A Secure Framework for Health Record Management Using Blockchain in Cloud Environment

Verma, Garima; Pathak, Nishith; Sharma, Neetu

doi:10.1088/1742-6596/1998/1/012019

Cited by 19 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…rough the application of artificial intelligence (AI) and machine learning (ML), growers can access increasingly sophisticated data and analytics tools, which enables better decisions, improved efficiencies, and reduced waste in food and biofuel production, all while minimizing negative environmental consequences. Subsequently, health-related issues will also decrease [26][27][28][29][30]. As a result, agricultural productivity is often assessed as the market value of the finished product.…”

Section: Introductionmentioning

confidence: 99%

Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

Rakhra

Bhargava

et al. 2022

Journal of Food Quality

View full text Add to dashboard Cite

Several industries have recently seen the replacement of human labor by automated machinery and equipment. Across the globe, farmers’ attitudes on the use of technology in agriculture are divergent. However, although some people are excited and ready to embrace technology, others are cautious and wary of trying new technologies for the first time. The third category is particularly prevalent in underdeveloped nations such as India, owing to a lack of competence, a lack of effective translation, and most crucially, a lack of financial resources. It is fruitless for the government to attempt to resolve these difficulties due to the fact that they do not take into consideration the changing circumstances and input needs of each agricultural group. Smart Tillage is a cutting-edge framework that was developed to solve the challenges listed above. In India, a decision-based smart engine for the rental and sharing of tools and equipment has been developed, which leverages machine learning methods to proceed towards a selection of tools and equipment. The option is entirely reliant on a variety of input variables, including crop kind, harvest time/month, crop equipment needed, harvest type, and the amount of money available for rental. Additionally, an ideal recommendation engine driven by content and collaborative-based filtering will provide the farmer’s requirements depending on their requirements. In terms of escalation, the proposals would be cost-effective and excellent since they would need little changes in training, technique improvements, and resource management via a new rent-share model similar to that used by Uber. In this work, demand and supply algorithms are used to define market equilibrium, and the results are shown in graphs. This includes discussion of a variety of demand and supply parameters, their impact on market equilibrium prices and quantities, and their effect on shifting demand and supply curves. The many sorts of elasticities (demand, cross-price, supply, income, and so on) are examined, as well as the ramifications for pricing systems that may result from these elasticities.

show abstract

Section: Introductionmentioning

confidence: 99%

Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

Rakhra

Bhargava

et al. 2022

Journal of Food Quality

View full text Add to dashboard Cite

show abstract

“…The development of artificial intelligence (AI) as a tool for improving health care provides tremendous prospects to improve clinical results and patient outcomes, reduce costs, and influence community health. A1 health check monitoring jobs can take care of executing certain basic and recommended rules of cleaning up data repositories that are not used to managing assets that have been referred for a long time deleting and that large corporations' contents and assets struggle to keep the server up and running 24 hours a day, seven days a week [ 1 , 2 ]. Due to population increase and the emergence of multiple ailments, the number of medical practitioners available to manage the community's health requirements is sometimes insufficient, particularly in less developed countries.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic System Implementation on the Performance Parameters of Health Data Management Frameworks

Vyas

Gupta

Bhargava

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

The development of wireless sensors and wearable devices has led health care services to the new paramount. The extensive use of sensors, nodes, and devices in health care services generate an enormous amount of health data which is generally unstructured and heterogeneous. Many generous methods and frameworks have been developed for efficient data exchange frameworks, security protocols for data security and privacy. However, very less emphasis has been devoted to structuring and interpreting health data by fuzzy logic systems. The wireless sensors and device performances are affected by the remaining battery/energy, which induces uncertainties, noise, and errors. The classification, noise removal, and accurate interoperation of health data are critical for taking accurate diagnosis and decision making. Fuzzy logic system and algorithms were found to be effective and energy efficient in handling the challenges of raw medical data uncertainties and data management. The integration of fuzzy logic is based on artificial intelligence, neural network, and optimization techniques. The present work entails the review of various works which integrate fuzzy logic systems and algorithms for enhancing the performance of healthcare-related apps and framework in terms of accuracy, precision, training, and testing data capabilities. Future research should concentrate on expanding the adaptability of the reasoning component by incorporating other features into the present cloud architecture and experimenting with various machine learning methodologies.

show abstract

“…20 Thus, there is a necessity to build a novel heart disease prediction technique that can enhance the classification accuracy and lessen the computational complexity as well as the errors. [23][24][25][26] However, the initial problem in the diagnosis develops from the data mining process, where there is a chance for the data to get corrupted. Then, the datasets being huge consume huge time for processing.…”

Section: Introductionmentioning

confidence: 99%

Adaptive local binary descriptor statistics: A novel feature set for heart disease diagnosis

Andhare

Ingle

2022

Concurrency and Computation

View full text Add to dashboard Cite

Summary Heart disease is among the most common causes of death worldwide, and it is critical to recognize the condition early on. Clinical professionals face a difficult task in determining whether or not a person is at risk of coronary artery disease since it necessitates extensive knowledge and the use of rigorous clinical tests. The goal of this study is to create a revolutionary heart disease prediction paradigm by going through three primary phases: “feature extraction, optimum feature selection, and prediction.” Initially, attributes such as “statistical and higher‐order statistical features” are retrieved from the provided input data. “local binary pattern (LBP), arithmetic mean based LBP, harmonic mean based LBP, and geometric mean based LBP features” are retrieved from the suggested higher‐order statistical characteristics. In addition, the original characteristics of the input datasets are retrieved. However, in this case, the “curse of dimensionality” appears to be the most serious problem, necessitating the extraction of the best features. As a result, a new lion using the suggested cub growth function algorithm selects the optimum features from the retrieved data. Finally, an optimized deep convolution neural network is used to forecast the specified characteristics.

show abstract

A Secure Framework for Health Record Management Using Blockchain in Cloud Environment

Cited by 19 publications

References 10 publications

Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

Fuzzy Logic System Implementation on the Performance Parameters of Health Data Management Frameworks

Adaptive local binary descriptor statistics: A novel feature set for heart disease diagnosis

Contact Info

Product

Resources

About