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Financial capability is considered to be an important concept that has drawn the attention of many world nations. While the literature suggests various studies on financial capability and financial wellbeing, focus on their combined significance has been limited. The purpose of this paper is to examine how financial capability affects the financial wellbeing of women in community-based organizations and how decision-making ability mediated this relationship.

In total, 1,000 women who are associated with the community-based organization – Kudumbashree in the state of Kerala, India participated in the survey-based study.

The structural equation modelling results show that there exists a significant relationship between financial capability and the financial wellbeing of women in CBOs. Further, decision-making ability was identified as a significant mediator in this relationship thus establishing a partial mediation effect.

The financial social workers can focus their activities on promoting financial capability and decision making aspects of women from middle/low income families to facilitate their financial wellbeing. The scope for financial socialisation and proper orientation is more for the women associated with the community based organisations. This opportunity can be made use by the government authorities and other practitioners to change their financial outlook and contribute towards the empowerment of these women from the grass root level.

The studies related to financial literacy and financial inclusion are available in the Indian context, but the conceptualization of financial capability is still an under-researched area in India. Hence, this study is an attempt to explain the capability-wellbeing relationship from a financial point of view in the Indian context, and further establishes its connection with the individual's decision-making ability. To strengthen the research base, the study was conducted among the women in the community-based organization who belong to middle and low-income families.

The mortality rate due to skin cancers has been increasing over the past decades. Early detection and treatment of skin cancers can save lives. However, due to visual resemblance of normal skin and lesion and blurred lesion borders, skin cancer diagnosis has become a challenging task even for skilled dermatologists. Hence, the purpose of this study is to present an image-based automatic approach for multiclass skin lesion classification and compare the performance of various models.

In this paper, the authors have presented a multiclass skin lesion classification approach based on transfer learning of deep convolutional neural network. The following pre-trained models have been used: VGG16, VGG19, ResNet50, ResNet101, ResNet152, Xception, MobileNet and compared their performances on skin cancer classification.

The experiments have been performed on HAM10000 dataset, which contains 10,015 dermoscopic images of seven skin lesion classes. The categorical accuracy of 83.69%, Top2 accuracy of 91.48% and Top3 accuracy of 96.19% has been obtained.

Early detection and treatment of skin cancer can save millions of lives. This work demonstrates that the transfer learning can be an effective way to classify skin cancer images, providing adequate performance with less computational complexity.

The current study aims to identify the impact of financial literacy, financial risk-tolerance, financial socialization, financial stress, socio-demographic factors and financial behavior on the individual financial wellbeing residing in India's National Capital Region (NCR) region. Understanding financial wellbeing is crucial as it helps individuals understand personal finance better and develop a more favorable financial attitude. The information can depict individuals' financial skills, knowledge and attitudes toward achieving financial wellbeing in emerging economies.

Through self-administered survey questionnaires, data are obtained using convenience sampling from 420 (394) respondents regarding individual financial wellbeing levels in India. The survey responses were collected between May 2022 and July 2022. The authors use the “partial least squares structural equation modeling” (PLS-SEM) technique to test the research hypotheses.

The present study's outcome confirms that five determinants, such as financial literacy, financial risk-tolerance, financial socialization, financial stress and socio-demographic factors, significantly influence the financial behavior of individuals. Further, financial behavior, financial literacy, financial risk-tolerance and financial socialization significantly influence financial wellbeing. However, financial stress and socio-demographic factors have statistically insignificant impacts on financial wellbeing.

The present study is exclusive in which an effort is being made to acquire relative importance on financial behavior and an individual's financial wellbeing. The present paper will help the government, financial services providers, and policymakers in offering innovative economic schemes and designing policies that may enhance the financial wellbeing of individuals. Finally, this article provides the road map for future research in this field.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

The recent economic changes in India and the gender discrimination practices of the patriarchal society have forced Indian women to turn to the financial sector as an essential means of generating returns. This study aims to identify the factors influencing investors’ investment frequency in India’s two most recognized metropolitan areas.

The authors applied structural equation modeling to augment Allport’s consumer behavior model and the social influence theory for assessing the frequency of investments made by 690 investors. The direct and indirect linkages in the proposed model were evaluated using moderation and mediation techniques.

The study’s findings show that investors’ perceptions of gender discrimination practices and social influence considerably increase investors’ involvement, magnifying their investment frequency. In addition, access to reliable information reinforces the relationship between investors’ involvement and their frequency of investments, whereas the low-risk tolerance weakens this association.

The findings could help policymakers, investors, financial media outlets, financial experts, educational institutions and society strengthen India’s financial sector by leveraging the linkage between the underlying constructs and investors’ behavior.

The aspects of involvement and gender inequality have not garnered enough attention in the previous studies on behavioral finance. The study delves deeper into investor behavior by establishing a link between the underlying constructs and broadening the horizons of prominent consumer behavior models. It also unfurls the moderating role of access to information and risk tolerance to comprehend the association better.

Sustainable development goals (SDGs) recognize the importance and interrelation between health and migration. Women migration and health is well researched, yet less attention is paid to their healthcare utilization, especially with regard to overall health and well-being. This paper aims to highlight the gap in the existing literature on health care utilization by women migrants.

A systematic review was carried out following the PRISMA guideline. For the review, the literature was taken from three electronic databases, which were Springer Link, Taylor and Francis and PubMed. From a total of 1,575 studies, seven studies cleared the eligibility screening.

Of seven studies, five were found to focus on the sexual and reproductive health of the women migrants than their general health and well-being, and less attention is paid to health promotion and illness prevention beyond reproductive and sexual health. While, studies on general health have focused on the influence of health status on health care utilization and the influence of health insurance in health care utilization. The review has revealed the disparities faced by migrant women in different countries while seeking health care.

Studies on women migration and health care utilization have largely focused on the reproductive and sexual health needs of women, and this overemphasis often undermines their accessibility and affordability to overall health and well-being. Therefore, the present study has moved away from the concept of sexual and reproductive health tot that of overall health and well-being of women migrants.

The present study aims to investigate the factors of loan access that affect entrepreneurial self-efficacy (ESE) and operating efficiency of Indian Micro, Small and Medium Enterprises (MSMEs). Furthermore, the study intended to investigate the influence of ESE on the operating efficiency of Indian MSMEs and its mediating role.

In this study, exploratory research design is used. The study heavily relies on the primary data which has been collected by using the survey research method from a cross-section of 617 women-owned MSMEs, located in urban, rural, suburban and exurban areas of Haryana, Uttarakhand, Himachal Pradesh and NCR-Delhi. The partial least square structural equation modeling method version 3.3.3 has been used to evaluate.

In terms of the selected factors affecting access to finance, it has been established that the Loan Formalities, Banking Process, Loan Process, Staff Responsiveness and Incentive Scheme have a positive and significant influence in enhancing accessibility to finance and improving the self-efficacy and operating performance of firms. The findings also show that ESE mediates the relationship between various factors of loan access and the operating efficiency of MSMEs.

The study’s findings show that entrepreneurial capacity is significantly and favorably impacted by attitudes toward entrepreneurship, ESE, perceived access to findings and business operations. It has also been demonstrated that entrepreneurial intentions are strongly and favorably influenced by entrepreneurial ability to access commercial bank financing for small businesses and the impact of the same on the women-owned MSMEs in India. It also revealed unfavorable loan terms, limited collateral, fear of repaying of loan and intricate loan application were among the many reasons for loan denial.

The study offers a comprehensive approach that simultaneously considers financial accessibility and ESE. This all-encompassing method offers a thorough grasp of the variables affecting MSMEs' operational efficiency (OE). In contrast to earlier research that might have concentrated only on direct relationships, this study explores the mediating mechanisms involved. This study examines how ESE modulates the influence of financing availability on OE, providing a comprehensive understanding of the underlying mechanisms. By taking into account particular MSME sector characteristics like size, industry or regional variations, the study may provide a unique contextual lens. Understanding how these contextual factors interact with entrepreneurial attributes and access to finance adds depth to the analysis.

Money laundering is the process of concealing unlawfully obtained funds by presenting them as coming from a legitimate source. Criminals use crypto money laundering to hide the illicit origin of funds using a variety of methods. The most simplified form of bitcoin money laundering leans hard on the fact that transactions made in cryptocurrencies are pseudonymous, but open data gives more power to investigators and enables the crowdsourcing of forensic analysis. With the motive to curb these illegal activities, there exist various rules, policies and technologies collectively known as anti-money laundering (AML) tools. When properly implemented, AML restrictions reduce the negative effects of illegal economic activity while also promoting financial market integrity and stability, but these bear high costs for institutions. The purpose of this work is to motivate the opportunity to reconcile the cause of safety with that of financial inclusion, bearing in mind the limitations of the available data. The authors use the Elliptic dataset; to the best of the authors' knowledge, this is the largest labelled transaction dataset publicly available in any cryptocurrency.

AML in bitcoin can be modelled as a node classification task in dynamic networks. In this work, graph convolutional decision forest will be introduced, which combines the potentialities of evolving graph convolutional network and deep neural decision forest (DNDF). This model will be used to classify the unknown transactions in the Elliptic dataset. Additionally, the application of knowledge distillation (KD) over the proposed approach gives finest results compared to all the other experimented techniques.

The importance of utilising a concatenation between dynamic graph learning and ensemble feature learning is demonstrated in this work. The results show the superiority of the proposed model to classify the illicit transactions in the Elliptic dataset. Experiments also show that the results can be further improved when the system is fine-tuned using a KD framework.

Existing works used either ensemble learning or dynamic graph learning to tackle the problem of AML in bitcoin. The proposed model provides a novel view to combine the power of random forest with dynamic graph learning methods. Furthermore, the work also demonstrates the advantage of KD in improving the performance of the whole system.

The purpose of this paper is to check the Solar Photovoltaic (PV) inverter working condition with modified unipolar switching pulse. The gate pulse for the inverter switches is generated in MATLAB simulation and interfaced with hardware protype. Simulation results can be compared with hardware results.

A considerable amount of research has been done on different Pulse Width Modulation (PWM) techniques. Based on the findings, a modified Unipolar Sinusoidal PWM technique was created with one reference signal and two carrier signals+ (one for the positive half cycle and the other for the negative half cycle) and simulated in the MATLAB/Simulink platform. The prototype inverter module receives the simulated switching pulses via dSPACE DS1104 hardware software interfacing board. The hardware implementation has been done, and the hardware results compared with simulation results for various input voltage levels using resistive load.

This modified switching pulse has dead band and additional hardware setup is not required. 3-phase multi-level inverter output waveform has been achieved with six switches in this method and with low filter values, pure sine wave output can be obtained in simulation. By this method of switching pulse generation and testing, for every modification in switching pulse hardware gate driver is not required. Resulting time consumption and money investment are lower.

Modified Unipolar SPWM pulse generation technique is novel method for solar PV inverter. The switching pulse has been designed and tested in both MATLAB/Simulation and hardware prototype inverter. Hardware and software results are identical. This method of pulse generation and hardware implementation has not been done anywhere before.

This study aims to comprehensively inspect the impact of blockchain technology in the context of globalization, focusing on the most influential research works, scientific production by countries, commonly used keywords, trending topics and the conceptual and intellectual structure of this field.

A bibliometric analysis was conducted in R Studio, involving the review and scrutiny of 194 research papers indexed in the Scopus database. This analytical approach allowed for a systematic examination of the literature and the identification of key patterns and trends in blockchain and globalization research.

The analysis revealed the most influential research contributions, highlighted countries with significant scientific production, identified frequently used keywords and unveiled emerging trends and areas of focus in the field of blockchain and digitization. Additionally, the study explores prospective research directions.

This research provides insights into the current state of blockchain and globalization research, offering valuable information for scholars, policymakers and industry professionals. The recognized themes provide a systematic structure for upcoming inquiries, directing studies toward vital domains like supply chain management and sustainable development. Moreover, the analysis emphasizes the significance of interdisciplinary collaboration and provides practical insights, enabling businesses and policymakers to enhance transparency, counteract fraud and adjust to the digital era for societal and economic advancement.

The study contributes to the prevailing literature by steering a comprehensive analysis of the interplay between blockchain technology and globalization, shedding light on influential works and emerging trends. It adds value by providing a structured overview of the field's intellectual landscape.