Lubna Alharbi
@ut.edu.sa
Computer Science
Tabuk University
Scopus Publications
Scopus Publications
Ahsan Waqar, Lubna A. Alharbi, Faiz Abdullah Alotaibi, Idris Othman, and Hamad Almujibah
Elsevier BV
YanRu Dong, Ahmed M. Alwakeel, Mohammed M. Alwakeel, Lubna A. Alharbi, and Sara A Althubiti
Springer Science and Business Media LLC
Bayan Alsaaidah, Zaid Mustafa, Moh’d Rasoul Al-Hadidi, and Lubna A. Alharbi
International Information and Engineering Technology Association
ABSTRACT
Lubna A. Alharbi
Institute of Electrical and Electronics Engineers (IEEE)
The Emergency Departments (EDs) in health centres located in the main areas of Saudi Arabia have heavy patient inflow because of the pandemic, viral infections, and even on some special occasions like Umrah or Hajj, where pilgrims who travel from one place to another with serious disorders. Other than the EDs, it was important to observe the patient’s activities from ED to other wards in the region or the hospital to track the spread of viral diseases. In this case, deep learning (DL) and machine learning (ML) methods have been used to track the target audience and classify the data into many classes. With this motivation, this study develops an artificial rabbit optimization with a machine learning-based healthcare data classification (AROML-HDC) technique for EDs. The AROML-HDC technique monitors and tracks the patient visit data, treatment given, and length of stay (LOS). In addition, the AROML-HDC technique designs an effective ARO algorithm for the optimal selection of feature subsets. Next, the class-specific cost regulation extreme learning machine (CSCR-ELM) classifier is applied for effective medical data classification. Finally, the grasshopper optimization algorithm (GOA) was used to adjust the parameters related to the CSCR-ELM classifier. The experimental outcome of the AROML-HDC approach is tested on the benchmark Cleveland dataset and the Statlog dataset comprising 297 and 270 samples, respectively. The simulation results signify the improved performance of the AROML-HDC technique over other recent methods with maximum accuracy of 93.22% and 94.05% Cleveland dataset and the Statlog dataset, respectively.
Nojood Alageel, Rahaf Alharbi, Rehab Alharbi, Maryam Alsayil, and Lubna A. Alharbi
The Science and Information Organization
Lubna A. Alharbi
The Science and Information Organization
Anwer Mustafa Hilal, Aisha Hassan Abdalla Hashim, Heba G. Mohamed, Lubna A. Alharbi, Mohamed K. Nour, Abdullah Mohamed, Ahmed S. Almasoud, and Abdelwahed Motwakel
Computers, Materials and Continua (Tech Science Press)
Hanan Abdullah Mengash, Lubna A. Alharbi, Saud S. Alotaibi, Sarab AlMuhaideb, Nadhem Nemri, Mrim M. Alnfiai, Radwa Marzouk, Ahmed S. Salama, and Mesfer Al Duhayyim
Computers, Materials and Continua (Tech Science Press)
Marwa Obayya, Nadhem NEMRI, Lubna A. Alharbi, Mohamed K. Nour, Mrim M. Alnfiai, Mohammed Abdullah Al-Hagery, Nermin M. Salem, and Mesfer Al Duhayyim
Computers, Materials and Continua (Tech Science Press)
Mohammad Hijji, Bilal Ahmad, Gulzar Alam, Ahmed Alwakeel, Mohammed Alwakeel, Lubna Abdulaziz Alharbi, Ahd Aljarf, and Muhammad Umair Khan
MDPI AG
Currently, researchers are working to contribute to the emerging fields of cloud computing, edge computing, and distributed systems. The major area of interest is to examine and understand their performance. The major globally leading companies, such as Google, Amazon, ONLIVE, Giaki, and eBay, are truly concerned about the impact of energy consumption. These cloud computing companies use huge data centers, consisting of virtual computers that are positioned worldwide and necessitate exceptionally high-power costs to preserve. The increased requirement for energy consumption in IT firms has posed many challenges for cloud computing companies pertinent to power expenses. Energy utilization is reliant upon numerous aspects, for example, the service level agreement, techniques for choosing the virtual machine, the applied optimization strategies and policies, and kinds of workload. The present paper tries to provide an answer to challenges related to energy-saving through the assistance of both dynamic voltage and frequency scaling techniques for gaming data centers. Also, to evaluate both the dynamic voltage and frequency scaling techniques compared to non-power-aware and static threshold detection techniques. The findings will facilitate service suppliers in how to encounter the quality of service and experience limitations by fulfilling the service level agreements. For this purpose, the CloudSim platform is applied for the application of a situation in which game traces are employed as a workload for analyzing the procedure. The findings evidenced that an assortment of good quality techniques can benefit gaming servers to conserve energy expenditures and sustain the best quality of service for consumers located universally. The originality of this research presents a prospect to examine which procedure performs good (for example, dynamic, static, or non-power aware). The findings validate that less energy is utilized by applying a dynamic voltage and frequency method along with fewer service level agreement violations, and better quality of service and experience, in contrast with static threshold consolidation or non-power aware technique.
A. Al-Qarafi, Hadeel Alsolai, Jaber S. Alzahrani, Noha Negm, Lubna A. Alharbi, Mesfer Al Duhayyim, Heba Mohsen, M. Al-Shabi, and Fahd N. Al-Wesabi
MDPI AG
A smart city is a sustainable and effectual urban center which offers a maximal quality of life to its inhabitants with the optimal management of their resources. Energy management is the most difficult problem in such urban centers because of the difficulty of energy models and their important role. The recent developments of machine learning (ML) and deep learning (DL) models pave the way to design effective energy management schemes. In this respect, this study introduces an artificial jellyfish optimization with deep learning-driven decision support system (AJODL-DSSEM) model for energy management in smart cities. The proposed AJODL-DSSEM model predicts the energy in the smart city environment. To do so, the proposed AJODL-DSSEM model primarily performs data preprocessing at the initial stage to normalize the data. Besides, the AJODL-DSSEM model involves the attention-based convolutional neural network-bidirectional long short-term memory (CNN-ABLSTM) model for the prediction of energy. For the hyperparameter tuning of the CNN-ABLSTM model, the AJO algorithm was applied. The experimental validation of the proposed AJODL-DSSEM model was tested using two open-access datasets, namely the IHEPC and ISO-NE datasets. The comparative study reported the improved outcomes of the AJODL-DSSEM model over recent approaches.
Likun Li, Yinsheng Fu, Kun Yu, Ahmed M. Alwakeel, and Lubna A. Alharbi
Springer Science and Business Media LLC
Naeem Ullah, Javed Ali Khan, Lubna Abdulaziz Alharbi, Asaf Raza, Wahab Khan, and Ijaz Ahmad
Institute of Electrical and Electronics Engineers (IEEE)
Crop pests are to blame for significant economic, social, and environmental losses worldwide. Various pests have different control strategies, and precisely identifying pests has become crucial to pest control and is a significant difficulty in agriculture. Many agricultural professionals are interested in deep learning (DL) models since they have shown significant promise in image recognition. Pest identification approaches in literature have relatively low accuracy in pest recognition and classification due to the complexity of their algorithms and limited data availability. Misclassification of insect pests sometimes leads to using the wrong pesticides, causing harm to agricultural yields and the surrounding environment. It necessitates developing an automated system capable of more accurate pest identification and classification. This paper presents a novel end-to-end DeepPestNet framework for pest recognition and classification. The proposed model has 11 learnable layers, including eight convolutional and three fully connected (FC) layers. We used image rotations techniques to increase the size of the dataset and image augmentations techniques to test the generalizability of the proposed DeepPestNet approach. We used the popular Deng’s crops data set to assess the proposed DeepPestNet framework. We used the proposed method to recognize and classify crop pests into 10-class pests, i.e., Locusta migratoria, Euproctis pseudoconspersa strand, chrysochus Chinensis, empoasca flavescens, Spodoptera exigua, larva of laspeyresia pomonella, parasa lepida, acrida cinerea, larva of S. exigua, and L.pomonella types of insects pests. The proposed method achieved optimal accuracy of 100%. We compared the proposed DeepPestNet approach with traditional pre-trained deep learning (DL) models. To verify the general adaptability of this model, we tested the proposed model on the standard Kaggle dataset “Pest Dataset” to recognize nine types of pests: aphids, armyworm, beetle, bollworm, grasshopper, mites, mosquito, sawfly, and stem borer and achieved an accuracy of 98.92%. The proposed model can provide specialists and farmers with immediate and effective aid in recognizing pests, potentially reducing economic and crop yield losses.
Lubna Alharbi, Floriana Grasso, and Phil Jimmieson
ACM