Browse Items (16481 total)
Sort by:
-
FinTech in India: A systematic literature review
India is the second-most populous country in the world, with a rapidly growing economy. Its population is highly tech-savvy and has a high level of adoption of digital technologies. The Indian government has taken several initiatives to promote digital transactions and financial inclusion. These initiatives have been instrumental in the growth of fintech in India. Fintech, or financial technology, is transforming the financial sector worldwide. Fintech solutions have led to the creation of new business models, streamlined operations, and enhanced customer experience. India is no exception to this trend, as it has witnessed a significant growth in fintech in recent years. The fintech ecosystem in India is highly diverse, consisting of startups, technology companies, banks, and non-banking financial companies (NBFCs). There are various challenges faced by fintech companies in India, such as lack of access to capital, regulatory hurdles, and competition from established players. This chapter proposal aims to provide a basic literature review on the development of fintech in India. 2023, IGI Global. All rights reserved. -
A STRUCTURAL EQUATION MODELLING APPROACH TOWARDS TAXPAYERS PERCEPTIONS ON GOODS AND SERVICES TAX IN INDIA; [UMA ABORDAGEM DE MODELAGEM DE EQUAES ESTRUTURAIS PARA AS PERCEPES DOS CONTRIBUINTES SOBRE O IMPOSTO SOBRE BENS E SERVIS NA DIA]; [UN ENFOQUE DE MODELADO DE ECUACIONES ESTRUCTURALES HACIA LAS PERCEPCIONES DE LOS CONTRIBUYENTES SOBRE EL IMPUESTO SOBRE BIENES Y SERVICIOS EN LA INDIA]
Purpose: The Purpose of this article is to comprehend how Indian taxpayers perceive the goods and services tax. Theoretical Framework: India has completed five years after the successful implementation of Goods and Services Tax (GST). Many economic benefits were promised at the time of implementation of this tax regime. Thus, it becomes essential to understand tax payers perceptions by developing a strong framework that influences their perceptions. Design/Methodology/Approach: A descriptive study approach was adopted for this objective. 200 replies were obtained in total. Using SPSS Amos, structural equation modelling was utilised to assess the assumptions produced. Attitude, knowledge, Equity, and fairness of taxpayers served as exogenous factors, while taxpayer impression served as the dependent variable. The real-world implication is used as a mediating variable in order to examine the impacts. Findings: The findings of the research indicate that tax knowledge, Equity, and fairness impact tax attitudes. This study provides some useful recommendations for further research in this sector. Research Implications: This study considers tax knowledge, tax equity and fairness and tax attitudes to measure tax payers perception. However, tax rates, regular amendments, circulars, technology and other variables could also be considered by future researchers on this study. Originality/Value: Using a Structural Equation Modelling in understanding Tax Payers Perceptions was hardly adopted in these types of studies. Variables considered for this study were also unique. 2023 AOS-Estratagia and Inovacao. All rights reserved. -
Quality enhanced framework through integration of blockchain with supply chain management
Recently, there has been significant growth in the consumption of the most widely diversified Internet of Things (IoT) technological knowledge, and devices, which has resulted in an impact on not only electrical items and the agricultural and food industries (Agri-Food) supply chain networks. This has sparked intense curiosity about the development of information sharing that is reliable, traceable, and transparent, and also increased significant research and advancement efforts. Existing IoT-based trace & authenticity methods for agri-food distribution networks are constructed on top of centralized architectures, which creates the potential for significant issues such as data security, manipulation, and standard points of weakness. A creative and scraping methodological approach to implementing decentralized trust-free networks is represented by blockchain technologies, the decentralized blockchain technologies that underpin cryptocurrencies. The fault tolerance, data integrity, visibility, and complete tracing of saved transactional data, along with cohesive digital information of property resources and independent transactions implementations, are in fact features built into this digitalization. This study introduces Agri-BlockIoT, a completely decentralized blockchain-based traceable platform for managing a global agro-food distribution network that can seamlessly connect IoT systems that produce and consume digital information all along the distribution chain. We implemented a use caseto achieve transparency and traceability. Lastly, we analyzed and contrasted the implementations' capability in terms of delay, CPU, or network utilization. 2022 The Authors -
Optimization of Flexible Manufacturing Production Line System Based on Digital Twin
This research presents a revolutionary Digital Twin (DT)driven method aimed at quick customization of computerized industrial processes. The DT includes dual components, the semi-physical replication that transfers system information and gives data input to the subsequent clause, which is enhanced. The outcomes of the optimum section are returned directly to the semi-physical replication used for validation. The term Open-Architecture Machine Tool (OAMT) led to a fundamental class of machine tools that consists of a basic unified platform and many individually designed modules that may be quickly added or replaced away. Designers can dynamically modify the production system for responding to process planning by inserting personalized components into its OAMTs. Major enabling approaches, along with how to identical virtual and substantial systems and how to instantly bi-level program the invention size and efficiency of developed structures to accommodate sudden variations of goods, are explained. A real execution is done to demonstrate the efficacy of the method to achieve increased enactment of the system by minimizing the overhead cost of the recompose method by systematizing and quickly enhancing it. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Deep learning based federated learning scheme for decentralized blockchain
Blockchain has the characteristics of immutability and decentralization, and its combination with federated learning has become a hot topic in the field of artificial intelligence. At present, decentralized, federated learning has the problem of performance degradation caused by non-independent and identical training data distribution. To solve this problem, a calculation method for model similarity is proposed, and then a decentralized, federated learning strategy based on the similarity of the model is designed and tested using five federated learning tasks: CNN model training fashion-mnist dataset, alexnet model training cifar10 dataset, TextRnn model training thusnews dataset, Resnet18 model training SVHN dataset and LSTM model training sentiment140 dataset. The experimental results show that the designed strategy performs decentralized, federated learning under the nonindependent and identically distributed data of five tasks, and the accuracy rates are increased by 2.51, 5.16, 17.58, 2.46 and 5.23 percentage points, respectively. 2024 selection and editorial matter, Arvind Dagur, Karan Singh, Pawan Singh Mehra & Dhirendra Kumar Shukla; individual chapters, the contributors. -
Deep learning based federated learning scheme for decentralized blockchain
Blockchain has the characteristics of immutability and decentralization, and its combination with federated learning has become a hot topic in the field of artificial intelligence. At present, decentralized, federated learning has the problem of performance degradation caused by non-independent and identical training data distribution. To solve this problem, a calculation method for model similarity is proposed, and then a decentralized, federated learning strategy based on the similarity of the model is designed and tested using five federated learning tasks: CNN model training fashion-mnist dataset, alexnet model training cifar10 dataset, TextRnn model training thusnews dataset, Resnet18 model training SVHN dataset and LSTM model training sentiment140 dataset. The experimental results show that the designed strategy performs decentralized, federated learning under the nonindependent and identically distributed data of five tasks, and the accuracy rates are increased by 2.51, 5.16, 17.58, 2.46 and 5.23 percentage points, respectively. 2024 The Author(s). -
Bipolar Disease Data Prediction Using Adaptive Structure Convolutional Neuron Classifier Using Deep Learning
The symptoms of bipolar disorder include extreme mood swings. It is the most common mental health disorder and is often overlooked in all age groups. Bipolar disorder is often inherited, but not all siblings in a family will have bipolar disorder. In recent years, bipolar disorder has been characterised by unsatisfactory clinical diagnosis and treatment. Relapse rates and misdiagnosis are persistent problems with the disease. Bipolar disorder has yet to be precisely determined. To overcome this issue, the proposed work Adaptive Structure Convolutional Neuron Classifier (ASCNC) method to identify bipolar disorder. The Imbalanced Subclass Feature Filtering (ISF2) for visualising bipolar data was originally intended to extract and communicate meaningful information from complex bipolar datasets in order to predict and improve day-to-day analytics. Using the Scaled Features Chi-square Testing (SFCsT), extract the maximum dimensional features in the bipolar dataset and assign weights. In order to select features that have the largest Chi-square score, the Chi-square value for each feature should be calculated between it and the target. Before extracting features for the training and testing method, evaluate the Softmax neural activation function to compute the average weight of the features before the feature weights. Diagnostic criteria for bipolar disorder are discussed as an assessment strategy that helps diagnose the disorder. It then discusses appropriate treatments for children and their families. Finally, it presents some conclusions about managing people with bipolar disorder. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Review on EMG-based Pattern Identification Methods for Effective Controlling of Hand Prostheses
The ability of amputees to do daily duties is significantly restricted by upper limb amputation. The myoelectric prosthesis uses impulses from the surviving muscles in the stump to gradually restore function to such severed limbs. Such myosignals are unfortunately tedious and challenging to gather and employ. The process of transforming it into a user control signal after it has been acquired often consumes a significant amount of processing resources. By modifying machine learning strategies for pattern recognition, the factors that influence the traditional electromyography (EMG)-pattern identification approaches may be significantly minimized. Although more recent developments in intelligent pattern recognition algorithms could discern between a variety of degrees of freedom with high levels of accuracy, their usefulness in practical (amputee) applications was less obvious. This review paper examined how well various pattern recognition algorithms for hand prostheses performed. Finally, we discussed the current difficulties and offered some suggestions for future research in our paper's conclusion. 2023 IEEE. -
Adaptive Fuzzy Heuristic Algorithm for Dynamic Data Mining in IoT Integrated Big Data Environments
The explosion of Internet of Things (IoT) devices has created enormous amounts of real-time data, requiring sophisticated Data Mining Methods (DMT) that can rapidly extract valuable insights. Managing the computational complexity of processing high data volumes, integrating various IoT data formats, and ensuring that the system can scale are among the most significant issues. Fuzzy Dynamic Adaptive Classifier Optimization Analysis (FDACOA) is a method that has been suggested as an approach to the difficulties caused by changes in data patterns, processing in real-time, and data heterogeneity. By incorporating Adaptive Fuzzy Logic (AFL) and heuristic optimization, FDACOA enhances data classification accuracy and efficiency while simultaneously assuring that the algorithm can adapt to changes in data streams. This adaptability is crucial in IoT applications, where data fluctuation might affect analysis quality. FDACOA uses dynamic adaptation to alter classifier parameters based on real-time feedback to improve prediction accuracy and reduce computing costs. An optimization layer fine-tunes fuzzy rules and membership functions to optimize performance across data situations. Simulation analyses proved the algorithm's capacity to classify with high accuracy and low computational cost. Smart healthcare, predictive maintenance in industrial IoT, and intelligent transportation systems use FDACOA for real-time decision-making and data-driven insights. FDACOA is a viable approach for dynamic data mining in IoT-enabled big data contexts because of its faster, more accurate, and more adaptable simulation results. 2025, Research Expansion Alliance (REA). All rights reserved. -
Effectiveness of Influencer Marketing Campaigns on Purchase Decision of Apparel Brands: A Study Among Millennial and Gen Z Consumers in India
The present study adopted the descriptive research design exploring the effectiveness of influencer marketing campaigns on the purchase decision processes of the Millennials and Generation Z consumers in India. Every reasonable effort will be made to reach these two, being otherwise alike: Millennials, aged 2642years and Generation Z, aged 1825years, identified by purposive sampling on the basis of their active usage of social media and their shopping for apparel products over the Internet, whether as a result of direct purchase or through influencer recommendations. The total sample size is to be 625 respondents to ensure statistical validity. The data will be collected by means of a structured questionnaire distributed to the targeted consumers across regions in India. The collected data is analysed with the help of the statistical tools Independent Sample t-test, Correlation and Regression. finally this study conclude that there is positive impact of Influencer Attributes (Trustworthiness, Authenticity, Expertise, Popularity) and Campaign Characteristics (Content Quality, Engagement Rates, Platform Choice, Frequency of Post) on consumer purchase decisions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Influence of agricultural wastes on larval growth phases of the black soldier fly, Hermetia illucens (Diptera: Stratiomyidae): An integrated approach
Insects are an effective tool for converting nutrients in agricultural by-products into protein-rich biomass and compost. Black soldier fly (BSF) (Hermetia illucens) larvae are currently one of the insect species widely used as a protein source in aquafeed globally. Although much effort has been spent on the use of BSF in aquafeed, there is not much information on the biology of the insect, especially with the morphology of the BSF. This study aimed to evaluate the influence of various organic wastes, such as fruit wastes (FW) and vegetable wastes (VW), on different growth phases of BSF larva (BSFL), using morphometric and scanning electron microscopic examinations, and the composition of the compost produced, as well as a method for upscaling of larval production of BSFL. Faster growth was observed in BSFL fed with VW substrate (40 days) compared to the FW (46 days). Based on the morphometric measurements such as length, larval head length, total length etc., five larval stages, prepupal and pupal stage of BSFL were differentiated and described. In addition, SEM imaging of BSF mouth parts found that the mouth morphology of the BSF larvae and prepupal stage differed, and the BSF prepupa had reduced mouthparts. Also, the mandibular-maxillary complex was well developed than the BSF prepupa. BSFL larvae have proven to convert fruit and vegetable waste into high-quality residue fertilizer for the soil. The BSF compost showed optimum nitrogen, phosphorous, potassium, calcium and sulphur content. This research establishes a baseline knowledge and guidance on the BSF-rearing facilities. Author (s). Publishing rights @ ANSF. -
Characteristics of chitin extracted from different growth phases of black soldier fly, Hermetia illucens, fed with different organic wastes
Insect chitin was isolated from different life stages of the black soldier fly Hermetia illucens, such as instar stages, prepupae and pupae, reared separately on fruit and vegetable waste substrates after removal of fat, protein and minerals. Chitin yield was high in prepupae fed with vegetable waste (11.78 0.13%) followed by fruit waste (6.82 0.36%). The extracted chitin was compared with a commercial chitin from shrimp by Fourier-transform infrared spectroscopy, X-ray diffraction, scanning electron microscopy and thermogravimetric analysis. The results revealed that both chitins from commercial shrimp source and the H. illucens had similar chemical structures and physicochemical properties indicating the H. illucens chitin samples to be of ?- chitin orientation type. With regards to the H. illucens samples, small physiochemical changes were noticed. Furthermore, the polymers derived from BSF are equivalent to commercial polymers in terms of purity and structural morphology, indicating their utility for industrial and medical applications. Thus, H. illucens prepupae is a promising alternative source of chitin. 2023, African Association of Insect Scientists. -
Influence of fruit and vegetable waste substrates on the nutritional profile of black soldier fly (Hermetia illucens) larvae and prepupa
The black soldier fly, Hermetia illucens, larva is widely recognized for efficiently converting organic biowaste into high-quality biomass, making it a key player in organic waste management. However, the nutritional value of the black soldier fly larvae (BSFL) is dependent on the substrates they feed on. This study investigated the nutritional profiles of different stages of BSFL- 3rd instar to 5th, and prepupa, reared on two distinct organic wastes, namely fruit (FW) and vegetable waste (VW). Analysis of crude protein, carbohydrate, crude lipid, minerals, and fatty acid composition was conducted across various growth stages, such as 3rd instar to 5th instar, and prepupa. The prepupa stage reared on FW exhibited the highest crude protein content (54.16 0.64%), while VW 5th instarhad the highest crude lipid content (12.4 0.20%). BSFL reared on FW displayed a high fatty acid composition with higher saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA), regardless of the substrate. Calcium and potassium were the most abundant minerals in BSFL, followed by magnesium, manganese and zinc, with substantial concentration variations between substrates. Amino acid profiling focused only on BSFL reared on FW, due to superior results in the chemical composition analysis, revealing that the prepupal stage contained the highest amount of essential and non-essential amino acids compared to the other stages. This study suggests that BSFL meal has the potential to serve as a novel and sustainable source of nutrient-rich animal feed ingredients in aquaculture and other animal husbandry practices. African Association of Insect Scientists 2025. -
Role of employee resource groups (ERGs) in fostering workforce diversity in information technology (IT) organizations after COVID-19
This chapter discovers how employee resource groups play an important role in fostering organizational diversity within information technology organizations. It examines the activities and practices to improve employee behaviour and also focuses on challenges faced by employees in spite of stress and mental health related issues during the COVID-19 pandemic. The data has been collected from secondary sources. The authors have used desk research and gray literature. The findings showcase increased employee engagement, improvements in diversity and inclusion, and an overall improvement in the inventive and creative skills of employees. It also helps the organization to brand itself better along with better recruitment strategies and practices. The key emphasis of the paper looks at the employees working within information technology organizations and how employee resource groups function to balance, motivate, and empower employees during COVID-19 Pandemic. 2023, IGI Global. All rights reserved. -
Valorization of agro-industrial fruit peel waste to fluorescent nanocarbon sensor: Ultrasensitive detection of potentially hazardous tropane alkaloid
Millions of tonnes of agro-industrial waste are generated each year globally, with the vast majority of it going untreated, underutilized, and disposed of by burning or landfilling, causing severe environmental distress and economic downturn. A practical solution to this global issue is to use green chemistry to convert this waste into value-added products. Accordingly, in the present study, agro-industrial orange peel waste was valorized into fluorescent nanodiamond-like carbon sensor via a green route involving hydrothermal treatment of microwave carbonized orange peel waste. The developed sensor, used for the fluorescence detection of potentially hazardous drug atropine sulfate, exhibits unique dual linearity over concentration ranges of 300 nM to 1 M and from 1 M to 10 M, as well as ultra-low sensitivity of 34.42 nM and 356.46 nM, respectively. Additionally, the sensor demonstrates excellent reproducibility, high stability, and satisfactory recovery when used to identify and quantify atropine sulfate in biological samples and commercially available pharmaceuticals, indicating promising multidisciplinary applications. [Figure not available: see fulltext.] 2021, Higher Education Press. -
Facile Synthesis of Few-Layer Graphene Oxide from Cinnamomum camphora
Abstract: This study presents a facile synthesis technique to produce few-layer graphene oxide from Cinnamomum camphora (Camphor L.). Camphor upon carbonization and chemical oxidation leads to the formation of few-layer graphene oxide sheets of around ten layers with a lateral size of 4.14 nm and stacking height of 3.10 nm. AFM and SEM analysis results reveal the wrinkled morphology of the graphene oxide sheets formed. The sharp G band and the relative intensity of the defect to the graphitic band in the Raman spectrum indicate the formation of nanocrystalline graphene oxide sheets with fewer defects. The FTIR spectrum and the deconvoluted C 1s XPS spectrum of graphene oxide synthesized reveal the presence of predominant sp2 hybridized carbon along with carbon bound to various oxygen functionalities. In brief, the formation of high-quality few-layer graphene oxide from an abundant, low-cost, and readily available botanical precursor is herein reported. 2021, Pleiades Publishing, Ltd. -
Extraction and characterization of wrinkled graphene nanolayers from commercial graphite
A report on the synthesis of wrinkled graphene nano carbon from bulk graphite is presented here. The obtained graphene nano carbon comprises mixed phase, sp2-sp3 bonded disordered carbon network. The as synthesized samples were intercalated by Hummer's method and are separated by centrifugation and sonication to obtain few layer graphene sheets. The structural and chemical changes of the nanostructure was elucidated by Raman spectroscopy, XRD, SEM-EDS, XPS, FTIR and UV-Vis-NIR spectroscopy. Raman spectra confirmed the existence of highly graphitized amorphous carbon with five peaks in the deconvoluted first order Raman spectrum. The IR and XPS analysis confirms the incorporation of functional groups to graphitic basal plane. There was a shift in the peaks position and intensity with intercalation. The synthesized graphene sheet is found to be in the graphite to nanocrystalline graphitic trajectory. The SEM analysis revealed the formation of large area wrinkled graphene sheets. The nanostructure formed is effortlessly scalable and ideally suitable for nano carbon composites based nano electronic devices and switches. -
Wrinkled graphene: Synthesis and characterization of few layer graphene-like nanocarbons from kerosene
Wrinkled graphene, derived from a facile thermal decomposition and chemical method, was subjected to various analysis techniques and the results have been reported here. Raman studies revealed the presence of highly graphitized amorphous carbon, which was evident by the appearance of five peaks in the deconvoluted first order spectrum. This result was very well corroborated by the XRD analysis. XPS and FT-IR spectra confirmed the incorporation of oxygen functionalities into the carbon backbone. AFM and SEM images of the sample disclosed a cluster of few-layer wrinkled graphene fragments. TEM images displayed a chain of nearly spherical aggregates of graphene, resembling nanohorns. The resistivity and sheet resistance of the sample were found to be low, making the obtained material a promising candidate for various device applications. Hence, kerosene soot proved to be an efficient precursor for facile synthesis of few layer graphene-like nanocarbon. 2016 Wroclaw University of Technology. -
Mesoporous onion-like carbon nanostructures from natural oil for high-performance supercapacitor and electrochemical sensing applications: Insights into the post-synthesis sonochemical treatment on the electrochemical performance
Although onion-like carbon nanostructures (OLCs) are attractive materials for energy storage, their commercialization is hampered by the absence of a simple, cost-effective, large-scale synthesis route and binder-free electrode processing. The present study employs a scalable and straightforward technique to fabricate sonochemically tailored OLCs-based high-performance supercapacitor electrode material. An enhanced supercapacitive performance was demonstrated by the OLCs when sonicated in DMF at 60 C for 15 min, with a specific capacitance of 647 F/g, capacitance retention of 97% for 5000 cycles, and a charge transfer resistance of 3 ?. Furthermore, the OLCs were employed in the electrochemical quantification of methylene blue, a potential COVID-19 drug. The sensor demonstrated excellent analytical characteristics, including a linear range of 100 pM to 1000 pM, an ultralow sensitivity of 64.23 pM, and a high selectivity. When used to identify and quantify methylene blue in its pharmaceutical formulation, the sensor demonstrated excellent reproducibility, high stability, and satisfactory recovery. 2021 The Author(s) -
A Congruent Approach to Normal Wiggly Interval-Valued Hesitant Pythagorean Fuzzy Set for Thermal Energy Storage Technique Selection Applications
Thermal energy is the energy from a substance in which molecules and atoms vibrate faster because of an increase in temperature. Thermal energy storage (TES) is an available energy resource for renewable energy platforms that enables them to meet sustainable technical requirements. The TES technique is divided into three categories; sensible TES, latent-heat TES, and thermo-chemical TES. The best of these techniques is selected in this research paper. Here the Interval-Valued Hesitant Pythagorean Fuzzy Set (IVPHFS) under the Normal Wiggly Mathematical Methodology is proposed and described for application to multi-criteria decision making (MCDM) technology. The MCDM methods, the Step-wise Weight Assessment Ratio Analysis (SWARA) method for determining weight values, and the Weighted Aggregated Sum Product Assessment (WASPAS) method for ranking alternative values are used employed here. The alternative values are selected based on the following criteria: capacity, efficiency, storage period, charging and discharging times, and cost 2021, Taiwan Fuzzy Systems Association.