Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
136,366
result(s) for
"Ibrahim, A"
Sort by:
Drone Deep Reinforcement Learning: A Review
Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios.
Journal Article
An efficient multilevel image thresholding method based on improved heap-based optimizer
Image segmentation is the process of separating pixels of an image into multiple classes, enabling the analysis of objects in the image. Multilevel thresholding (MTH) is a method used to perform this task, and the problem is to obtain an optimal threshold that properly segments each image. Methods such as the Kapur entropy or the Otsu method, which can be used as objective functions to determine the optimal threshold, are efficient in determining the best threshold for bi-level thresholding; however, they are not effective for MTH due to their high computational cost. This paper integrates an efficient method for MTH image segmentation called the heap-based optimizer (HBO) with opposition-based learning termed improved heap-based optimizer (IHBO) to solve the problem of high computational cost for MTH and overcome the weaknesses of the original HBO. The IHBO was proposed to improve the convergence rate and local search efficiency of search agents of the basic HBO, the IHBO is applied to solve the problem of MTH using the Otsu and Kapur methods as objective functions. The performance of the IHBO-based method was evaluated on the CEC’2020 test suite and compared against seven well-known metaheuristic algorithms including the basic HBO, salp swarm algorithm, moth flame optimization, gray wolf optimization, sine cosine algorithm, harmony search optimization, and electromagnetism optimization. The experimental results revealed that the proposed IHBO algorithm outperformed the counterparts in terms of the fitness values as well as other performance indicators, such as the structural similarity index (SSIM), feature similarity index (FSIM), peak signal-to-noise ratio. Therefore, the IHBO algorithm was found to be superior to other segmentation methods for MTH image segmentation.
Journal Article
Oil leaders : an insider's account of four decades of Saudi Arabia and OPEC's global energy policy
'Oil Leaders' offers a glimpse into the strategic thinking of top figures in the energy world from the 1980s through the recent past. Ibrahim AlMuhanna - a close adviser to four different Saudi oil ministers over that span of time - examines the role of individual and collective decision making in shaping market movements.
BOOK
Green synthesis of silver nanoparticles using Sudanese Candida parapsilosis: a sustainable approach to combat antimicrobial resistance
Background
Antimicrobial resistance (AMR) is a critical global health challenge, particularly in Sudan, where the overuse and misuse of antibiotics have driven the rise of multidrug-resistant (MDR) pathogens. Conventional antimicrobial strategies often fall short due to rapid resistance development and limited efficacy, highlighting the need for novel approaches. Nanotechnology offers promising alternatives, with silver nanoparticles (AgNPs) demonstrating potent broad-spectrum antimicrobial activity. This study aims to develop an eco-friendly synthesis of AgNPs using
Candida parapsilosis
(
C. parapsilosis
), an untapped yeast strain isolated from Sudanese soil, to combat AMR.
Results
Biosynthesis of AgNPs using
C. parapsilosis
was successfully confirmed through UV-Vis spectroscopy, X-ray diffraction (XRD), and high-resolution transmission electron microscopy (HRTEM), revealing well-defined nanoparticles. The biosynthesized AgNPs exhibited strong antibacterial activity against both ATCC reference strains and MDR clinical isolates of Gram-positive and Gram-negative bacteria, with inhibition zones increasing in a concentration-dependent manner. At optimal concentrations, inhibition zones reached 29 mm for
Pseudomonas aeruginosa (P.aeruginosa)
(ATCC 27853), while clinical isolates of
Salmonella typhi
(
S. typhi
) (24.5 ± 0.58 mm) and
Escherichia coli
(
E. coli
) (23.8 ± 0.79 mm) exhibited significant susceptibility. Minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) assays demonstrated potent bactericidal activity, particularly against
E. coli
and
Klebsiella pneumoniae
(
K. pneumoniae
) at 0.3125 mg/mL. Furthermore, AgNPs synergistically enhanced the efficacy of conventional antibiotics in a species- and antibiotic-dependent manner. The strongest synergy was observed in
Enterococcus faecalis (E. faecalis)
(up to 9.84-fold with Colistin) and
Acinetobacter baumannii
(
A. baumannii
) (up to 5.11-fold with Ceftazidime), suggesting that AgNP-enhanced antibiotic efficacy varies depending on bacterial species, nanoparticle synthesis method, and antibiotic type.
Conclusions
This study presents a novel and sustainable approach to tackling AMR by leveraging Sudanese yeast strains for the green synthesis of AgNPs. The findings underscore the potential of AgNPs as an effective antibacterial agent, both independently and in combination with conventional antibiotics, to combat MDR pathogens. By integrating microbiology and nanotechnology, this research offers a cost-effective and environmentally friendly solution for AMR mitigation. These findings provide a strong foundation for future clinical applications and public health interventions, particularly in resource-limited settings.
Journal Article
Detection and Classification of Unhealthy Heartbeats Using Deep Learning Techniques
Arrhythmias are a common and potentially life-threatening category of cardiac disorders, making accurate and early detection crucial for improving clinical outcomes. Electrocardiograms are widely used to monitor heart rhythms, yet their manual interpretation remains prone to inconsistencies due to the complexity of the signals. This research investigates the effectiveness of machine learning and deep learning techniques for automated arrhythmia classification using ECG signals from the MIT-BIH dataset. We compared Gradient Boosting Machine (GBM) and Multilayer Perceptron (MLP) as traditional machine learning models with a hybrid deep learning model combining one-dimensional convolutional neural networks (1D-CNNs) and long short-term memory (LSTM) networks. Furthermore, the Grey Wolf Optimizer (GWO) was utilized to automatically optimize the hyperparameters of the 1D-CNN-LSTM model, enhancing its performance. Experimental results show that the proposed 1D-CNN-LSTM model achieved the highest accuracy of 97%, outperforming both classical machine learning and other deep learning baselines. The classification report and confusion matrix confirm the model’s robustness in identifying various arrhythmia types. These findings emphasize the possible benefits of integrating metaheuristic optimization with hybrid deep learning.
Journal Article
Islam in the West : perceptions and reactions
This text focuses on the way Muslims and mainstream societies in the West, especially in America, Australia, and Europe, perceive each other. It focuses on the meaning of being a Muslim in a multicultural, multi-religious, and technologically developed world. The essays in the volume explore the socio-political, cultural, and historical differences between the two groups, Muslims and Western societies, while attempting to reconcile some of these differences in creative ways by initiating constructive dialogues between them. It also takes into account the tensions, challenges, and complexities between these communities across various contexts, including, schools, universities, media, government, private, and public institutions. This volume thus explores this interplay between perceptions and misperceptions by delving into the societal structures of Western host and immigrant communities.
Book
Considerable Production of Ulvan from Ulva lactuca with Special Emphasis on Its Antimicrobial and Anti-fouling Properties
Abstract In the current study, a significant amount of ulvan was extracted from Ulva lactuca collected from Alexandria coastline, Egypt, using a simple extraction method. According to the chemical analysis, the obtained polysaccharide content is estimated to be 36.50 g/100 g with a high sulfate content of 19.72%. Physio-chemically, the FTIR analysis confirmed the presence of sulfated groups attached to the carbohydrate backbone. The GC–MS results revealed the presence of various monosaccharides with relative abundances in the order: fucopyranose (22.09%) > L-rhamnose (18.17%) > L-fucose (17.46%) > rhamnopyranose (14.29%) > mannopyranose (8.59%) > α-D-glactopyranose (7.64%) > galactopyranose (6.14%) > β-arabinopyranose (5.62%). In addition, the SEM–EDX depicted an amorphous architecture with a majority wt% for the elements of C, O, and S. The partially purified ulvan demonstrated potent antimicrobial activity against some fish and human pathogenic microbes. The inhibition zone diameter ranged from 11 to 18 mm. On the other hand, the prepared ulvan-chitosan hydrogel significantly improved the antimicrobial activity as the inhibition zone diameter ranged from 12 to 20. Moreover, when compared to the controls, the extracted ulvan demonstrated anti-fouling properties and successfully disrupted the biofilm formed on a glass slide submerged in seawater.
Journal Article