The decentralised nature of cryptocurrency, coupled with its potential for significant financial returns, has elevated its status as a sought-after investment opportunity on a global scale. Nonetheless, the inherent unpredictability and volatility of the cryptocurrency market present considerable challenges for investors aiming to forecast price movements and secure profitable investments. In response to this challenge, the current investigation was conducted to assess the efficacy of three Machine Learning (ML) algorithms, namely, Gradient Boosting (GB), Random Forest (RF), and Bagging, in predicting the daily closing prices of six major cryptocurrencies, namely, Binance, Bitcoin, Ethereum, Solana, USD, and XRP. The study utilised historical price data spanning from January 1, 2015 to January 26, 2024 for Bitcoin, from January 1, 2018 to January 26, 2024 for Ethereum and XRP, from January 1, 2021 to January 26, 2024 for Solana, and from January 1, 2019 to January 26, 2024 for USD. A novel approach was adopted wherein the lagging prices of the cryptocurrencies were employed as features for prediction, as opposed to the conventional method of using opening, high, and low prices, which are not predictive in nature. The data set was divided into a training set (80%) and a testing set (20%) for the evaluation of the algorithms. The performance of these ML algorithms was systematically compared using a suite of metrics, including R2, adjusted R2, Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The findings revealed that the GB algorithm exhibited superior performance in predicting the prices of Bitcoin and Solana, whereas the RF algorithm demonstrated greater efficacy for Ethereum, USD, and XRP. This comparative analysis underscores the relative advantages of RF over GB and Bagging algorithms in the context of cryptocurrency price prediction. The outcomes of this study not only contribute to the existing body of knowledge on the application of ML algorithms in financial markets but also provide actionable insights for investors navigating the volatile cryptocurrency market.

The advent of fifth-generation (5G) long-term evolution (LTE) technology represents a critical leap forward in telecommunications, enabling unprecedented high-speed data transfer essential for today’s digital society. Despite the advantages, the transition introduces significant challenges, including elevated bit error rate (BER), diminished signal-to-noise ratio (SNR), and the risk of jitter, undermining network reliability and efficiency. In response, a novel low-density parity check (LDPC) decoder optimized for 5G LTE applications has been developed. This decoder is tailored to significantly reduce BER and improve SNR, thereby enhancing the performance and reliability of 5G communications networks. Its design accommodates advanced switching and parallel processing capabilities, crucial for handling complex data flows inherent in contemporary telecommunications systems. A distinctive feature of this decoder is its dynamic adaptability in adjusting message sizes and code rates, coupled with the augmentation of throughput via reconfigurable switching operations. These innovations allow for a versatile approach to optimizing 5G networks. Comparative analyses demonstrate the decoder’s superior performance relative to the quasi-cyclic low-density check code (QCLDC) method, evidencing marked improvements in communication quality and system efficiency. The introduction of this LDPC decoder thus marks a significant contribution to the evolution of 5G networks, offering a robust solution to the pressing challenges faced by next-generation communication systems and establishing a new standard for high-speed wireless connectivity.

In the realm of fog computing (FC), a vast array of intelligent devices collaborates within an intricate network, a synergy that, while promising, has not been without its challenges. These challenges, including data loss, difficulties in workload distribution, a lack of parallel processing capabilities, and security vulnerabilities, have necessitated the exploration and deployment of a variety of solutions. Among these, software-defined networks (SDN), double-Q learning algorithms, service function chains (SFC), virtual network functions (VNF) stand out as significant. An exhaustive survey has been conducted to explore workload distribution methodologies within Internet of Things (IoT) architectures in FC networks. This investigation is anchored in a parameter-centric analysis, aiming to enhance the efficiency of data transmission across such networks. It delves into the architectural framework, pivotal pathways, and applications, aiming to identify bottlenecks and forge the most effective communication channels for IoT devices under substantial workload conditions. The findings of this research are anticipated to guide the selection of superior simulation tools, validate datasets, and refine strategies for data propagation. This, in turn, is expected to facilitate optimal power consumption and enhance outcomes in data transmission and propagation across multiple dimensions. The rigorous exploration detailed herein not only illuminates the complexities of workload distribution in FC networks but also charts a course towards more resilient and efficient IoT ecosystems.

In the realm of automated image captioning, which entails generating descriptive text for images, the fusion of Natural Language Processing (NLP) and computer vision techniques is paramount. This study introduces the Fully Convolutional Localization Network (FCLN), a novel approach that concurrently addresses localization and description tasks within a singular forward pass. It maintains spatial information and avoids detail loss, streamlining the training process with consistent optimization. The foundation of FCLN is laid by a Convolutional Neural Network (CNN), adept at extracting salient image features. Central to this architecture is a Localization Layer, pivotal in precise object detection and caption generation. The FCLN architecture amalgamates a region detection network, reminiscent of Faster Region-CNN (R-CNN), with a captioning network. This synergy enables the production of contextually meaningful image captions. The incorporation of the Faster R-CNN framework facilitates region-based object detection, offering precise contextual understanding and inter-object relationships. Concurrently, a Long Short-Term Memory (LSTM) network is employed for generating captions. This integration yields superior performance in caption accuracy, particularly in complex scenes. Evaluations conducted on the Microsoft Common Objects in Context (MS COCO) test server affirm the model's superiority over existing benchmarks, underscoring its efficacy in generating precise and context-rich image captions.

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.

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.

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.

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.

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.

Cyclins, commonly referred to as co-enzymes, are a pivotal family of proteins that modulate cellular growth by activating cell-cycle mediators, proving essential for the cell cycle. Due to the marked dissimilarity in their sequences, effective differentiation among cyclins remains a challenging endeavour. In this study, an innovative methodology was proposed, wherein the amino acid composition was utilized to inform an SVM-based classification approach. SVMs, being supervised machine learning algorithms, are typically employed for classification and regression tasks. From the data analyzed, eighteen (18) feature labels were extracted, culminating in an extensive set of thirteen thousand one hundred and fifty-one (13,151) discernible features. Employing the jackknife cross-validation technique revealed that this SVM-informed approach facilitated the identification of cyclins with an accuracy rate of 91.9%, a notable improvement from prior studies. Such advancements underscore the potential for more accurate and efficient differentiation of cyclins in future endeavours.