Browse Items (5511 total)
Sort by:
-
A comprehensive molecular docking-based study to identify potential drug-candidates against the novel and emerging severe fever with thrombocytopenia syndrome virus (SFTSV) by targeting the nucleoprotein
Severe fever with thrombocytopenia syndrome (SFTS) is a newly emerging haemorrhagic fever that is caused by an RNA virus called Severe fever with Thrombocytopenia Syndrome virus (SFTSV). The disease has spread globally with a case fatality rate of 30%. The nucleoprotein (N) of the virus has a pivotal role in replication and transcription of RNA inside the host. Considering that no specific treatment regime is suggested for the disease, N protein may be regarded as the potential candidate drug target. In the present study, in silico molecular docking was performed with 130 compounds (60 natural compounds and 70 repurposed synthetic drugs) against the N protein. Based on the binding affinity (kcal mol?1), we selected Cryptoleurine (?10.323kcalmol?1) and Ivermectin (?10.327kcalmol?1) as the top-ranked ligands from the natural compounds and repurposed synthetic drugs groups respectively, and pharmacophore analysis of these compounds along with other high performing ligands revealed that two aromatic and one acceptor groups could strongly interact with the target protein. Finally, molecular dynamic simulations of Cryptoleurine and Ivermectin showed stable interactions with the N protein of SFTSV. To conclude, Cryptoleurine and Ivermectin can be considered as a potential therapeutic agent against the infectious SFTS virus. Graphical abstract: (Figure presented.) The Author(s) under exclusive licence to Archana Sharma Foundation of Calcutta 2024. -
Exploring the adsorption efficacy of Cassia fistula seed carbon for Cd (II) ion removal: Comparative study of isotherm models
The current study demonstrates the potential of Cassia fistula seed carbon (CFSC), a waste lignocellulosic biomass, to eliminate Cd (II) ion-from saturated liquid samples. The efficient removal of about 93.2% (w/v) of Cd (II) ions from 10 mg/L concentration was achieved within 80 min of treatment. The CFSC dosage of 100 mg/50 mL accounted as optimal for enhanced Cd (II) removal. Cd (II) adsorption onto CFSC was observed to be maximum at pH 6. The investigational trials were assessed with three isotherm models such Dubinin-Radushkevich, Freundlich, and Langmuir. The specifications obtained from this experimental study align well with the Langmuir isotherm model, which describes the maximal adsorption capacity of 68.02 mg/g. Cd (II) adsorption data from this study exhibited the R2 of 0.9 under pseudo-second-order. Maximum desorption (76.3% w/v) was obtained with 0.3 M HCL. This study revealed that thermally activated C. fistula seed carbon (CFSC) can be tuned to be lucrative adsorbent for Cd (II) elimination from water and waste-water. 2023 Elsevier Inc. -
Cost-effective cryptographic architecture in quantum dot cellular automata for secured nano-communication
Quantum dot cellular automata (QCA) provide rapid computational efficiency, high density and low power consumption, which is an alternative for CMOS technology. In digital world, cryptography is an important feature to protect digital data. To ensure the data protection in nano-communication, a QCA-based cryptographic architecture is proposed in this article. In the proposed design, the encryption and decryption are done with the help of random keys which is produced by the pseudo random number generator (PRNG). In this paper, architectural component of cryptographic architecture includes XOR block, 1 to 4 de-multiplexer and PRNG, which are realised using QCA. Finally, an integration of the individual components through clock zone-based crossover, lead to the generation of a novel cryptographic architecture. This design achieves low cost compared to the existing literature, as it uses minimum number of majority gate and inverters with clock zone-based crossover. Copyright 2024 Inderscience Enterprises Ltd. -
An enhancing reversible data hiding for secured data using shuffle block key encryption and histogram bit shifting in cloud environment
Nowadays there are numerous intruders trying to get the privacy information from cloud resources and consequently need a high security to secure our data. Moreover, research concerns have various security standards to secure the data using data hiding. In order to maintain the privacy and security in the cloud and big data processing, the recent crypto policy domain combines key policy encryption with reversible data hiding (RDH) techniques. However in this approach, the data is directly embedded resulting in errors during data extraction and image recovery due to reserve leakage of data. Hence, a novel shuffle block key encryption with RDH technique is proposed to hide the data competently. RDH is applied to encrypted images by which the data and the protection image can be appropriately recovered with histogram bit shifting algorithm. The hidden data can be embedded with shuffle key in the form of text with the image. The proposed method generates the room space to hide data with random shuffle after encrypting image using the definite encryption key. The data hider reversibly hides the data, whether text or image using data hiding key with histogram shifted values. If the requestor has both the embedding and encryption keys, can excerpt the secret data and effortlessly extract the original image using the spread source decoding. The proposed technique overcomes the data loss errors competently with two seed keys and also the projected shuffle state RDH procedure used in histogram shifting enhances security hidden policy. The results show that the proposed method outperforms the existing approaches by effectively recovering the hidden data and cover image without any errors, also scales well for large amount of data. 2018, Springer Science+Business Media, LLC, part of Springer Nature. -
Structural and Optical Properties of Alumino Lead Borate Glasses Containing Copper Oxide
The alumino lead borate glasses with small amounts of copper oxide were synthesized by melting and quenching according to the relation 50B2O3-30PbO-(20x)Al2O3-xCuO with x = 0, 0.10, 0.25, 0.50, 0.75 and 1.00 mol%. The powder XRDs had no sharp peaks which show that the samples are amorphous. Density of the glasses increased as the content of the CuO increased. FTIR spectroscopic studies reveal the presence of BO3, BO4, PbO4, AlO4, pentaborate [B5O8], diborate [B4O72] and dipentaborate B512 structural units. The UV-visible absorption studies showed that the refractive index, indirect energy gap, oxide ion polarizability and optical basicity had composition dependence which were related to the glass structure. As the CuO concentration increased, the refractive index decreased, indirect energy gap increased, oxide ion polarizability decreased and optical basicity decreased. Optical band gap increased with increasing CuO content as the band gap for bridging oxygens is higher than that for non-bridging oxygens. 2024 Indian Ceramic Society. -
National Development through women empowerment
International Journal of Physical and Social Sciences Vol.3, Issue 3, pp.77-89 -
Modelling for working capital efficiency: integrating SBM-DEA and artificial neural networks in Indian manufacturing
Purpose: This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN). Design/methodology/approach: A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME. Findings: Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME. Originality/value: The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively. 2024, Emerald Publishing Limited. -
A cross-country analysis of the relationship between human capital and foreign direct investment
Purpose: The ZhangMarkusen (Z-M) inverse U-shape theory uses education as a human capital variable to investigate the impact of educational attainment on foreign direct investment (FDI) inflows to a country. The objective of this research is to empirically test this theory in a cross-country framework. Design/methodology/approach: Fixed effect panel regression has been used to test the Z-M hypothesis for 172 countries for the period 19902015. For the purpose of this study, countries were divided into four groups as per the World Bank classification: Low-income economies, lower middle-income countries, upper middle-income economies and high-income economies. Findings: The findings of this study reinforce the proposition that macroeconomic factors are the major determinants of FDI inflows into various countries. The authors find that the size of the market measured by gross domestic product (GDP), the growth potential of the market measured by real GDP growth rate and the availability of infrastructure are the major factors that enhance the attractiveness of a country as an FDI destination. Originality/value: Though the Z-M theory has been empirically tested in cross-country frameworks, no consensus has been reached. Thus, it is interesting to look again at the validity of the Z-M hypothesis using data covering longer and more recent periods. The study includes both macroeconomic and human capital determinants of FDI, so as to arrive at a comprehensive model explaining the FDI flows into various countries. 2021, Emerald Publishing Limited. -
An adept approach to ascertain and elude probable social bots attacks on twitter and twitch employing machine learning approach
There has been a tremendous increase in the popularity of social media such as blogs, Instagram, twitter, online websites etc. The increasing utilization of these platforms have enabled the users to share information on a regular basis and also publicize social events. Nevertheless, most of the multimedia events are filled with social bots which raise concerns on the authenticity of the information shared in these events. With the increasing advancements of social bots, the complexity of detecting and fact-checking is also increasing. This is mainly due to the similarity between authorized users and social bots. Several researchers have introduced different models for detecting social bots and fact checking. However, these models suffer from various challenges. In most of the cases, these bots become indistinguishable from existing users and it is challenging to extract relevant attributes of the bots. In addition, it is also challenging to collect large scale data and label them for training the bot detection models. The performance of existing traditional classifiers used for bot detection processes is not satisfactory. This paper presents: A machine learning based adaptive fuzzy neuro model integrated with a hist gradient boosting (HGB) classifier for identifying the persisting pattern of social bots for fake news detection. And Harris Hawk optimization with Bi-LSTM for social bot prediction. Results validate the efficacy of the HGB classifier which achieves a phenomenal accuracy of 95.64 % for twitter bot and 98.98 % for twitch bot dataset. 2023 -
Performance analysis of semantic veracity enhance (SVE) classifier for fake news detection and demystifying the online user behaviour in social media using sentiment analysis
The increased propagation of fake news is the significant concern in the digital era. Identification of fake news from social media platforms is critical to strengthen public trust and ensure social stability. This research presents an effective and accurate framework for identifying fake news that combines different steps of natural language processing (NLP) technique along with a neural network architecture. A novel semantic veracity enhancement (SVE) classifier is designed and implemented in this work for detecting fake news. The proposed approach leverages the effectiveness of sentiment analysis for identifying misleading or deceptive content and its subsequent implications on the sentiment and behaviour of social media users. A BERT model is used in this research for analysing the sentiments and classifying the texts from the social media platform. By examining the sentiments, the SVE classifier differentiates between real news and fabricated content. To achieve this, three different datasets comprising both actual content and fabricated (tweaked) tweets are employed for training the SVE classifier. The potentiality of the SVE classifier is evaluated and compared with different optimization techniques. The outcome of the experimental analysis shows that the proposed approach exhibits an excellent performance in terms of classifying misinformation from the original information with an outstanding accuracy of 99% compared to other state of art methods. 2024, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature. -
Inter-State Migration, Footloose Labour and Accessibility to Health Care: An Exploration among Metro Workers of a Camp in Bengaluru
The neoliberal political economy that India adopted in 1991 has brought in huge Foreign Direct Investments, which has led to a perceptible increase in the number of migrants in the major cities of India due to various structural reasons in their place of origin and rapid developmental activities in the cities. Bengaluru has the second largest migrant population after Mumbai, and as per the labour department of the government of Karnataka; there are more than 65 lakh migrant workers in Karnataka, who are involved in various developmental projects, including the metro railway project in Bangalore. Even though the Karnataka Building and Other Construction Workers Welfare Board (KBOCWWB) offers certain social security, including health care for registered migrants, they must wait more than a year to get these benefits. With privatisation and increased out-of-pocket expenditure for health related issues, the migrants face a major hurdle in surviving at the migrated workplaces. Many of them are unaware of welfare boards, and the number of migrants who are registered with them is very small. This paper aims to understand the accessibility of health facilities for migrant workers working in the Bengaluru Metro Project. This research will understand the legal, economic and psychological aspects related to the health status of migrant workers through qualitative study. The study used in-depth interviews to elicit responses from selected inter-state migrant workers to understand their access towards health facilities. The thematic analysis of the interview transcripts revealed a substantive gap in workers access to health facilities. The unregulated working conditions have added more stress to the workers, and due to poverty and unemployment back home, these hurdles are not forcing them to go back. More awareness creating interventions from the government can transform their lives. (2024), (University of Duisburg). All rights reserved. -
Offline Handwritten Character and Numeral Recognition: A Kernel-Based Approach
Automatic character recognition for the handwritten Indic script is a challenging area for research in the field of pattern recognition. Although a great amount of research work has been reported, all the state-of-the-art methods are limited with optimal features. This article aims to suggest a well-defined recognition model which harnessed upon handwritten Odia characters and numerals by implementing a novel process of decomposition in terms of 3rd level fast discrete curvelet transform (FDCT) to get higher dimension feature vector. After that, kernel-principal component analysis (K-PCA) is considered to obtain optimal features from FDCT feature. Finally, the classification is performed by using probabilistic neural network (PNN) on a handwritten Odia character and numeral dataset from both NIT Rourkela and IIT Bhubaneswar. The outcome of the proposed scheme performs better compared to existing models with optimized Gaussian kernel-based feature sets. Copyright 2022, IGI Global. -
A New Facile Iodine-Promoted One-Pot Synthesis of Dihydroquinazolinone Compounds
A one-pot iodine catalyzed reaction has been developed for the preparation of dihydroquinazolinones from isatoic anhydride, enaminones, and amines in modest to good yields. The reaction has been screened in various catalysts and solvents and a gram scale experiment has been performed based on the optimum conditions. A possible mechanism has been proposed based on the control experiments. The reaction has been checked with broad range of substrates. 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim -
Digital Gender Gap, Gender Equality and National Institutional Freedom: A Dynamic Panel Analysis
While digital gender gap is a growing field of research in Information Systems (IS), there remains a dearth of research focusing on it. The objective of this study is to investigate the relationship between the digital gender gap in mobile and internet usage and gender equality. Additionally, this study also examines the impact of national institutional freedoms on the aforementioned relationship. Utilizing the theoretical framework of intersecting inequalities and building upon existing literature on the gender digital divide, this study aims to explore the associations between disparities in mobile and internet usage, gender equality, and the extent of national institutional freedoms encompassing economic, political, and media domains. In pursuit of this objective, we undertake a dynamic panel data analysis using publicly accessible archival data at the country level. The results indicate that national institutions have a significant impact on the relationship between the digital gender gap in internet and mobile phone usage and gender equality. The discussion encompasses the significance of our findings for both study and practice. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Is ChatGPT Enhancing Youths Learning, Engagement and Satisfaction?
Integration of artificial intelligence (AI) in educational practices necessitates the understanding of the influence of tools such as ChatGPT. Self-determination Theory (SDT) has been used to examine the impact of ChatGPT usage by students for the improvement of perceived learning, engagement and satisfaction. The moderating role of students AI literacy between ChatGPT and the antecedents of intrinsic motivation, autonomy, competence and relatedness. The data was collected through questionnaire from 481 students and structural equation modeling was used to analyze the data. The findings of the study shows that ChatGPT usage impacts students perceived autonomy, competence, and relatedness, enhancing intrinsic motivation. Also, there is a moderation of AI literacy between ChatGPT usage and these psychological needs. This study extends SDT to student interactions with ChatGPT and underscores the pivotal role of AI literacy. The findings contribute to the discourse on AI and education, offering valuable perspectives on students use of ChatGPT and its effect on their academic experience. 2024 International Association for Computer Information Systems. -
Sex determination using finger print ridge density among the medical students of NIMS medical college, Jaipur
To determine the sex of an individual plays an important role among forensic pathologists and scientists particularly when the fingerprints recovered from the crime scene does not match any of the criminal record so in that case fingerprint ridge density plays an important role in determining the sex of an individual. The present study was done among the 100 medical students of NIMS Medical college (50 males and 50 females) shobha nagar, Jaipur. Finger ridge density was counted on the radial border of each print. Result of the study shows that females have higher number of finger ridge density count as compared to males. Application of Bayes theorem suggests that finger print ridge density count <14ridges/25mm2 is more likely to be male while finger print ridge density count >14ridges/25mm2 is more likely to be female. 2016, World Informations Syndicate. All rights reserved. -
An efficient classification of cirrhosis liver disease using hybrid convolutional neural network-capsule network
Liver cirrhosis is the diffuse and advanced phase of liver disease. Several morphological methods are used for imaging modalities. But, these modalities are biased and lack in higher detection accuracy. Hence, this work introduces automated cirrhosis liver disease classification using an optimized hybrid deep learning model. In this work, Magnetic Resonance Image (MRI) is considered for the process. Initially, an Extended Guided Filter (EGF) is used for eliminating the noise from input MRI images. Binomial thresholding is used to segment the tumor from the image. Then, Feature Extraction (FE) phase is carried out by Grey Level Co-occurrence Matrix (GLCM) and Gray level Run-length Matrix (GRLM). Finally, a hybrid of two Deep Learning (DL) algorithms Convolutional Neural Network and Capsule Network (HCNN-CN) are integrated to classify the Cirrhosis liver disease. Moreover, for fine tuning the parameters of the neural network, an optimization approach Adaptive Emperor Penguin Optimization (AEPO) is used. The proposed HCNN-CN-AEPO is compared over several approaches and depicted accuracy and sensitivity value of 0.993 and 0.986 on the real time dataset. The experimental results proved that the proposed HCNN-CN-AEPO can exactly diagnose the tumour. 2022 Elsevier Ltd -
Mentha spicata assisted AgCuO nanocomposite enables anti-diabetic and vitamin-C sensing activities
Diabetes mellitus (DM), a multifactorial chronic health condition, affects a sizable portion of the global population, and more people are expected to contract it in the future, according to the World Health Organisation (WHO). Diabetes mellitus can be treated with conventional drugs, but most of the medications have a variety of side effects. The use of nanocomposites (NCs) to treat diabetes has been prioritized in this scenario. In this study, AgCuO NCs were synthesized using a green method using Mentha spicata leaf extract and their physicochemical properties were investigated with a variety of analytical techniques. According to an extensive in vivo and in vitro analysis of the biological activities of as-synthesized AgCuO NCs, AgCuO NCs possess effective antibacterial, anti-diabetic, and anti-hyperlipidemic characteristics. When AgCuO NCs are administered to STZ-induced animals in a concentration-based manner, the blood levels of inflammatory and liver marker enzymes are reduced and antioxidant enzyme levels are increased. Besides, AgCuO NCs exhibit excellent sensing activity with a limit of detection of 86 nM against Vitamin-C. This study reveals that AgCuO NCs derived from Mentha spicata may, therefore, prove to be a very successful anti-diabetic and biosensor candidate in the future. 2024 Elsevier B.V. -
Experimental instigating a counter cultural film platform in Bangalore /
Moving Image Review & Art Journal (MIRAJ), Vol.7, Issue 2, pp.189-297, ISSN No: 2045-6298. -
Gaussian MutationSpider Monkey Optimization (GM-SMO) Model for Remote Sensing Scene Classification
Scene classification aims to classify various objects and land use classes such as farms, highways, rivers, and airplanes in the remote sensing images. In recent times, the Convolutional Neural Network (CNN) based models have been widely applied in scene classification, due to their efficiency in feature representation. The CNN based models have the limitation of overfitting problems, due to the generation of more features in the convolutional layer and imbalanced data problems. This study proposed Gaussian MutationSpider Monkey Optimization (GM-SMO) model for feature selection to solve overfitting and imbalanced data problems in scene classification. The Gaussian mutation changes the position of the solution after exploration to increase the exploitation in feature selection. The GM-SMO model maintains better tradeoff between exploration and exploitation to select relevant features for superior classification. The GM-SMO model selects unique features to overcome overfitting and imbalanced data problems. In this manuscript, the Generative Adversarial Network (GAN) is used for generating the augmented images, and the AlexNet and Visual Geometry Group (VGG) 19 models are applied to extract the features from the augmented images. Then, the GM-SMO model selects unique features, which are given to the Long Short-Term Memory (LSTM) network for classification. In the resulting phase, the GM-SMO model achieves 99.46% of accuracy, where the existing transformer-CNN has achieved only 98.76% on the UCM dataset. 2022 by the authors.