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.

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.

Heart disease causes major death across the entire globe. Hence, heart disease prediction is a vital part of medical data analysis. Recently, various data mining and machine learning practices have been utilized to detect heart disease. However, these techniques are inadequate for effectual heart disease prediction due to the deficient test data. In order to progress the efficacy of detection performance, this research introduces the hybrid feature selection method for selecting the best features. Moreover, the missed value from the input data is filled with the quantile normalization and missing data imputation method. In addition, the best features relevant to disease detection are selected through the proposed hybrid Congruence coefficient Kumar–Hassebrook similarity. In addition, heart disease is predicted using SqueezeNet, which is tuned by the dwarf mongoose optimization algorithm (DMOA) that adapts the feeding aspects of dwarf mongoose. Moreover, the experimental result reveals that the DMOA-SqueezeNet method attained a maximum accuracy of 0.925, sensitivity of 0.926, and specificity of 0.918.

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.

INTRODUCTION: Most Indians rely on agricultural work as their primary means of support, making it an essential part of the country’s economy. Disasters and the expected loss of farmland by 2050 as a result of global population expansion raise concerns about food security in that year and beyond. The Internet of Things (IoT), Big Data and Analytics are all examples of smart agricultural technologies that can help the farmers enhance their operation and make better decisions. OBJECTIVES: In this paper, machine learning based system has been developed for solving the problem of crop disease and pest prediction, focussing on the chilli crop as a case study. METHODS: The performance of the suggested system has been assessed by employing performance metrics like accuracy, Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). RESULTS: The experimental results reveals that the proposed method obtained accuracy of 0.90, MSE of 0.37, MAE of 0.15, RMSE of 0.61 CONCLUSION: This model will predict pests and diseases and notify farmers using a combination of the Random Forest Classifier, the Ada Boost Classifier, the K Nearest Neighbour, and Logistic Regression. Random Forest is the most accurate model.