Journal of Al-Qadisiyah for Computer Science and Mathematics

In today's world, abnormal activity indicates threats and risks to others. An anomaly can be defined as something that deviates from what is expected, common, or normal. Because it is difficult to continuously monitor public spaces, intelligent video surveillance is necessary. When artificial intelligence, machine learning, and deep learning were introduced into the system, the technology had progressed much too far. Different methods are in place using the above combinations to help distinguish various suspicious activities from the live tracking of footage. Human behavior is the most unpredictable, and determining whether it is suspicious or normal is quite tough. In an academic setting, a deep learning Technique is utilized to detect normal or abnormal behavior and sends an alarm message to the appropriate authorities if suspicious activity is predicted. Monitoring is frequently carried out by extracting successive frames from a video. The framework is split into two sections. The features are calculated from video frames in the first phase, and the classifier predicts whether the class is suspicious or normal in the second part based on the obtained features. This paper proposes an effective method to design a system that automatically detects any unexpected or abnormal circumstance and alerts the appropriate authority and it can be used in both indoor and outdoor settings in an academic area. The proposed system was able to achieve an accuracy rate of 95.3 percent.

In this paper,the researcher recalls the definitionsof bornologicalset & bornological group and gives someexamplesin detalis. Additionally, the primary goal of this research is to introduce bornological transformation group, which are formulatedon bornological group acts on bornological set. We observethatinthe bornological transformation group bornological setcan be partitionedinto orbits. The mainimportant part was that, researcher shows that the bornological transformation group is bornological isomorphism

This study introduces ℷ-convergent for double sequence spaces of fuzzy numbers described by
double Orlicz functions and consider some properties, such as, (

With the development of modern communications technology, data compression is becoming more important to save space and reduce transmission costs. Because of this, various types and strategies of image compression were provided by several researchers, some of these studies were discussed in this review. The two main types of image compression are Lossless and lossy compression, with many methods for each of them. This research also described various lossless and lossy compression algorithms that were used by many researchers studied that reported in this literature. Lastly, certain conclusions have been provided based on the results of the conducted survey.

In this paper, a hybrid PSO (NLPSO*) is adapted to improve the obtained local optimal solution for the Permutation Flow Shop Scheduling Problem (PFSP) with minimizing the makespan. In this method, an improved NEH heuristic called -NEH+ is used to generate a good initial population. Then the PSO is triggered, followed by Iterated Local Search (ILS) to increase the coverage of exploration and exploitation search in the solution space. Both of the -NEH+ and ILS are simple and efficient algorithms. A computational study is performed to show the efficiency of the proposed technique. Several of well-known PFSP instances of small, medium, and large sizes were used in this study. The experimental study shows that the NLPSO* algorithm is significantly efficient in reaching better local optimal solutions.

Due to progress in the field of deep learning in order to find and track objects through the use of computer vision in the service of large segments of the population, as it was adopted in the field of serving people b with special needs for the sake of dialogue and implementation of many requests in this research, a series of commands for use by people with special needs with speech problems or paralysis, where the ability to use the eye blink is very useful for social communication, were developed. In this research, the orders needed by the target people with special needs were studied, and (11) commands were identified that can be increased according to the intended sample. A table of commands was built depending on the length of the eye blink period. By creating a modified CNN: Convolutional Neural Network structure and training it on 2 different database, deep learning was used to identify and determine if the eye is closed or open (mrlEye2018 and Closed Eye in the Wlid:CEW).It was then followed by a test on all of the chosen examples, in different contexts and at various ages. The test on the data yielded excellent results, with 99% percent accuracy on data from test samples and 97.5% and 96% percent accuracy on training data in each case. To check the cases (11) suggested commands, the proposed system was evaluated on a collection of videos taken in real time and under normal recording circumstances through a camera, and the correctness of generating the codes that was proposed was 94% percent on four tries in each of the above instances.