Nowadays, the big data marketplace is rising rapidly. The big challenge is finding a system that can store and handle a huge size of data and then processing that huge data for mining the hidden knowledge. This paper proposed a comprehensive system that is used for improving big data analysis performance. It contains a fast big data processing engine using Apache Spark and a big data storage environment using Apache Hadoop. The system tests about 11 Gigabytes of text data which are collected from multiple sources for sentiment analysis. Three different machine learning (ML) algorithms are used in this system which is already supported by the Spark ML package. The system programs were written in Java and Scala programming languages and the constructed model consists of the classification algorithms as well as the pre-processing steps in a figure of ML pipeline. The proposed system was implemented in both central and distributed data processing. Moreover, some datasets manipulation manners have been applied in the system tests to check which manner provides the best accuracy and time performance. The results showed that the system works efficiently for treating big data, it gains excellent accuracy with fast execution time especially in the distributed data nodes.

Computer vision is a significant field of application of mathematical models developed to monitor 3D anatomical locations of the human body, particularly in robotics, surveillance, and medicine. In this paper, we present a new model that applies the Kalman filter (KF) to track 3D anatomical features in real time with increased precision. The approach separates video frames, identifies objects using the pixel characteristics, and uses the Kalman filter to forecast and correct landmark locations. As shown, experimental results demonstrate that the proposed solution is much more efficient than conventional tracking mechanisms. The system's tracking accuracy has improved, with a mean squared error (MSE) of 0.035 compared to 0.048 for the baseline Kalman filter. The suggested approach provides a processing time of 15 ms per frame, which is sufficient for real-time operation. The fact that the system can cope with noisy data and any high-speed movements makes it robust and is applicable in cases where the application needs high precision and low latency. The test confirms that Kalman filtering and deep learning feature-extraction frameworks improve the tracking accuracy and stability of 3D anatomical landmarks in dynamic systems. This study opens the way to more trustworthy real-time medical surveillance, rehabilitation, and robotic interaction tracking solutions.