Software defect prediction approaches play an essential role in the software development life cycle to help developers predict defects early, thus, preventing wasted time and effort. Defect prediction techniques based on semantic features have recently gained success over approaches based on traditional features. Existing semantic features-based defect prediction approaches use a single source code representation. Most studies focus on contextual syntax represented by abstract syntax trees, and some studies use a control flow graph to represent code graphs. However, a single representation is still limited for predicting defects that call multiple functions and have a high probability of false positives. To close the gap between source code representations on software defect prediction, we propose a defect prediction model based on multiple source code representations. The proposed model is a deep hierarchical convolutional neural network (DH-CNN). The syntax features extracted from abstract syntax trees using Word2vec are fed into syntax-level DH-CNN, and the semantic-graph features extracted from the control flow graph and data dependence graph using Node2vec are fed into semantic-level DH-CNN. In addition, the proposed model includes a gated merging mechanism that combines DH-CNN outputs to estimate the combination ratio of both types of features. Experimental results indicate that DH-CNN outperforms existing methods under cross-project and within-project scenarios.

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.

Head pose estimation (HPE) has been extensively investigated over the past decade due to its wide range of applications across several domains of artificial intelligence (AI), resulting in progressive improvements in accuracy. The problem becomes more challenging when the application requires full-range angles, particularly in unconstrained environments, making HPE an active research topic. This paper presents a comprehensive survey of recent AI-based HPE tasks in digital images. We also propose a novel taxonomy based on the main steps to implement each method, broadly dividing these steps into eleven categories under four groups. Moreover, we provide the pros and cons of ten categories of the overall system. Finally, this survey sheds some light on the public datasets, available codes, and future research directions, aiding readers and aspiring researchers in identifying robust methods that exhibit a strong baseline within the subcategory for further exploration in this fascinating area. The review compared and analyzed 113 articles published between 2018 and 2024, distributing 70.5% deep learning, 24.1% machine learning, and 5.4% hybrid approaches. Furthermore, it included 101 articles related to datasets, definitions, and other elements for AI-based HPE systems published over the last two decades. To the best of our knowledge, this is the first paper that aims to survey HPE strategies based on artificial intelligence, with detailed explanations of the main steps to implement each method. A regularly updated project page is provided: (github).

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.

Arabic handwriting recognition and conversion are crucial for financial operations, particularly for processing handwritten amounts on cheques and financial documents. Compared to other languages, research in this area is relatively limited, especially concerning Arabic. This study introduces an innovative AI-driven method for simultaneously recognizing and converting Arabic handwritten legal amounts into numerical courtesy forms. The framework consists of four key stages. First, a new dataset of Arabic legal amounts in handwritten form (“.png” image format) is collected and labeled by natives. Second, a YOLO-based AI detector extracts individual legal amount words from the entire input sentence images. Third, a robust hybrid classification model is developed, sequentially combining ensemble Convolutional Neural Networks (CNNs) with a Vision Transformer (ViT) to improve the prediction accuracy of single Arabic words. Finally, a novel conversion algorithm transforms the predicted Arabic legal amounts into digital courtesy forms. The framework’s performance is fine-tuned and assessed using 5-fold cross-validation tests on the proposed novel dataset, achieving a word level detection accuracy of 98.6% and a recognition accuracy of 99.02% at the classification stage. The conversion process yields an overall accuracy of 90%, with an inference time of 4.5 s per sentence image. These results demonstrate promising potential for practical implementation in diverse Arabic financial systems.