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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 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 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 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. -
A comprehensive review of AI based intrusion detection system
In today's digital world, the tremendous amount of data poses a significant challenge to cyber security. The complexity of cyber-attacks makes it difficult to develop efficient tools to detect them. Signature-based intrusion detection has been the common method used for detecting attacks and providing security. However, with the emergence of Artificial Intelligence (AI), particularly Machine Learning, Deep Learning and ensemble learning, promising results have been shown in detecting attacks more efficiently. This review discusses how AI-based mechanisms are being used to detect attacks effectively based on relevant research. To provide a broader view, the study presents taxonomy of the existing literature on Machine Learning (ML), Deep learning (DL), and ensemble learning. The analysis includes 72 research papers and considers factors such as the algorithm and performance metrics used for detection. The study reveals that AI-based intrusion detection methods improve accuracy, but researchers have primarily focused on improving performance for detecting attacks rather than individual attack classification. The main objective of the study is to provide an overview of different AI-based mechanisms in intrusion detection and offer deeper insights for future researchers to better understand the challenges of multi-classification of attacks. 2023 -
A comprehensive review of the Indian retail industry growth /
International Journal of Social And Allied Research, Vol.7, Issue 2, pp.49-53, ISSN No: 2319-3611. -
A comprehensive review on natural macromolecular biopolymers for biomedical applications: Recent advancements, current challenges, and future outlooks
Versatile material properties coupled with high degree of biocompatibility and biodegradability has made biopolymers as potential candidates for diverse applications in the biomedical field. Natural biopolymers derived from various plant, animal and microbial sources with different biochemical compositions are extensively used in biomaterial industry with or without further medication to their native form. Biopolymeric biomaterials have been employed in a wide range of biomedical applications like tissue engineering, drug delivery, bone regeneration, wound dressings and cardiovascular surgery. Carbohydrate based biopolymers and protein based biopolymers are extensively used for several applications in the biomedical field including cartilage regeneration, periodontal tissue regeneration, bone regeneration, corneal regeneration, drug delivery and wound healing. This review work presents a comprehensive outlook on the applications of various biopolymers in biomedical field. The work elaborates the biochemistry of these polymers with special focus on their crucial properties in the biomedical industry. Further a detailed description on the most recent application of various biopolymers in the biomedical filed is presented in this review. This work further summarizes the current challenges and future prospects in the use of biopolymers in biomedical field. 2024 The Author(s) -
A comprehensive review on the need for integrated strategies and process modifications for per- and polyfluoroalkyl substances (PFAS) removal: Current insights and future prospects
Alarming concern over the persistence and toxicity of per- and polyfluoroalkyl substances (PFAS) in the environment has created an imperative need for designing and redesigning strategies for their detection and remediation. Conventional PFAS removal technologies that uses physical, chemical, or biological methods. Increase in the diversity and quantity of PFAS entering the environment has necessitated the need for developing more advanced and integrated strategies for their removal. Despite of the advances reported in this domain, there exist a huge research gap that need to be mentored to tackle the problems associated with mitigation of combined toxicity of wide variety of PFAS in the environment. The possibility of PFAS to combine with other emerging contaminants poses an additional threat to the existing treatment methods thereby stressing the need for a continuous monitoring and updating the treatment processes. This review work aims at understanding the structure, entry, and fate of different types of PFAS in to the environment. Further an in-depth discussion regarding the different levels of toxicity associated with PFAS is elaborated in the review. The process description of recent PFAS remediation techniques along with their significance, limitations and possibility of integration is discussed in detail. Further a detailed outlook on the advantages and limitations of PFAS removal methods and an insight into the recently developed PFAS removal methods is outlined in this review. 2024 The Authors -
A Comprehensive Study on Parametric Optimization of Plasma-Sprayed Cr2C3 Coatings on Al6061 Alloy
Plasma spray, a widely employed thermal spray method, is known for enhancing coatings with heightened microhardness, density, and bonding strength. In this study, Taguchis approach was applied to optimize processing parameters for plasma spray-coated surfaces, aiming to reduce porosity, increase hardness, and fortify the connection between Cr2C3 coatings. The design of experiments method facilitated the optimization of process parameters, utilizing signal-to-noise ratios and ANOVA analysis to assess the significance of each processing parameter and identify optimal parameter combinations. Powdered feed rate and stand-off distance emerged as the two most critical processing variables influencing permeability and hardness, contingent on signal-to-noise ratios. S/N ratio analysis was employed to determine the optimal processing parameters for permeability, hardness, and bonding strength. For porosity, the optimal stand-off distance, powdered feed rate, and current density were identified as 60rpm, 50g/min, and 460ampsmm/s, respectively. Exemplary process conditions for hardness included a powdered feed rate of 60g/min, a stand-off distance of 80rpm, and a current density of 480 amps. Lastly, for strength properties, the ideal process variables were a stand-off distance of 80rpm, a current density of 480amps, and a powdered feed rate of 60g/min. Despite small differences between projected R2 and modified R2 values in statistical data on permeability, hardness, and bonding strength, the proximity to the one emphasizing the fit of the linear regression used for analysis was evident. Fracture results from the binding strength test postulate mixed adhesion-cohesion type failures in the Cr2C3 coatings. The Institution of Engineers (India) 2024. -
A comprehensive study on the assessment of chemically modified Azolla pinnata as a potential cadmium sequestering agent
The major environmental issue raised throughout the world is the egression of toxic pollutants in water bodies. Hence, employment of novel technological interventions such as bioremediation and phytoremediation for mitigating the toxic effects caused by the pollutants has gained attention. The aquatic macrophyte, Azolla pinnata is utilized as a biofiltering agent in the present study for the chelation of metal toxicants from the artificial wastewater system. The nutritive value of A. pinnata was determined to be 268.99Kcal/100g energy and the mineral profiling showed the highest amount of calcium (54.7ppm), iron (14.04ppm) and manganese (7.96 ppm). The quantitative screening of total phenolic and total flavonoid contents showed a maximum of 402.334.29 mg/g GAE and 105.253.81 mg/g QE respectively and the sample exhibited strong antioxidant activity in quenching the DPPH radicals with an IC50 value of 88.27?g/ml. Similarly, the highest bioactivity was observed in methanolic and chloroform extract of A. pinnata biomass showing the zone of growth inhibition against E. coli (17mm) and S. aureus (18mm). The results recorded from the SEM-EDX, GCMS, FTIR and XRD confirmed the adsorptive properties of biomass. The chemically modified and unmodified Azolla exposed to cadmium metal solution showed the maximum adsorption of about 0.470.001 and 0.480.003 ppm in 60mins using the unmodified biomass with dosage of 0.75 and 1.0g respectively. Moreover, the results recorded from the instrumental characterization for the adsorptive properties of Azolla biomass proved that cadmium chelation is due to the modifications caused in porosity, surface structure and the addition of functional groups in the treated biomass surface. 2023 The Ceramic Society of Japan. -
A Comprehensive Survey on Deep Learning Techniques for Digital Video Forensics
With the help of advancements in connected technologies, social media and networking have made a wide open platform to share information via audio, video, text, etc. Due to the invention of smartphones, video contents are being manipulated day-by-day. Videos contain sensitive or personal information which are forged for one's own self pleasures or threatening for money. Video falsification identification plays a most prominent role in case of digital forensics. This paper aims to provide a comprehensive survey on various problems in video falsification, deep learning models utilised for detecting the forgery. This survey provides a deep understanding of various algorithms implemented by various authors and their advantages, limitations thereby providing an insight for future researchers. 2024 World Scientific Publishing Co. -
A comprehensive survey on machine learning techniques to mobilize multi-camera network for smart surveillance
Deploying a web of CCTV cameras for surveillance has become an integral part of any smart citys security procedure. This, however, has led to a steady increase in the number of cameras being deployed. These cameras generate a large amount of data, which needs to be further analyzed. Our next step is to achieve a network of cameras spread across a city that does not require any human assistance to detect, recognize and track a person. This paper incorporates various algorithmic techniques used in order to make surveillance systems and their use cases so as to enable less human intervention dependent as much as possible. Even though many of these methods do carry out the task graciously, there are still quite a few obstructions such as computational resources required for model building, training time for the models, and many more issues that hinder the process and hence, constrain the possibility of easy implementation. In this paper, we also intend to shift the paradigm by providing evidence toward the use of technologies like Fog computing and edge computing coupled with the surveillance technology trends, which can help to achieve the goal in a sustainable manner with lesser overheads. 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. -
A compression system for Unicode files using an enhanced Lzw method
Data compression plays a vital and pivotal role in the process of computing as it helps in space reduction occupied by a file as well as to reduce the time taken to access the file.This work relates to a method for compressing and decompressing a UTF-8 encoded stream of data pertaining to Lempel-Ziv-welch (LZW) method. It is worth to use an exclusive-purpose LZW compression scheme as many applications are utilizing Unicode text. The system of the present work comprises a compression module, configured to compress the Unicode data by creating the dictionary entries in Unicode format. This is accomplished with adaptive characteristic data compression tables built upon the data to be compressed reflecting the characteristics of the most recent input data. The decompression module is configured to decompress the compressed file with the help of unique Unicode character table obtained from the compression module and the encoded output. We can have remarkable gain in compression, wherein the knowledge that we gather from the source is used to explore the decompression process. Universiti Putra Malaysia Press. -
A computational approach for shallow water forced KortewegDe Vries equation on critical flow over a hole with three fractional operators
The KortewegDe Vries (KdV) equation has always provided a venue to study and generalizes diverse physical phenomena. The pivotal aim of the study is to analyze the behaviors of forced KdV equation describing the free surface critical flow over a hole by finding the solution with the help of q-homotopy analysis transform technique (q-HATT). he projected method is elegant amalgamations of q-homotopy analysis scheme and Laplace transform. Three fractional operators are hired in the present study to show their essence in generalizing the models associated with power-law distribution, kernel singular, non-local and non-singular. The fixed-point theorem employed to present the existence and uniqueness for the hired arbitrary-order model and convergence for the solution is derived with Banach space. The projected scheme springs the series solution rapidly towards convergence and it can guarantee the convergence associated with the homotopy parameter. Moreover, for diverse fractional order the physical nature have been captured in plots. The achieved consequences illuminates, the hired solution procedure is reliable and highly methodical in investigating the behaviours of the nonlinear models of both integer and fractional order. 2021 Balikesir University. All rights reserved. -
A computational approach for the generalised GenesioTesi systems using a novel fractional operator
This article presents the novel fractional-order GenesioTesi system, along with discussions of its boundedness, stability of the equilibrium points, Lyapunov stability, uniqueness of the solution and bifurcation. The efficient predictorcorrector approach is employed to quantitatively analyse the GenesioTesi system in fractional order. The findings enable conceptualisation and visualisation of the presented novel fractional-order GenesioTesi systems. The modified systems are proposed for future study on chaos control and applying the same for secure communication. Bifurcation analysis is carried out to see the variation in the systems behaviour from stability to chaos. The results of the bifurcation analysis support the results obtained for the stability of the equilibrium points. The system behaves chaotically since all the equilibrium points are unstable. The findings demonstrate a torus attractor for some of the suggested systems and a chaotic attractor for some of the novel fractional-order GenesioTesi systems. The systems torus attractor changes into a steady state when the order is reduced from integer to fractional. Changing the parameter values for one of the modified systems also shifts the systems behaviour, with the point attractor replacing the torus attractor. The point attractor of one of the systems changes into a steady character when the systems order is reduced from integer to fractional. The behaviour for one modified system is the same for fractional and integer orders. This discovery paves the way for the future study of the modified GenesioTesi system. This article gives a new direction to utilise these proposed GenesioTesi systems and study them extensively. The chaotic behaviour of the modified system can be used for secure communication. The synchronisation and chaos control of the modified system is recommended. 2024, Indian Academy of Sciences.