Showing papers by "University of Jordan published in 2019"
TL;DR: The statistical results and comparisons show that the HHO algorithm provides very promising and occasionally competitive results compared to well-established metaheuristic techniques.
2,871 citations
TL;DR: This article analyzes the interaction of nanoparticle surface and ligands with different chemical groups, the types of bonding, the final dispersibility of ligand-coated nanoparticles in complex media, their reactivity, and their performance in biomedicine, photodetectors, photovoltaic devices, light-emitting devices, sensors, memory devices, thermoelectric applications, and catalysis.
Abstract: The design of nanoparticles is critical for their efficient use in many applications ranging from biomedicine to sensing and energy. While shape and size are responsible for the properties of the inorganic nanoparticle core, the choice of ligands is of utmost importance for the colloidal stability and function of the nanoparticles. Moreover, the selection of ligands employed in nanoparticle synthesis can determine their final size and shape. Ligands added after nanoparticle synthesis infer both new properties as well as provide enhanced colloidal stability. In this article, we provide a comprehensive review on the role of the ligands with respect to the nanoparticle morphology, stability, and function. We analyze the interaction of nanoparticle surface and ligands with different chemical groups, the types of bonding, the final dispersibility of ligand-coated nanoparticles in complex media, their reactivity, and their performance in biomedicine, photodetectors, photovoltaic devices, light-emitting devices, sensors, memory devices, thermoelectric applications, and catalysis.
616 citations
TL;DR: The present review article summarizes the progress of recent research on luteolin against several human cancers.
417 citations
TL;DR: Binary variants of the recent Grasshopper Optimisation Algorithm are proposed in this work and employed to select the optimal feature subset for classification purposes within a wrapper-based framework and the comparative results show the superior performance of the BGOA and B GOA-M methods compared to other similar techniques in the literature.
Abstract: Feature Selection (FS) is a challenging machine learning-related task that aims at reducing the number of features by removing irrelevant, redundant and noisy data while maintaining an acceptable level of classification accuracy. FS can be considered as an optimisation problem. Due to the difficulty of this problem and having a large number of local solutions, stochastic optimisation algorithms are promising techniques to solve this problem. As a seminal attempt, binary variants of the recent Grasshopper Optimisation Algorithm (GOA) are proposed in this work and employed to select the optimal feature subset for classification purposes within a wrapper-based framework. Two mechanisms are employed to design a binary GOA, the first one is based on Sigmoid and V-shaped transfer functions, and will be indicated by BGOA-S and BGOA-V, respectively. While the second mechanism uses a novel technique that combines the best solution obtained so far. In addition, a mutation operator is employed to enhance the exploration phase in BGOA algorithm (BGOA-M). The proposed methods are evaluated using 25 standard UCI datasets and compared with 8 well-regarded metaheuristic wrapper-based approaches, and six well known filter-based (e.g., correlation FS) approaches. The comparative results show the superior performance of the BGOA and BGOA-M methods compared to other similar techniques in the literature.
318 citations
TL;DR: Security and performance analysis indicates that the proposed scheme is highly resistant to various cryptanalytic attacks, is statistically superior and more secure than previously proposed chaos-based image ciphers.
277 citations
01 Sep 2019
TL;DR: It is shown and proved that the proposed stochastic training algorithm GOAMLP is substantially beneficial in improving the classification rate of MLPs.
Abstract: This paper proposes a new hybrid stochastic training algorithm using the recently proposed grasshopper optimization algorithm (GOA) for multilayer perceptrons (MLPs) neural networks. The GOA algorithm is an emerging technique with a high potential in tackling optimization problems based on its flexible and adaptive searching mechanisms. It can demonstrate a satisfactory performance by escaping from local optima and balancing the exploration and exploitation trends. The proposed GOAMLP model is then applied to five important datasets: breast cancer, parkinson, diabetes, coronary heart disease, and orthopedic patients. The results are deeply validated in comparison with eight recent and well-regarded algorithms qualitatively and quantitatively. It is shown and proved that the proposed stochastic training algorithm GOAMLP is substantially beneficial in improving the classification rate of MLPs.
224 citations
TL;DR: An intelligent detection system that is based on Genetic Algorithm and Random Weight Network is proposed to deal with email spam detection tasks and can automatically identify the most relevant features of the spam emails.
210 citations
TL;DR: The aim of the present review is to collect information pertaining to the effective role of kaempferol against various degenerative disorders, summarize the antioxidant, anti‐inflammatory, anticancer, antidiabetic, and antiaging effects of kaEMPferol, and to review the progress of recent research and available data on ka Kempferol as a protective and chemotherapeutic agent against several ailments.
Abstract: Kaempferol, a natural flavonoid present in several plants, possesses a wide range of therapeutic properties such as antioxidant, anticancer, and anti-inflammatory. It has a significant role in reducing cancer and can act as a therapeutic agent in the treatment of diseases and ailments such as diabetes, obesity, cardiovascular diseases, oxidative stress, asthma, and microbial contamination disorders. Kaempferol acts through different mechanisms: It induces apoptosis (HeLa cervical cancer cells), decreases cell viability (G2/M phase), downregulates phosphoinositide 3-kinase (PI3K)/AKT (protein kinase B) and human T-cell leukemia/lymphoma virus-I (HTLV-I) signaling pathways, suppresses protein expression of epithelial-mesenchymal transition (EMT)-related markers including N-cadherin, E-cadherin, Slug, and Snail, and metastasis-related markers such as matrix metallopeptidase 2 (MMP-2). Accordingly, the aim of the present review is to collect information pertaining to the effective role of kaempferol against various degenerative disorders, summarize the antioxidant, anti-inflammatory, anticancer, antidiabetic, and antiaging effects of kaempferol and to review the progress of recent research and available data on kaempferol as a protective and chemotherapeutic agent against several ailments.
193 citations
TL;DR: Aptamers are short single-stranded RNA or DNA oligonucleotides capable of folding into complex 3D structures, enabling them to bind to a large variety of targets ranging from small ions to an entire organism, and they are superior regarding a longer shelf life, simple production and chemical modification.
Abstract: Soon after they were first described in 1990, aptamers were largely recognized as a new class of biological ligands that can rival antibodies in various analytical, diagnostic, and therapeutic applications. Aptamers are short single-stranded RNA or DNA oligonucleotides capable of folding into complex 3D structures, enabling them to bind to a large variety of targets ranging from small ions to an entire organism. Their high binding specificity and affinity make them comparable to antibodies, but they are superior regarding a longer shelf life, simple production and chemical modification, in addition to low toxicity and immunogenicity. In the past three decades, aptamers have been used in a plethora of therapeutics and drug delivery systems that involve innovative delivery mechanisms and carrying various types of drug cargos. However, the successful translation of aptamer research from bench to bedside has been challenged by several limitations that slow down the realization of promising aptamer applications as therapeutics at the clinical level. The main limitations include the susceptibility to degradation by nucleases, fast renal clearance, low thermal stability, and the limited functional group diversity. The solution to overcome such limitations lies in the chemistry of aptamers. The current review will focus on the recent arts of aptamer chemistry that have been evolved to refine the pharmacological properties of aptamers. Moreover, this review will analyze the advantages and disadvantages of such chemical modifications and how they impact the pharmacological properties of aptamers. Finally, this review will summarize the conjugation strategies of aptamers to nanocarriers for developing targeted drug delivery systems.
179 citations
TL;DR: The comprehensive experiments results show that the proposed algorithm outperforms all other algorithms in terms of sentiment analysis classification accuracy through finding the best solutions, while its also minimizes the number of selected features.
Abstract: To help individuals or companies make a systematic and more accurate decisions, sentiment analysis (SA) is used to evaluate the polarity of reviews. In SA, feature selection phase is an important phase for machine learning classifiers specifically when the datasets used in training is huge. Whale Optimization Algorithm (WOA) is one of the recent metaheuristic optimization algorithm that mimics the whale hunting mechanism. However, WOA suffers from the same problem faced by many other optimization algorithms and tend to fall in local optima. To overcome these problems, two improvements for WOA algorithm are proposed in this paper. The first improvement includes using Elite Opposition-Based Learning (EOBL) at initialization phase of WOA. The second improvement involves the incorporation of evolutionary operators from Differential Evolution algorithm at the end of each WOA iteration including mutation, crossover, and selection operators. In addition, we also used Information Gain (IG) as a filter features selection technique with WOA using Support Vector Machine (SVM) classifier to reduce the search space explored by WOA. To verify our proposed approach, four Arabic benchmark datasets for sentiment analysis are used since there are only a few studies in sentiment analysis conducted for Arabic language as compared to English. The proposed algorithm is compared with six well-known optimization algorithms and two deep learning algorithms. The comprehensive experiments results show that the proposed algorithm outperforms all other algorithms in terms of sentiment analysis classification accuracy through finding the best solutions, while its also minimizes the number of selected features.
176 citations
San Diego State University1, Charles University in Prague2, Cairo University3, Monash University4, University of Notre Dame5, Delft University of Technology6, University of Liverpool7, Utrecht University8, Kavli Institute of Nanoscience9, Aix-Marseille University10, New York University11, University of California, Davis12, University of Otago13, University of Groningen14, Katholieke Universiteit Leuven15, University of Los Andes16, Norwegian Institute of Public Health17, Universidad Mayor18, Saint Petersburg State University of Information Technologies, Mechanics and Optics19, National Autonomous University of Mexico20, Technical University of Denmark21, University of Jordan22, University of Alicante23, University of Illinois at Urbana–Champaign24, Autonomous University of Barcelona25, Pontifical Catholic University of Chile26, University of Warsaw27, Dartmouth College28, University of Khartoum29, University of Chicago30, University of Barcelona31, Scripps Research Institute32, University College Cork33, University of Tampere34, Northern Illinois University35, University of Sydney36, Columbus Zoo and Aquarium37, McGill University38, University of Western Australia39, University of Colorado Boulder40, Virginia Tech41, Tel Aviv University42, Instituto Superior Técnico43, Makerere University44, University of California, San Diego45, Hawaii Pacific University46, Stockholm University47, University of California, Irvine48, University of Buenos Aires49, Fudan University50, University of Padua51, University of Pittsburgh52, Ebonyi State University53, Andrés Bello National University54, University of Copenhagen55, Radboud University Nijmegen56
TL;DR: It is concluded that crAssphage is a benign cosmopolitan virus that may have coevolved with the human lineage and is an integral part of the normal human gut virome.
Abstract: Microbiomes are vast communities of microorganisms and viruses that populate all natural ecosystems. Viruses have been considered to be the most variable component of microbiomes, as supported by virome surveys and examples of high genomic mosaicism. However, recent evidence suggests that the human gut virome is remarkably stable compared with that of other environments. Here, we investigate the origin, evolution and epidemiology of crAssphage, a widespread human gut virus. Through a global collaboration, we obtained DNA sequences of crAssphage from more than one-third of the world's countries and showed that the phylogeography of crAssphage is locally clustered within countries, cities and individuals. We also found fully colinear crAssphage-like genomes in both Old-World and New-World primates, suggesting that the association of crAssphage with primates may be millions of years old. Finally, by exploiting a large cohort of more than 1,000 individuals, we tested whether crAssphage is associated with bacterial taxonomic groups of the gut microbiome, diverse human health parameters and a wide range of dietary factors. We identified strong correlations with different clades of bacteria that are related to Bacteroidetes and weak associations with several diet categories, but no significant association with health or disease. We conclude that crAssphage is a benign cosmopolitan virus that may have coevolved with the human lineage and is an integral part of the normal human gut virome.
TL;DR: A novel GSA-based algorithm with evolutionary crossover and mutation operators is proposed to deal with feature selection (FS) tasks and the extensive results and comparisons demonstrate the superiority of the proposed algorithm in solving FS problems.
TL;DR: The superiority and applicability of the present technique is illustrated by handling linear and nonlinear numerical examples and the outcomes obtained are compared with exact solutions and existing methods to confirm the effectiveness of the reproducing kernel method.
TL;DR: The protocol described in the current study was used to search for publications from Jordanian authors in the years 2013-2017 and showed a yearly trend in the number of publications, the disciplines that have the most publications, and the countries of collaboration.
Abstract: Literature databases (i.e., PubMed, Scopus, and Web of Science) differ in terms of their coverage, focus, and the tool they provide. PubMed focuses mainly on life sciences and biomedical disciplines, whereas Scopus and Web of Science are multidisciplinary. The protocol described in the current study was used to search for publications from Jordanian authors in the years 2013-2017. In this protocol, how to use each database to conduct this type of search is explained in detail. A Scopus search resulted in the highest number of documents (11,444 documents), followed by a Web of Science search (10,943 documents). PubMed resulted in a smaller number of documents due to its narrower scope and coverage (4,363 documents). The results also show a yearly trend in: (1) the number of publications, (2) the disciplines that have the most publications, (3) the countries of collaboration, and (4) the number of open access publications. In contrast, PubMed has a sophisticated keyword optimization service (i.e., Medical Subject Heading, or MeSH), while both Scopus and Web of Science provide search analysis tools that can produce representative figures. Finally, the features of each database are explained in detail and several indices that can be extracted using the search results are provided. This study provides a base for using literature databases for bibliometric analysis.
TL;DR: The synthesis and degradation mechanisms of chitosan micro/nanoparticles frequently used in drug delivery especially in pulmonary drug delivery are reviewed to understand whether these nanoparticles are biodegradable.
01 Aug 2019
TL;DR: A framework named HaBiTs (blockchain-based secure and flawless inter-operable telesurgery), where security can be achieved with immutability and interoperability by Smart Contracts (SCs).
Abstract: Telesurgery has a huge potential to deliver a real-time healthcare surgical services to the remote or distant locations with high quality and accuracy over the wireless communication channel. It provides benefits to the society with improved precision and accuracy in diagnosis procedures. However, the existing telesurgery system has the security, privacy, and interoperability issues, which limits its applicability in healthcare centers across the world in future. To mitigate these issues, in this paper, we propose, a framework named as HaBiTs (blockchain-based secure and flawless inter-operable telesurgery), where security can be achieved with immutability and interoperability by Smart Contracts (SCs). SC is a piece of code written in solidity or other blockchain specific languages to establish the trust between all the parties connected through blockchain and also eliminate the need of an intermediary for data sharing. Finally, we highlight some issues of the traditional telesurgery system and how they are mitigated with usage of the proposed HaBiTs framework.
01 Jan 2019
TL;DR: A batch-based clustering and routing protocol in which the network topology divides the sensor field into equal-sized layers and clusters, and introduces a routing algorithm in which a new node role called “Forwarder” which is capable of relaying the collected data from the layer, it resides in, and far away forwarders toward the base station are introduced.
Abstract: Advances in sensor technology has enabled the development of small, relatively inexpensive, and low-power sensors, which are connected together through wireless medium, forming what is so called Wireless Sensor Networks (WSNs). WSNs have huge number of applications out of which military target tracking and surveillance. However, sensors operate on limited power resources; therefore, utilizing those resources has brought the attention of current researchers. In this paper, we propose a Balanced Power-Aware Clustering and Routing protocol (BPA-CRP). Specifically, we developed a batch-based clustering and routing protocol in which the network topology divides the sensor field into equal-sized layers and clusters. The clustering algorithm allows any cluster to operate multiple rounds (a batch) without any need for set-up overhead. BPA-CRP assigns four different broadcast ranges for each sensor. Not only to this extent, but rather, BPA-CRP introduces a routing algorithm in which a new node role called “Forwarder” which is capable of relaying the collected data from the layer, it resides in, and far away forwarders toward the base station. As a complementary to prior described protocol, BPA-CRP proposes that a batch ends when the energy of any of the forwarders dips below a certain threshold. Additionally, BPA-CRP introduces the “Only Normal” operation mode, which primarily prevents exhausted nodes from serving as cluster heads or forwarders any longer. In fact, all of just mentioned enhancements not only are energy-aware, but also contributes in accomplishing efficient load balancing. Finally, we put proper node death-handling rules, which guarantee that each node dies smoothly without any loss of data, neither causing disruption for the network. Simulation results showed an exceptional performance of BPA-CRP over different relevant works in terms of network lifetime and network energy utilization. The load balancing capability of BPA-CRP is validated as well.
TL;DR: In this review, viruses as "biological" nanoparticles are analyzed towards their ligand density, which is then compared to the liganddensity of engineered nanoparticles, and results help to understand which ligand densities should be attempted for better targeting.
TL;DR: Using immersive VR as an adjuvant intervention is more effective than morphine alone in relieving pain and anxiety; furthermore, VR is a safe intervention more than pharmacological treatment.
Abstract: ObjectiveThe goal of this study was to assess the effectiveness of immersive virtual reality (VR) distraction technology in reducing pain and anxiety among female patients with breast cancer.MethodA randomized control trial design was used with a sample of 80 female patients with breast cancer at a specialized cancer center in Jordan. Participants were randomly assigned into intervention and comparison groups.ResultThe study findings showed that one session of the immersive VR plus morphine made a significant reduction in pain and anxiety self-reported scores, compared with morphine alone, in breast cancer patients.Significance of resultsImmersive VR is an effective distraction intervention for managing pain and anxiety among breast cancer patients. Using immersive VR as an adjuvant intervention is more effective than morphine alone in relieving pain and anxiety; furthermore, VR is a safe intervention more than pharmacological treatment.
TL;DR: In this paper, the reproducing kernel Hilbert space is used to approximate the solution to those class of fractional differential equations in the form of uniformly convergent series with respect to space variables.
Abstract: This article is concerned with design and comprehensive study of a numerical approach for solving Riccati and Bernoulli equations in the Atangana-Baleanu fractional sense. The proposed technique is using the reproducing kernel Hilbert space to approximate the solution to those class of fractional differential equations in the form of uniformly convergent series with respect to space variables. An accurate computational algorithm is presented to confirm the gained analysis. The relevant theorems and characterizations related to Riccati and Bernoulli equations are included among the reproducing kernel theory. Supplementary problems at the end of the article serve as a complete review of the utilized method and theories. The numerical consequences of the proposed approach are practically effective and impressive, whilst the utilized theories lay the foundation stone for addressing such issues. In light of the summary, conclusions and future recommendations are also provided.
TL;DR: Developing an extension of Technology Acceptance Model by including four more constructs: namely, content quality, service quality, information quality and quality of the system to make it more relevant for the developing countries, like the United Arab Emirates (UAE).
Abstract: There is a widespread use of Internet technology in the present times, because of which universities are making investments in Mobile learning to augment their position in the face of extensive competition and also to enhance their students’ learning experience and efficiency. Nonetheless, Mobile Learning Platform are only going to be successful when students show acceptance and adoption of this technology. Our literature review indicates that very few studies have been carried out to show how university students accept and employ Mobile Learning Platform. In addition, it is asserted that behavioral models of technology acceptance are not equally applied in different cultures. The purpose of this study is to develop an extension of Technology Acceptance Model (TAM) by including four more constructs: namely, content quality, service quality, information quality and quality of the system. This is proposed to make it more relevant for the developing countries, like the United Arab Emirates (UAE). An online survey was carried out to obtain the data. A total of 221 students from the UAE took part in this survey. Structural equation modeling was used to determine and test the measurement and structural model. Data analysis was carried out, which showed that ten out of a total of 12 hypotheses are supported. This shows that there is support for the applicability of the extended TAM in the UAE. These outcomes suggest that Mobile Learning Platform should be considered by the policymakers and education developers as being not only a technological solution but also as being new e-learning platform especially for distance learning students.
TL;DR: This work focuses on reviewing a heuristic global optimization method called particle swarm optimization (PSO), the mathematical representation of PSO in contentious and binary spaces, the evolution and modifications ofPSO over the last two decades and a comprehensive taxonomy of heuristic-based optimization algorithms.
Abstract: Swarm intelligence is a kind of artificial intelligence that is based on the collective behavior of the decentralized and self-organized systems. This work focuses on reviewing a heuristic global optimization method called particle swarm optimization (PSO). This includes the mathematical representation of PSO in contentious and binary spaces, the evolution and modifications of PSO over the last two decades. We also present a comprehensive taxonomy of heuristic-based optimization algorithms such as genetic algorithms, tabu search, simulated annealing, cross entropy and illustrate the advantages and disadvantages of these algorithms. Furthermore, we present the application of PSO on graphics processing unit and show various applications of PSO in networks.
TL;DR: In this article, a high-order algorithm for numerical solutions of fractional order Volterra integro-differential equations using Atangana-Baleanu approach by employing the reproducing kernel approximation was proposed.
Abstract: This paper focuses on providing a novel high-order algorithm for the numerical solutions of fractional order Volterra integro-differential equations using Atangana–Baleanu approach by employing the reproducing kernel approximation. For this purpose, we investigate couples of Hilbert spaces and kernel functions, as well as, the regularity properties of Atangana–Baleanu derivative, and utilize that the representation theorem of its solution. To remove the singularity in the kernel function, using new Atangana–Baleanu approach the main operator posses smoothing solution with a better regularity properties and the reproducing kernel algorithm is designed for the required equation. The convergence properties of the proposed algorithm are also studied which proves that the new strategy exhibits a high-order of convergence with decreasing error bound. Some numerical examples of single and system formulation illustrate the performance of the approach. Summary and some notes are also provided in the case of conclusion and highlight.
01 Aug 2019
TL;DR: A framework named as BloHosT (Blockchain Enabled Smart Tourism and Hospitality Management), which allows tourists to interact with various stakeholders through a single wallet identifier linked with a cryptocurrency server to initiate payments and achieves a high Return of Investment (ROI) in tourism sector as compared to traditional frameworks.
Abstract: In the era of Industry 4.0, e-tourism uses bulk of digital payments through applications supported by heterogeneous payment gateways. These heterogeneous payment gateways open the doors for the attackers to perform malicious activities such as-hacking of wallet accounts, identity theft, attacks on payment clearance cycles. In e-tourism, financial data is maintained in a centralized cloud server, which can lead to payment failures during peak traffic. The aforementioned issues can be addressed by the usage of a decentralized mechanism such as-blockchain, which enables trust and reputation management among various stakeholders such as-banks, travel agencies, airports, railways, cruises, hotels, restaurants, and local taxis. Motivated by the above discussion, we propose a framework named as BloHosT (Blockchain Enabled Smart Tourism and Hospitality Management), which allows tourists to interact with various stakeholders through a single wallet identifier linked with a cryptocurrency server to initiate payments. BloHosT uses an immutable ledger, where no proofs are required during travel that provides a hassle-free experience to tourists. Also, a Tourism enabled Deep-Learning (TeDL) framework is presented as a part of BloHosT framework, which is trained on experience of previous visited travelers. It provides rating scores to prospective travelers about the recently visited locations by previous travelers. Finally, through case studies, we demonstrate that BloHosT achieves a high Return of Investment (ROI) in tourism sector as compared to traditional frameworks.
TL;DR: Hydrogels of AuNRs could be a promising nano-platform for wound healing and show excellent colloidal stability and demonstrated slow and prolonged release behavior over a 48-h of exposure using in vitro model.
TL;DR: Comparing students’ experience results between the traditional method (Physical Heart Model) and the VR heart anatomy system, the mean scores showed a distinct increase in the values, indicating that the developed system enhanced their experience in anatomy learning and the provided tools improved their understanding of heart anatomy.
Abstract: The aim of using virtual reality (VR) as a medical training tool is to offer additional means to teach students and to improve the quality of medical skills. A novel system was developed to fulfil the requirements of modern medical education and overcome the challenges faced by both students and lecturers in the process of knowledge transfer. A heart three-dimensional model presented in a virtual reality (VR) environment has been implemented in order to facilitate a new educational modality. This paper reports the outcome of a comparative study between traditional medical teaching modalities and virtual reality technology. This study was conducted in the Faculty of Medicine in the University of Jordan. The participants were asked to perform system trials and experiment with the system by navigating through the system interfaces, as well as being exposed to the traditional physical model of the human heart that is currently used in the faculty during practical anatomy sessions. Afterwards, they were asked to provide feedback via a comparative questionnaire. The participants’ replies to the questions regarding the Physical Heart Model and VR heart anatomy system were assessed for reliability using Cronbach’s alpha. The first group’s (Physical Heart Model questions) α value was 0.689. The second group’s (VR heart anatomy system questions) α value was 0.791. Comparing students’ experience results between the traditional method (Physical Heart Model) and the VR heart anatomy system, the mean scores showed a distinct increase in the values. This indicates that the developed system enhanced their experience in anatomy learning and the provided tools improved their understanding of heart anatomy. Results demonstrated the usefulness of the system by showing a higher satisfaction rate for the provided tools regarding structure and visualisation.
TL;DR: A comprehensive ensemble approach composed by optimized and diversified Artificial Neural Networks (ANNs) is proposed for improving the 24h-ahead solar PV power production predictions, which would allow balancing power supplies and demands across centralized grid networks through economic dispatch decisions between the energy sources that contribute to the energy mix.
Abstract: The use of data-driven ensemble approaches for the prediction of the solar Photovoltaic (PV) power production is promising due to their capability of handling the intermittent nature of the solar energy source. In this work, a comprehensive ensemble approach composed by optimized and diversified Artificial Neural Networks (ANNs) is proposed for improving the 24h-ahead solar PV power production predictions. The ANNs are optimized in terms of number of hidden neurons and diversified in terms of the diverse training datasets used to build the ANNs, by resorting to trial-and-error procedure and BAGGING techniques, respectively. In addition, the Bootstrap technique is embedded to the ensemble for quantifying the sources of uncertainty that affect the ensemble models' predictions in the form of Prediction Intervals (PIs). The effectiveness of the proposed ensemble approach is demonstrated by a real case study regarding a grid-connected solar PV system (231 kWac capacity) installed on the rooftop of the Faculty of Engineering at the Applied Science Private University (ASU), Amman, Jordan. The results show that the proposed approach outperforms three benchmark models, including smart persistence model and single optimized ANN model currently adopted by the PV system's owner for the prediction task, with a performance gain reaches up to 11%, 12%, and 9%, for RMSE, MAE, and WMAE standard performance metrics, respectively. Simultaneously, the proposed approach has shown superior in quantifying the uncertainty affecting the power predictions, by establishing slightly wider PIs that achieve the highest confidence level reaches up to 84% for a predefined confidence level of 80% compared to three other approaches of literature. These enhancements would, indeed, allow balancing power supplies and demands across centralized grid networks through economic dispatch decisions between the energy sources that contribute to the energy mix.