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The primary aim of this article is to propose a novel orchestration framework based on Revocable Dynamic Hash Table (RDHT) for dynamic and secure access of Electronic Health Records (EHRs) in multi-cloud environments. The proposed orchestration framework includes four processes namely, Function-Level Rewriting (FLR), Optional Function Generator (OFG), Entry Recognition (ER), and Optional File Elimination (OFE) for effectively managing the storage of EHRs. This proposed orchestration framework's performance is analysed using a web application related to hospital management systems. Once the orchestration layer authenticates the health professionals, the RDHT effectively routes the request and distributes EHRs across different cloud providers. The orchestration layer enforces access control after identifying the designated cloud providers. This process confirms that the health professionals have appropriate permission for the specifically requested operation. In comparison to the conventional methods like Quick-Functions as a Service (FaaS), Fast Healthcare Interoperability Resources (FHIR), ElGamal encryption, and Deletable Consortium Blockchain (DS-Chain), the RDHT method consumes minimal memory and running time. More precisely, the RDHT method demands a minimum response time of 1070.52 milliseconds with a memory usage of 89.68 megabyte, access time of 1187.12 milliseconds, and computation overhead of 124.56 megabyte on the Medical Information Mart for Intensive Care (MIMIC-III) dataset, especially for 300 patient records. [ABSTRACT FROM AUTHOR]

Scrum is an important and essential technique for developing software. Scrum framework is a project management technique that can emphasize software development teamwork. If you are starting, consider scrum as a method for accomplishing tasks as a team in manageable chunks simultaneously, with ongoing experimentation and feedback loops to learn and improve as you go. Scrum enables individuals and groups to collaboratively and gradually generate value. Being an agile framework, scrum gives people and teams the perfect structure to fit into their workflows while still including the best practices to cater to their requirements. There are some important considerations for security in the agile software development process such as threat modeling, secure coding practices, continuous security testing, authentication, and authorization. In this work, we reviewed various scrum developing techniques to find out the research gaps. This article consider the scrum technique with blockchain technologies for review because it is preferred by most of the software industries. [ABSTRACT FROM AUTHOR]

Today, methodologies based on learning models are utilized to generate precise conversion techniques for renewable sources. The methods based on Computational Intelligence (CI) are considered an effective way to generate renewable instruments. The energy-related complexities of developing such methods are dependent on the vastness of the data sets and number of parameters needed to be covered, both of which need to be carefully examined. The most recent and significant researchers in the field of learning-based approaches for renewable challenges are addressed in this article. There are several different Deep Learning (DL) and Machine Learning (ML) approaches that are utilized in solar, wind, hydro, and tidal energy sources. A new taxonomy is formed in the process of evaluating the effectiveness of the strategies that are described in the literature. This survey evaluates the advantages and the drawbacks of the existing methodologies and helps to find an effective approach to overcome the issues in the existing methods. In this study, various methods based on energy conversion systems in renewable source of energies like solar, wind, hydro power, and tidal energies are evaluated using ML and DL approaches. [ABSTRACT FROM AUTHOR]

In the present scenario, Alzheimer's Disease (AD) is one of the incurable neuro-degenerative disorders, which accounts for nearly 60% to 70% of dementia cases. Currently, several machine-learning approaches and neuroimaging modalities are utilized for diagnosing AD. Among the available neuroimaging modalities, functional Magnetic Resonance Imaging (fMRI) is extensively utilized for studying brain activities related to AD. However, analyzing complex brain structures in fMRI is a time-consuming and complex task; so, a novel automated model was proposed in this manuscript for early diagnosis of AD using fMRI images. Initially, the fMRI images are acquired from an online dataset: Alzheimer's Disease Neuroimaging Initiative (ADNI). Further, the quality of the acquired fMRI images was improved by implementing a normalization technique. Then, the Segmentation by Aggregating Superpixels (SAS) method was implemented for segmenting the brain regions (AD, Normal Controls (NC), Mild Cognitive Impairment (MCI), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Significant Memory Concern (SMC)) from the denoised fMRI images. From the segmented brain regions, feature vectors were extracted by employing Gabor and Gray Level Co-Occurrence Matrix (GLCM) techniques. The obtained feature vectors were dimensionally reduced by implementing Honey Badger Optimization Algorithm (HBOA) and fed to the Multi-Layer Perceptron (MLP) model for classifying the fMRI images as AD, NC, MCI, EMCI, LMCI, and SMC. The extensive investigation indicated that the presented model attained 99.44% of classification accuracy, 88.90% of Dice Similarity Coefficient (DSC), 90.82% of Jaccard Coefficient (JC), and 88.43% of Hausdorff Distance (HD). The attained results are better compared with the conventional segmentation and classification models. [ABSTRACT FROM AUTHOR]