Gold is one of the valuable materials that is used for funding trading purchases. Nowadays, more investors are interested in gold investments due to the sudden increase in gold prices. However, transactions involving gold are risky, the price of gold fluctuates wildly due to the unpredictability of the gold market. Hence, there is a need for the development of gold price prediction scheme to assist and support investors, marketers, and financial institutions in making effective economic and monetary decisions. This paper analyzes the correlation between gold price movements and sentiments of

Quercetin and its glycosides have gained a lot of interest due to their potential applications in preventing and treating various diseases. Quercetin is naturally available in free and glycoside forms such as isoquercitrin, rutin, avicularin, hyperoside, quercitrin, and others. These glycosides are more soluble and hence more bioavailable than quercetin. Determining the amount of quercetin in plants and foods is crucial to assess the quality of its natural sources, because the quercetin content changes with the species, variety, and the cultivation season. Furthermore, the concentrations of

The global rise in Metabolic dysfunction-associated steatotic liver disease (MASLD)/Metabolic dysfunction-associated steatohepatitis (MASH) highlights the urgent necessity for noninvasive biomarkers to detect these conditions early. To address this, we endeavored to construct a diagnostic model for MASLD/MASH using a combination of bioinformatics, molecular/biochemical data, and machine learning techniques. Initially, bioinformatics analysis was employed to identify RNA molecules associated with MASLD/MASH pathogenesis and enriched in ferroptosis and exophagy. This analysis unveiled specific

Lung cancer (LC) represents most of the cancer incidences in the world. There are many types of LC, but Lung Adenocarcinoma (LUAD) is the most common type. Although RNA-seq and microarray data provide a vast amount of gene expression data, most of the genes are insignificant to clinical diagnosis. Feature selection (FS) techniques overcome the high dimensionality and sparsity issues of the large-scale data. We propose a framework that applies an ensemble of feature selection techniques to identify genes highly correlated to LUAD. Utilizing LUAD RNA-seq data from the Cancer Genome Atlas (TCGA)

Internet of Vehicles is considered one of the most unprecedented outputs of the Internet of Things. No one has realized or even expected the rapidly-growing revolution regarding autonomous connected vehicles. Nowadays, Internet of Vehicles is massively progressing from Vehicular Ad-Hoc Networks as a huge futuristic research and development discipline. This paper proposes a novel reliable and secure architecture for ubiquitously controlling remote connected cars' internal systems, such as engine, doors' locks, sunroof, horn, windows' and lights' control systems. The main contribution is that

The accurate prediction of the required backhaul transmission capacity for cellular networks is critical to ensure efficient and reliable network performance, especially with the increasing demand for high-speed data services and the introduction of new radio technologies. This paper presents a framework for predicting the required capacity of backhaul networks based on the base stations' radio resources utilization and serving radio conditions. The proposed framework utilizes machine learning techniques to accurately estimate the required backhaul capacity by analyzing the base stations'

Introduction: With the increasing prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need to discover effective therapeutic targets for this complex condition. Coding and non-coding RNAs, with traditional biochemical parameters, have shown promise as viable targets for therapy. Machine learning (ML) techniques have emerged as powerful tools for predicting drug responses. Method: In this study, we developed an ML-based model to identify the most influential features for drug response in the treatment of type 2 diabetes using three medicinal plant-based drugs (Rosavin, Caffeic

Following cybersecurity st and ards nowadays is becoming one of the highest priorities to the digital specialists. Due to the global direction to apply digital transformation, data security is a concern. It becomes crucial to ensure data confidentiality, integrity, and availability whether while transmitting, at rest or even while processing it. The difficulty being faced by organizations, is the challenge of applying the needed security measures. Also, implementing, and maintaining the cryptographic algorithms that ensure the wellness of the data encryption. Having a crypto library or a

This study addresses the challenge of multi-label text classification in the Arabic language, focusing on movie genre categorization using plot summaries. Even though over 400 million people speak Arabic, its natural language processing (NLP) advances are not keeping up with those of other languages because of data shortages and quality difficulties. Three key contributions are made by this research to narrow this gap: a thorough analysis of prior research on Arabic multi-label text classification; the introduction of a newly curated dataset containing 22 genre labels for Egyptian movies; and

Flavonoids are plant-derived compounds that have several health benefits, including antioxidative, anti-inflammatory, anti-mutagenic, and anti-carcinogenic effects. Quercetin is a flavonoid that is widely present in various fruits, vegetables, and drinks. Accurate determination of quercetin in different samples is of great importance for its potential health benefits. This review, is an overview of sample preparation and determination methods for quercetin in diverse matrices. Previous research on sample preparation and determination methods for quercetin are summarized, highlighting the