A notable association between Alzheimer's Disease and Epilepsy, two divergent neurological conditions, has been established through previous research, illustrating an elevated seizure development risk in individuals diagnosed with Alzheimer’s Disease (AD). The hippocampus, fundamental in both seizure and tumour pathology, is intricately investigated herein. The subsequent aberrant electrical activity within this brain region, frequently implicated in seizure onset and propagation, underpins a complex relationship between degenerative cerebral changes and seizure incidence. Symptomatic manifestations in hippocampal glioma include, but are not limited to, seizures, memory deficits, and language difficulties, contingent upon the tumour's location and size. Thus, the cruciality of proficient seizure detection and analysis is underscored. Employing canny edge detection and thresholding to delineate contours and boundaries within images, an analysis was conducted by transmuting grayscale or colour images into a binary format. The input dataset, utilised for the training and testing of machine and deep-learning models, comprised images of seizures. These models were subsequently trained to discern patterns and features within the images, facilitating the differentiation between two predefined classes. Resultantly, the models predicted, with a defined accuracy level, the presence or absence of a seizure within a new image. The Support Vector Machine (SVM) and Convolutional Neural Network (CNN) models demonstrated classification accuracies of 96% and 95%, respectively. By analysing performance metrics on a per-slice basis, the localization of seizure activity within the brain could be visualised, offering valuable insights into regions affected by this activity. The amalgamation of edge detection, feature extraction, and classification models proficiently discriminated between seizure and non-seizure activities, providing pivotal insights for the diagnosis and therapeutic strategies for epilepsy. Further, studying these neurological alterations can illuminate the progression and severity of cognitive and emotional deficits within affected individuals, whilst advancements in diagnostic methodologies, such as Magnetic Resonance Imaging (MRI), facilitate an enriched comparative analysis.

In the rapidly evolving landscape of digital healthcare, the integration of cloud computing, Internet of Things (IoT), and advanced computational methodologies such as machine learning and artificial intelligence (AI) has significantly enhanced early disease detection, accessibility, and diagnostic scope. However, this progression has concurrently elevated concerns regarding the safeguarding of sensitive patient data. Addressing this challenge, a novel secure healthcare system employing a blockchain-based IoT framework, augmented by deep learning and biomimetic algorithms, is presented. The initial phase encompasses a blockchain-facilitated mechanism for secure data storage, authentication of users, and prognostication of health status. Subsequently, the modified Jellyfish Search Optimization (JSO) algorithm is employed for optimal feature selection from datasets. A unique health status prediction model is introduced, leveraging a Deep Convolutional Gated Recurrent Unit (DCGRU) approach. This model ingeniously combines Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) processes, where the GRU network extracts pivotal directional characteristics, and the CNN architecture discerns complex interrelationships within the data. Security of the data management system is fortified through the implementation of the twofish encryption algorithm. The efficacy of the proposed model is rigorously evaluated using standard medical datasets, including Diabetes and EEG Eyestate, employing diverse performance metrics. Experimental results demonstrate the model's superiority over existing best practices, achieving a notable accuracy of 0.884. Furthermore, comparative analyses with the Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) models reveal enhanced performance metrics, with the proposed model achieving a processing time and throughput of 40 and 45.42, respectively.

The swift global spread of Corona Virus Disease 2019 (COVID-19), identified merely four months prior, necessitates rapid and precise diagnostic methods. Currently, the diagnosis largely depends on computed tomography (CT) image interpretation by medical professionals, a process susceptible to human error. This research delves into the utility of Convolutional Neural Networks (CNNs) in automating the classification of COVID-19 from medical images. An exhaustive evaluation and comparison of prominent CNN architectures, namely Visual Geometry Group (VGG), Residual Network (ResNet), MobileNet, Inception, and Xception, are conducted. Furthermore, the study investigates ensemble approaches to harness the combined strengths of these models. Findings demonstrate the distinct advantage of ensemble models, with the novel deep learning (DL)+ ensemble technique notably surpassing the accuracy, precision, recall, and F-score of individual CNNs, achieving an exceptional rate of 99.5%. This remarkable performance accentuates the transformative potential of CNNs in COVID-19 diagnostics. The significance of this advancement lies not only in its reliability and automated nature, surpassing traditional, subjective human interpretation but also in its contribution to accelerating the diagnostic process. This acceleration is pivotal for the effective implementation of containment and mitigation strategies against the pandemic. The abstract delineates the methodological choices, highlights the unparalleled efficacy of the DL+ ensemble technique, and underscores the far-reaching implications of employing CNNs for COVID-19 detection.

The landscape of livestock farming is undergoing a significant transformation, primarily influenced by the integration of the Internet of Things (IoT) technology. This systematic literature review (SLR) critically examines the role of IoT in enhancing cow health monitoring, a burgeoning field of research drawing considerable attention in recent years. Spanning articles published from 2017 to 2023 in eminent academic forums, this study meticulously selected and analyzed thirty publications. These were chosen through a structured process, evaluating each for its relevance based on title and abstract. The review encapsulates a thorough investigation of the applications, sensors, and devices underpinning IoT-based cow health monitoring systems. It is observed that the current research landscape is dynamically evolving, marked by emerging trends and noticeable gaps in technology and application. This synthesis of existing literature offers an insightful overview of the potential and limitations inherent in current IoT solutions, highlighting their efficacy in real-world scenarios. Furthermore, this review delineates the challenges faced and posits future research directions to address unresolved issues in cow health monitoring. The primary objective of this systematic analysis is to consolidate research findings, thereby advancing the understanding of IoT's impact in this field. It also aims to foster a comprehensive dialogue on the technological advancements and their implications for future research endeavors in livestock farming.

This study examines the aspects that can impact an organization's cloud security posture and the consequences for their cloud adoption strategies. Based on a thorough examination of existing literature, a conceptual framework is developed that includes several aspects such as organisational, technical, regulatory, operational, and human elements. The cloud security readiness is influenced by these five types of characteristics. A research instrument is utilised to evaluate the hypotheses pertaining to those aspects. The pilot survey showcases the research tool within the framework of a representative sample of organisations. In addition to conducting instrument testing, the initial responses also validate the importance of several elements that impact cloud security. The prominence of technical capabilities as a key factor underscores their vital contribution to bolstering cybersecurity readiness. Regulatory factors have a significant role in emphasising the necessity of compliance in cloud security. Organisational elements, such as managerial support, training, budget allocation, policy adherence, and governance, have a moderate impact. The presence of human elements also appears to contribute to and emphasise the necessity of promoting security awareness and alertness. This study enhances the existing body of knowledge on cloud security by offering insights into the various complex issues involved. The results can provide guidance to professionals seeking to enhance the cloud security of enterprises and scholars studying the changing cloud environment.