Left Ventricular Hypertrophy (LVH) is a critical predictor of cardiovascular disease, making it essential to incorporate it as a fundamental parameter in both diagnostic screening and clinical management. Addressing the need for efficient, accurate, and scalable medical image analysis, we introduce a state-of-the-art preprocessing pipeline coupled with a novel Deep Convolutional Neural Network (DCNN) architecture. This paper details our choice of the HMC-QU dataset, selected for its robustness and its proven efficacy in enhancing model generalization. We also describe innovative preprocessing techniques aimed at improving the quality of input data, thereby boosting the model's feature extraction capabilities. Our multi-disciplinary approach includes deploying a DCNN for automated LVH diagnosis using echocardiography A4C and A2C images. We evaluated the model using architectures based on VGG16, ResNet50, and InceptionV3, where our proposed DCNN exhibited enhanced performance. In our study, 93 out of 162 A4C recordings and 68 out of 130 A2C recordings confirmed the presence of LVH. The novel DCNN model achieved an impressive 99.8% accuracy on the training set and 98.0% on the test set. Comparatively, ResNet50 and InceptionV3 models showed lower accuracy and higher loss values both in training and testing phases. Our results underscore the potential of our DCNN architecture in enhancing the precision of MRI echocardiograms in diagnosing LVH, thereby providing critical support in the screening and treatment of cardiovascular conditions. The high accuracy and minimal losses observed with the novel DCNN model indicate its utility in clinical settings, making it a valuable tool for improving patient outcomes in cardiovascular care.

ABSTRACT The increasing complexity of software development processes has heightened the need for robust security measures. Although technical safeguards are essential, the role of human factors in securing software development remains underexplored. This paper presents a novel approach that integrates people's maturity with a fuzzy analytic hierarchy process (Fuzzy‐AHP) decision‐making framework to enhance the security in software development. The framework provides a systematic method for evaluating and prioritizing human factors that influence an organization's security posture, such as team‐expertized communication and adherence to security protocols. Using the decision‐making model allows the project managers and stakeholders to determine the appropriate areas for improvement and develop the right strategies and actions to nurture a secure and mature development culture. The paper identifies 24 human success factors (HSFs) and human security vulnerabilities (HSVs) and 38 practices for addressing these HSFs and HSVs through systematic literature review (SLR) and empirical survey. Furthermore, we discuss the local and global ranks of each HSF and HSV practice and categorize the identified practices into nine categories to determine the ranks and weight of each category. Based on collected data, Fuzzy‐AHP prioritized these practices; the category “C4: Skill development and stakeholder engagement” is ranked highest at rank‐1 and possesses the most significant weight of 0.12435. Similarly, the highest global weight is 0.051506, and the global ranked (rank‐1) HSF and HSV practice is “P15: Hands‐on practice and stakeholder communication.” The proposed approach complements existing technical methods by addressing the human element of security, making it adaptable to diverse organizational environments. Through this integration of people maturity and Fuzzy‐AHP, the paper contributes a new dimension to securing software development, emphasizing the critical role of human factors in achieving comprehensive security.

ABSTRACT Information System Security (ISS) is the primary economic lever for the global economy. It is the cornerstone for value generation, and its absence undeniably affects technology, people, and finances. The emergence of the worldwide information society has introduced fresh economic and legal challenges attributed to the surge in Internet utilization and advancements in the digital economy. Ensuring the security of advancements within information systems has emerged as a primary concern in propelling the evolution of information processes within the software development industry. This study aims to develop and propose a Fuzzy Analytic Hierarchy Process (Fuzzy‐AHP) framework to enhance decision‐making for software maintenance and deployment in ISS. This framework aims to provide a systematic, flexible method for evaluating and prioritizing multiple conflicting criteria under conditions of uncertainty. The study initially adopts an empirical survey to identify software security maintenance and deployment risks and their practices for ISS organizations. Then adopts the Fuzzy‐AHP method to handle the imprecision of expert judgments and organizes decision‐making into a hierarchical structure. The framework is applied to evaluate key criteria related to software maintenance and deployment, including security risks, system performance, operational costs, and compliance requirements. Data from 50 ISS experts were collected and used to validate the framework. The paper identifies 52 security risks in maintenance and deployment (SRMD) processes in ISS and also identified 139 best practices for ensuring security, including regular updates, patch management, and adherence to industry‐standard security protocols. The Fuzzy‐AHP framework effectively structured the decision‐making process by prioritizing criteria and sub‐criteria. The results demonstrated that the framework helps mitigate the subjective biases in expert judgment and provides a more balanced assessment of maintenance and deployment strategies. Prioritizing security risks and compliance emerged as key factors in the decision‐making process. The proposed Fuzzy‐AHP framework provides an innovative and adaptable solution for optimizing ISS organizations' software maintenance and deployment decisions. It addresses the complexity and uncertainty involved in such decisions, offering a transparent and structured approach that improves the accuracy and reliability of outcomes. Future research should focus on empirical validation of the framework in real‐world case studies and expand its application to other industries with similar decision‐making needs.

Deep learning (DL) is a new machine learning (ML) methodology that has found successful implementations in many application domains. However, its usage in communications systems has not been well explored. This paper investigates the use of the DL in modulation classification, which is a major task in many communications systems. The DL relies on a massive amount of data and, for research and applications, this can be easily available in communications systems. Furthermore, unlike the ML, the DL has the advantage of not requiring manual feature selections, which significantly reduces the task complexity in modulation classification. In this paper, we use two convolutional neural network (CNN)-based DL models, AlexNet and GoogLeNet. Specifically, we develop several methods to represent modulated signals in data formats with gridlike topologies for the CNN. The impacts of representation on classification performance are also analyzed. In addition, comparisons with traditional cumulant and ML-based algorithms are presented. Experimental results demonstrate the significant performance advantage and application feasibility of the DL-based approach for modulation classification.