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A Compatible Hexadecimal Encryption-Booster Algorithm for Augmenting Security in the Advanced Encryption Standard
Among the most prominent encryption algorithms, Advanced Encryption Standard ranks first. Even so, many familiar characters can be seen when an AES encrypted file is opened. As of today, there have been very few contributions to research on suppressing known characters in AES encrypted files. It is possible to identify encrypted files not only by their name and content, but also by their size. As a result, hackers can identify files at source and target locations by comparing their sizes. In this paper, a methodology is presented to address these two research gaps. As a result of the proposed algorithm, almost all characters are transformed into an unintelligible format not only for humans, but also for computer interpreters. As an additional benefit, the proposed method makes the encrypted file appear smaller and conceals its actual size. The proposed Encryption Booster algorithm is also easily integrated with Advanced Encryption Standard. 2023 IEEE. -
A compilation of interstellar column densities
We have collated absorption line data toward 3008 stars in order to create a unified database of interstellar column densities. These data have been taken from a number of different published sources and include many different species and ionizations. The preliminary results from our analysis show a tight relation [N(H)/E(B - V)= 6.1210 21] between N(H) and E(B - V). Similar plots have been obtained with many different species, and their correlations along with the correlation coefficients are presented. 2012 The American Astronomical Society. All rights reserved. -
A complete person re-identification model using Kernel-PCA-based Gabor-filtered hybrid descriptors
Person re-identification is a challenging problem in computer vision. Lots of research interest is observed in this area over the past few years. A model for complete person re-identification can prove useful in this direction. Use of convolutional neural networks for pedestrian detection can improve the accuracy of detection to a larger extent. Deriving a descriptor which is invariant to the changes in the illumination, background and the pose can make the difference in the re-identification process. The predominant part of our work focuses on building a robust descriptor which can tackle such challenges. We have concentrated on building a descriptor by employing appearance-based features extracted both at local and global levels. Further, the dimensionality of the descriptor is reduced using kernel PCA. Distance metric learning algorithms are used to evaluate the descriptor on three major benchmark datasets. We propose a complete person re-identification system which involves both pedestrian detection and person re-identification. Major contributions of this work are to detect pedestrians from surveillance videos using CNN-based learning and to generate a kernel-PCA-based spatial descriptor and evaluate the descriptor using known distance metric learning methods on benchmark datasets. 2018, Springer-Verlag London Ltd., part of Springer Nature. -
A Component Selection Framework of Cohesion and Coupling Metrics
Component-based software engineering is concerned with the development of software that can satisfy the customer prerequisites through reuse or independent development. Coupling and cohesion measurements are primarily used to analyse the better software design quality, increase the reliability and reduced system software complexity. The complexity measurement of cohesion and coupling component to analyze the relationship between the component module. In this paper, proposed the component selection framework of Hexa-oval optimization algorithm for selecting the suitable components from the repository. It measures the interface density modules of coupling and cohesion in a modular software system. This cohesion measurement has been taken into two parameters for analyzing the result of complexity, with the help of low cohesion and high cohesion. In coupling measures between the component of inside parameters and outside parameters. The final process of coupling and cohesion, the measured values were used for the average calculation of components parameter. This paper measures the complexity of direct and indirect interaction among the component as well as the proposed algorithm selecting the optimal component for the repository. The better result is observed for high cohesion and low coupling in component-based software engineering. 2022 CRL Publishing. All rights reserved. -
A Comprehensive Analysis of Digital Technologies for Climate Change
This study examines how digital technologies can help us to solve climate change through mitigation and adaptation. Through a literature review of the evolution of digital technologies from simple communication applications to artificial intelligence tools, this analysis identifies urgent applications needed at each stage of technological development and critiques their utility in climate action. The findings point out that the digital technologies have the potential to play a large role in combating climate change, but implementation faces challenges that need to be addressed through coordinated action internationally. The research concludes that by integrating approach that are combining of technological innovation and suitable policy frameworks presents climate action's most auspicious path forward. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
A Comprehensive Analysis of the Impact of Demographic, Socioeconomic Factors, and Social Media Advertisements on Consumers Online Shopping Behavior
This research comprehensively explores the intricate relationship between social media, demographic factors, socioeconomic status, and online shopping behavior. It investigates how various elements, such as brand, quality, price, and reviews, intersect to influence consumers virtual shopping experiences. Recognizing the diversity among consumer populations, the study considers demographic traits like age, gender, and socioeconomic status. It identifies a research gap concerning the moderating effects of these factors on virtual shopping behavior and the influence of different advertisement sources. The research proposes a methodology involving machine learning approaches to address these gaps. Using a dataset collected from 331 respondents in Kolkata, the study employs statistical tests and machine learning models to analyze the correlations and predict online shopping behavior. The results underscore the importance of understanding consumer behavior in the evolving e-commerce landscape. Logistic Regression emerges as the most accurate algorithm for predicting online shopping decisions. Additionally, the research investigates the reasons limiting consumers use of online shopping platforms, providing practical insights for tailored marketing strategies. These findings empower businesses to adapt their marketing strategies to the evolving landscape of consumer behavior. Overall, the study enhances our understanding of the multifaceted variables shaping customers decisions in online marketplaces. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A comprehensive analysis of various structural parameters of Indian coals with the aid of advanced analytical tools
An exhaustive structural analysis was carried out on three Indian coals (ranging from sub-bituminous to high volatile bituminous coal) using a range of advanced characterization tools. Detailed investigations were carried out using UVVisible spectroscopy, X-ray diffraction, scanning electron microscopy coupled energy dispersive spectroscopy, Raman spectroscopy and Fourier transform infrared spectroscopy. The X-ray and Raman peaks were deconvoluted and analyzed in details. Coal crystallites possess turbostratic structure, whose crystallite diameter and height increase with rank. The H/C ratio plotted against aromaticity exhibited a decreasing trend, confirming the graphitization of coal upon leaching. It is also found that, with the increase of coal rank, the dependency of I20/I26 on La is saturated, due to the increase in average size of sp2 nanoclusters. In Raman spectra, the observed G peak (1585cm?1) and the D2 band arises from graphitic lattices. In IR spectrum, two distinct peaks at 2850 and 2920cm?1 are attributed to the symmetric and asymmetric CH2 stretching vibrations. The intense peak at ~1620cm?1, is either attributed to the aromatic ring stretching of C=C nucleus. 2016, The Author(s). -
A Comprehensive Analysis on Computational Thinking in Education: Open Issues and Challenges
Computational thinking (CT) is a cognitive approach for solving problems using the concepts of algorithmic thinking, decomposition of a problem into components, identifying patterns among commonly occurring activities, and abstraction. CT promotes interdisciplinary learning and enhances problem-solving and logical reasoning abilities. In this study, a comprehensive analysis of the current issues and challenges of applying CT in the educational landscape is presented with a focus on the various assessment tools and their implementation in teaching methods. The study identifies the various techniques that can be used by educators to evaluate the skills of students based on their ability to solve problems that require CT. A systematic review of the available literature and related works was conducted to analyze their importance in CT, as well as their issues and challenges. This study finds that there is a need for a unified definition and implementation guidelines on CT. The available assessment tools mainly focus on programming constructs, leaving little room for evaluating abstract concepts as challenges in the field; hence, designing and developing assessment mechanisms are also required for effective implementation of CT in an academic context. 2025 by the authors. -
A Comprehensive Comparison of MobileNet, ResNet50 and InceptionV3 for Efficient Plant Pathology Detection
Plant diseases have a strong impact on agricultural productivity due to economic factors and a reduction in crop quality. This work focuses on the classification of apple leaves into four classes: healthy, rust-infected, scab-infected, and infected by both diseases, using the Plant Pathology FGVC7-2020 dataset that contains 3,642 images in total. The work involves the analysis of three sophisticated deep learning architectures: ResNet50, MobileNet and InceptionV3.It turned out that MobileN et showed the highest performance, reaching a 92% accuracy rate; it was followed by ResNet50 with 75% accuracy and InceptionV3 at 73%, hence underlining its better generalization capability and efficiency in classifying. We discuss the proposed methodology, which includes data preprocessing techniques, experimental results and final conclusions, is discussed in detail. These results underline the fundamental importance of determining an appropriate neural network architecture for the recognition of plant diseases, which is of prime importance to improve agricultural productivity. 2025 IEEE. -
A comprehensive examination of factors influencing intention to continue usage of health and fitness apps: a two-stage hybrid SEM-ML analysis
This research developed a theoretical framework based on the uses and gratification theory to investigate the intention to continue usage of Health and Fitness Apps (HFAs). In addition, this study explored how health valuation moderates the relationship between determinants and users intention to continue usage. A total of 447 HFA users data was collected from Delhi NCR, India through a purposive sampling technique. Partial least square-structure equation modeling was used to test the role of potential predictors influencing users behavioral intention to continue. The machine learning algorithms were employed to identify the features of importance. The results revealed that system quality, networkability, recordability, and task technology fit have a positive influence on hedonic motivation and utilitarian motivation. While information quality influences hedonic motivation but does not affect utilitarian motivation. Health valuation positively moderates the relationship between information quality, system quality, and networkability to intention to continue usage. We also observed that hedonic motivation emerged as a key predictor of users intention to continue usage of HFAs. The results would possibly offer useful recommendations for HFA developers, marketers, and health policymakers. The quality of fitness apps should be the primary concern of app developers. Furthermore, gamification can be incorporated into HFAs as it may influence the users hedonic motivations. The research contributes by developing a uses and gratification theory tailored for the HFAs. Additionally, this research incorporates hedonic and utilitarian motivation as mediating variables and health valuation as a moderator. 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
A Comprehensive Investigation of Blockchain Technology's Role in Cyber Security
In recent years, blockchain has become an extremely trending technology, capable of solving a variety of problems. One of these domains is cybersecurity, where blockchain technology has a huge scope. To dive deeper into this topic, we first need to understand the cybersecurity domain, the need for this field, and how it has become crucial to the current Information-Technology industry. Once we have a good understanding of the field of cybersecurity, we next focus on blockchain technology, its basic working process, and what makes it a trending infrastructural technology in today's world. The basic idea about the field of cybersecurity and blockchain technology can help us understand how the two different fields can be integrated to solve several problems in the cybersecurity domain. Eventually, we discuss the pros and cons of blockchain technology in cybersecurity and how the integration of the two different fields can make a difference. This study aims to explore various possibilities where blockchain technology can be utilized in several applications to solve a variety of problems in the field of cybersecurity. 2023 IEEE. -
A comprehensive investigation of ethyl 2-(3-methoxybenzyl) acrylate substituted pyrazolone analogue: Synthesis, computational and biological studies
In this study, we successfully synthesized ethyl 2-(3-methoxybenzyl) acrylate-substituted pyrazolones derivative (EMH) through the reaction of Baylis-Hillman acetate with pyrazolones. We conducted comprehensive screenings to evaluate its invitro antifungal, antibacterial, and antioxidant properties. The molecule demonstrated notable in vitro antifungal and antibacterial activities attributed to the presence of anisole, enhancing absorption rates through increased lipid solubility and improving pharmacological effects. Structure-activity relationship (SAR) studies supported these findings. Additionally, insilico studies delved into the molecular interactions of the synthesized molecule with DNA Gyrase, Lanosterol 14 alpha demethylase, and KEAP1-NRF2 proteins, revealing strong binding interactions at specific sites. Furthermore, we employed ab-initio techniques to theoretically estimate the photophysical properties of the compounds. Ground state optimization, dipole moment, and HOMO-LUMO energy levels were calculated using the DFT-B3LYP-6-31G(d) basis set. The theoretical HOMO-LUMO values indicated high electronegativity and electrophilicity index. NBO analysis confirmed the presence of intermolecular ONH hydrogen bonds resulting from the interaction of the lone pair of oxygen with the anti-bonding orbital. Overall, our results suggest that anisole-substituted pyrazolones derivatives exhibit promising applications in both photophysical and biological domains. 2024 -
A comprehensive investigation of the effect of mineral additives to bituminous concrete
Research efforts to employ sustainable materials for road construction have been on the rise in recent years. In particular, the use of polymers as additives in asphalt mix has been actively explored by several researchers. Bituminous pavementsnormally constructed in India, have increasing number of premature failures, due to increase in traffic density and noteworthy variations in road temperatures. The modified binders have proven to improve numerous properties of bituminous surfaces such as temperature susceptibility, fatigue life, creep, resistance to permanent deformation and rutting. The present study has focussed on the experimental investigations conducted to evaluate the influence of mineral additives, such as wollostonite and Rice Husk Ash (RHA) on Indirect Tensile Strength (ITS) and Tensile Strength Ratio (TSR) of bituminous concrete (BC)maintaining uniformity of aggregate properties.The results establish that the bituminous concrete blends modified using rice husk ash at 20% and wollostonite at 8%, with hydrated lime are most suitable for practical applications. 2021 Elsevier Ltd. All rights reserved. -
A comprehensive investigation on machine learning techniques for diagnosis of down syndrome
Down Syndrome is a chromosomal disease which causes many physical and cognitive disabilities. Down Syndrome patients are more vulnerable than any other patient. Medical experts started knowing it now with keen awareness. In recent years it has become a field of interest for many researchers, medical experts and social organisation. For the researchers it is an area of interest where very little work is done and a lot to be explored. Machine Learning consists of different processing levels like pre-processing, segmentation, feature selection and classification. Each level contains a vast set of techniques like filters, segmentation algorithms and classifiers. Machine Learning is one of the most popular algorithm, which is used to automate the decision making process with higher rate of accuracy in less time with least error rate. Machine Learning proved its significance with highest rate of accuracy in decision making and problem solving in almost all the fields but automated decision making in medical science is still a challenge. This paper reviews the different works done in the field of Down Syndrome using Machine Learning applied on different medical images, and the techniques like pre-processing, segmentation, feature selection and classification. The aim of this research work is to analyse and identify the Machine Learning methodologies that works efficiently to detect Down Sundrome. 2017 IEEE. -
A comprehensive literature review on financial inclusion /
Asian Journal Of Research In Banking And Financial, Vol.7, Issue 8, pp.119-133, ISSN: 2249-7323. -
A comprehensive LR model for predicting banks stock performance in Indian stock market
The study focusses on developing a Logistic Regression model to distinguish between Good and Poor Performance of Bank-stocks which are traded in Indian stock market with regard to the financial ratios. The study- sample comprises of financialratios of 40 nationalised and private banks, for a period of six years. The study ascertains and scrutinizes eleven financial ratios that can categorize the Banksbroadly into two categories as good or poor, up to the accuracy level of 78 percent, based on their rate of return. First, the study predicts the performance of banks by using financial ratios and tries to build the goodness of fit by using Logistic Regression approach. The study also emphasizes that this model can enrich an investors ability to forecast the price of various stocks. However, the paper confers the real-world implications of Logistic Regression model to envisage the performance of Banks in the stock market. The study reveals that the model could be useful to potential investors, fund managers, and investment companies to improve their strategies and to select the out-performing Bank-stocks. Serials Publications Pvt. Ltd. -
A Comprehensive Meta-Analysis on Animal Identification Using Machine Learning and Deep Learning
Artificial Intelligence (AI)-based models have shown promising results in the identification of animals breeds. The surge in the development of new models has opened up new avenues for computer vision. The growing need to achieve cent percent accuracy in the prediction, identification and classification of data/images has motivated researchers to develop innovative strategies seamlessly. The results of various AI models are analyzed in terms of their classification accuracy. EfficientNet-B0 provided an accuracy of 95% in cat breed identification. InceptionV3 deep learning model reached the maximum accuracy of 96.75%, 96.57%, and 100% on dog, goat, and pig breed identification, respectively. ResNet attained an accuracy of 85.77% on snake species identification. This article provides an in-depth analysis of animal classification/species identification models. The inferences drawn out of this literature review would help the researchers in the selection of an ideal AI model to develop an automated animal classification model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Comprehensive Methodical Strategy for Forecasting Anticipated Time of Delivery in OnlineFood Delivery Organizations
Determining the cost of shipping has long been a cornerstone of urban logistics, but today's effective outcomes need acceptable precision. Around the globe, internet-based meal ordering and distribution services have surpassed public expectations; for example, in India, platform-to-consumer distributions and delivery of food and drinks reached an astounding amount of more than 290 million transactions in 2023. Businesses are required to provide customers with precise details on the time it will take for their food to be delivered, starting from the moment the purchase is placed until it reaches the customer's door. Customers won't place orders if the result measure is greater than the actual delivery date, but a greater number of consumers are going to contact the customer service line if the period of waiting falls shorter than their actual shipment period. This study's primary goals are to identify critical variables that affect the availability of nutritious food inspiring leaders as well as to provide an approach for making accurate predictions. Analyzing and contrasting the primary effects and challenges of distribution and shipping in the nation's many different sectors. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
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. -
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. The Author(s) under exclusive licence to Archana Sharma Foundation of Calcutta 2024.