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In this day and age, the D.C motors have been commonly used in many enterprises, sports, kind toys, and in several home appliances since of its outstanding features like reliability, a extensive limit of torque-speed, better performance, good starting torque, lower electrical control range, weight/torque ratio and noise. In contemporary Gulf Arab societies for centuries, camel racing has been part of Arabian culture, in one type or another, with some historians dating races to the seventh century. In this project work, the camel is fitted with the DC motor on its back depend on the speed of the motor the beat stick attached at the end will slash the camel to move fast forward. This allows the camel to acknowledge that still a person is sitting back and driving them. By adhering to the latest technologies like the Internet of Things (IoT) the camel may be driven from a remote location. This proposed method will have a controller like Arduino which acts as a central processing unit for the controlled action of the motor. The ESP8266 module is connected with the Arduino for providing an internet facility for supporting IoT. Blynk, a mobile application provides the platform to control the connected devices online through IoT. Here the DC motor speed set control is given as the slide switch in the Blynk app which in turn controls the speed of the motor through Wi-Fi employing IoT. This makes the racing environment without the case of a vehicle which may drastically reduce the air pollution. [ABSTRACT FROM AUTHOR]

Fraud detection systems (FDS) are an important factor in e-commerce trading as online fraud grows and spreads quickly. The incidence of fraud involving online payments by credit cards has recently increased considerably, forcing all banks and e-commerce companies to carry out large-scale operational logs to create automatic methods to detect fraud. The controlled binary classification systems that are appropriately trained from pre-determined sampled data sets appear to be one of the most promising techniques for the detection of unlawful transactions, separating fraudulent and non-fraudulent cases. So the problems related to fraud detection are also classified as a novel challenge that is an assessment based on several advanced algorithms, such as random woodlands, Naive Bays (NB), and generative opponent networks. The problem is a classification problem. In this article, random forest and generative adversary networks are developed to further increase the accuracy of the detection rate of fraud via credit cards. Extensive experiments have shown that the technique offered exceeds current techniques. [ABSTRACT FROM AUTHOR]

It is crucial that Breast Cancer should be detected early. Breast cancer time series forecasting is a novel data - driven approach to breast cancer diagnosis. Instead of looking at static images of the medical records, it analyses the dynamics in the tumour's growth rate, especially in its early stages. It uses machine learning models to find patterns that are not readily observable in static images, but are predictive of later outcomes. During COVID-19 it is necessary to monitor patient from home and IOT devices can be used that give moment forecast to the client and doctor during their typical day by day routine. Various Machine learning models are reviewed for classification of Breast Cancer symptoms. It is observed that data visualization and feature engineering play a crucial role in the classification before applying any model on data set. For human protection during COVID-19 it is better to depend on IoT enabled wearable device for automatic detection and appointment. The IoT enabled devices can use power of cloud computing and machine learning models to complete the framework of getting treated at home. Security of the data is another aspect to be taken into consideration. Solutions are available for the whole process and their aggregation will result in generating the desired model. In this paper, model is proposed to diagnose breast cancer at home using IoT, Blockchain, Machine learning and Cloud Computing. [ABSTRACT FROM AUTHOR]