Dark Web Data Classification Using Neural Network

There are several issues associated with Dark Web Structural Patterns mining (including many redundant and irrelevant information), which increases the numerous types of cybercrime like illegal trade, forums, terrorist activity, and illegal online shopping. Understanding online criminal behavior is challenging because the data is available in a vast amount. To require an approach for learning the criminal behavior to check the recent request for improving the labeled data as a user profiling, Dark Web Structural Patterns mining in the case of multidimensional data sets gives uncertain results. Uncertain classification results cause a problem of not being able to predict user behavior. Since data of multidimensional nature has feature mixes, it has an adverse influence on classification. The data associated with Dark Web inundation has restricted us from giving the appropriate solution according to the need. In the research design, a Fusion NN (Neural network)-S3VM for Criminal Network activity prediction model is proposed based on the neural network; NN- S3VM can improve the prediction

Enrollment in community based health insurance schemes in rural Bihar and Uttar Pradesh, India

This paper assesses insurance uptake in three community based health insurance (CBHI) schemes located in rural parts of two of India’s poorest states and offered through women’s self-help groups (SHGs). We examine what drives uptake, the degree of inclusive practices of the schemes, and the influence of health status on enrollment. The most important finding is that a household’s socio-economic status does not appear to substantially inhibit uptake. In some cases Scheduled Caste/ Scheduled Tribe (SC/ST) households are more likely to enroll. Second, households with greater financial liabilities find insurance more attractive. Third, access to the hospital insurance scheme (RSBY) does not dampen CBHI uptake, suggesting that the potential for greater development of insurance markets and products beyond existing ones would respond to a need. Fourth, recent episodes of illness and selfassessed health status do not influence uptake. Fifth, insurance coverage is prioritized within households, with the household head, the spouse of the household head and both male and female children of the household head, more likely to be insured as compared to other relatives. Sixth, offering insurance through women’s SHGs appears to mitigate concerns about the inclusiveness and sustainability of CBHI schemes. Given the pan-Indian spread of SHGs, offering insurance through such groups offers the potential to scale-up CBHI

Renewing membership in three community-based health insurance schemes in rural India

Low renewal rate is a key challenge facing the sustainability of Community-based Health Insurance (CBHI) schemes. While there is a large literature on initial enrolment into such schemes, there is limited evidence on the factors that impede renewal. This paper uses longitudinal data to analyse what determines renewal, both one and two years after the introduction of three CBHI schemes, which have been operating in rural Bihar and Uttar Pradesh since 2011. We find that initial scheme uptake is about 23-24 % and that two years after scheme operation, only about 20 % of the initial enrolees maintain their membership. A household’s socio-economic status does not seem to play a large role in impeding renewal. In some instances, a greater understanding of the scheme boosts renewal. The link between health status and use of health care in maintaining renewal is mixed. The clearest effect is that individuals living in households that have received benefits from the scheme are substantially more likely to renew their contracts. We find that having access to a national health insurance scheme is not a substitute for the CBHI. We conclude that the low retention rates may be attributed to limited benefit packages, slow claims processing times and the gaps between the amounts claimed and amounts paid out by insurance

Smart scalable ML-blockchain framework for large-scale clinical information sharing

Large-scale clinical information sharing (CIS) provides significant advantages for medical treatments, including enhanced service standards and accelerated scheduling of health services. The current CIS suffers many challenges such as data privacy, data integrity, and data availability across multiple healthcare institutions. This study introduces an innovative blockchain-based electronic healthcare system that incorporates synchronous data backup and a highly encrypted data-sharing mechanism. Blockchain technology, which eliminates centralized organizations and reduces the number of fragmented patient files, could make it easier to use machine learning (ML) models for predictive diagnosis and analysis. In turn, it might lead to better medical care. The proposed model achieved an improved patient-centered CIS by personalizing the separation of information with an intelligent ”allowed list“ for clinician data access. This work introduces a hybrid ML-blockchain solution that combines traditional data storage and blockchain-based access. The experimental analysis evaluated the proposed model against the competing models in comparative and quantitative studies in large-scale CIS examples in terms of model viability, stability, protection, and robustness, with improved results

Healthcare seeking behavior among self-help group households in rural Bihar and Uttar Pradesh

Abstract Background: In recent years, supported by non-governmental organizations (NGOs), a number of communitybased health insurance (CBHI) schemes have been operating in rural India. Such schemes design their benefit packages according to local priorities. This paper examines healthcare seeking behaviour among self-help group households with a view to understanding the implications for the benefit packages offered by such schemes. Methods: We use cross-sectional data collected from two of India’s poorest states and estimate an alternativespecific conditional logit model to examine healthcare seeking behaviour. Results: We find that the majority of respondents do access some form of care and that there is overwhelming use of private providers. Non-degree allopathic providers (NDAP) also called rural medical practitioners are the most popular providers. In the case of acute illnesses, proximity plays an important role in determining provider choice. For chronic illnesses, cost of care influences provider choice. Conclusion: Given the importance of proximity in determining provider choice, benefit packages offered by CBHI schemes should consider coverage of transportation costs and reimbursement of foregone earnings

Healthcare Seeking Behavior among Self-help Group Households in Rural Bihar and Uttar Pradesh, India

In recent years, supported by non-governmental organizations (NGOs), a number of demand-driven community-based health insurance (CBHI) schemes have been functioning in rural India. These CBHI schemes may design their benefit packages according to local priorities. In this paper we examine healthcare seeking behavior among self-help group households, with a view to understanding the implications for benefit packages offered by such schemes. This study is based on data from rural locations in two of India’s poorest states.1 We find that the majority of respondents do access some form of care and that there is overwhelming use of private services. Within private services, non-degree allopathic providers (NDAP) also called rural medical practitioners account for a substantial share and the main reason to access such unqualified providers is their proximity. The direct cost of care does not appear to have a bearing on choice of provider. Given the importance of proximity in determining provider choices, several solutions could be foreseen, such as mobile medical tours to villages, and/or that insurance schemes consider coverage of transportation costs and reimbursement of foregone earnings

Chronic Kidney Disease: A single day screening on World Kidney Day for five consecutive years

Introduction: Chronic kidney disease is a costly and burdensome public health concern. Delayed recognition and treatment of CKD may predispose patients to unfavorable future outcomes and burden the healthcare services. The early detection of disease via screening programs is widely recommended. The present study is a hospital camp-based screening for detecting patients with chronic kidney disease in Varanasi from 2014-18. Methods: The study subjects constituted 436 apparently healthy adults (age ≥18 years) of Varanasi. Information on socio-demographic profile, personal characteristics and clinical investigations were recorded. Stepwise binary logistic regression analysis was applied to find the significant predictors of chronic kidney disease. Results: Median age of the study subjects was 40.5 years. There were 39.7% males and 60.3% females. Chronic kidney disease was found in 23.9% subjects. Underweight, diabetes mellitus, hypertension, smoking status and higher creatinine levels came out as significant predictors of chronic kidney disease. Conclusion: We screened apparently healthy individuals and found very high percentages of chronic kidney disease and its predictors. Henceforth, understanding the preventable and modifiable risk factors of chronic kidney disease becomes a prerequisite to intervene before risk populations reaches to irreversible stages of adverse future outcomes