This study research attempts to prohibit privacy and loss of money for individuals and organization by creating a reliable model which can detect the fraud exposure in the online recruitment environments. This research presents a major contribution represented in a reliable detection model using ensemble approach based on Random forest classifier to detect Online Recruitment Fraud (ORF). The detection of Online Recruitment Fraud is characterized by other types of electronic fraud detection by its modern and the scarcity of studies on this concept. The researcher proposed the detection model to achieve the objectives of this study. For feature selection, support vector machine method is used and for classification and detection, ensemble classifier using Random Forest is employed. A freely available dataset called Employment Scam Aegean Dataset (EMSCAD) is used to apply the model. Pre-processing step had been applied before the selection and classification adoptions. The results showed an obtained accuracy of 97.41%. Further, the findings presented the main features and important factors in detection purpose include having a company profile feature, having a company logo feature and an industry feature.

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-Automated quality control inspection is essential in manufacturing industries in these days. The purpose of this study is to improve product quality as well as increasing productivity by integrating three crucial systems in manufacturing lines; a robotic arm, 2D-vision, and conveyor system. As most of integrated systems have a tricky setup process to connect them to work as a single fully automated system, the process is a challenging, important step; for example, to connect a robot arm with 2D-vision based robotic inspection, and conveyor system controlled by a PLC, communicating with each other via communication network (Ethernet, Modbus). To determine main factors among selected characteristics, Minitab software is applied especially, to analyse 2D-vision system's ability and identify defective products while developing the entire process. A letter "O" was assigned as a testbed to simulate five defective work pieces: abrasion, chip, extra-material, pitting, and warp defect types, along with a template workpiece attached on Mock Bottle Cap. After two vision analyses using Minitab are applied to the integrated system, the result of 2D-vision inspection system is then able to identify four of the five simulated defects and the template workpiece, but not the warp defect due to the vision software with hardware. The vision inspection rate is over 95% successful among the identified defects. Analysis of experimental data has been developed to find highly accurate relationship between conveyor motor speed (RPM) and the time interval to pick up defects by the robotic system for real-world applications with the help of Minitab software and tools of statistical analyses. Therefore, the research demonstrates that an integrated three-subsystem could have huge potential and promising in the future if we are able keep improving each subsystem while testing with a variety of different letters or shapes, to enhance manufacturing production line to increase quality control and quality assurance.

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