Video surveillance systems obtain a great interest as application-oriented studies that have been growing rapidly in the past decade. The most recent studies attempt to integrate computer vision, image processing, and artificial intelligence capabilities into video surveillance applications. Although there are so many achievements in the acquisition of datasets, methods, and frameworks published, there are not many papers that can provide a comprehensive picture of the current state of video surveillance system research. This paper provides a comprehensive and systematic review on the literature from various video surveillance system studies published from 2010 through 2019. Within a selected study extraction process, 220 journal-based publications were identified and analyzed to illustrate the research trends, datasets, methods, and frameworks used in the field of video surveillance, to provide an in-depth explanation about research trends that many topics raised by researchers as a focus in their researches, to provide references on public datasets that are often used by researchers as a comparison and a means of developing a test method, and to give accounts on the improvement and integration of network infrastructure design to meet the demand for multimedia data. In the end of this paper, several opportunities and challenges related to researches in the video surveillance system are mentioned.INDEX TERMS Artificial intelligence, cloud video surveillance, intelligent video surveillance, video surveillance framework.This study is conducted as follows: the methodology of the study is presented in Section 2. The outcomes and answers to the research questions are then discussed in Section 3. Finally, the study is summarized in the last section.

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Currently, the spread of hoax news has increased significantly, especially on social media networks. Hoax news is very dangerous and can provoke readers. So, this requires special handling. This research proposed a hoax news detection system using searching, snippet and cosine similarity methods to classify hoax news. This method is proposed because the searching method does not require training data, so it is practical to use and always up to date. In addition, one of the drawbacks of the existing approaches is they are not equipped with a sentiment analysis feature. In our system, sentiment analysis is carried out after hoax news is detected. The goal is to extract the true hidden sentiment inside hoax whether positive sentiment or negative sentiment. In the process of sentiment analysis, the Naïve Bayes (NB) method was used which was optimized using the Particle Swarm Optimization (PSO) method. Based on the results of experiment on 30 hoax news samples that are widely spread on social media networks, the average of hoax news detection reaches 77% of accuracy, where each news is correctly identified as a hoax in the range between 66% and 91% of accuracy. In addition, the proposed sentiment analysis method proved to has a better performance than the previous analysis sentiment method.

Hitting the ball is a complicated ability in softball academicie, because in appreciation to prioritizing movement skills, cognitive appearances additionally need to be investigated, one of which is attention. Meanwhile, going to hit the ball requires excellent attention for an individual student. Exhibiting gamma waves further influence arrangements of hitting performance. The objectives of this study were first, to determine the negative functional correlation between gamma brain waves and hitting skills, then second to know the positive functional correlation between attention and hitting skills and third to determine the effect of attention on increasing hitting skills. The method used in this research is an experimental method with a one-group pretest-posttest design research design. The sampling technique in this study was using a saturated sampling technique. This investigation amounted to 20 subjects, in the calculation of the Pearson product-moment correlation test using SPSS v.23. The first results collected were p-value 0.026 with an r square value of 0.25, so there is a significant negative functional correlation between gamma brain waves and hitting skills in softball learning of 25%. The second issue obtained p-value 0.017 with an r square value of 0.28, so there is a significant positive functional correlation between attention and hitting skills in softball learning by 28%. Furthermore, thirdly, the results obtained p-value 0.0001 between pre-test and post-test, with a significant increase in skills of 70.17%, so there is a significant influence between attention and hitting skills.

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