Application of an Image-Based Model of the Elastic Modulus of Porous Thermal Barrier Coatings

Moteb Alotaibi 대한금속·재료학회 2022 METALS AND MATERIALS International Vol.28 No.8

The elastic modulus of plasma-sprayed thermal barrier coatings (TBCs), which have been utilized for gas turbine enginecomponents at elevated temperatures, has been investigated using a proposed model. The main purpose of this paper is toexplore the alterations in porous TBC microstructure that lead to alterations in its mechanical properties, including elasticmodulus. This paper investigates the effect of different types of defects, i.e., nonflat porosity, microcracks and interlamellarporosity, on the elastic modulus of porous TBC materials. The first part of this paper quantitively studies the microstructuralcharacterization of plasma-sprayed TBCs by means of an image analysis approach. The second part of this paper predictsthe elastic modulus of plasma-sprayed TBCs based on microstructural changes, i.e., defects. The volumetric fraction of differenttypes of defects and their shapes and orientations are also taken into account. It is found that both microcracks andinterlamellar porosity exhibit a crucial optimization on the elastic modulus of porous TBCs, while nonflat porosity showsa lesser effect on the elastic modulus. The predicted data of the proposed model show relatively good agreement with FEAmodel results and experimentally measured results. These simulation results could help to further the understanding of theimpact of porous TBC microstructural alterations on elastic modulus.

KAB: Knowledge Augmented BERT2BERT Automated Questions-Answering system for Jurisprudential Legal Opinions

Alotaibi, Saud S.,Munshi, Amr A.,Farag, Abdullah Tarek,Rakha, Omar Essam,Al Sallab, Ahmad A.,Alotaibi, Majid International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.6

The jurisprudential legal rules govern the way Muslims react and interact to daily life. This creates a huge stream of questions, that require highly qualified and well-educated individuals, called Muftis. With Muslims representing almost 25% of the planet population, and the scarcity of qualified Muftis, this creates a demand supply problem calling for Automation solutions. This motivates the application of Artificial Intelligence (AI) to solve this problem, which requires a well-designed Question-Answering (QA) system to solve it. In this work, we propose a QA system, based on retrieval augmented generative transformer model for jurisprudential legal question. The main idea in the proposed architecture is the leverage of both state-of-the art transformer models, and the existing knowledge base of legal sources and question-answers. With the sensitivity of the domain in mind, due to its importance in Muslims daily lives, our design balances between exploitation of knowledge bases, and exploration provided by the generative transformer models. We collect a custom data set of 850,000 entries, that includes the question, answer, and category of the question. Our evaluation methodology is based on both quantitative and qualitative methods. We use metrics like BERTScore and METEOR to evaluate the precision and recall of the system. We also provide many qualitative results that show the quality of the generated answers, and how relevant they are to the asked questions.

Radio Resource Scheduling Approach For Femtocell Networks

Alotaibi, Sultan International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.4

The radio resources available in a wireless network system are limited. Therefor, job of managing resources is not easy task. Because the resources are shared among the UEs that are connected, the process of assigning resources must be carefully controlled. The packet scheduler in an LTE network is in charge of allocating resources to the user equipment (UE). Femtocells networks are being considered as a promising solution for poor channel performance for mulitple environments. The implementation of femtocells into a macrocell (traditional base station) would boost the capacities of the cellular network. To increase femtocells network capacity, a reliable Packet Scheduler mechanism should be implemented. The Packet Scheduler technique is introduced in this paper to maximize capacity of the network while maintaining fairness among UEs. The proposed solution operates in a manner consistent with this principle. An analysis of the proposed scheme's performance is conducted using a computer simulation. The results reveal that it outperforms the well-known PF scheduler in terms of cell throughput and average throughput of UEs.

The Effect of the Sentence Location on Arabic Sentiment Analysis

Alotaibi, Saud S. International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.5

Rich morphology language such as Arabic needs more investigation and method to improve the sentiment analysis task. Using all document parts in the process of the sentiment analysis may add some unnecessary information to the classifier. Therefore, this paper shows the ongoing work to use sentence location as a feature with Arabic sentiment analysis. Our proposed method employs a supervised sentiment classification method by enriching the feature space model with some information from the document. The experiments and evaluations that were conducted in this work show that our proposed feature in the sentiment analysis for Arabic improves the performance of the classifier compared to the baseline model.

Autonomous Transmission Power Adjustment Strategy for Femtocell Base Station

Alotaibi, Sultan International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.4

Femtocells have recently been recognized for their potential to boost network capacity, improve end-user QoS and throughput, and do so at a cheap cost and with ease of implementation. The use of femtocells in indoor environments, such as residential buildings with neighboring homes, is becoming more popular. Femtocells are subject to interference from other femtocells, and the unwanted effects of interference are amplified when femtocells are deployed in close proximity to one another. As a consequence, the network's overall performance is degraded to a significant degree. One of the strategies that is thought to be effective in reducing the impact of interference is altering the transmission power of the femtocells. In this paper, a dynamic downlink transmission power of femtocells is suggested. In accordance with the observed cost function unit, each femtocell automatically changes its transmission power. If a femtocell causes too much interference for its neighbors, its transmission power level will be limited by that interference's rate. A simulation experiment is conducted to validate the effectiveness of the suggested system when compared with other schemes. When compared to previous schemes, which are addressed in this study, the numerical results show that the proposed strategy could provide more capacity while also ideally mitigating the influence of interference among co-channel deployed femtocells.

Using Machine Learning to Improve Evolutionary Multi-Objective Optimization

Alotaibi, Rakan International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.6

Multi-objective optimization problems (MOPs) arise in many real-world applications. MOPs involve two or more objectives with the aim to be optimized. With these problems improvement of one objective may led to deterioration of another. The primary goal of most multi-objective evolutionary algorithms (MOEA) is to generate a set of solutions for approximating the whole or part of the Pareto optimal front, which could provide decision makers a good insight to the problem. Over the last decades or so, several different and remarkable multi-objective evolutionary algorithms, have been developed with successful applications. However, MOEAs are still in their infancy. The objective of this research is to study how to use and apply machine learning (ML) to improve evolutionary multi-objective optimization (EMO). The EMO method is the multi-objective evolutionary algorithm based on decomposition (MOEA/D). The MOEA/D has become one of the most widely used algorithmic frameworks in the area of multi-objective evolutionary computation and won has won an international algorithm contest.

LTE Packet Scheduling with Bandwidth Type Consideration

Alotaibi, Sultan International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.4

LTE (Long-Term Evolution, sometimes known as 4G LTE) is a wireless high-speed data communication technology for mobile phones and data terminals. The Packet Scheduler (PS) is an important component in improving network performance. Physical Resource Blocks (PRBs) are assigned to associated User Equipment by the packet scheduler (UEs). The primary contribution of this study is a comparison of the eNodeB throughput between a suggested method and the Round Robin (RR) Algorithm. The RR Algorithm distributes PRBs among all associated UEs without taking channel circumstances into account. In this research, we present a new scheduling method that takes into account the number of PRBs and associated UEs and produces higher throughput than the RR algorithm.

Key Challenges of Mobility Management and Handover Process In 5G HetNets

Alotaibi, Sultan International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.4

Wireless access technologies are emerging to enable high data rates for mobile users and novel applications that encompass both human and machine-type interactions. An essential approach to meet the rising demands on network capacity and offer high coverage for wireless users on upcoming fifth generation (5G) networks is heterogeneous networks (HetNets), which are generated by combining the installation of macro cells with a large number of densely distributed small cells Deployment in 5G architecture has several issues because to the rising complexity of network topology in 5G HetNets with many distinct base station types. Aside from the numerous benefits that dense small cell deployment delivers, it also introduces key mobility management issues such as frequent handover (HO), failures, delays and pingpong HO. This article investigates 5G HetNet mobility management in terms of radio resource control. This article also discusses the key challenges for 5G mobility management.

HetNet Characteristics and Models in 5G Networks

Alotaibi, Sultan International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.4

The fifth generation (5G) mobile communication technology is designed to meet all communication needs. Heterogeneous networks (HetNets) are a new emerging network structure. HetNets have greater potential for radio resource reuse and better service quality than homogeneous networks since they can evolve small cells into macrocells. Effective resource allocation techniques reduce inter-user interference while optimizing the utilization of limited spectrum resources in HetNets. This article discusses resource allocation in 5G HetNets. This paper explains HetNets and how they work. Typical cell types in HetNets are summarized. Also, HetNets models are explained in the third section. The fourth component addresses radio resource control and mobility management. Moreover, future study in this subject may benefit from this article's significant insights on how HetNets function.

Development of the Recommender System of Arabic Books Based on the Content Similarity

Alotaibi, Shaykhah Hajed,Khan, Muhammad Badruddin International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.8

This research article develops an Arabic books' recommendation system, which is based on the content similarity that assists users to search for the right book and predict the appropriate and suitable books pertaining to their literary style. In fact, the system directs its users toward books, which can meet their needs from a large dataset of Information. Further, this system makes its predictions based on a set of data that is gathered from different books and converts it to vectors by using the TF-IDF system. After that, the recommendation algorithms such as the cosine similarity, the sequence matcher similarity, and the semantic similarity aggregate data to produce an efficient and effective recommendation. This approach is advantageous in recommending previously unrated books to users with unique interests. It is found to be proven from the obtained results that the results of the cosine similarity of the full content of books, the results of the sequence matcher similarity of Arabic titles of the books, and the results of the semantic similarity of English titles of the books are the best obtained results, and extremely close to the average of the result related to the human assigned/annotated similarity. Flask web application is developed with a simple interface to show the recommended Arabic books by using cosine similarity, sequence matcher similarity, and semantic similarity algorithms with all experiments that are conducted.