Nb2O5-carbon nanospheres (suNb2O5-CNS) is achieved by in-situ growth of Nb2O5 in the presence of CNS without compromising on morphology, phase, and crystallinity. suNb2O5-CNS composite is shown to possess higher charge storage capacity compared to its constituents for supercapacitor applications. 2023 Wiley-VCH GmbH. -
A Simple and Efficient Ligand-Free Copper-Catalyzed C-N Bond Formation of Aryl (Hetero) Halides and N-Heteroaryl Amines
In this protocol, we report a simple, inexpensive, and user-friendly conventional method for C-N cross coupling between aryl/heteroaryl halides and hetero aryl amines using copper iodide as a catalyst in DMSO as a solvent to prepare pyrimidines and pyrazines derivatives. The reaction conditions were optimized by screening in various copper catalysts and bases. The substrate scope of the reaction was also carried out to prepare novel functionalized N-arylated compounds in good yields. 2021 Taylor & Francis Group, LLC. -
Metal and Ligand-Free Approach Towards the Efficient One-Pot Synthesis of Dipyridopyrimidinimine Derivatives
We report a facile, expeditious, user-friendly, and convenient metal-free synthesis employing base catalysis in a one-pot procedure to construct 11H-dipyrido[1,2-a : 3?,2?-d]pyrimidin-11-imine derivatives. This protocol involves a domino process leading to the formation of double C?N bonds utilising KOtBu as the base and DMAc as the superior solvent at 25 C for 2 h. The versatility of this methodology was demonstrated by its successful application to substrates with both electron-withdrawing and electron-donating functional groups, yielding novel functionalized stable 11H-dipyrido[1,2-a : 3?,2?-d]pyrimidin-11-imine derivatives in good to excellent yields. Additionally, we have discussed a plausible reaction pathway for the synthesis. 2024 Wiley-VCH GmbH. -
An Efficient Copper-Catalyzed Regioselective One-Pot Synthesis of Pyrido[1,2-a]benzimidazole and Its Derivatives
A facile and effectual regioselective one-pot synthesis protocol has been developed for the construction of pyrido[1,2-a]benzimidazole and its derivatives using Copper(I) bromide as the catalyst, 1,10-phenanthroline as ligand, and K3PO4 (Tripotassium phosphate) as the base in Dimethyl sulfoxide as solvent at 110 C for 12 h. The reaction conditions were optimized by screening various copper catalysts, ligands, solvents, and bases. The substrate scope of the reaction was also carried out with electron-withdrawing and donating functional groups to prepare novel functionalized regioselective benzimidazole compounds in good to excellent yields. All the isolated compounds were characterized by 1H, 13C, and 19F NMR. 2023 Wiley-VCH GmbH. -
A Quantum-Inspired Self-Supervised Network model for automatic segmentation of brain MR images
The classical self-supervised neural network architectures suffer from slow convergence problem and incorporation of quantum computing in classical self-supervised networks is a potential solution towards it. In this article, a fully self-supervised novel quantum-inspired neural network model referred to as Quantum-Inspired Self-Supervised Network (QIS-Net) is proposed and tailored for fully automatic segmentation of brain MR images to obviate the challenges faced by deeply supervised Convolutional Neural Network (CNN) architectures. The proposed QIS-Net architecture is composed of three layers of quantum neuron (input, intermediate and output) expressed as qbits. The intermediate and output layers of the QIS-Net architecture are inter-linked through bi-directional propagation of quantum states, wherein the image pixel intensities (quantum bits) are self-organized in between these two layers without any external supervision or training. Quantum observation allows to obtain the true output once the superimposed quantum states interact with the external environment. The proposed self-supervised quantum-inspired network model has been tailored for and tested on Dynamic Susceptibility Contrast (DSC) brain MR images from Nature data sets for detecting complete tumor and reported promising accuracy and reasonable dice similarity scores in comparison with the unsupervised Fuzzy C-Means clustering, self-trained QIBDS Net, Opti-QIBDS Net, deeply supervised U-Net and Fully Convolutional Neural Networks (FCNNs). 2020 Elsevier B.V. -
Qutrit-Inspired Fully Self-Supervised Shallow Quantum Learning Network for Brain Tumor Segmentation
Classical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due to forceful termination. Qubits or bilevel quantum bits often describe quantum neural network models. In this article, a novel self-supervised shallow learning network model exploiting the sophisticated three-level qutrit-inspired quantum information system, referred to as quantum fully self-supervised neural network (QFS-Net), is presented for automated segmentation of brain magnetic resonance (MR) images. The QFS-Net model comprises a trinity of a layered structure of qutrits interconnected through parametric Hadamard gates using an eight-connected second-order neighborhood-based topology. The nonlinear transformation of the qutrit states allows the underlying quantum neural network model to encode the quantum states, thereby enabling a faster self-organized counterpropagation of these states between the layers without supervision. The suggested QFS-Net model is tailored and extensively validated on the Cancer Imaging Archive (TCIA) dataset collected from the Nature repository. The experimental results are also compared with state-of-the-art supervised (U-Net and URes-Net architectures) and the self-supervised QIS-Net model and its classical counterpart. Results shed promising segmented outcomes in detecting tumors in terms of dice similarity and accuracy with minimum human intervention and computational resources. The proposed QFS-Net is also investigated on natural gray-scale images from the Berkeley segmentation dataset and yields promising outcomes in segmentation, thereby demonstrating the robustness of the QFS-Net model. 2012 IEEE. -
Auto-diagnosis of covid-19 using lung ct images with semi-supervised shallow learning network
In the current world pandemic situation, the contagious Novel Coronavirus Disease 2019 (COVID-19) has raised a real threat to human lives owing to infection on lung cells and human respiratory systems. It is a daunting task for the researchers to find suitable infection patterns on lung CT images for automated diagnosis of COVID-19. A novel integrated semi-supervised shallow neural network framework comprising a Parallel Quantum-Inspired Self-supervised Network (PQIS-Net) for automatic segmentation of lung CT images followed by Fully Connected (FC) layers, is proposed in this article. The proposed PQIS-Net model is aimed at providing fully automated segmentation of lung CT slices without incorporating pre-trained convolutional neural network based models. A parallel trinity of layered structure of quantum bits are interconnected using an N -connected second order neighborhood-based topology in the suggested PQIS-Net architecture for segmentation of lung CT slices with wide variations of local intensities. A random patch-based classification on PQIS-Net segmented slices is incorporated at the classification layers of the suggested semi-supervised shallow neural network framework. Intensive experiments have been conducted using three publicly available data sets, one for purely segmentation task and the other two for classification (COVID-19 diagnosis). The experimental outcome on segmentation of CT slices using self-supervised PQIS-Net and the diagnosis efficiency (Accuracy, Precision and AUC) of the integrated semi-supervised shallow framework is found to be promising. The proposed model is also found to be superior than the best state-of-the-art techniques and pre-trained convolutional neural network-based models, specially in COVID-19 and Mycoplasma Pneumonia (MP) screening. 2013 IEEE. -
Prediction of indoor air circulation of residential room with adaptation of solar chimney using numerical technique
With the exponential increase in consumption of electrical power during the summer season by household, there is a great need for households to withhold sustainability. To maintain the temperature of the household a passive heating and cooling system is used i.e. Solar Chimney. Ventilation, through a natural convection process, is gaining a lot of attention to be an alternative technique for mechanical air conditioning ventilation because of its reduced power usage when compared to the external cooling devices used in residential buildings of hot regions. The present study, involve solar chimney of horizontal and vertical designs in comparison with different width and height. The following paper studies the effect of a solar chimney on the indoor thermal behavior using Numerical Technique for a prototype of a residential room. The performance on the ventilation velocity and air temperature operation inside the room with varying air gap width is studied based on multiple numerical analysis solutions. The present study deals with two different architectures of a two dimensional model and results have shown that the ventilation velocity has increased to 0.017626444 kg/s and operative air temperature has been decreased by 7.26C for the vertical model while the horizontal model has shown a mass flow rate of 0.018027636 kg/s and a temperature decrease of 9.15C. The most efficient chimney was found to be model 7 which is horizontal solar chimney 3 with an air gap width of 0.05625m and a height of 0.3175 m, when compared to the other models from model number one to six. BEIESP. -
Art Therapy for Children With Autism Spectrum Disorder in India
The purpose of this study was to examine the effects of art therapy for 9 children with autism spectrum disorder in India using a prepost experimental design with a control group. The Childhood Autism Rating Scale was used to measure symptoms before and after 8 individual art therapy sessions, and changes in the childrens art development was also examined. Analysis of covariance results showed that art therapy was effective and content analysis of the drawings indicated progress seen in the developmental art stages, based on Lowenfeld theory. The positive changes were notable in the participants cognitive, social, and motor skills. 2019, AATA, Inc. 2019. -
Solution Focused vs Problem Focused Questions on Affect and Processing Speed among Individuals with Depression
The present study investigated the effect of solution-focused and problem-focused questions on affect and processing speed in a sample of 60 individuals diagnosed with depression. Participants were equally and randomly assigned to the solution focused question group, problem focused question group, and delayed experimental group. The Beck depression inventory-II was used to assess the severity of depressive symptoms of the participants. The positive and negative affect schedule was used to measure affect. Symbol search and coding were used to measure the processing speed. Solution-focused questions significantly reduced negative affect and improved coding compared to problem-focused questions. Even though there was no significant interaction between the groups in positive affect and symbol search test performance, solution-focused questions caused simple effects in both. Findings imply the scope of solution-focused questions as psychological first aid in intervening depression. Possible long-term effects of solution-focused questions on individuals with depression were discussed. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
In silico analysis of NHP2 membrane protein, a novel vaccine candidate present in the RD7 region of Mycobacterium tuberculosis
Mycobacterium tuberculosis, the etiological agent of tuberculosis, is one of the trickiest pathogens. We have only a few protective shields, like the BCG vaccine against the pathogen, which itself has poor efficacy in preventing adult tuberculosis. Even though different vaccine trials for an alternative vaccine have been conducted, those studies have not shown much promising results. In the current study, advanced computational technology was used to study the potential of a novel hypothetical mycobacterial protein, identified by subtractive hybridization, to be a vaccine candidate. NHP2 (Novel Hypothetical Protein 2), housed in the RD7 region of the clinical strains of M. tuberculosis, was studied for its physical, chemical, immunological and structural properties using different computational tools. PFAM studies and Gene ontology studies depicted NHP2 protein to be functionally active with a possible antibiotic binding domain too. Different computational tools used to assess the toxicity, allergenicity and antigenicity of the protein indicated its antigenic nature. Immune Epitope Database (IEDB) tools were used to study the T and B cell determinants of the protein. The 3D structure of the protein was designed, refined and authenticated using bioinformatics tools. The validated tertiary structure of theprotein was docked against the TLR3 immune receptor to study the binding affinity and docking scores. Molecular dynamic simulation of the protein-protein complex formed were studied. NHP2 was found to activate host immune response against tubercle bacillus and could be explored as a potential vaccine in the fight against tuberculosis. 2023, The Author(s), under exclusive licence to Plant Science and Biodiversity Centre, Slovak Academy of Sciences (SAS), Institute of Zoology, Slovak Academy of Sciences (SAS), Institute of Molecular Biology, Slovak Academy of Sciences (SAS). -
Structural and functional characterization of a hypothetical protein in the RD7 region in clinical isolates of Mycobacterium tuberculosis an in silico approach to candidate vaccines
Background: Mycobacterium tuberculosis has been ravaging humans by inflicting respiratory tuberculosis since centuries. Bacillus Calmette Guerine (BCG) is the only vaccine available for tuberculosis, and it is known to be poorly effective against adult tuberculosis. Proteins belonging to the ESAT-6 family and PE/PPE family show immune responses and are included in different vaccine trials. Herein, we study the functional and structural characterization of a 248 amino acid long putative protein novel hypothetical protein 1 (NHP1) present in the RD7 region of Mycobacterium tuberculosis (identified first by subtractive hybridization in the clinical isolate RGTB123) using bioinformatics tools. Results: Physicochemical properties were studied using Expasy ProtParam and SMS software. We predicted different B-cell and T-cell epitopes by using the immune epitope database (IEDB) and also tested antigenicity, immunogenicity, and allergenicity. Secondary structure of the protein predicted 30% alpha helices, 20% beta strands, and 48% random coils. Tertiary structure of the protein was predicted using the Robetta server using the Mycobacterium smegmatis protein as the putative protein with homology. Structural evaluations were done with Ramachandran plot analysis, ProSA-web, and VERIFY3D, and with GalaxyWEB server, a more stable structure was validated with good stereo chemical properties. Conclusion: The present study of a subtracted genomic locus using various bioinformatics tools indicated good immunological properties of the putative mycobacterial protein, NHP1. Evidence obtained from the analyses of NHP1 using structure prediction tools strongly point to the fact that NHP1 is an ancient protein having flavodoxin folding structure with ATP binding sites. Positive scores were obtained for antigenicity, immunogenicity, and virulence too, implying the possibility of NHP1 to be a potential vaccine candidate. Such computational studies might give clues for developing newer vaccines for tuberculosis, which is the need of the hour. 2022, The Author(s).