Computational Intelligence

The use of technology in cricket has seen a significant increase in recent years, leading to overlapping computer vision-based research efforts. This study aims to extract front pitch view shots in cricket broadcasts by utilizing deep learning. The front pitch view (FPV) shots include ball delivery by the bowler and the stroke played by the batter. FPV shots are valuable for highlight generation, automatic commentary generation and bowling and batting techniques analysis. We classify each broadcast video frame as FPV and non-FPV using deep-learning models.

Now a day’s education plays a great role in development of any country. Many of education organizations try to increase education quality. One of the aspects of this improvement is managing of school resources.

The CyberAlert-25 Dataset is a comprehensive collection of curated cyber threat data, developed to support advanced research in vulnerability detection, classification, and threat intelligence. Aggregated from authoritative sources such as the National Critical Information Infrastructure Protection Center (NCIIPC) and the MITRE Corporation, the dataset focuses on Common Vulnerabilities and Exposures (CVEs), encompassing a total of 29,650 entries.

The shift towards cloud-native applications has been accelerating in recent years. Modern applications are increasingly distributed, taking advantage of cloud-native features such as scalability, flexibility, and high availability. However, this evolution also introduces various security challenges. From a networking perspective, the large number of interconnected components and their intricate communication patterns make detecting and mitigating traffic anomalies a complex task.

The TUROS-TS encompasses 5,357 Google Street View images with 8,775 traffic sign instances covering 9 categories and 28 classes. Three subsets of the dataset were created: test (10%-1050 images 579), validation (20% -1050 images), and training (70% - 3728 images). It is available upon request. If you want to train and test the data set. Please send an email to  afef.zwidi@regim.usf.tn

Online Quiz System is a web-based quiz system for accessing students. It is a system by which students can

sit in a quiz which need no pencil and paper. Nowadays, students take quiz manually .Lecturers need spend

more time on grading. Other than that, the quiz paper maybe will be missing. Students need to wait for

lecturers finish grading to get their result. Therefore, this system will help lecturers save their time because

of automated marking. Lecturers can set up a quiz which is it will auto-grade itself. Students can answer the

Online Quiz System is a web-based quiz system for accessing students. It is a system by which students can

sit in a quiz which need no pencil and paper. Nowadays, students take quiz manually .Lecturers need spend

more time on grading. Other than that, the quiz paper maybe will be missing. Students need to wait for

lecturers finish grading to get their result. Therefore, this system will help lecturers save their time because

of automated marking. Lecturers can set up a quiz which is it will auto-grade itself. Students can answer the

Race track dataset for a mobile fun racer. Used for the evaluation of a not yet published journal paper. Specific reference will be added here once the paper has been accepted and published.

The TripAdvisor online airline review dataset, spanning from 2016 to 2023, provides a comprehensive collection of passenger feedback on airline services during the COVID-19 pandemic. This dataset includes user-generated reviews that capture sentiments, preferences, and concerns, allowing for an in-depth analysis of shifting customer priorities in response to pandemic-related disruptions. By examining these reviews, the dataset facilitates the study of evolving passenger expectations, changes in service perceptions, and the airline industry's adaptive strategies.

This dataset can be used for vulnerability detection. This repository is devised to explain vulEmbedding,

1. First "altKlasörTaraTahmin.R" file is for searching code files to generate suitable numeric matrix,

2. createKeywordMatrix.R is for generating keyword matrix, thereby checking vulnerabilities,

3. sphericalLabeling.R is for generating spherical labeling.

4. Final you can run deepnetVersion2.R to produce vulnerability prediction.

   If you are not concered with generating data from source code,