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A Comparison of Similarity Measures in an Online Book Recommendation System
To assist users in identifying the right book, recommendation systems are crucial to e-commerce websites. Methodologies that recommend data can lead to the collection of irrelevant data, thus losing the ability to attract users and complete their work in a swift and consistent manner. Using the proposed method, information can be used to offer useful information to the user to help enable him or her to make informed decisions. Training, feedback, management, reporting, and configuration are all included. Our research evaluated user-based collaborative filtering (UBCF) and estimated the performance of similarity measures (distance) in recommending books, music, and goods. Several years have passed since recommendation systems were first developed. Many people struggle with figuring out what book to read next. When students do not have a solid understanding of a topic, it can be difficult determining which textbook or reference they should read. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A COMPARITIVE ANALYSIS BETWEEN HOLLYWOOD AND INDIAN MOVIES DEALING WITH THE TOPIC CAPITAL PUNISHMENT
Capital Punishment is a much debated topic in the country and around the world. Films are a major of information where one can propagate an idea to a wide range of audience. Being a topic of general concern, it is important to discuss the relevance of capital punishment on screen (in a movie) and carry forth the happening debate onscreen. The paper tries to do a comparative study between English and Indian movies dealing with the topic. -
A comparitive study on traditonal healthcare system and present healthcare system using cloud computing and big data
Cloud computing is one the emerging technology which provides all the necessary resources required for day to day operations of an organization in a virtual environment. It is also known as green computing as it reduces the physical existence of the hardware resources. Health is being considered as a basic right for an individual. Even though there are advancements in the healthcare sector of India when compared to earlier stages, there is still a need for betterment in this sector. In order to make progress in this field, constant learning and better economic standards are needed. This paper provides a comparative view of the progress made by India in the healthcare sector after the introduction of two major technologies such as cloud computing and big data. 2017 IEEE. -
A Compartmental Mathematical Model of Novel Coronavirus-19 Transmission Dynamics
The COVID-19 pandemic has spread quickly throughout the world, posing a serious threat to human-to-human transmission. The novel coronavirus pandemic is described quantitatively in this paper using a mathematical model of COVID-19 driven by a system of ordinary differential equations. The suggested model is used to provide predictions regarding the behavior of a COVID-19 outbreak over a shorter time frame. It is demonstrated that the system of model equations has a unique and existing solution. Furthermore, the answer is positive and bounded. Thus, it is argued that the model created and discussed in this work is both mathematically and biologically sound. A threshold parameter that controls the disease transmission is used in a qualitative analysis of the model to confirm the existence and stability of disease-free and endemic equilibrium points. Additionally, the key parameters undergo sensitivity analysis to ascertain their relative significance and potential influence on the COVID-19 virus dynamics. 2024 NSP Natural Sciences Publishing Cor. -
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 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 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 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 Model for Forecasting the Nifty50 Index Using MAchine and Deep Learning Methodologgy with Reference to National Stock Exchange
The volatility and uncertainty make stock and stock price index predictions challenging. Many financial professionals and academics are interested in stock price/index prediction studies. This study presents computational ML and DL intelligence techniques for estimating the NIFTY50 index closing value on the Indian NSE using Fundamental Analysis and Technical Analysis. To forecast the NIFTY50 index, we first employed Fundamental Analysis and max voting, bagging, boosting, and stacking ensemble learning techniques. An embedded feature selection algorithm is utilized to determine the model's best fundamental indicators, and a grid search is performed to tweak hyperparameters for each base regressor. Our results demonstrate that the bagging and stacking regressor model 2 beat all other models, with the lowest RMSE of 0.0084 and 0.0085, respectively, indicating an improved fit of ensemble regressors. Subsequently, TA research was done to exhibit the influence of deep learning on the NIFTY50. This method employs a data augmentation mechanism and three GRU model variations. It is examined using two datasets, TA1 and TA2, which include technical indicators from the NIFTY50 index. The GRU model enhanced the NIFTY50 index prediction using the TA1 technical indicator dataset. Finally, the study examines a hybrid model to estimate equity market trends, combining PCA with ML methods such as ANN, SVM, NB, and RF. The proposed approach uses the trend deterministic data preparation layer to convert the continuous data to a discrete form denoted by +1 or -1. The empirical findings of this hybrid model demonstrate that the RF model with the first three principal components obtains precision of 0.9969, F1-score 0.9968 and AUC score of 1. Overall, the suggested research design outperforms baseline models in our experiments and shows promising results using fundamental and technical analysis indicators. Thus, this study provides an ideal tool for stock market prediction and financial decision-makers. -
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 novel model for network speech anomaly detection system using deep learning approach
Network Intrusion Detection System (NIDS) is the key technology for information security, and it plays significant role for classifying various attacks in the networks accurately. An NIDS gains an understanding of normal and anomalous behavior by examining the network traffic and can identify unknown and new attacks. Analyzing and Identifying unfamiliar attacks are one of the big challenges in Network IDS research. A huge response has been given to deep learning over the past several years and novelty in deep learning techniques are also improved regularly. Deep learning based Network Intrusion Detection approach is highly essential for improved performance. Nowadays, Machine learning algorithms made a revolution in the area of human computer interaction and achieved significant advancement in imitating human brain exactly. Convolutional Neural Network (CNN) is a powerful learning algorithm in deep learning model for improving the machine learning ability in order to achieve high attack classification accuracy and low false alarm rate. In this article, an overview of deep learning methodologies for commonly used NIDS such as Auto Encoder (AE), Deep Belief Network (DBN), Deep Neural Network (DNN), Restricted Boltzmann Machine (RBN). Moreover, the article introduces the most recent work on network anomaly detection using deep learning techniques for better understanding to choose appropriate method while implementing NIDS through widespread literature analysis. The experimental results designate that the accuracy, false alarm rate, and timeliness of the proposed CNN-NIDS model are superior than the traditional algorithms. 2020, Springer Science+Business Media, LLC, part of Springer Nature.