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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.

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.

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 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.

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.

The aim of this paper is to investigate the impacts of the noise from the diesel engine power generators used for production activities in an urban environment.

This study has used the Enterprise Edition of NoiseMap 2000 Version 2.7.1 to investigate the impacts of the noise from the diesel engines electric power generators used in a factory in Ikorodu, an urban environment in Lagos, Nigeria. Five sections of the factory with diesel engines electric power generators were considered. The immediate and distant environments covering about 10 km of the factory host environment were considered as receptors to the noise for this study.

It was found out that when all the generators operate simultaneously in the factory, the ambient noise was 30.0-152.5 dB(A) with the minimum contribution within the factory being 70.0-84.4 dB(A) and the maximum contribution of 57.2-70.8 dB(A) outside the factory fence line. Though the maximum noise is 152.5 dB(A), the maximum noise of 70.8 dB(A) beyond the fence line shows a compliance with 70 dB(A) industrial and commercial area limit but breaches the 45 dB(A) and 55 dB(A) residential area limit of the World Bank.

As much as it would be desirable ambient noise level could not be measured in all the receptors’ locations covered by the modeling. However, the capability of the modeling software adopted makes this to have no negative impact on the quality of the findings of this study.

The study will assist the public to determine the noise level safe region around diesel engine electric power generators.

The paper highlights the challenges in which ambient noise from the use of off-grid generators used for industrial purposes could pose to the neighboring receptor environments.

The purpose of this paper is to describe a method that has been set up to schedule preventive maintenance (PM) tasks for power and water plants with all constraints such as production and maintenance.

The proposed methodology relies on the zero-one integer programming model that finds the maximum number of power and water units available in separate generating units. To verify this, the model was implemented and tested as a case study in Kuwait for the Cogeneration Station.

An effective solution can be achieved for scheduling the PM tasks and production at the power and water cogeneration plant.

The proposed model offers a practical method to schedule PM of power and water units, which are expensive equipment.

This proposed model is an effective decision-making tool that provides an ideal solution for preventive maintenance scheduling problems for power and water units in a cogeneration plant, effectively and complies with all constraints.

Dynamic economic and emission dispatch (DEED) aims to optimally set the active power generation with constraints in a power system, which should target minimum operation cost and at the same time minimize the pollution in terms of emission when the load dynamically changes hour to hour. The purpose of this study is to achieve optimal economic and emission dispatch of an electrical system with a renewable generation mix, consisting of 3-unit thermal, 2-unit wind and 2-unit solar generators for dynamic load variation in a day. An improved version of a simple, easy to understand and popular optimization algorithm particle swarm optimization (PSO) referred to as a constriction factor-based particle swarm optimization (CFBPSO) algorithm is deployed to get optimal solution as compared to PSO, modified PSO and red deer algorithm (RDA).

Different model with and without wind and solar power generating systems; with valve point effect is analyzed. The thermal generating system (TGs) are the major green house gaseous emission producers on earth. To take up this ecological issue in addition to economic operation cost, the wind and solar energy sources are integrated with the thermal system in a phased manner for electrical power generation and optimized for dynamic load variation. This DEED being a multi-objective optimization (MO) has contradictory objectives of fuel cost and emission. To get the finest combination of the two objectives and to get a non-dominated solution the fuzzy decision-making (FDM) method is used herein, the MO problem is solved by a single objective function, including min-max price penalty factor on emission in the total cost to treat as cost. Further, the weight factor accumulation (WFA) technique normalizes the pair of objectives into a single objective by giving each objective a weightage. The weightage is decided by the FDM approach in a systematic manner from a set of non-dominated solutions. Here, the CFBPSO algorithm is applied to lessen the total generation cost and emission of the thermal power meeting the load dynamically.

The efficacy of the contribution of stochastic wind and solar power generation with the TGs in the dropping of net fuel cost and emission in a day for dynamic load vis-à-vis the case with TGs is established.

Cost and emission are conflicting objectives and can be handled carefully by weight factors and penalty factors to find out the best solution.

The proposed methodology and its strategy are very useful for thermal power plants incorporating diverse sources of generations. As the execution time is very less, practical implementation can be possible.

As the cheaper generation schedule is obtained with respect to time, cost and emission are minimized, a huge revenue can be saved over the passage of time, and therefore it has a societal impact.

In this work, the WFA with the FDM method is used to facilitate CFBPSO to decipher this DEED multi-objective problem. The results reveal the competence of the projected proposal to satisfy the dynamic load demand and to diminish the combined cost in contrast to the PSO algorithm, modified PSO algorithm and a newly developed meta-heuristic algorithm RDA in a similar system.

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.

This paper aims to update with information about edible coating on minimally processed and fresh fruits, focussing on the composition, active ingredients, antimicrobial concentration and their effect on ripening rate, phytonutrients retention and shelf-life of fruits. In future, the data will be helpful for the processors to select the best coating material and its effective concentration for different fresh and minimally processed fruits.

Major scientific information was collected from Scopus, Web of Science, Mendeley and Google Scholar. Several key words such as postharvest, edible coating, phytonutrients, shelf-life enhancement, bioactive compounds, minimally processed fruits and antimicrobial compounds were used to find the data. Relevant information was collected by using 90 recent research and review articles.

The main findings of this comprehensive review are to improve the quality of fruits, to meet the next-generation food security needs. However, in the process of achieving the goal of improving quality of food produce, embrace of synthetic, non-biodegradable packaging materials have increased, creating serious pollution problem. Amidst several alternatives for replacement of synthetic packaging, the option of biodegradable films and coatings showed promising results.

The paper represents recent information about the edible coating used for the enhancement of shelf-life of fresh and minimally processed fruits.