Yasser M.K. Omar's research while affiliated with Arab Academy for Science, Technology & Maritime Transport and other places

Publications (16)

Article
Alzheimer's disease (AD) is a complex disorder with strong genetic factors. The proposed framework is applied to Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We present a novel framework integrating ensemble learning and MDR constructive induction algorithm to discover epistasis interactions associated with AD in a computationally ef...
Article
Alzheimer's disease (AD) is the most common form of dementia. Single Nucleotide Polymorphisms (SNPs) are single nucleotide alterations that can be used as genomic markers disclosing susceptibility to complex diseases like AD. Epistasis has long been significant for recognizing the function of genetic pathways and the evolutionary dynamics of diffic...
Article
Full-text available
Precision agriculture is a challenging task to achieve. Several studies have been conducted to forecast agricultural yields using machine learning algorithms (MLA), but few studies have used ensemble machine learning algorithms (EMLA). In the current study, we use a dataset generated by a computer simulation program, and meteorological data obtaine...
Preprint
Full-text available
Precision agriculture is a challenging task to achieve. Several studies have been conducted to forecast agricultural yields using machine learning algorithms (MLA), but few studies have used ensemble machine learning algorithms (EMLA). In the current study, we use a dataset generated by a computer simulation program, and meteorological data obtaine...
Chapter
Over the past years, the application of electronic nose devices has been investigated as an important tool for monitoring meat quality. Unfortunately, the standard of meat can easily degrade if not handled properly and become a significant hazard if consumed. Hence, the food safety system is extremely important to ensure the standard of consumable...
Article
Background Gene regulation is a complex and a dynamic process that not only depends on the DNA sequence of genes, but also is influenced by a key factor called Epigenetic Mechanisms. This factor along with other factors contributes to change the behavior of DNA. While these factors cannot affect the structure of DNA, they can control the behavior o...
Article
Alzheimer’s disease (AD) is a progressive disease that attacks the brain’s neurons and causes problems in memory, thinking, and reasoning skills. Personalized Medicine (PM) needs a better and more accurate understanding of the relationship between human genetic data and complex diseases like AD. The goal of PM is to tailor the treatment of a case p...
Article
Full-text available
Parkinson’s disease (PD) is a clinical neurodegenerative disease having symptoms like tremor, rigidity, and postural disability. According to Harvard, about 60,000 of American citizens are diagnosed with PD yearly, with more than 10 million people infected worldwide. An estimate of 4% of the people have PD before they reach the age 50; however, the...
Chapter
Human Activity Recognition (HAR) has become a subject of high interest within many fields. In this work, the target is to build a classifier with high accuracy, so two approaches are used to build a classifier. The first approach represented by model (A), and the second represented by the model (B), both models are built using four machine learning...
Article
Full-text available
Virtual screening is the most critical process in drug discovery, and it relies on machine learning to facilitate the screening process. It enables the discovery of molecules that bind to a specific protein to form a drug. Despite its benefits, virtual screening generates enormous data and suffers from drawbacks such as high dimensions and imbalanc...
Article
Full-text available
Nowadays, the massive amount of data that needs to be processed is increased. High-performance computing (HPC) and big data analytics are required. In the identical context, research on drug discovery has reached an area where it has no preference, but the use of HPC and huge data processing systems to perform its targets at a reasonable time. Virt...
Conference Paper
Drug discovery is an important step before drug development. Drug discovery is the process of identifying, testing a drug before medical use. Drugs are used to cure diseases by interacting with the target, which is the protein in the human cells. Many resources are wasted (cost and time) on lab experiments to discover drugs and its application. Yet...
Conference Paper
Full-text available
The multi agent agriculture system is a new application in AI to solve the problem of food shortage in the world and to decrease the gap between agriculture production and the need of people. In this paper we explorer the modeling and simulation of the new trend of the agriculture using AI with Markov processes. Simulation is used to check the perf...
Article
Full-text available
Cancer is one of the most influential factors causing death in the world. Adenosine which is a molecule, found in all human cells by coupling with G protein it turns into an adenosine receptor. Adenosine receptor is an important target for cancer therapy. Adenosine stops the growth of malignant tumor cells such as lymphoma, melanoma and prostate ca...

Citations

... In literature, feature selection has been motivated variously and provides several advantages over using the entire feature set [27], [42]. Besides a significant reduction of training time as well as complexity by excluding correlated and counterproductive features, especially the potential increase of classifier performance has to be mentioned as a key advantage [43], [44]. ...
... Stacked regression is a very powerful approach that has been successfully applied to a wide array of fields including anticancer drug response prediction, prediction of photosynthetic capacities, image quality assessment, and mortality forecasting, among others [67][68][69][70]. The aforementioned technique has also shown its superior performance in several agricultural applications such as in the estimation of the leaf area index, wild blueberry yield prediction, and crop yield prediction, among others [71][72][73]. However, its particular application in the simultaneous prediction of these seven important chemical components in fresh cattle and poultry manure has not been studied. ...
... Based on the T1-weighted MRI image, Zhang et al. added a normalization layer to the fifth layer of the original AlexNet network to improve the overall learning rate and accelerate the convergence of the model [1]. Mohamed et al. used Siamese neural network for the diagnosis of Parkinson's disease to enhance the distribution of similar samples among groups by clustering data sets before applying classification [15]. Suvita et al. propesed the binary versions of Rao algorithms and applied to four publicly available Parkinson's disease datasets [16].Basnin et al. combining the DenseNet model and LSTM to enhance the ability of model feature selection and used LSTM to discover the relationship between temporal characteristics [7]. ...
... As a result, MI signals have been proven useful for human body parts movement. Besides that, some other related methods used multimodal learning in the HAR systems, such as methods presented by Elmadany et al. [23], Ahmad et al. [24], and Gamal et al. [25]. However, the above methods are still having challenges [26]. ...
... In this paper, the PSO-SVM-AdaBoost-integrated learning classification algorithm is proposed, which is compared with the SVM classification method only using particle swarm optimization algorithm [26]. At the same time, predictive contrasts were also performed on the postsampled dataset. ...
... Tian et al. (2018) used the ARIMA model to predict industrial water consumption, using Guangzhou, China, as a case study, and the results showed that the ARIMA model could be used for industrial water consumption. From the above, it can be seen that there are many methods for forecasting water consumption at home and abroad (Savun et al., 2020;Dalhuisen et al., 2002;Zeng 2021;Ibrahim et al., 2020), mainly through time series, structural analysis, system dynamics, indicator analysis, and machine learning methods. However, these methods have their advantages and also have some problems: (1) time series method, which generally only requires past water use data, but the accuracy of the results is difficult to guarantee; (2) structural analysis method, which is suitable for medium and long-term water use forecasting but requires the industrial structure to be stable, otherwise large deviations will occur; (3) system dynamics and chunking forecasting methods, which are suitable for situations where there are sudden changes in water use or the composition of industrial water consumption is extremely unbalanced; and (4) indicator analysis method, i.e., quota method, which determines the water consumption quota by analyzing the characteristics of water consumption and the economic level and social progress and then calculates the future water consumption according to the water consumption quota. ...
... Spark can compare the cost of different methods and automatically select the optimal data recovery method. Method and the equivariant method are relatively stable, and the AST method has a certain degree of improvement in F1 value compared with the equivariant method [19]. e experimental dataset needs to be not too large compared to the serial algorithm; otherwise it will cause the serial algorithm to fail in mining. ...
... While there have been several previous attempts to use machine learning for ARs (Saad et al., 2019;Wang et al., 2021), few have performed external validation. One recent study used deep learning combined with pharmacophore and docking approaches to identify novel A 1 /A 2A antagonists (Wang et al., 2021). ...
... A subset of the tree is selected form its root to leaf node. Features are extracted from each tree to represent the final tree [13]. ...