Machine learning is known as a significant pattern of AI that gives an effective allowance to the software applications to become precise at forecasting outcomes without explicitly programmed in doing that. In addition, machine learning is important as this gives service sectors a suitable view of trends in “business operational patterns” and consumer behaviors. Service sectors are mainly known as the healthcare sectors, tourism sectors, and transportation sectors. In several developed countries, AI is maximizing labor productivity by more than 30% in the coming 15 years. The requirement of showing the usage of machine learning and the way it handles the multi-dimensional data have also been shown in this entire work. Machine learning shows some ways through that it helps in providing improvement to all the service sectors such as enhancing consumer analytics, giving rapid and effective assistance, providing effective personalization, identifying the fraud cases and also enhancing customer experiences. Though, in this research work it has been highlighted that, in terms of implementing ML in service sectors, service sectors are facing several challenges. Moreover, in terms of showing the effectiveness of ML two algorithms with flowcharts have been shown in this work. On the other hand, in this research work, a secondary data collection method has been utilized and a qualitative data analysis method has also been used in this research work. In addition, secondary data resources have been assembled from books, scholarly articles, journals, and newspapers. Index Terms : Machine learning, secondary data resources, AI, Service sectors.
Verification for access is used in software to secure information of the user. There are some kinds of verification process, however, as per the secondary information; major users prefer the biometric verification process. On the other hand, block chain based data storage is used in businesses, banking sectors, and other sectors. In this case, this process helps to store confidential information with proper security. This research is focused on the implementation process of block chain based data storage and verification access control. The aim of this research is to demonstrate the importance of block chain data storage and verification access control in various sectors to store and secure information of the users. This research has used the quantitative research data collection method to collect information on block chain and verification access. As per the information, it can be stated that the user has increased demand for block chain due to its verification ability, and it’s other benefits such as increased speed of work, traceability, track of confidential data, and others. In this research, the implementation process of block chain has been discussed with an algorithm flowchart. As per the flowchart, there is a node that helps to store the information of the user and increase the value of block chain data storage. As per the result of this research, there are few steps to implement data storage and those are increasing knowledge on block chain and verification, and strategizing block chain, and plan to implement that. After that, the simulation process needs to be entered in this process to check the progress of implementation. In this research work, the FMS model is discussed to focus on the implementation of verification for access. Keyword : Blockchain, data storage, verification access control, technology, CSE
Here, the main aim of this paper is to discuss the process and procedure of big data analytics that can develop smart and sustainable solutions for the agricultural industries. This paper also tends to collect relevant and reliable information or data input regarding developing smart and sustainable solutions using big data analytics for betterment of the overall agricultural industry. As many previous researchers have proposed the fact that big data analytics can be used to tackle increasing challenges of agricultural production such as granular data on rainfall patterns, water cycles, fertilizer requirements, and more. Here, this particular study module aims to show the importance of sustainable yet smart solutions for the agricultural industry. Moreover, it has also shown different roles of data analytics in terms of providing smart agricultural solutions that has conversely shown the both beneficial and non-beneficial sides of big data analytics during developing smart solutions for better agricultural prediction. As it has already been highlighted the main objective of this specific research is to analyse the process of developing smart and sustainable solutions for the agricultural industry different methodologies or random analytical process such as positivism research philosophy, descriptive research design and secondary data collection method has been applied to acknowledge the methods of implementing or developing smart and solutions by using big data analytics in agricultural industry. At the end a brief discussion and analysis has been shown through using two tables of implementation and a big data analytics flow chart in order to outline the steps that needed to be performed for developing smart and sustainable agricultural solutions. Keyword : Big data analysis, BigData tools and systems, smart and sustainable solution and agriculture industry.
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