Intelligent Grapevine Disease Detection Using IoT Sensor Network.

The Internet of Things (IoT) has gained significance in agriculture, using remote sensing and machine learning to help farmers make high-precision management decisions. This technology can be applied in viticulture, making it possible to monitor disease occurrence and prevent them automatically. The study aims to achieve an intelligent grapevine disease detection method, using an IoT sensor network that collects environmental and plant-related data.

A White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-Based Cybersecurity Solution for a Smart City Environment.

Smart grids (SGs) play a vital role in the smart city environment, which exploits digital technology, communication systems, and automation for effectively managing electricity generation, distribution, and consumption. SGs are a fundamental module of smart cities that purpose to leverage technology and data for enhancing the life quality for citizens and optimize resource consumption. The biggest challenge in dealing with SGs and smart cities is the potential for cyberattacks comprising Distributed Denial of Service (DDoS) attacks.

Brief tele-mindfulness-based intervention: A multicenter randomized controlled trial.

Background: Coronavirus disease 2019 (COVID-19) has proven to be detrimental to the psychological well-being of healthcare providers (HCP). This study was a psychological intervention during the COVID-19 pandemic to check extent to which brief mindfulness-based interventions (MBIs) and progressive muscle relaxation (PMR) affect psychological well-being, resilience, and anxiety of HCPs.

Materials And Methods: A randomized trial study conducted from July to August 2020.

A Systematic Literature Review on Cyber Threat Intelligence for Organizational Cybersecurity Resilience.

Cybersecurity is a significant concern for businesses worldwide, as cybercriminals target business data and system resources. Cyber threat intelligence (CTI) enhances organizational cybersecurity resilience by obtaining, processing, evaluating, and disseminating information about potential risks and opportunities inside the cyber domain. This research investigates how companies can employ CTI to improve their precautionary measures against security breaches.

Detection of Alzheimer's Disease Based on Cloud-Based Deep Learning Paradigm.

Deep learning is playing a major role in identifying complicated structure, and it outperforms in term of training and classification tasks in comparison to traditional algorithms. In this work, a local cloud-based solution is developed for classification of Alzheimer's disease (AD) as MRI scans as input modality. The multi-classification is used for AD variety and is classified into four stages.

MRI-Based Effective Ensemble Frameworks for Predicting Human Brain Tumor.

The diagnosis of brain tumors at an early stage is an exigent task for radiologists. Untreated patients rarely survive more than six months. It is a potential cause of mortality that can occur very quickly.

Digital Transformation and Cybersecurity Challenges for Businesses Resilience: Issues and Recommendations.

This systematic literature review explores the digital transformation (DT) and cybersecurity implications for achieving business resilience. DT involves transitioning organizational processes to IT solutions, which can result in significant changes across various aspects of an organization. However, emerging technologies such as artificial intelligence, big data and analytics, blockchain, and cloud computing drive digital transformation worldwide while increasing cybersecurity risks for businesses undergoing this process.

Joint Diagnosis of Pneumonia, COVID-19, and Tuberculosis from Chest X-ray Images: A Deep Learning Approach.

Pneumonia, COVID-19, and tuberculosis are some of the most fatal and common lung diseases in the current era. Several approaches have been proposed in the literature for the diagnosis of individual diseases, since each requires a different feature set altogether, but few studies have been proposed for a joint diagnosis. A patient being diagnosed with one disease as negative may be suffering from the other disease, and vice versa.

SBXception: A Shallower and Broader Xception Architecture for Efficient Classification of Skin Lesions.

Skin cancer is a major public health concern around the world. Skin cancer identification is critical for effective treatment and improved results. Deep learning models have shown considerable promise in assisting dermatologists in skin cancer diagnosis.

A robust algorithmic cum integrated approach of interval-valued fuzzy hypersoft set and OOPCS for real estate pursuit.

Due to the vast variety of aspects that must be made-many of which are in opposition to one another-choosing a home can be difficult for those without much experience. Individuals need to spend more time making decisions because they are difficult, which results in making poor choices. To overcome residence selection issues, a computational approach is necessary.

Efficient thermal face recognition method using optimized curvelet features for biometric authentication.

Biometric technology is becoming increasingly prevalent in several vital applications that substitute traditional password and token authentication mechanisms. Recognition accuracy and computational cost are two important aspects that are to be considered while designing biometric authentication systems. Thermal imaging is proven to capture a unique thermal signature for a person and thus has been used in thermal face recognition.

Some stable and closed-shell structures of anticancer drugs by graph theoretical parameters.

The eigenvalues are significant in mathematics, but they are also relevant in other domains like as chemistry, economics, and a variety of others. In terms of our research, eigenvalues are used in chemistry to represent not only the form of energy but also the various physicochemical aspects of a chemical substance. We must comprehend the connection between mathematics and chemistry.

Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain.

The emergence of the Internet of Things (IoT) and its subsequent evolution into the Internet of Everything (IoE) is a result of the rapid growth of information and communication technologies (ICT). However, implementing these technologies comes with certain obstacles, such as the limited availability of energy resources and processing power. Consequently, there is a need for energy-efficient and intelligent load-balancing models, particularly in healthcare, where real-time applications generate large volumes of data.

Smart Flood Detection with AI and Blockchain Integration in Saudi Arabia Using Drones.

Global warming and climate change are responsible for many disasters. Floods pose a serious risk and require immediate management and strategies for optimal response times. Technology can respond in place of humans in emergencies by providing information.

Intelligent lung cancer MRI prediction analysis based on cluster prominence and posterior probabilities utilizing intelligent Bayesian methods on extracted gray-level co-occurrence (GLCM) features.

Lung cancer is the second foremost cause of cancer due to which millions of deaths occur worldwide. Developing automated tools is still a challenging task to improve the prediction. This study is specifically conducted for detailed posterior probabilities analysis to unfold the network associations among the gray-level co-occurrence matrix (GLCM) features.

Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations.

Genome-wide association studies have proven their ability to improve human health outcomes by identifying genotypes associated with phenotypes. Various works have attempted to predict the risk of diseases for individuals based on genotype data. This prediction can either be considered as an analysis model that can lead to a better understanding of gene functions that underlie human disease or as a black box in order to be used in decision support systems and in early disease detection.

Cyber Attack Detection for Self-Driving Vehicle Networks Using Deep Autoencoder Algorithms.

Connected and autonomous vehicles (CAVs) present exciting opportunities for the improvement of both the mobility of people and the efficiency of transportation systems. The small computers in autonomous vehicles (CAVs) are referred to as electronic control units (ECUs) and are often perceived as being a component of a broader cyber-physical system. Subsystems of ECUs are often networked together via a variety of in-vehicle networks (IVNs) so that data may be exchanged, and the vehicle can operate more efficiently.

Phishing URLs Detection Using Sequential and Parallel ML Techniques: Comparative Analysis.

In today's digitalized era, the world wide web services are a vital aspect of each individual's daily life and are accessible to the users via uniform resource locators (URLs). Cybercriminals constantly adapt to new security technologies and use URLs to exploit vulnerabilities for illicit benefits such as stealing users' personal and sensitive data, which can lead to financial loss, discredit, ransomware, or the spread of malicious infections and catastrophic cyber-attacks such as phishing attacks. Phishing attacks are being recognized as the leading source of data breaches and the most prevalent deceitful scam of cyber-attacks.

Robust Classification and Detection of Big Medical Data Using Advanced Parallel -Means Clustering, YOLOv4, and Logistic Regression.

Big-medical-data classification and image detection are crucial tasks in the field of healthcare, as they can assist with diagnosis, treatment planning, and disease monitoring. Logistic regression and YOLOv4 are popular algorithms that can be used for these tasks. However, these techniques have limitations and performance issue with big medical data.

DRLBTS: deep reinforcement learning-aware blockchain-based healthcare system.

Industrial Internet of Things (IIoT) is the new paradigm to perform different healthcare  applications with different services in daily life. Healthcare applications based on IIoT paradigm are widely used to track patients health status using remote healthcare technologies. Complex biomedical sensors exploit wireless technologies, and remote services in terms of industrial workflow applications to perform different healthcare tasks, such as like heartbeat, blood pressure and others.

Comprehensible Machine-Learning-Based Models for the Pre-Emptive Diagnosis of Multiple Sclerosis Using Clinical Data: A Retrospective Study in the Eastern Province of Saudi Arabia.

Multiple Sclerosis (MS) is characterized by chronic deterioration of the nervous system, mainly the brain and the spinal cord. An individual with MS develops the condition when the immune system begins attacking nerve fibers and the myelin sheathing that covers them, affecting the communication between the brain and the rest of the body and eventually causing permanent damage to the nerve. Patients with MS (pwMS) might experience different symptoms depending on which nerve was damaged and how much damage it has sustained.

The Reasons for Physicians and Pharmacists' Acceptance of Clinical Support Systems in Saudi Arabia.

This research aims to identify the technological and non-technological factors influencing user acceptance of the CDSS in a group of healthcare facilities in Saudi Arabia. The study proposes an integrated model that indicates the factors to be considered when designing and evaluating CDSS. This model is developed by integrating factors from the "Fit between Individuals, Task, and Technology" (FITT) framework into the three domains of the human, organization, and technology-fit (HOT-fit) model.

Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review.

This research aims to review and evaluate the most relevant scientific studies about deep learning (DL) models in the omics field. It also aims to realize the potential of DL techniques in omics data analysis fully by demonstrating this potential and identifying the key challenges that must be addressed. Numerous elements are essential for comprehending numerous studies by surveying the existing literature.