ET-Network: A novel efficient transformer deep learning model for automated Urdu handwritten text recognition.

Automatic Urdu handwritten text recognition is a challenging task in the OCR industry. Unlike printed text, Urdu handwriting lacks a uniform font and structure. This lack of uniformity causes data inconsistencies and recognition issues.

VOC-DL: Deep learning prediction model for COVID-19 based on VOC virus variants.

Background And Objective: The ever-mutating COVID-19 has infected billions of people worldwide and seriously affected the stability of human society and the world economic development. Therefore, it is essential to make long-term and short-term forecasts for COVID-19. However, the pandemic situation in different countries and regions may be dominated by different virus variants, and the transmission capacity of different virus variants diversifies.

Deep learning-based automatic segmentation of images in cardiac radiography: A promising challenge.

Background: Due to the advancement of medical imaging and computer technology, machine intelligence to analyze clinical image data increases the probability of disease prevention and successful treatment. When diagnosing and detecting heart disease, medical imaging can provide high-resolution scans of every organ or tissue in the heart. The diagnostic results obtained by the imaging method are less susceptible to human interference.

Attention based GRU-LSTM for software defect prediction.

Software defect prediction (SDP) can be used to produce reliable, high-quality software. The current SDP is practiced on program granular components (such as file level, class level, or function level), which cannot accurately predict failures. To solve this problem, we propose a new framework called DP-AGL, which uses attention-based GRU-LSTM for statement-level defect prediction.

Group-based local adaptive deep multiple kernel learning with lp norm.

The deep multiple kernel Learning (DMKL) method has attracted wide attention due to its better classification performance than shallow multiple kernel learning. However, the existing DMKL methods are hard to find suitable global model parameters to improve classification accuracy in numerous datasets and do not take into account inter-class correlation and intra-class diversity. In this paper, we present a group-based local adaptive deep multiple kernel learning (GLDMKL) method with lp norm.

Weight prioritized slicing based on constraint logic programming for fault localization.

Fault localization, a technique to fix and ensure the dependability of software, is rapidly becoming infeasible due to the increasing scale and complexity of multilingual programs. Compared to other fault localization techniques, slicing can directly narrow the range of the code which needed checking by abstracting a program into a reduced one by deleting irrelevant parts. Only minority slicing methods take into account the fact that the probability of different statements leading to failure is different.