A mathematical model for the spread of the COVID-19 disease based on a fractional Atangana–Baleanu operator is studied. Some fixed point theorems and generalized Gronwall inequality through the AB fractional integral are applied to obtain the existence and stability results. The fractional Adams–Bashforth is used to discuss the corresponding numerical results. A numerical simulation is presented to show the behavior of the approximate solution in terms of graphs of the spread of COVID-19 in the Chinese city of Wuhan. We simulate our table for the data of Wuhan from February 15, 2020 to April 25, 2020 for 70 days. Finally, we present a debate about the followed simulation in characterizing how the transmission dynamics of infection can take place in society.

Text line extraction from a text document image and segmenting it into isolate words and segmenting these words into individual characters are considered as one of the most critical processes in OCR systems development and turning the document into a searchable electronic representation, this paper presents a new approach to analyze the Arabic text documents, the proposed approach contains four steps, preprocessing, text line segmentation, word segmentation, character segmentation. The horizontal projection method are used to detect and extract the text line from preprocessed text documents image, in word segmentation step The space threshold are computed to determine the spaces among connected components in text line as within-word space or between-words space for segmenting the text line into isolate words, finally thinning method applied to find the skeleton of segmented word and analyses geometric characteristics of the characters to detect ligatures and characters. The proposed approach was tested and evaluated on a set of 115 text images, this set contains images from the KFUPM Handwritten Arabic TexT (KHATT) database and some images produced by the authors. The experiment results are extremely encouraging, with a success rate of 98.6% for lines segmentation, 96% for words segmentation, and 87.1% for characters segmentation.

Software defect prediction (SDP) methodology could enhance software’s reliability through predicting any suspicious defects in its source code. However, developing defect prediction models is a difficult task, as has been demonstrated recently. Several research techniques have been proposed over time to predict source code defects. However, most of the previous studies focus on conventional feature extraction and modeling. Such traditional methodologies often fail to find the contextual information of the source code files, which is necessary for building reliable prediction deep learning models. Alternatively, the semantic feature strategies of defect prediction have recently evolved and developed. Such strategies could automatically extract the contextual information from the source code files and use them to directly predict the suspicious defects. In this study, a comprehensive survey is conducted to systematically show recent software defect prediction techniques based on the source code’s key features. The most recent studies on this topic are critically reviewed through analyzing the semantic feature methods based on the source codes, the domain’s critical problems and challenges are described, and the recent and current progress in this domain are discussed. Such a comprehensive survey could enable research communities to identify the current challenges and future research directions. An in-depth literature review of 283 articles on software defect prediction and related work was performed, of which 90 are referenced.

Visual SLAM (Simultaneous Localization and Mapping) is widely used in autonomous robots and vehicles for autonomous navigation. Trajectory estimation is one part of Visual SLAM. Trajectory estimation is needed to estimate camera position in order to align the real image locations. In this paper, we present a new framework for trajectory estimation aided by Monocular Visual Odometry. Our proposed method combines the feature points extracting and matching based on ORB (Oriented FAST and Rotated BRIEF) and PnP (Perspective-n-Point). Thus, it was used a Matlab® dynamic model and an OpenCV/C++ computer graphics platform to perform a very robust monocular Visual Odometry mechanism for trajectory estimation in outdoor environments. Our proposed method displays that meaningful depth estimation can be extracted and frameto-frame image rotations can be successfully estimated and can be translated in large view even texture-less. The best key-points has been extracted from ORB key point detectors depend on their key-point response value. These extracted key points are used to decrease trajectory estimation errors. Finally, the robustness and high performance of our proposed method were verified on image sequences from public KITTI dataset.

The cheque field extraction is a critical step in automating bank cheque processing and is the first step in implementing a cheque recognition system. Many approaches for extracting the bank cheques components have been suggested. However, the complexity of the backdrop, the design variety of bank cheques, the variety of font sizes, and different patterns of writing remain a difficulty that necessitates the employment of precise algorithms. In this paper, we present a novel approach to extract the bank cheque components, in presented approach we used an innovative model called Faster R-CNN. This model represents the pinnacle of object recognition since it eliminates the need to manually extract image features and instead segments images to provide candidate region suggestions automatically. The IDRBT Cheque Image Dataset is used to train and test the Faster R-CNN model. The findings demonstrate that the model is capable of properly detecting the bank cheque fields. The extraction of bank cheque fields using Faster R-CNN achieves an accuracy of 97.4%, which outperforms other techniques.