Browse Items (11810 total)
Sort by:
-
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 comparative study of text mining algorithms for anomaly detection in online social networks
Text mining is a process by which information and patterns are extracted from textual data. Online Social Networks, which have attracted immense attention in recent years, produces enormous text data related to the human behaviours based on their interactions with each other. This data is intrinsically unstructured and ambiguous in nature. The data involves incorrect spellings and inaccurate grammars leading to lexical, syntactic and semantic ambiguities. This causes wrong analysis and inappropriate pattern identification. Various Text Mining approaches are being used by researchers which can help in Anomaly Detection through Topic Modeling, identification of Trending Topics, Hate Speeches and evolution of the communities in Online Social Networks. In this paper, a comparative analysis of the performance of four classification algorithms, Support Vector Machine (SVM), Rocchio, Decision Trees and K-Nearest Neighbour (KNN) for a Twitter data set is presented. The experimental study revealed that SVM outperforms better than other classifiers, and also classifies the dataset into anomalous and non-anomalous users opinions. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Adaptive artificial bee colony (aabc)-based malignancy pre-diagnosis
Lung cancer is one of the leading causes of death. The survival rate of the patients diagnosed with lung cancer depends on the stage of the detection and the timely prognosis. Hence, early detection of anomalous malignant cells is needed for pre-diagnosis of lung cancer as it plays a major role in the prognosis and treatment. In this work, an innovative pre-diagnosis approach is suggested, wherein the size of the dataset comprising risk factors and symptoms is considerably decreased and optimized by means of an Adaptive Artificial Bee Colony (AABC) algorithm. Subsequently, the optimized dataset is fed to the Feed-Forward Back-Propagation Neural Network (FFBNN) to perform the training task. For the testing, supplementary data is furnished to well-guided FFBNN-AABC to authenticate whether the supplied investigational data is competent to effectively forecast the lung disorder or not. The results obtained show a considerable improvement in the classification performance compared to other approaches like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Twitter Hate Speech Detection using Stacked Weighted Ensemble (SWE) Model
Online Social Media has expanded the freedom of expression in the internet, which has become a disturbing problem if it has an impact on the situation or the interest of a country. Hate speech refers to the use of hostile, abusive or offensive language, directed at a certain group of people who share common property, whether it is their gender, ethnicity or race (i.e. racism), faith and religion. Therefore, auto detection of hate speeches has an increased importance in Online Social Media for filtering any message that has hatred language before posting it to the network. In this paper, a Stacked Weighted Ensemble (SWE) model is proposed for the detection of hate speeches. The model ensembles five standalone classifiers: Linear Regression, Nae Bayes', Random Forest, Hard Voting and Soft Voting. The experimental results on a Twitter dataset has shown an accuracy of 95.54% in binary classification of tweets into hateful speech and an improved performance is noted compared to the standalone classifiers. 2020 IEEE. -
Deducing Water Quality Index (WQI) by Comparative Supervised Machine Learning Regression Techniques for India Region
Water quality is of paramount importance for the wellbeing of the society at large. It plays avery important role in maintaining the health of the living being. Several attributes like biological oxygen demand (BOD), power of hydrogen (pH), dissolved oxygen (DO) content, nitrate content (NC) and so on help to identify the appropriateness of the water to be used for different purposes. In this research study, the focus is to deduce the Water Quality Index (WQI) by means of artificial intelligence (AI)-based machine learning (ML) models. Six parameters, namely BOD, DO, pH, NC, total coliform (CO) and electrical conductivity (EC) are used to measure, analyze and predict WQI using nine supervised regression machine learning techniques. Bayesian Ridge regression (BRR) and automatic relevance determination regression (ARD regression) yielded a low mean squared error (MSE) value when compared to other regression techniques. ARD regression model parameters as independent a priori so that non-zero coefficients do not exploit vectors that are not just sparse, but they are dependent. In the estimation process, BRR contains regularization parameters; regularization parameters are not set hard but are adjusted to the relevant data. Due to these reasons, ARD regression and BRR models performed better. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
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. -
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. -
Curvature Ductility of Reinforced Masonry Walls and Reinforced Concrete Walls
Research conducted in this work proposes an equation to evaluate and compares the curvature ductility of reinforced masonry (RM) and reinforced concrete (RC) walls. The curvature ductilities are measured at varying levels of axial stresses for walls for aspect ratio (l/h) of 0.5, 1.0 and 1.5. The percentage of reinforcement is increased from 0.25% (minimum reinforcement for RC walls as per IS-13920) to 1.00%. The curvature ductilities are evaluated by plotting flexural strength (M) versus curvature (?) for the walls. The stressstrain curves of masonry, concrete and reinforcing steel are all adopted from existing literature. The compressive strength of masonry and concrete has been chosen as 10MPa and 25MPa, respectively. The yield strength of the steel is fixed as 415MPa. The height and thickness of the wall are 3000 and 230mm, respectively, and the length of the wall is varied to obtain different aspect ratios. Results obtained from this paper imply due to increase curvature ductility, RM walls provide a better alternative for the construction of structural walls compared to RC walls in regions of significant seismicity. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Lateral Load Behavior of Unreinforced Masonry Spandrels
Spandrels, are usually classified as secondary elements and even though their behaviour has not received adequate focus unlike piers, they significantly affect the seismic capacity of the structure. Masonry spandrels are often damaged and the first structural components that crack within Unreinforced Masonry structures. Despite this, existing analytical methods typically consider a limit case in which the strength of spandrels is either neglected, considered to be infinitely rigid and strong or treated as rotated piers. It is clearly evident that such an assumption is not plausible. Hence, reliable predictive strength models are required. This thesis attempts to re-examine the flexural behaviour of spandrels and proposes an analytical model. The model is based on the interlocking phenomena of the joints at the end-sections of the spandrel and the contiguous masonry. The proposed analytical model is incorporated within a simplified approach to account for the influence of spandrel response on global capacity estimate of URM buildings. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
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.