In the modern era business intelligence (BI) has a pivotal role in articulating a strategy and taking correct measures based on data. Business intelligence plays a pivotal role in an inevitable decision support system that enables the enterprise to perform analysis on data and throughout the process of business. Machine learning predicts the forecasting of future demands of the enterprises. Demand forecasting is one of the main decision-making tasks of enterprise. For demand forecasting first raw sales data is collected from the market, then according to data, the future sale/product demands are forecasted. This prediction is based on collected data that compiles through different sources. The machine learning engine executes data from different modules and determines the weekly, monthly, and quarterly demands of goods/commodities. In demand forecasting, its perfect accuracy is non-compromising, the more accurate system model is more efficient. Furthermore, we test the efficiency by comparing the predicted data with actual data and determine the percentage error. Simulation results show that after applying the purposed solution on real-time organization data, we get up to 92.38 % accuracies for the store in terms of intelligent demand forecasting. INDEX TERMS Business intelligence, demand forecasting, prediction, machine learning, AWS sage maker, sale forecasting.

Financial Technology (FinTech) has attracted a wide range of attention and is rapidly proliferating. As a result of its consistent growth new terms have been introduced in this domain. The term 'FinTech' is one such terminology. This term is used for describing various operations that are being frequently employed in the financial technology sector. These operations are usually practiced in enterprises or organizations and provide requested services by using Information Technology based applications. The term does take into account various other sensitive issues, like, security, privacy, threats, cyber-attacks, etc. This is important to note that the development of FinTech is indebted to the mutual integration of different state of the art technologies, for example, technologies related to a mobile embedded system, mobile networks, mobile cloud computing, big data, data analytics techniques, and cloud computing etc. However, this technology is facing several security and privacy issues that are much needed to be addressed in order to improve the acceptability of this new technology among its users. In an effort to secure FinTech, this article provides a comprehensive survey of FinTech by reviewing the most recent as well as anticipated financial industry privacy and security issues. It provides a comprehensive analysis of current security issues, detection mechanisms and security solutions proposed for FinTech. Finally, it discusses future challenges to ensure the security and privacy of financial technology applications.

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Hepatitis C is a prevalent disease in the world. Around 3 to 4 million new cases of Hepatitis C are reported every year across the globe. Effective, timely prediction of the disease can help people know about their Stage of Hepatitis C. To identify the Stage of disease, various noninvasive serum biochemical markers and clinical information of the patients have been used. Machine learning techniques have been an effective alternative tool for determining the Stage of this chronic disease of the liver to prevent biopsy side effects. In this study, an Intelligent Hepatitis C Stage Diagnosis System (IHSDS) empowered with machine learning is presented to predict the Stage of Hepatitis C in a human using Artificial Neural Network (ANN). The dataset obtained from the UCI machine learning repository contains 29 features, out of which the 19 most reverent are selected to conduct the study; 70% of the dataset is used for training and 30% for validation purposes. The precision value is compared with the proposed IHSDS with previously presented models. The proposed IHSDS has achieved 98.89% precision during training and 94.44% precision during validation.

The death ratio caused by heart diseases is threating around the world. Efficient and accurate diagnosis through information technology can turn over this picture. This article proposed Diagnosis Heart Disease using Mamdani Fuzzy Inference (DHD-MFI) based expert system which intelligently diagnoses heart disease. In an explorative pattern, the current research has taken six conducive variables for the purpose of fuzzy logic technical enhancement in the diagnosis of heart disease. The input fields comprise of age, chest pain, electrocardiography, blood pressure systolic, diabetic and cholesterol are transmitted with the help of Fuzzy rules which are framed in the light of low, normal, high and very high intensity among the input variations. The single output is obtained as a clinical decision support system for the heart diagnosis by using the Mamdani Inference method. The proposed DHD-MFI based expert system gives 94% overall accuracy.