Background image subtraction algorithm is a common approach which detects moving objects in a video sequence by finding the significant difference between the video frames and the static background model. This paper presents a developed system which achieves vehicle detection by using background image subtraction algorithm based on blocks followed by deep learning data validation algorithm. The main idea is to segment the image into equal size blocks, to model the static reference background image (SRBI), by calculating the variance between each block pixels and each counterpart block pixels in the adjacent frame, the system implemented into four different methods: Absolute Difference, Image Entropy, Exclusive OR (XOR) and Discrete Cosine Transform (DCT). The experimental results showed that the DCT method has the highest vehicle detection accuracy.

No abstract

Ontology is a descriptive model representing domain knowledge with robust specifications that solve interoperability between humans and machines. In this work, a practical methodology presented for Arabic Storytelling ontology construction for domain ontology extraction from unstructured Arabic story documents. However, the manual construction of ontologies is a time-consuming and challenging process. Still, ontology construction and learning, which extracts ontological knowledge from various data types automatically or semiautomatically, can overcome the bottleneck of knowledge acquisition. This paper intends to investigate the problem of automatically construct and build an Arabic storytelling ontology based on Arabic named entity recognition (NER) from unstructured story text. This paper presents a system designed based on Machine Learning (ML) approach. The system framework is a combination of five main stages: The first stage determines the requirement analysis-second document pre-processing using NLP tasks. The third is Conceptualization. The fourth stage is formal design and construction, and the final step is evaluation.

No abstract