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Chaotic Elephant Herd Optimization with Machine Learning for Arabic Hate Speech Detection

Intelligent Automation & Soft Computing 2024年第3期39卷 567-583页

作者： Badriyya B.al-onazi Jaber S.alzahrani Najm alotaibi Hussain alshahrani Mohamed Ahmed Elfaki Radwa Marzouk Heba Mohsen Abdelwahed Motwakel Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Prince saud alFaisal Institute for Diplomatic Studies Riyadh12735Saudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqra11911Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in Egypt New CairoNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipAl-Kharj16278Saudi Arabia

In recent years,the usage of social networking sites has considerably increased in the Arab *** has empowered individuals to express their opinions,especially in ***,various organizations that operate in the Arab countries have embraced social media in their day-to-day business activities at different *** is attributed to business owners’understanding of social media’s importance for business ***,the Arabic morphology is too complicated to understand due to the availability of nearly 10,000 roots and more than 900 patterns that act as the basis for verbs and *** speech over online social networking sites turns out to be a worldwide issue that reduces the cohesion of civil *** this background,the current study develops a Chaotic Elephant Herd Optimization with Machine Learning for Hate Speech Detection(CEHOML-HSD)model in the context of the Arabic *** presented CEHOML-HSD model majorly concentrates on identifying and categorising the Arabic text into hate speech and *** attain this,the CEHOML-HSD model follows different sub-processes as discussed *** the initial stage,the CEHOML-HSD model undergoes data pre-processing with the help of the TF-IDF ***,the Support Vector Machine(SVM)model is utilized to detect and classify the hate speech texts made in the Arabic ***,the CEHO approach is employed for fine-tuning the parameters involved in *** CEHO approach is developed by combining the chaotic functions with the classical EHO *** design of the CEHO algorithm for parameter tuning shows the novelty of the work.A widespread experimental analysis was executed to validate the enhanced performance of the proposed CEHOML-HSD *** comparative study outcomes established the supremacy of the proposed CEHOML-HSD model over other approaches.

关键词： Arabic language machine learning elephant herd optimization TF-IDF vectorizer hate speech detection

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Intelligent IoT-Aided Early Sound Detection of Red Palm Weevils

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Computers, Materials & Continua 2021年第12期69卷 4095-4111页

作者： Mohamed Esmail Karar Omar Reyad Abdel-Haleem Abdel-Aty saud Owyed Mohd F.Hassan College of Computing and Information Technology Shaqra UniversityShaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityEgypt Faculty of Science Sohag UniversitySohag82524Egypt Department of Physics College of SciencesUniversity of BishaBisha61922Saudi Arabia Department of Mathematics College of SciencesUniversity of BishaBisha61922Saudi Arabia Department of Computer and Information Science CeRDaSUniversiti Teknologi PetronasMalaysia

Smart precision agriculture utilizes modern information and wireless communication technologies to achieve challenging agricultural ***,Internet of Things(IoT)technology can be applied to monitor and detect harmful insect pests such as red palm weevils(RPWs)in the farms of date palm *** this paper,we propose a new IoT-based framework for early sound detection of RPWs using fine-tuned transfer learning classifier,namely *** sound sensors,namely TreeVibes devices are carefully mounted on each palm trunk to setup wireless sensor networks in the *** trees are labeled based on the sensor node number to identify the infested ***,the acquired audio signals are sent to a cloud server for further on-line analysis by our fine-tuned deep transfer learning model,i.e.,*** proposed infestation classifier has been successfully validated on the public TreeVibes *** includes total short recordings of 1754 samples,such that the clean and infested signals are 1754 and 731 samples,*** to other deep learning models in the literature,our proposed InceptionResNet-V2 classifier achieved the best performance on the public database of TreeVibes audio *** resulted classification accuracy score was 97.18%.Using 10-fold cross validation,the fine-tuned InceptionResNet-V2 achieved the best average accuracy score and standard deviation of 94.53%and±1.69,*** the proposed intelligent IoT-aided detection system of RPWs in date palm farms is the main prospect of this research work.

关键词： Red palm weevils smart precision agriculture internet of things artificial intelligence

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Analysis of within-host CHIKV dynamics models with general incidence rate

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International Journal of Biomathematics 2018年第5期11卷 17-41页

作者： Ahmed M. Elaiw Taofeek O. alade saud M. alsulami Department of Mathematics Faculty of Science King Abdulaziz University P.O. Box 80203 Jeddah 21589 Saudi Arabia

In this paper we study the stability analysis of two within-host Chikungunya virus （CHIKV） dynamics models. The incidence rate between the CHIKV and the uninfected mouocytes is modeled by a general nonlinear function. The second model considers two types of infected monocytes （i） latently infected monocytes which do not generate CHIKV and （ii） actively infected monocytes which produce the CHIKV particles. Sufficient conditions are found which guarantee the global stability of the positive steady states. Using the Lyapunov function, we established the global stability of the steady states of the models. The theoretical results are confirmed by numerical simulations.

关键词： Global stability chikungunya virus infection general incidence rate Lyapunov function.

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Sign Language Recognition and Classification Model to Enhance Quality of Disabled People

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Computers, Materials & Continua 2022年第11期73卷 3419-3432页

作者： Fadwa alrowais saud S.alotaibi Sami Dhahbi Radwa Marzouk Abdullah Mohamed Anwer Mustafa Hilal Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia University of Tunis EL Manar Higher Institute of ComputerResearch Team on Intelligent Systems in Imaging and Artificial Vision(Siiva)–Lab LimticAryanah2036Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Sign language recognition can be considered as an effective solution for disabled people to communicate with *** helps them in conveying the intended information using sign languages without any *** advancements in computer vision and image processing techniques can be leveraged to detect and classify the signs used by disabled people in an effective *** optimization algorithms can be designed in a manner such that it fine tunes the hyper parameters,used in Deep Learning(DL)models as the latter considerably impacts the classification *** this motivation,the current study designs the Optimal Deep Transfer Learning Driven Sign Language Recognition and Classification(ODTL-SLRC)model for disabled *** aim of the proposed ODTL-SLRC technique is to recognize and classify sign languages used by disabled *** proposed ODTL-SLRC technique derives EfficientNet model to generate a collection of useful feature *** addition,the hyper parameters involved in EfficientNet model are fine-tuned with the help of HGSO ***,Bidirectional Long Short Term Memory(BiLSTM)technique is employed for sign language *** proposed ODTL-SLRC technique was experimentally validated using benchmark dataset and the results were inspected under several *** comparative analysis results established the superior performance of the proposed ODTL-SLRC technique over recent approaches in terms of efficiency.

关键词： Sign language image processing computer vision disabled people deep learning parameter tuning

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Automated Machine Learning Enabled Cybersecurity Threat Detection in Internet of Things Environment

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Computer Systems Science & Engineering 2023年第4期45卷 687-700页

作者： Fadwa alrowais Sami althahabi saud S.alotaibi Abdullah Mohamed Manar Ahmed Hamza Radwa Marzouk Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Recently,Internet of Things(IoT)devices produces massive quantity of data from distinct sources that get transmitted over public *** becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be *** development of automated tools for cyber threat detection and classification using machine learning(ML)and artificial intelligence(AI)tools become essential to accomplish security in the IoT *** is needed to minimize security issues related to IoT gadgets ***,this article introduces a new Mayfly optimization(MFO)with regularized extreme learning machine(RELM)model,named MFO-RELM for Cybersecurity Threat Detection and classification in IoT *** presented MFORELM technique accomplishes the effectual identification of cybersecurity threats that exist in the IoT *** accomplishing this,the MFO-RELM model pre-processes the actual IoT data into a meaningful *** addition,the RELM model receives the pre-processed data and carries out the classification *** order to boost the performance of the RELM model,the MFO algorithm has been employed to *** performance validation of the MFO-RELM model is tested using standard datasets and the results highlighted the better outcomes of the MFO-RELM model under distinct aspects.

关键词： Cybersecurity threats classification internet of things machine learning parameter optimization

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Design and Performance Analysis of DSS (Dual Sink Based Scheme) Protocol for WBASNs

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Advances in Remote Sensing 2017年第4期6卷 245-259页

作者： Abdul Ahad saud al Faisal Farzand ali Bahlool Jan Niamat Ullah Department of Computer Science Virtual University of Lahore Lahore Pakistan Department of Electronics Engineering Comsats University Islamabad Pakistan Department of Computer Science Preston University Islamabad Pakistan Department of Computer Science University of Swat Swat Pakistan Department of Computer Science Abdul Wali Khan University Mardan Pakistan

After a large improvement in health care services, Wireless Body Area Sensor Network (WBASN) is used in different fields where health care monitoring can be done continuously and from a distance. Many selected protocols are given in literature work which can improve the performance of WBASN by focusing on delay, routing and energy efficiency. In our research, we will focus on improving throughput, network life time, and decrease end-to-delay. In given research, the two sink nodes utilized and AnyCasting concept will be used. Dual Sinks based Scheme (DSS) for WBASN will be compared with existing DARE, SIMPLE, LAEEBA and M-ATTEMPT protocols. DSS performance found more efficient than DARE, SIMPLE, LAEEBA and M-ATTEMPT respectively in throughput. From results, DSS network life time is greater than DARE, SIMPLE, LAEEBA and M-ATTEMPT with minimum delay. In DSS, energy parameter is in tradeoff with the improved parameters, because of a computation of RSSI which do more process and utilize more energy.

关键词： WBASNs Network Life Time End-to-End Delay Throughput Energy Consumption AnyCasting RSSI

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Intelligent Traffic Surveillance through Multi-Label Semantic Segmentation and Filter-Based Tracking

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Computers, Materials & Continua 2023年第9期76卷 3707-3725页

作者： Asifa Mehmood Qureshi Nouf Abdullah almujally saud S.alotaibi Mohammed Hamad alatiyyah Jeongmin Park Department of Creative Technologies Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Information Systems Department Umm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Sciences and Humanities in AflajPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Engineering Tech University of KoreaSiheung-siGyeonggi-do15073Korea

Road congestion,air pollution,and accident rates have all increased as a result of rising traffic density andworldwide population *** the past ten years,the total number of automobiles has increased significantly over the *** this paper,a novel method for intelligent traffic surveillance is *** proposed model is based on multilabel semantic segmentation using a random forest classifier which classifies the images into five *** improve the results,mean-shift clustering was applied to the segmented ***,the pixels given the label for the vehicle were extracted and blob detection was applied to mark each *** the validation of each detection,a vehicle verification method based on the structural similarity index is *** tracking of vehicles across the image frames is done using the Identifier(ID)assignment technique and particle ***,vehicle counting in each frame along with trajectory estimation was done for each *** proposed system demonstrated a remarkable vehicle detection rate of 0.83 over Vehicle Aerial Imaging from Drone(VAID),0.86 over AU-AIR,and 0.75 over the Unmanned Aerial Vehicle Benchmark Object Detection and Tracking(UAVDT)dataset during the experimental *** proposed system can be used for several purposes,such as vehicle identification in traffic,traffic density estimation at intersections,and traffic congestion sensing on a road.

关键词： Traffic surveillance multi-label segmentation random forest particle filter computer vision

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Gastric antisecretory and antiulcer activity of bovine hemoglobin

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World Journal of Gastroenterology 2013年第21期19卷 3291-3299页

作者： Abdulrahman K al Asmari saud al Omani Ibrahim Elfaki Mohammad Tariq Ahmed al Malki Saeed al Asmary Research Center Prince Sultan Military Medical CityRiyadh 11159Saudi Arabia Department of Surgery Prince Sultan Military Medical CityRiyadh 11159Saudi Arabia Department of Gastroenterology Prince Sultan Military Medical CityRiyadh 11159Saudi Arabia Family and Community Medicine Prince Sultan Military Medical CityRiyadh 11159Saudi Arabia

AIM: To investigate gastric antisecretory and gastro- protective activity of bovine hemoglobin (B-Hb) in rats. METHODS: Adult albino-Wistar rats were divided into groups of 6 animals each. B-Hb in doses of 100, 300 and 900 mg/kg body weight was tested for gastric acid secretion and antiulcer activity. Gastric secretions were measured 6 h after pylorus ligation in rats pretreated with B-Hb. The acidity was measured by titrating gastric contents against 0.01 mol/L NaOH to pH 7. Indomethacin ulcers were produced by oral administration of 30 mg/kg bw in the rats pretreated with B-Hb one hour before indomethacin. Six hours after indomethacin stomach removed and ulcer index was recorded. Ethanol ulcer were produced by 1 mL of ethanol in the rats pretreated with B-Hb 30 min before the *** hour after ethanol stomach were cut open to score ulcers. Histological examination and analysis of gastric wall mucus, non-protein sulfhydryl groups (NP-SH), and myeloperoxidase (MPO) were carried in gastric tissue following ethanol administration. RESULTS: In control rats pylorus ligation for 6 h resulted in the accumulation of 8.1 ± 0.61 mL of gastric secretion. The treatment of the rats with 100, 300 and 900 mg/kg of B-Hb produced a significant decrease in the volume of gastric secretion 5.6 ± 0.63, 5.5 ± 0.75 and 4.7 ± 0.58 mL respectively as compared to the control group [analysis of variance (ANOVA) F = 4.77, P < 0.05]. The lesion area in the control group was found to be 22.4 ± 3.2 mm2 six hours after the administration of indomethacin. Treatment of rats with B-Hb at doses of 100 mg/kg (24.3 ± 3.29 mm2), 300 mg/kg (16.2 ± 1.45 mm2) and 900 mg/kg (12.6 ± 1.85 mm2) produced a dose dependent decreased the lesion scores (ANOVA F = 4.50, P < 0.05). The ulcer index following one hour after 1 mL ethanol was 7.1 ± 0.31. Pretreatment of rats with B-Hb at the doses of 100 mg/kg (2.5 ± 0.42), 300 mg/kg (2.1 ± 0.4) and 900 mg/kg (0.7 ± 0.21) significantly inhibited the formation of gas

关键词： Bovine hemoglobin Gastric ulcers Indomethacin Ethanol Ischemic injury Rats

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Delayed Grafting as a Valid Technique in Burn Management: Experience from a Burn Unit in Oman

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Modern Plastic Surgery（现代整形外科） 2024年第1期

作者： Taif alsafy Joe Jacob Anish Thadathil Elias Ahmed Salah El Din Salim Mohammed Ghawas Majid Tariq saud albusaidi Ahmed al Jabri al Anood al Hadhrami Mutazz Obeid al Khaldi Moath Abdelhamid Shummo Karim Mohamed Haridi Aml Eid Saleh Said al-Busaidi

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Pregnancy Outcomes in Pregnant Patients with COVID-19: A Multicenter Study

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Open Journal of Obstetrics and Gynecology 2023年第1期13卷 13-25页

作者： Mufareh Asiri Salem al Suwaidan Afaf altweijry Abdulsamed almdefa Saad alonze Theba saud Maternity Hospital King Saud Medical City Riyadh KSA Research Center King Saud Medical City Riyadh KSA

Introduction: Acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was declared a pandemic by the World Health Organization (WHO) on March 11, 2020. The presence of COVID-19 in a pregnant patient can raise concerns, as other types of coronaviruses were associated with many adverse outcomes. This study aims to study the effect of COVID-19 on pregnancy outcomes. Methods: A prospective cross-sectional cohort study within Central First Health Care Cluster (multicentric), Riyadh, included all pregnant women with a singleton pregnancy diagnosed as COVID-19-positive. The primary outcome is the severity of COVID during pregnancy in terms of ICU admission and mortality. The participants were divided into three groups (preterm less than 37 weeks, the term from 37 - 40 weeks, and late-term after 40 weeks. In addition, parameters included: Gestational age at diagnosis, symptoms at presentation (cough, fever), presence of congenital anomalies, IUFD, mode of delivery, presence of PPH, newborn Apgar score, cord PH, need for NICU admission, and the newborn becoming infected with COVID-19 were also measured as secondary outcomes. Results: One hundred pregnant, COVID-19-positive women met the inclusion criteria;the average age of participants was 31.2 years (SD ± 6.4). Asymptomatic patients represented 54% of participants. Most of the deliveries occurred at 36 weeks or less as preterm delivery. Cesarean sections represented 55% of our population. Four-term mothers (12.5%), more than 37 weeks, need ICU admission compared to 13 (25%) preterm cases diagnosed with preeclampsia. No maternal death. Conclusion: COVID-19 during pregnancy can increase ICU admission. A high rate of preterm labor, miscarriage, cesarean section, and newborn testing positive for COVID-19 were observed among our population. No congenital anomalies related to COVID-19 were observed.

关键词： Pregnancy COVID-19 Preterm Labor Miscarriage saudi Arabia

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