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Alanazi, Ibrahim O,Khan, Zahid Asian Pacific Journal of Cancer Prevention 2016 Asian Pacific journal of cancer prevention Vol.17 No.2
Epidermal growth factor receptors (EGFRs/HERs) and downstream signaling pathways have been implicated in the pathogenesis of several malignancies including breast cancer and its resistance to treatment with chemotherapeutic drugs. Consequently, several monoclonal antibodies as well as small molecule inhibitors targeting these pathways have emerged as therapeutic tools in the recent past. However, studies have shown that utilizing these molecules in combination with chemotherapy has yielded only limited success. This review describes the current understanding of EGFRs/HERs and associated signaling pathways in relation to development of breast cancer and responses to various cancer treatments in the hope of pointing to improved prevention, diagnosis and treatment. Also, we review the role of breast cancer stem cells (BCSCs) in disease and the potential to target these cells.
Deconstructing Opinion Survey: A Case Study
Alanazi, Entesar International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.4
Questionnaires and surveys are increasingly being used to collect information from participants of empirical software engineering studies. Usually, such data is analyzed using statistical methods to show an overall picture of participants' agreement or disagreement. In general, the whole survey population is considered as one group with some methods to extract varieties. Sometimes, there are different opinions in the same group, but they are not well discovered. In some cases of the analysis, the population may be divided into subgroups according to some data. The opinions of different segments of the population may be the same. Even though the existing approach can capture the general trends, there is a risk that the opinions of different sub-groups are lost. The problem becomes more complex in longitudinal studies where minority opinions might fade over time. Longitudinal survey data may include several interesting patterns that can be extracted using a clustering process. It can discover new information and give attention to different opinions. We suggest using a data mining approach to finding the diversity among the different groups in longitudinal studies. Our study shows that diversity can be revealed and tracked over time using the clustering approach, and the minorities have an opportunity to be heard.
Malicious Trust Managers Identification (MTMI) in Peer to Peer Networks
Alanazi, Adwan Alownie International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.9
Peer to Peer Networks play an increasing role in today's networks, also it's expected that this type of communication networks evolves more in the future. Since the number of users that is involved in Peer to Peer Networks is huge and will be increased more in the future, security issues will appear and increase as well. Thus, providing a sustainable solution is needed to ensure the security of Peer to Peer Networks. This paper is presenting a new protocol called Malicious Trust Managers Identification (MTMI). This protocol is used to ensure anonymity of trust manager, that computes and stores the trust value for another peer. The proposed protocol builds a secure connection between trust managers by using public key infrastructure. As well as experimental testing has been conducted to validate the proposed protocol.
Resource Prediction for Big Data Processing in a Cloud Data Center
Alanazi Rayan,Yunmook Nah 대한전자공학회 2018 IEIE Transactions on Smart Processing & Computing Vol.7 No.6
The high demand for big data applications, such as the Internet of Things (IoT), healthcare, business, and academia, as well as government, fosters the creation of large-scale cloud data centers. Cloud data centers contain thousands of physical machines (PMs), so resource management is necessary for allocating the tremendous amount of data to them. Knowing the workload demand in advance enables control of those resources, saving energy, reducing CPU and memory usage, and improving service. Workload prediction can be used to determine how many resources need to be allocated in the future. In this paper, we propose machine learning–based techniques to predict the daily operational workload. The proposed approach can predict the amount of power consumption (PC) and the number of PMs required to fulfill the demands of the cloud data center. Workload prediction accuracy varies based on the prediction methods used and the type of workload. In this work, we investigate three different methods: polynomial regression, support vector regression, and random forest regression (RFR). Considering both accuracy and computation time, results show that RFR provides the best performance, in our case, with a minimum root-mean-square error of 11.68 for PMs and 4869.08 for PC prediction. The computation time solidifies our selection with 2 seconds training time in all instances.
Comprehensive Analysis and Evaluation of Mobile S-MAC Protocol in Wireless Sensor Network
Alanazi, Adwan Alownie International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.4
Wireless sensor networks (WSN) are becoming widely used in collecting and sensing information in different fields such as in the medical area, smart phone industry and military environment. The main concern here is reducing the power consumption because it effects in the lifetime of wireless sensor during commutation because it may be work in some environment like sensor in the battlefields where is not easy to change the battery for a node and that may decrease the efficiency of that node and that may affect the network traffic may be interrupted because one or more nodes stop working. In this paper we implement, simulate, and investigate S-MAC protocol with mobility support and show the sequence of events the sender and receiver go through. We tested some parameters and their impacts of on the performance including System throughput, number of packets successfully delivered per second, packet delay, average packet delay before successful transmission.
Alanazi, Afrah,Li, Alice,Soh, Ben International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.8
In Saudi Arabia, female students tend to struggle with the basics of computer programming, especially coding. To better understand why female students sometimes perform poorly in this discipline, this qualitative study aims to obtain the views of female computer programming teachers at a Saudi university on using mobile learning (m-learning) methods in computer programming lectures. Ten teachers from the all-female Aljouf University were interviewed to assess their perceptions of m-learning, in particular, the usefulness of ViLLE visualisation software. Data were analysed using thematic analysis. Most interview responses about m-learning and ViLLE were positive, although there were some notable negative responses. The Saudi culture-related responses were evenly divided between positive and negative, reflecting the culture's limitations.
Developing Cloud Computing Time Slot-availability Predictions Using an Artificial Neural Network
Alanazi Rayan,Muhammad Ashfaq khan,Fawaz Alhazemi,Hamoud Alshammari,Yunmook Nah 대한전자공학회 2020 IEIE Transactions on Smart Processing & Computing Vol.9 No.1
Over the last decade, cloud computing has exponentially transformed the ways of computing. In spite of its various advantages, cloud computing suffers from several challenges that affect performance. Two of the fundamental challenges are power consumption and dynamic resource scaling. An efficient resource allocation strategy could help cloud computing to improve overall performance and operational costs. In this paper, we design a novel approach to available time slot prediction in a data node, based on an artificial neural network (ANN), which predicts the time at which the required resources will be available. We conducted experiments on several nodes, obtaining up to 98%, and outperforming state-of-the-art available time slot prediction approaches. We claim that available time–slot prediction for cloud computing based on an ANN will lead to optimum resource allocation and to minimizing energy consumed while maintaining the essential performance level.