Advancing Phishing Email Detection: A Comparative Study of Deep Learning Models.

Phishing is one of the most dangerous attacks targeting individuals, organizations, and nations. Although many traditional methods for email phishing detection exist, there is a need to improve accuracy and reduce false-positive rates. Our work investigates one-dimensional CNN-based models (1D-CNNPD) to detect phishing emails in order to address these challenges.

Role of Optimization in RNA-Protein-Binding Prediction.

RNA-binding proteins (RBPs) play an important role in regulating biological processes, such as gene regulation. Understanding their behaviors, for example, their binding site, can be helpful in understanding RBP-related diseases. Studies have focused on predicting RNA binding by means of machine learning algorithms including deep convolutional neural network models.

Molecular Identification of Newly Recorded Louse Columbicola tschulyschman Eichler (Phthiraptera: Philopteridae) From Saudi Arabia With a Note on Genus Columbicola spp. Phylogeny.

Chewing lice comprise a large group of ectoparasites that colonize and adversely affect several domestic and wild birds including pigeons. In Saudi Arabia, there is a lack of studies describing such ectoparasites and their infestation rates. Through this work, a new record, Columbicola, tschulyschman Eichler (C.

Development of a deep learning model for automatic localization of radiographic markers of proposed dental implant site locations.

Objectives: To develop a Deep Learning Artificial Intelligence (AI) model that automatically localizes the position of radiographic stent gutta percha (GP) markers in cone beam computed tomography (CBCT) images to identify proposed implant sites within the images, and to test the performance of the newly developed AI model.

Materials And Methods: Thirty-four CBCT datasets were used for initial model training, validation and testing. The CBCT datasets were those of patients who had a CBCT examination performed wearing a radiographic stent for implant treatment planning.

Optimal Deep Transfer Learning-Based Human-Centric Biomedical Diagnosis for Acute Lymphoblastic Leukemia Detection.

Human-centric biomedical diagnosis (HCBD) becomes a hot research topic in the healthcare sector, which assists physicians in the disease diagnosis and decision-making process. Leukemia is a pathology that affects younger people and adults, instigating early death and a number of other symptoms. Computer-aided detection models are found to be useful for reducing the probability of recommending unsuitable treatments and helping physicians in the disease detection process.

Incremental Ant-Miner Classifier for Online Big Data Analytics.

Internet of Things (IoT) environments produce large amounts of data that are challenging to analyze. The most challenging aspect is reducing the quantity of consumed resources and time required to retrain a machine learning model as new data records arrive. Therefore, for big data analytics in IoT environments where datasets are highly dynamic, evolving over time, it is highly advised to adopt an online (also called incremental) machine learning model that can analyze incoming data instantaneously, rather than an offline model (also called static), that should be retrained on the entire dataset as new records arrive.

Molecular Detection of Tick-Borne Rickettsial Pathogens Associated with the Arabian Camel (Camelus dromedarius) in Riyadh and the Eastern Region, Saudi Arabia.

Background: In Saudi Arabia, records on molecular identification of tick-borne infections in camels are relatively scarce; few molecular epidemiological studies have been conducted.

Objective: This study aimed to find Anaplasma species and Piroplasma spp. in camels from Riyadh and the Eastern Region, Saudi Arabia.

Identification of spp. in sheep and goats from Jeddah, Saudi Arabia, using molecular techniques.

Background: Thileriosis is a tick -born disease caused by hemoprotozoan parasites which has global veterinary and economic implications.

Methods: Blood samples were collected from 216 sheep and 83 goats from Jeddah, Saudi Arabia, were analyzed to determine whether the animals were infected with spp. parasites.

Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia.

COVID-19 is a disease-causing coronavirus strain that emerged in December 2019 that led to an ongoing global pandemic. The ability to anticipate the pandemic's path is critical. This is important in order to determine how to combat and track its spread.

A deep learning approach to predict blood-brain barrier permeability.

The blood-brain barrier plays a crucial role in regulating the passage of 98% of the compounds that enter the central nervous system (CNS). Compounds with high permeability must be identified to enable the synthesis of brain medications for the treatment of various brain diseases, such as Parkinson's, Alzheimer's, and brain tumors. Throughout the years, several models have been developed to solve this problem and have achieved acceptable accuracy scores in predicting compounds that penetrate the blood-brain barrier.

A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection.

Cybersecurity protects and recovers computer systems and networks from cyber attacks. The importance of cybersecurity is growing commensurately with people's increasing reliance on technology. An anomaly detection-based network intrusion detection system is essential to any security framework within a computer network.

Molecular Identification of Isolated from Arabian Camels () in Riyadh and Al-Qassim, Saudi Arabia.

We analyzed the blood from 400 one-humped camels, () in Riyadh and Al-Qassim, Saudi Arabia to determine if they were infected with the parasite spp. Polymerase chain reaction (PCR) targeting the internal transcribed spacer 1 (ITS1) gene was used to detect the prevalence of spp. in the camels.

Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review.

The existence of widespread COVID-19 infections has prompted worldwide efforts to control and manage the virus, and hopefully curb it completely. One important line of research is the use of (ML) to understand and fight COVID-19. This is currently an active research field.

Prevalence Rate and Molecular Characteristics of L. (Diptera, Oestridae) in Sheep and Goats from Riyadh, Saudi Arabia.

Heads of sheep ( = 600) and goats ( = 800) slaughtered at Al-Aziziah Abattoir in Riyadh, Saudi Arabia, were inspected for the presence of larvae (L). Heads were split along the longitudinal axes, and larvae (L1, L2, and L3) were gathered. The infestation rate was significantly higher in goats (44.

Early Detection of Red Palm Weevil, (Olivier), Infestation Using Data Mining.

In the past 30 years, the red palm weevil (RPW), (Olivier), a pest that is highly destructive to all types of palms, has rapidly spread worldwide. However, detecting infestation with the RPW is highly challenging because symptoms are not visible until the death of the palm tree is inevitable. In addition, the use of automated RPW weevil identification tools to predict infestation is complicated by a lack of RPW datasets.

A Recurrent Neural Network model to predict blood-brain barrier permeability.

The rapid development of computational methods and the increasing volume of chemical and biological data have contributed to an immense growth in chemical research. This field of study is known as "chemoinformatics," which is a discipline that uses machine-learning techniques to extract, process, and extrapolate data from chemical structures. One of the significant lines of research in chemoinformatics is the study of blood-brain barrier (BBB) permeability, which aims to identify drug penetration into the central nervous system (CNS).

Identification of Spp. in One-humped Camels () from Riyadh and Dammam, Saudi Arabia, via Histological and Phylogenetic Approaches.

spp. are intracellular protozoan parasites that infect birds and animals, resulting in substantial commercial losses. spp.

Analyzing Passive BCI Signals to Control Adaptive Automation Devices.

Brain computer interfaces are currently considered to greatly enhance assistive technologies and improve the experiences of people with special needs in the workplace. The proposed adaptive control model for smart offices provides a complete prototype that senses an environment's temperature and lighting and responds to users' feelings in terms of their comfort and engagement levels. The model comprises the following components: (a) sensors to sense the environment, including temperature and brightness sensors, and a headset that collects (EEG) signals, which represent workers' comfort levels; (b) an application that analyzes workers' feelings regarding their willingness to adjust to a space based on an analysis of collected data and that determines workers' attention levels and, thus, engagement; and (c) actuators to adjust the temperature and/or lighting.

Molecular Characterization of Species Isolated from Sheep and Goats in Riyadh, Saudi Arabia.

Sarcocystosis is induced by species of , which is an intracellular protozoan parasite in the phylum Apicomplexa. The diversity and importance of species in sheep and goats in Saudi Arabia are poorly understood. In this study, the tongue, esophagus, heart, diaphragm, and skeletal muscles were collected from 230 sheep and 84 goats, and the tissues were examined for the presence of species by macroscopic examination and light microscopy.

Gene-based molecular characterization of and in isolated from naturally infected mice and rats in Saudi Arabia.

Mice and rats are animals commonly used in research and laboratory testing. Compared with other animal species, they harbor many more zoonotic agents. () is a common tapeworm that parasitizes both humans and rodents.

Gene-based molecular analysis of COX1 in Echinococcus granulosus cysts isolated from naturally infected livestock in Riyadh, Saudi Arabia.

The diversity and importance of Echinococcus species in domesticated animals in Saudi Arabia are poorly understood. In this study, 108 singular (hydatid) cysts were collected from goats (n = 25), sheep (n = 56) and camels (n = 27). DNA was extracted from the protoscoleces of individual fertile cysts and used for polymerase chain reaction (PCR) amplification of mitochondrial subunit 1 of the cytochrome c oxidase 1 (cox1) gene.

Building predictive models for MERS-CoV infections using data mining techniques.

Background: Recently, the outbreak of MERS-CoV infections caused worldwide attention to Saudi Arabia. The novel virus belongs to the coronaviruses family, which is responsible for causing mild to moderate colds. The control and command center of Saudi Ministry of Health issues a daily report on MERS-CoV infection cases.

TrieAMD: a scalable and efficient apriori motif discovery approach.

Motif discovery is the problem of finding recurring patterns in biological sequences. It is one of the hardest and long-standing problems in bioinformatics. Apriori is a well-known data-mining algorithm for the discovery of frequent patterns in large datasets.

IncMD: incremental trie-based structural motif discovery algorithm.

The discovery of common RNA secondary structure motifs is an important problem in bioinformatics. The presence of such motifs is usually associated with key biological functions. However, the identification of structural motifs is far from easy.

Classification and assessment tools for structural motif discovery algorithms.

Background: Motif discovery is the problem of finding recurring patterns in biological data. Patterns can be sequential, mainly when discovered in DNA sequences. They can also be structural (e.