Now a days, drowsy driving is becoming biggest challenges leading to the traffic collision. Based on the existing data and statistics, several road accidents and causalities happen due to the drowsy driving which leads to severe injuries. To overcome these challenges, various studies have been done in designing systems that can predict the driver fatigue and alert him beforehand, thus avoiding him to fall asleep behind the wheel and cause an accident. Few existing approaches used psychological measures to give higher accuracy in checking the drowsiness of the driver. However, such approaches are actually intrusive as electrodes are supposed to be incorporated on the head as well as the body. In this paper, a nonintrusive and real-time system has been proposed. This system considers eye closure ratio as input to find driver drowsiness. If this ratio decreases from the standard ratio, the driver is alerted with the help of a notification and also an email alert is communicated to the owner of the vehicle. For our system, a Pi camera is used to catch the photos of the driver’s eye and the entire framework is incorporated in a vehicle using Raspberry-Pi.

We have concentrated on a range of strategies, methodologies, and distinct fields of research in this article, all of which are useful and relevant in the field of data mining technologies. As we all know, numerous multinational corporations and major corporations operate in various parts of the world. Each location of business may create significant amounts of data. Corporate decision-makers need access to all of these data sources in order to make strategic decisions. The data warehouse adds substantial value to the firm by increasing the efficiency of management decision-making. The significance of strategic information systems like these is immediately recognised in an uncertain and highly competitive corporate climate, but in today's business world, efficiency or speed is not the sole route to competitiveness. This massive amount of data is available in the form of terabytes to petabytes, which has profoundly impacted research and engineering. To evaluate, manage, and make decisions with such a large volume of data, we need data mining tools, which will alter numerous fields. This work provides a greater number of data mining applications as well as a more focused scope of data mining, which will be useful in future research.

Friction stir processing is a technique for improving the characteristics of metals by causing local, severe plastic deformation. This deformation is achieved by forcing a non-consumable tool into the workpiece and rotating it in a stirring action while being pushed laterally through it. The primary goal of this experiment is to investigate Friction Stir processing parameters, surface composite production using friction stirs, and material mechanical characteristics. The various ceramic particles, such as SiC and Al2O3, were used as reinforcement particles. The parameter for this experiment is considered as the traveling speed, tool rotation speed, and tilt angles. The main effect of the reinforcement is to improve mechanical properties, such as hardness, impact strength, and strength.

This stud introduces a microstrip patch antenna that contains radiating patch of FR-4 substrate material on the one side of microstrip antenna with dimensions 30mm×50mm×2mm, dielectric constant =4.4 and excited by microstrip line feed. HFSS is used in this research. This antenna composes of rectangular slots to improve gain and cylindrical shorted pins to enhance performance of antenna in desired band. Return loss -20.17dB, -17.82dB, -25.19dB at 8.36GHz, 9.30GHz, 11.30GHz respectively, radiation efficiency 73.6% and gain 5.9dBi in desired band