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In ophthalmic diagnostics, achieving precise segmentation of retinal blood vessels is a critical yet challenging task, primarily due to the complex nature of retinal images. The intricacies of these images often hinder the accuracy and efficiency of segmentation processes. To overcome these challenges, we introduce the cognitive DL retinal blood vessel segmentation (CoDLRBVS), a novel hybrid model that synergistically combines the deep learning capabilities of the U-Net architecture with a suite of advanced image processing techniques. This model uniquely integrates a preprocessing phase using a matched filter (MF) for feature enhancement and a post-processing phase employing morphological techniques (MT) for refining the segmentation output. Also, the model incorporates multi-scale line detection and scale space methods to enhance its segmentation capabilities. Hence, CoDLRBVS leverages the strengths of these combined approaches within the cognitive computing framework, endowing the system with human-like adaptability and reasoning. This strategic integration enables the model to emphasize blood vessels, accurately segment effectively, and proficiently detect vessels of varying sizes. CoDLRBVS achieves a notable mean accuracy of 96.7%, precision of 96.9%, sensitivity of 99.3%, and specificity of 80.4% across all of the studied datasets, including DRIVE, STARE, HRF, retinal blood vessel and Chase-DB1. CoDLRBVS has been compared with different models, and the resulting metrics surpass the compared models and establish a new benchmark in retinal vessel segmentation. The success of CoDLRBVS underscores its significant potential in advancing medical image processing, particularly in the realm of retinal blood vessel segmentation., (© 2024. The Author(s).)

This research proposes a novel Hybrid Success History Intelligent Optimizer with Gaussian Transformation (SHIOGT) for solving different complexity level optimization problems and for Convolutional Neural Network (CNNs) hyperparameter tuning. SHIOGT algorithm is designed to balance exploration and exploitation phases through the addition of Gaussian Transformation to the original Success History Intelligent Optimizer. The inclusion of Gaussian Transformation enhances solution diversity enables SHIO to avoid local optima. SHIOGT also demonstrates robustness and adaptability by dynamically adjusting its search strategy based on problem characteristics. Furthermore, the combination of Gaussian and SHIO facilitates faster convergence, accelerating the discovery of optimal or near-optimal solutions. Moreover, the hybridization of these two techniques brings a synergistic effect, enabling SHIOGT to overcome individual limitations and achieve superior performance in hyperparameter optimization tasks. SHIOGT was thoroughly assessed against an array of benchmark functions of varying complexities, demonstrating its ability to efficiently locate optimal or near-optimal solutions across different problem categories. Its robustness in tackling multimodal and deceptive landscapes and high-dimensional search spaces was particularly notable. SHIOGT has been benchmarked over 43 challenging optimization problems and have been compared with state-of-the art algorithm. Further, SHIOGT algorithm is applied to the domain of deep learning, with a case study focusing on hyperparameter tuning of CNNs. With the intelligent exploration–exploitation balance of SHIOGT, we hypothesized it could effectively optimize the CNN's hyperparameters. We evaluated the performance of SHIOGT across a variety of datasets, including MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100, with the aim of optimizing CNN model hyperparameters. The results show an impressive accuracy rate of 98% on the MNIST dataset. Similarly, the algorithm achieved a 92% accuracy rate on Fashion-MNIST, 76% on CIFAR-10, and 70% on CIFAR-100, underscoring its effectiveness across diverse datasets. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) mobile networks have already marked their presence globally, revolutionizing entertainment, business, healthcare, and other domains. While this leap forward brings numerous advantages in speed and connectivity, it also poses new challenges for security protocols. Machine learning (ML) and deep learning (DL) have been employed to augment traditional security measures, promising to mitigate risks and vulnerabilities. This paper conducts an exhaustive study to assess ML and DL algorithms' role and effectiveness within the 5G security landscape. Also, it offers a profound dissection of the 5G network's security paradigm, particularly emphasizing the transformative role of ML and DL as enabling security tools. This study starts by examining the unique architecture of 5G and its inherent vulnerabilities, contrasting them with emerging threat vectors. Next, we conduct a detailed analysis of the network's underlying segments, such as network slicing, Massive Machine-Type Communications (mMTC), and edge computing, revealing their associated security challenges. By scrutinizing current security protocols and international regulatory impositions, this paper delineates the existing 5G security landscape. Finally, we outline the capabilities of ML and DL in redefining 5G security. We detail their application in enhancing anomaly detection, fortifying predictive security measures, and strengthening intrusion prevention strategies. This research sheds light on the present-day 5G security challenges and offers a visionary perspective, highlighting the intersection of advanced computational methods and future 5G security. [ABSTRACT FROM AUTHOR]