The pathological development of abnormal blood vessels results in neovascularization as a major vision‐threatening condition of diabetic retinopathy. The main factor behind pathological vessel growth results from retinal capillary depletion of oxygen that causes abnormal vascular development patterns. Early detection of these fundus image abnormalities requires precision because it enables ophthalmologists to provide effective treatment and make proper diagnoses. A multiple‐step image processing system treats this problem. A fusion‐based contrast enhancement method begins the process of enhancing diabetic retinopathy fundus image brightness and contrast. After the initial process, the system applies detail weighted histogram equalization to the green channel for better structural detail visualization. In the second stage, the proposed online tiger‐claw algorithm segments abnormal neovascularization from normal blood vessels. Next, the combination of fuzzy zone‐based clustering with optimization and classifier thresholding performs local identification along with highlight generation for neovascularized areas. Neovascularization detection makes use of a YOLOv5 neural network in the third stage through feature extraction and classification operations. A refined segmentation process occurs with the application of multistage gray wolf optimization. The proposed algorithm underwent testing through its application to the public datasets STARE, DRIVE, MESSIDOR, and DIARETDB1. Experimental tests indicate that the neovascularization region marking performed with 98.19% sensitivity and 96.56% specificity while reaching 99.27% accuracy. The proposed approach demonstrates 97.03% accuracy and 98.94% sensitivity, together with 97.17% specificity in neovascularization detection.

Medical image fusion (IF) is a process of registering and combining numerous images from multiple‐ or single‐imaging modalities to enhance image quality and lessen randomness as well as redundancy for increasing the clinical applicability of the medical images to diagnose and evaluate clinical issues. The information that is acquired additionally from fused images can be effectively employed for highly accurate positioning of abnormality. Since diverse kinds of images produce various information, IF becomes more complicated for conventional methods to generate fused images. Here, a unified image fusion convolutional neural network (UIFCNN) is designed for IF utilizing medical images. To execute the IF process, two input images, namely, native T1 and T2 fluid‐attenuated inversion recovery (T2‐FLAIR) are taken from a dataset. An input image‐T1 is preprocessed employing bilateral filter (BF), and it is segmented by a recurrent prototypical network (RP‐Net) to obtain segmented output‐1. Simultaneously, input image‐T2‐FLAIR is also preprocessed by BF and then segmented using RP‐Net to acquire segmented output‐2. The two segmented outputs are fused utilizing the UIFCNN that is introduced by assimilating unified and unsupervised end‐to‐end IF network (U2Fusion) with IF framework based on the CNN (IFCNN). In addition, the UIFCNN obtained maximal Dice coefficient and Jaccard coefficient of 0.928 and 0.920 as well as minimal mean square error (MSE) of 0.221.

Heart disease is a leading cause of death worldwide, affecting millions of lives each year. Earlier and more accurate heart disease detection helps people to save their valuable lives. Many existing systems remain costly and inaccurate. To overcome these issues, an exponential fossa optimization algorithm–based parallel convolutional neural network (EFOA‐PCNN) is proposed in this paper for efficient heart disease detection. Initially, the heart disease data are allowed for data normalization, which is performed by min–max normalization. These normalized data are forwarded to the feature selection phase, which is conducted based on chord distance. Finally, heart disease detection is performed using a parallel convolutional neural network (PCNN) that is trained using the EFOA. Here, the EFOA is developed by the combination of the fossa optimization algorithm (FOA) and exponentially weighted moving average (EWMA). The performance of the proposed EFOA‐PCNN is analysed by three metrics, such as specificity, sensitivity, and accuracy, and the F 1 score that gained superior values of 91.95%, 91.76%, 91.86%, and 92.39%. These results highlight the robustness and reliability of the proposed method in comparison to traditional approaches.

The field of generative artificial intelligence (AI) is experiencing rapid advancements, impacting a multitude of sectors, from computer vision to healthcare. This paper provides a comprehensive review of generative AI’s evolution, significance, and applications, including the foundational architectures such as generative adversarial networks (GANs), variational autoencoders (VAEs), autoregressive models, flow‐based models, and diffusion models. We delve into the impact of generative algorithms on computer vision, natural language processing, artistic creation, and healthcare, demonstrating their revolutionary potential in data augmentation, text and speech synthesis, and medical image interpretation. While the transformative capabilities of generative AI are acknowledged, the paper also examines ethical concerns, most notably the advent of deepfakes, calling for the development of robust detection frameworks and responsible use guidelines. As generative AI continues to evolve, driven by advances in neural network architectures and deep learning methodologies, this paper provides a holistic overview of the current landscape and a roadmap for future research and ethical considerations in generative AI.

Cloud computing is a vast revolution in information technology (IT) that inhibits scalable and virtualized sources to end users with low infrastructure cost and maintenance. They also have much flexibility and these resources are supervised by various management organizations and provided over the Internet by known standards, formats, and networking protocols. Legacy protocols and underlying technologies consist of vulnerabilities and bugs which open doors for intrusion by network attackers. Attacks as distributed denial of service (DDoS) are one of most frequent attacks, which impose heavy damage and affect performance of the cloud. In this research work, DDoS attack detection is easily identified in an optimized way through a novel algorithm, namely, the proposed gradient hybrid leader optimization (GHLBO) algorithm. This optimized algorithm is responsible to train a deep stacked autoencoder (DSA) that detects the attack in an efficient manner. Here, fusion of features is carried out by deep maxout network (DMN) with an overlap coefficient, and augmentation of data is carried out by the oversampling process. Furthermore, the proposed GHLBO is generated by integrating the gradient descent and hybrid leader‐based optimization (HLBO) algorithm. Also, this proposed method is assessed by various performance metrics, such as the true positive rate (TPR), true negative rate (TNR), and testing accuracy with values attained as 0.909, 0.909, and 0.917, accordingly.