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.
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.
IoT Based Air Pollution Monitoring System '' supports showing the quality of air in PPM over the LCD along with that on the web sheet therefore, as this can simply be scrutinized. In order to show the impact of “IoT based Air Quality Monitoring System '', flowcharts, graphs, digraphs and tables have been shown in an effective manner. According to the Statista, it has been estimated that “air quality monitoring IoT '' services along with products would be a significant economic value of 50 up to 60 billion US dollars by the year of 2025. In the present times the condition of air is not that good due to the increasing pollution rate in the past several years. In order to conduct this research work in an effective manner, a secondary data collection method has been taken and all the secondary data resources have been taken to conduct the research in a suitable manner. “IoT based Air Quality Monitoring system” has been suitably designed to quantity the mutual “air quality index” and it also helps in standardizing the results with “India’s Air Pollutant Index'' to suitably indicate the strictness of air superiority. The most harmful air pollutant gasses are NOx, LPG, CO2, NH3, Benzene, alcohol and benzene. In addition, with the help of this system, temperature along with humidity both can effectively be monitored. LPG gas has been suitably detected to use MQ135 and MQ6 sensors and this has been used for monitoring the air’s quality as it helps in detecting all these harmful elements. In this research work, it has been shown that focusing on “Triple Bottom Line '' theory can be effective as this theory helps in showing the ways through which environmental sustainability can be maintained easily. Keyword : IoT based Air Quality Monitoring system, India’s Air Pollutant Index, air quality monitoring IoT .
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