Retinal Vessel Extraction Assisted Multi-Channel Feature Map and U-Net.

Early detection of vessels from fundus images can effectively prevent the permanent retinal damages caused by retinopathies such as glaucoma, hyperextension, and diabetes. Concerning the red color of both retinal vessels and background and the vessel's morphological variations, the current vessel detection methodologies fail to segment thin vessels and discriminate them in the regions where permanent retinopathies mainly occur. This research aims to suggest a novel approach to take the benefit of both traditional template-matching methods with recent deep learning (DL) solutions.

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models.

Coronavirus disease 2019 (COVID-19) is a novel disease that affects healthcare on a global scale and cannot be ignored because of its high fatality rate. Computed tomography (CT) images are presently being employed to assist doctors in detecting COVID-19 in its early stages. In several scenarios, a combination of epidemiological criteria (contact during the incubation period), the existence of clinical symptoms, laboratory tests (nucleic acid amplification tests), and clinical imaging-based tests are used to diagnose COVID-19.

Cybersecurity Awareness Based on Software and E-mail Security with Statistical Analysis.

The aim of this study is to discover the impact of software security and e-mail security on overall cybersecurity among the students of Imam Abdulrahman Bin Faisal University in Dammam. Another main purpose to conduct this study is to know the level of knowledge students have in the developing countries about the cybersecurity and how much are they mindful of cyber-attacks and the level of awareness among the university students. Two important hypotheses were studied to discover their importance in awareness of cybersecurity.

Computational Intelligence Approaches in Developing Cyberattack Detection System.

The Internet plays a fundamental part in relentless correspondence, so its applicability can decrease the impact of intrusions. Intrusions are defined as movements that unfavorably influence the focus of a computer. Intrusions may sacrifice the reputability, integrity, privacy, and accessibility of the assets attacked.

Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices.

With the rapid expansion of the use of smartphone devices, malicious attacks against Android mobile devices have increased. The Android system adopted a wide range of sensitive applications such as banking applications; therefore, it is becoming the target of malware that exploits the vulnerabilities of the security system. A few studies proposed models for the detection of mobile malware.

EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm.

The electroencephalogram (EEG) introduced a massive potential for user identification. Several studies have shown that EEG provides unique features in addition to typical strength for spoofing attacks. EEG provides a graphic recording of the brain's electrical activity that electrodes can capture on the scalp at different places.

A Novel Framework for Arabic Dialect Chatbot Using Machine Learning.

With the advent of artificial intelligence and proliferation in the demand for an online dialogue system, the popularity of chatbots is growing on various industrial platforms. Their applications are getting widely noticed with intelligent tools as they are able to mimic human behavior in natural languages. Chatbots have been proven successful for many languages, such as English, Spanish, and French, over the years in varied fields like entertainment, medicine, education, and commerce.

Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms.

Autism spectrum disorder (ASD) is a type of mental illness that can be detected by using social media data and biomedical images. Autism spectrum disorder (ASD) is a neurological disease correlated with brain growth that later impacts the physical impression of the face. Children with ASD have dissimilar facial landmarks, which set them noticeably apart from typically developed (TD) children.

Sentiment analysis for cruises in Saudi Arabia on social media platforms using machine learning algorithms.

Social media has great importance in the community for discussing many events and sharing them with others. The primary goal of this research is to study the quality of the sentiment analysis (SA) of impressions about Saudi cruises, as a first event, by creating datasets from three selected social media platforms (Instagram, Snapchat, and Twitter). The outcome of this study will help in understanding opinions of passengers and viewers about their first Saudi cruise experiences by analyzing their feelings from social media posts.

A Multi-Agent Deep Reinforcement Learning Approach for Enhancement of COVID-19 CT Image Segmentation.

Currently, most mask extraction techniques are based on convolutional neural networks (CNNs). However, there are still numerous problems that mask extraction techniques need to solve. Thus, the most advanced methods to deploy artificial intelligence (AI) techniques are necessary.

Prediction of generalized anxiety levels during the Covid-19 pandemic: A machine learning-based modeling approach.

The rapid spread of the Covid-19 outbreak led many countries to enforce precautionary measures such as complete lockdowns. These lifestyle-altering measures caused a significant increase in anxiety levels globally. For that reason, decision-makers are in dire need of methods to prevent potential public mental crises.

EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications.

Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain's electrical activities, measured by placing electrodes (channels) in various scalp positions.

Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray.

The coronavirus pandemic (COVID-19) is disrupting the entire world; its rapid global spread threatens to affect millions of people. Accurate and timely diagnosis of COVID-19 is essential to control the spread and alleviate risk. Due to the promising results achieved by integrating machine learning (ML), particularly deep learning (DL), in automating the multiple disease diagnosis process.

Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction.

Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability.

An Industrial IoT-Based Blockchain-Enabled Secure Searchable Encryption Approach for Healthcare Systems Using Neural Network.

The IoT refers to the interconnection of things to the physical network that is embedded with software, sensors, and other devices to exchange information from one device to the other. The interconnection of devices means there is the possibility of challenges such as security, trustworthiness, reliability, confidentiality, and so on. To address these issues, we have proposed a novel group theory (GT)-based binary spring search (BSS) algorithm which consists of a hybrid deep neural network approach.

Brain Tumor Detection and Classification by MRI Using Biologically Inspired Orthogonal Wavelet Transform and Deep Learning Techniques.

Radiology is a broad subject that needs more knowledge and understanding of medical science to identify tumors accurately. The need for a tumor detection program, thus, overcomes the lack of qualified radiologists. Using magnetic resonance imaging, biomedical image processing makes it easier to detect and locate brain tumors.

Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity.

Rapid technological development has changed drastically the automotive industry. Network communication has improved, helping the vehicles transition from completely machine- to software-controlled technologies. The autonomous vehicle network is controlled by the controller area network (CAN) bus protocol.

COVID-19 outbreak and the role of digital twin.

COVID-19 has transformed the life of human beings and digital twin infrastructure can facilitates working remotely during COVID-19 outbreak by reducing burden on services and infrastructure. Currently, many organizations are installing and developing devices such as thermal cameras, sensors aiming to minimize human contact and so forth, in addition to enforcing social distancing resulting in reducing the risk of transmission. Due to economic reasons, lockdown restrictions are being relaxed/lifted in many countries and Pakistan which is one of the most densely populated countries in the world with a population of 220 + million is no exception.

Federated learning enables intelligent reflecting surface in fog-cloud enabled cellular network.

The intelligent reflecting surface (IRS) is a ground-breaking technology that can boost the efficiency of wireless data transmission systems. Specifically, the wireless signal transmitting environment is reconfigured by adjusting a large number of small reflecting units simultaneously. Therefore, intelligent reflecting surface (IRS) has been suggested as a possible solution for improving several aspects of future wireless communication.

Sustainable Program Assessment Practices: A Review of the ABET and NCAAA Computer Information Systems Accreditation Process.

Program outcome assessment is a complex process that demands careful planning and resources in order to accurately assess higher-order thinking skills. A well-defined assessment approach provides detailed insights into program weaknesses and leads to continuous improvement. Whereas a poor assessment approach does not reflect the underlying weaknesses and may result in a useless effort.

COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images.

The term COVID-19 is an abbreviation of Coronavirus 2019, which is considered a global pandemic that threatens the lives of millions of people. Early detection of the disease offers ample opportunity of recovery and prevention of spreading. This paper proposes a method for classification and early detection of COVID-19 through image processing using X-ray images.

A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries.

Accurate prediction models have become the first goal for aiding pandemic-related decisions. Modeling and predicting the number of new active cases and deaths are important steps for anticipating and controlling COVID-19 outbreaks. The aim of this research was to develop an accurate prediction system for the COVID-19 pandemic that can predict the numbers of active cases and deaths in the Gulf countries of Saudi Arabia, Oman, the United Arab Emirates (UAE), Kuwait, Bahrain, and Qatar.

Intelligent Techniques for Detecting Network Attacks: Review and Research Directions.

The significant growth in the use of the Internet and the rapid development of network technologies are associated with an increased risk of network attacks. Network attacks refer to all types of unauthorized access to a network including any attempts to damage and disrupt the network, often leading to serious consequences. Network attack detection is an active area of research in the community of cybersecurity.

Deepening into the suitability of using pre-trained models of ImageNet against a lightweight convolutional neural network in medical imaging: an experimental study.

Transfer learning (TL) has been widely utilized to address the lack of training data for deep learning models. Specifically, one of the most popular uses of TL has been for the pre-trained models of the ImageNet dataset. Nevertheless, although these pre-trained models have shown an effective performance in several domains of application, those models may not offer significant benefits in all instances when dealing with medical imaging scenarios.

Multi-Agent Systems in Fog-Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring.

In the last decade, the developments in healthcare technologies have been increasing progressively in practice. Healthcare applications such as ECG monitoring, heartbeat analysis, and blood pressure control connect with external servers in a manner called cloud computing. The emerging cloud paradigm offers different models, such as fog computing and edge computing, to enhance the performances of healthcare applications with minimum end-to-end delay in the network.