High-performance computing (HPC) and its supercomputers are essential for solving the most difficult issues in many scientific computing domains. The proliferation of computational resources utilized by HPC systems has resulted in an increase in the associated error rates. As such, modern HPC systems promote a hybrid programming style that integrates the message-passing interface (MPI) and open multi-processing (OpenMP). However, this integration often leads to complex defects, such as deadlocks and race conditions, that are challenging to detect and resolve. This paper presents a novel approach: using machine learning algorithms to predict defects in C++-based systems by employing hybrid MPI and OpenMP models. We focus on employing a balanced dataset to enhance prediction accuracy and reliability. Our study highlights the effectiveness of the support vector machine (SVM) classifier, enhanced with term frequency (TF) and recursive feature elimination (RFE) techniques, which demonstrates superior accuracy and performance in defect prediction when compared to other classifiers. This research contributes significantly to the field by providing a robust method for early defect detection in hybrid programming environments, thereby reducing development time, costs and improving the overall reliability of HPC systems.

In the last decade, many computer-aided diagnosis (CAD) systems that utilize a broad range of diagnostic techniques have been proposed. Due to both the inherently complex structure of the breast tissues and the low intensity contrast found in most mammographic images, CAD systems that are based on conventional techniques have been shown to have missed malignant masses in mammographic images that would otherwise be treatable. On the other hand, systems based on fuzzy image processing techniques have been found to be able to detect masses in cases where conventional techniques would have failed. In the current chapter, recent advances in fuzzy image segmentation techniques as applied to mass detection in digital mammography are reviewed. Image segmentation is an important step in CAD systems since the quality of its outcome will significantly affect the processing downstream that can involve both detection and classification of benign versus malignant masses.

Hybrid Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) parallel programs are pivotal for scalability and efficiency in high-performance computing (HPC), especially as systems approach exa-scale operations. These programs leverage distributed and shared memory systems, making them indispensable for scientific simulations, data analysis, and numerical computations. However, synchronization defects such as deadlocks and race conditions pose significant challenges to reliability and performance, often eluding traditional static and dynamic analysis tools due to the complexity of hybrid systems. This study introduces a deep learning-based models for automated defect prediction in hybrid MPI and OpenMP programs. Using a balanced dataset of 1,500 C++ files, three neural architectures—Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM model—were evaluated. To preprocess the code, Abstract Syntax Tree (AST)-based token extraction was employed, capturing structural and semantic relationships critical for detecting defects. Token extraction methods included detailed C++ syntax analysis via the Clang library and a custom regular expression approach. The results reveal that Clang-token-based representation provided the most effective input for defect prediction, enabling CNN models to achieve an accuracy of 97%. By integrating AST-based structural insights with the predictive power of deep learning, this research offers a scalable solution for enhancing the reliability of hybrid parallel programs. The findings establish a foundation for future advancements in automated defect detection, addressing the pressing needs of high-performance computing systems.

The visual similarity between normal breast tissues and abnormal lesions in digital mammogram images makes computer-aided diagnosis of breast cancer using automatically detected features a highly error-prone task. Our contribution in this paper is a novel feature reduction framework for selecting the most discriminative features that achieves both efficiency and classification accuracy. Our approach applies five individual feature-ranking methods including Fisher score, minimum redundancy-maximum relevance, relief-f, sequential forward feature selection, and genetic algorithm for sorting the extracted features and selecting the features with highest ranking to setup a classifier. Our method achieves an accuracy of 94.27% and a sensitivity of 98.36% with a specificity of 99.27% on a set of 1,100 mammogram patches taken from image retrieval in medical applications database using a neural network classifier, which competes with state-of-the-art classification accuracy 93.11%. Furthermore, we demonstrate that only 49 out of the 119 extracted features are sufficient to achieve the reported accuracy of normal vs. abnormal classification.