Leveraging data analytics to revolutionize cybersecurity with machine learning and deep learning.

The exponential growth of digital technologies has brought about a surge in the complexity and frequency of cyber-attacks, necessitating robust cyber security measures. This study introduces an innovative approach to cyber security data analysis by leveraging Convolutional Neural Network (CNN) technology. The primary objective is to explore the potential of CNNs in accurately and efficiently detecting and classifying cyber security threats.

Improving CNN predictive accuracy in COVID-19 health analytics.

The COVID-19 pandemic has underscored the critical necessity for robust and accurate predictive frameworks to bolster global healthcare infrastructures. This study presents a comprehensive examination of convolutional neural networks (CNNs) applied to the prediction of COVID-19-related health outcomes, with an emphasis on core challenges, methodological constraints, and potential remediation strategies. Our investigation targets two principal aims: the identification of COVID-19 infections through chest radiographic imaging, specifically X-rays, and the prognostication of disease severity by integrating clinical parameters and electronic health records.

Multivariate and predictive modelling of arsenic and cadmium in rice: Influence of origin, grain type, and processing.

Rice is a global dietary staple and understanding the accumulation of toxic and essential elements in rice grains is vital for public health, particularly in high-consumption regions. While prior studies have assessed elemental contamination, applying advanced machine learning (ML) to predict and analyze geographic patterns remains limited. This study analyzed arsenic (As), cadmium (Cd), and six essential elements (Zn, Fe, Cu, Mn, Se, and Mo) in 46 rice samples sold in Sydney, Australia, using ICP-MS.

Hybrid AI and semiconductor approaches for power quality improvement.

This research presents a novel approach to improving electric power quality using semiconductor devices by integrating Machine Learning (ML), Deep Learning (DL), and advanced control strategies. The research addresses key power quality challenges - including voltage sags, swells, harmonics, and transient disturbances - through a data-driven framework that combines traditional control techniques with adaptive learning models. A variety of algorithms, including Support Vector Machines (SVM), Random Forests, Neural Networks, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, were tested using real-time data.

COVID-19 health data prediction: a critical evaluation of CNN-based approaches.

The COVID-19 pandemic has significantly accelerated the demand for accurate and efficient prediction models to support effective disease management, containment strategies, and informed decision-making. Predictive models capable of analyzing complex health data are essential for monitoring disease trends, evaluating risk factors, and optimizing resource allocation during the pandemic. Among various machine learning approaches, convolutional neural networks (CNNs) have emerged as powerful tools due to their ability to process large volumes of high-dimensional health data, such as medical images, time-series data, and patient demographics, with impressive precision.

Enhancing cybersecurity through script development using machine and deep learning for advanced threat mitigation.

This scholarly paper explores the utilization of Machine Learning (ML) and Deep Learning (DL) methodologies to enhance the cybersecurity aspects of script development. Given the increasing panorama of threats in contemporary software creation, cybersecurity has ascended to a critical realm of concern. Traditional security measures frequently prove inadequate in countering complex breaches.

Comparative immobilization of 30 PFAS mixtures onto biochar, clay, nanoparticle, and polymer derived engineered adsorbents: Machine learning insights into carbon chain length and removal mechanism.

Per- and polyfluoroalkyl substances (PFAS) are a group of fluorinated chemicals that cause potential risk in PFAS-impacted soil and water. The adsorption efficiency of 30 PFAS mixtures using different adsorbents in environmentally relevant concentrations was investigated. Different meso/microporous designed adsorbents (n = 7) were used for PFAS adsorption and their interfacial interactions.

Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images.

COVID-19 has sparked a global pandemic, with a variety of inflamed instances and deaths increasing on an everyday basis. Researchers are actively increasing and improving distinct mathematical and ML algorithms to forecast the infection. The prediction and detection of the Omicron variant of COVID-19 brought new issues for the health fraternity due to its ubiquity in human beings.

COVID-19 Data Analytics Using Extended Convolutional Technique.

The healthcare system, lifestyle, industrial growth, economy, and livelihood of human beings worldwide were affected due to the triggered global pandemic by the COVID-19 virus that originated and was first reported in Wuhan city, Republic Country of China. COVID cases are difficult to predict and detect in their early stages, and their spread and mortality are uncontrollable. The reverse transcription polymerase chain reaction (RT-PCR) is still the first and foremost diagnostical methodology accepted worldwide; hence, it creates a scope of new diagnostic tools and techniques of detection approach which can produce effective and faster results compared with its predecessor.