Numerous alternative methods for text classification have been created because of the increase in the amount of online text information available. The cosine similarity classifier is the most extensively utilized simple and efficient approach. It improves text classification performance. It is combined with estimated values provided by conventional classifiers such as Multinomial Naive Bayesian (MNB). Consequently, combining the similarity between a test document and a category with the estimated value for the category enhances the performance of the classifier. This approach provides a text document categorization method that is both efficient and effective. In addition, methods for determining the proper relationship between a set of words in a document and its document categorization is also obtained.

A model of the Internet of things was made to grasp the prevalent collaboration and correlation of smart things that were either already in use or anticipated to be utilized in the near future. The definition of the Internet of Things was therefore the expansion of the internet to umbrella the existent and future demands for the purpose of enhancing our everyday lives, including but not limited to, opening new business opportunities, building towns, improving transportation, and focusing on many aspects to make people’s daily routines smarter. The tracking of this information has been made easier by a smart system. Nevertheless, these advantages came with privacy threats. If not handled properly, this will limit the fame of the Internet of Things, which was already becoming a problem. Unfamiliar threats were arising such as the omnipresent, privacy related management of an individual’s information that may forbid the tracking required. The privacy concerns related to the Internet of Things have been considered in this paper. Firstly, we will review the emerging features and movements within this subject and then inspect their privacy concerns. Then, a classification of privacy threats will be carried out in hopes of identifying all the threats that require removal and therefore achieving the maximum smoothness within the Internet of Things and confirming its feasibility.

In this study, the large data extraction techniques; include detection of patterns and secret relationships between factors numbering and bring in the required information. Rapid analysis of massive data can lead to innovation and concepts of the theoretical value. Compared with results from mining between traditional data sets and the vast amount of large heterogeneous data interdependent it has the ability expand the knowledge and ideas about the target domain. We studied in this research data mining on the Internet. The various networks that are used to extract data onto different locations complex may appear sometimes and has been used to extract information on the web technology to extract and data analysis (Marwah et al., 2016). In this research, we extracted the information on large quantities of the web pages and examined the pages of the site using Java code, and we added the extracted information on a special database for the web page. We used the data network function to get accurate results of evaluating and categorizing the data pages found, which identifies the trusted web or risky web pages, and imported the data onto a CSV extension. Consequently, examine and categorize these data using WEKA to obtain accurate results. We concluded from the results that the applied data mining algorithms are better than other techniques in classification and extraction of data and high performance.

The field internet of things and Big Data has become a necessity in our everyday lives due to the broadening of its technology and the exponential increase in devices, services, and applications that drive different types of data. This survey shows the study of Internet of Things (IoT), Big Data, data management, and intermediate data. The survey discusses intermediate data on Big Data and Internet of Things (IoT) and how it is managed. Internet of Things (IoT) is an essential concept of a new technology generation. It is a vision that allows the embedded devices or sensors to be interconnected over the Internet. The future Internet of Things (IoT) will be greatly presented by the massive quantity of heterogeneous networked embedded devices that generate intensively "Big data". Referring to the term intermediate data as the information that is provoked as output data along the process. However, this data is temporary and is erased as soon as you run a model or a sample tool. Also, the existence of intermediate data in both of the Internet of Things (IoT) and Big Data are explained. Here, various aspects of the internet of things, Big Data, intermediate data and data management will be reviewed. Moreover, the schemes for managing this data and its framework are discussed. Â

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.