Role of quality determinants of the internal audit function in corporate governance effectiveness. Senior management support as moderator: Evidence from Yemeni commercial banks

The purpose of this study was to determine if senior management support (SMSI) in Yemeni commercial banks mediates the association between internal audit function (IAF) quality characteristics and improved corporate governance effectiveness (CGE). Internal auditors, heads of internal audit, chairmen and participants in audit committees, CEOs, and financial management of Yemeni commercial banks were given a list of questions to answer. 158 full lists were obtained to evaluate after distributing the survey. For data analysis and hypothesis testing in this work, Smart PLS 3 was used. The study findings demonstrate a substantial relationship between CGE and IAF competence and due professional care (CPCI), IAF independence and objectivity (INOI), and IAF professional ethics (PEI). The outcomes of the study also demonstrate that there is no relationship between CGE and chief audit executive (CAE) Leadership Style (CLS). In terms of the moderate variable’s influence, the findings revealed that SMSI positively changes the link between CLS, CPCI, and corporate governance effectiveness. SMSI, on the other hand, has no influence on the link between INOI, PEI, and the efficacy of corporate governance. The findings add to the knowledge on IAF factors affecting the efficacy of CG and the role of SMSI in changing this relationship in developing countries such as Yemen. AcknowledgmentThe authors extend their appreciation to the deanship of scientific research at King Khalid University for funding this work through large groups project under grant number (RGP.2/189/44)

Henry Gas Solubility Optimization Algorithm based Feature Extraction in Dermoscopic Images Analysis of Skin Cancer

Artificial Intelligence (AI) techniques have changed the general perceptions about medical diagnostics, especially after the introduction and development of Convolutional Neural Networks (CNN) and advanced Deep Learning (DL) and Machine Learning (ML) approaches. In general, dermatologists visually inspect the images and assess the morphological variables such as borders, colors, and shapes to diagnose the disease. In this background, AI techniques make use of algorithms and computer systems to mimic the cognitive functions of the human brain and assist clinicians and researchers. In recent years, AI has been applied extensively in the domain of dermatology, especially for the detection and classification of skin cancer and other general skin diseases. In this research article, the authors propose an Optimal Multi-Attention Fusion Convolutional Neural Network-based Skin Cancer Diagnosis (MAFCNN-SCD) technique for the detection of skin cancer in dermoscopic images. The primary aim of the proposed MAFCNN-SCD technique is to classify skin cancer on dermoscopic images. In the presented MAFCNN-SCD technique, the data pre-processing is performed at the initial stage. Next, the MAFNet method is applied as a feature extractor with Henry Gas Solubility Optimization (HGSO) algorithm as a hyperparameter optimizer. Finally, the Deep Belief Network (DBN) method is exploited for the detection and classification of skin cancer. A sequence of simulations was conducted to establish the superior performance of the proposed MAFCNN-SCD approach. The comprehensive comparative analysis outcomes confirmed the supreme performance of the proposed MAFCNN-SCD technique over other methodologies