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Big Data Analytics with Artificial Intelligence Enabled Environmental Air Pollution monitoring Framework

Computers, materials & Continua 2022年第11期73卷 3235-3250页

作者： manar Ahmed Hamza Hadil Shaiba Radwa marzouk Ahmad Alhindi mashael m.asiri Ishfaq Yaseen Abdelwahed motwakel mohammed Rizwanullah Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia&Research and InnovationSalla Holding LimitedMakkahSaudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil Asir62529Saudi Arabia

Environmental sustainability is the rate of renewable resourceharvesting, pollution control, and non-renewable resource exhaustion. Airpollution is a significant issue confronted by the environment particularlyby highly populated countries like India. Due to increased population, thenumber of vehicles also continues to increase. Each vehicle has its individualemission rate;however, the issue arises when the emission rate crosses thestandard value and the quality of the air gets degraded. Owing to the technological advances in machine learning (mL), it is possible to develop predictionapproaches to monitor and control pollution using real time data. With thedevelopment of the Internet of Things (IoT) and Big Data Analytics (BDA),there is a huge paradigm shift in how environmental data are employed forsustainable cities and societies, especially by applying intelligent *** this view, this study develops an optimal AI based air quality prediction andclassification (OAI-AQPC) model in big data environment. For handling bigdata from environmental monitoring, Hadoop mapReduce tool is *** addition, a predictive model is built using the hybridization of ARImAand neural network (NN) called ARImA-NN to predict the pollution *** improving the performance of the ARImA-NN algorithm, the parametertuning process takes place using oppositional swallow swarm optimization(OSSO) algorithm. Finally, Adaptive neuro-fuzzy inference system (ANFIS)classifier is used to classify the air quality into pollutant and non-pollutant.A detailed experimental analysis is performed for highlighting the betterprediction performance of the proposed ARImA-NN method. The obtainedoutcomes pointed out the enhanced outcomes of the proposed OAI-AQPCtechnique over the recent state of art techniques.

关键词： Sustainability environmental air quality predictive model pollution monitoring statistical models artificial intelligence

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metaheuristics Enabled Clustering with Routing Scheme for Wireless Sensor Networks

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Computers, materials & Continua 2022年第12期73卷 5491-5507页

作者： mashael m.asiri Saud S.Alotaibi Dalia H.Elkamchouchi Amira Sayed A.Aziz manar Ahmed Hamza Abdelwahed motwakel Abu Sarwar Zamani Ishfaq Yaseen 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 Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Digital media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Wireless Sensor Network(WSN)is a vital element in Internet of Things(IoT)as the former enables the collection of huge quantities of data in energy-constrained *** offers independent access to the target region and performs data collection in an effective *** energy constraints remain a challenging issue in WSN since it operates on in-built *** studies conducted earlier recommended that the energy spent on communication processmust be considerably reduced to improve the efficiency of *** organization and optimal selection of the routes are considered as NP hard optimization problems which can be resolved with the help of metaheuristic *** and routing are considered as effective approaches in enhancing the energy effectiveness and lifespan of *** this background,the current study develops an Improved Duck and Traveller Optimization(IDTO)-enabled cluster-based multi-Hop Routing(IDTOmHR)technique for ***,IDTO algorithm is exploited for the selection of Cluster Head(CH)and construction of ***,Artificial Gorilla Troops Optimization(ATGO)technique is also used to derive an optimal set of routes to the *** clustering and routing approaches derive a fitness function with the inclusion of multiple input *** proposed IDTOmHR model was experimentally validated for its performance under different *** extensive experimental results confirmed the better performance of IDTOmHR model over other recent approaches.

关键词： Wireless sensor network clustering routing metaheuristics energy efficiency lifetime

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malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

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Computers, materials & Continua 2023年第1期74卷 607-621页

作者： Anwer mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.mohamed mohamed K.Nour mashael m.asiri Ali m.Al-Sharafi mahmoud Othman Abdelwahed motwakel Department of Electrical and Computer Engineering International Islamic University Malaysia 53100 Kuala LumpurMalaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital *** Uniform Resource Locators(URLs)can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their *** may result in compromised security of the systems,scams,and other such *** attacks hijack huge quantities of the available data,incurring heavy financial *** the same time,machine Learning(mL)and Deep Learning(DL)models paved the way for designing models that can detect malicious URLs accurately and classify *** this motivation,the current article develops an Artificial Fish Swarm Algorithm(AFSA)with Deep Learning Enabled malicious URL Detection and Classification(AFSADL-mURLC)*** presented AFSADL-mURLC model intends to differentiate the malicious URLs from genuine *** attain this,AFSADL-mURLC model initially carries out data preprocessing and makes use of glove-based word embedding *** addition,the created vector model is then passed onto Gated Recurrent Unit(GRU)classification to recognize the malicious ***,AFSA is applied to the proposed model to enhance the efficiency of GRU *** proposed AFSADL-mURLC technique was experimentally validated using benchmark dataset sourced from Kaggle *** simulation results confirmed the supremacy of the proposed AFSADL-mURLC model over recent approaches under distinct measures.

关键词： malicious URL cybersecurity deep learning machine learning metaheuristics gated recurrent unit

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Hybrid metaheuristics Feature Selection with Stacked Deep Learning-Enabled Cyber-Attack Detection model

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Computer Systems Science & Engineering 2023年第5期45卷 1679-1694页

作者： mashael m.asiri Heba G.mohamed mohamed K Nour mesfer Al Duhayyim Amira Sayed A.Aziz Abdelwahed motwakel Abu Sarwar Zamani mohamed I.Eldesouki Department of Computer Science College of Science&Art atMahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Digital media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Due to exponential increase in smart resource limited devices and high speed communication technologies,Internet of Things(IoT)have received significant attention in different application ***,IoT environment is highly susceptible to cyber-attacks because of memory,processing,and communication *** traditional models are not adequate for accomplishing security in the IoT environment,the recent developments of deep learning(DL)models find *** study introduces novel hybrid metaheuristics feature selection with stacked deep learning enabled cyber-attack detection(HmFS-SDLCAD)*** major intention of the HmFS-SDLCAD model is to recognize the occurrence of cyberattacks in the IoT *** the preliminary stage,data pre-processing is carried out to transform the input data into useful *** addition,salp swarm optimization based on particle swarm optimization(SSOPSO)algorithm is used for feature selection ***,stacked bidirectional gated recurrent unit(SBiGRU)model is utilized for the identification and classification of ***,whale optimization algorithm(WOA)is employed for optimal hyperparameter optimization *** experimental analysis of the HmFS-SDLCAD model is validated using benchmark dataset and the results are assessed under several *** simulation outcomes pointed out the improvements of the HmFS-SDLCAD model over recent approaches.

关键词： Cyberattacks security deep learning internet of things feature selection data classification

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Historical Arabic Images Classification and Retrieval Using Siamese Deep Learning model

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Computers, materials & Continua 2022年第7期72卷 2109-2125页

作者： manal m.Khayyat Lamiaa A.Elrefaei mashael m.Khayyat Computer Science Department Umm Al-Qura UniversityMakkahSaudi Arabia Computer Science Department King Abdulaziz UniversityJeddahSaudi Arabia Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairoEgypt Department of Information Systems and Technology College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia

Classifying the visual features in images to retrieve a specific image is a significant problem within the computer vision field especially when dealing with historical faded colored ***,there were lots of efforts trying to automate the classification operation and retrieve similar images *** reach this goal,we developed a VGG19 deep convolutional neural network to extract the visual features from the images ***,the distances among the extracted features vectors are measured and a similarity score is generated using a Siamese deep neural *** Siamese model built and trained at first from scratch but,it didn’t generated high evaluation ***,we re-built it from VGG19 pre-trained deep learning model to generate higher evaluation ***,three different distance metrics combined with the Sigmoid activation function are experimented looking for the most accurate method formeasuring the similarities among the retrieved *** that the highest evaluation parameters generated using the Cosine distance ***,the Graphics Processing Unit(GPU)utilized to run the code instead of running it on the Central Processing Unit(CPU).This step optimized the execution further since it expedited both the training and the retrieval time *** extensive experimentation,we reached satisfactory solution recording 0.98 and 0.99 F-score for the classification and for the retrieval,respectively.

关键词： Visual features vectors deep learning models distance methods similar image retrieval

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Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones model

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Computers, materials & Continua 2021年第3期66卷 3161-3182页

作者： Ihsan Jasim Hussein m.A.Burhanuddin mazin Abed mohammed mohamed Elhoseny Begonya Garcia-Zapirain marwah Suliman maashi mashael S.maashi BIOCORE Research Group Universiti Teknikal Malaysia MelakaMelaka76100Malaysia Director of UTem International Centre BIOCORE Research GroupUniversiti Teknikal Malaysia MelakaMelaka76100Malaysia College of Computer Science and Information Technology University of AnbarRamadi31001Iraq Department of Computer Science College of Computer Information TechnologyAmerican University in the Emirates503000United Arab Emirates eVIDA Laboratory University of DeustoBilbao48007Spain medical Laboratory Science Department King Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia

One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of ***,this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound *** main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer *** addition,proposed an approach that can efficiently generate region-of-interest(ROI)and new features that can be used in characterizing lesion *** study uses two databases in training and testing the proposed segmentation *** breast cancer database contains 250 images,while that of the ovarian tumor has 100 images obtained from several hospitals in *** of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound *** segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%.By contrast,the segmentation result of the proposed system in the ovarian tumor data set was 79.2%.In the classification results,we achieved 95.43%accuracy,92.20%sensitivity,and 97.5%specificity when we used the breast cancer data *** the ovarian tumor data set,we achieved 94.84%accuracy,96.96%sensitivity,and 90.32%specificity.

关键词： Viola-Jones model breast cancer segmentation ovarian tumor ovarian tumor segmentation breast cancer ultrasound images active contour cascade model

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Exendin-4 inhibits the survival and invasiveness of two colorectal cancer cell lines via suppressing GS3Kβ/β-catenin/NF-κB axis through activating SIRT1

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BIOCELL 2021年第5期45卷 1337-1353页

作者： ATTALLA F.EL-KOTT AYmAN E.EL-KENAWY EmAN R.ELBEALY ALI S.ALSHEHRI HEBA S.KHALIFA mashael mOHAmmED BIN-mEFERIJ EHAB E.mASSOUD AmIRA m.ALRAmLAWY Biology Department College of ScienceKing Khalid UniversityAbha61413Saudi Arabia Zoology Department College of ScienceDamanhour UniversityDamanhour22511Egypt Pathology Department College of MedicineTaif UniversityTaif11099Saudi Arabia Biology Department College of Science for GirlsKing Khalid UniversityAbha61413Saudi Arabia Biology Department College of SciencePrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Biology Department Faculty of Science and ArtsDahran AljnoubKing Khalid UniversityAbha61413Saudi Arabia Research Center for Advanced materials Science King Khalid UniversityAbha61413Saudi Arabia Agriculture Research Centre SoilWater and Environment Research InstituteGiza12619Egypt mansoura Research Centre for Cord Stem Cell(mARC-CSC) Stem Cells BankChildren’s HospitalMansoura UniversityMansoura35516Egypt

This study examined if the anti-tumorigenesis effect of Exendin-4 in HT29 and HCT116 colorectal cancer(CRC)involves modulation of SIRT1 and Akt/GSR3K/β-catenin/NF-κB ***29 and HCT116 cells were treated either with increasing levels of Exendin-4(0.0-200μm)or with Exendin-4(at its IC50)in the presence or absence of EX-527(10μm/a selective SIRT1 inhibitor)or Exendin-4(9-39)amide(E(9-39)A)(1μm/an Exendin-4 antagonist).In a dose-dependent manner,Exendin-4 inhibited cell survival,but enhanced levels of lactate dehydrogenase(LDH)and single-stranded DNA(ssDNA)in both HT29 and *** both cell lines and at it has an IC50(45μm for HT29 and 35μm for HCT1165),Exendin-4 also significantly reduced cell survival,migration,and invasion of both cell types,with no effect on the expression GLP-1 receptors(GLPRs)nor of the activity of *** these doses,Exendin-4 also increased the expression of SIRT1 but reduced the acetylation of NF-κB and the expression of Bax and cleaved caspase-3 and in both cell ***,protein levels of p-GS3Kβ(Ser9),total and acetylatedβ-catenin,and Anix2 were significantly decreased,but levels of p-GS3Kβ(Ser9)and p-β-catenin(Ser33/37/Thr41)were significantly increased in both HT29 and HCT116-exendin-4 treated *** the effects exerted by Exendin-4 were completely prevented by Ex527 or E(9-39)*** conclusion,Exendin-4 suppresses the tumorigenesis of HT29 and HCT116 CRC cell activation of GS3Kβ-induced inhibition ofβ-catenin and NF-κβin a SIRT1-dependent mechanism.

关键词： Colorectal cancer Exendin-4 SIRT1 GS3Kβ β-catenin NF-κB

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Pterostilbene induces cell apoptosis and inhibits lipogenesis in SKOV3 ovarian cancer cells by activation of AmPK-induced inhibition of Akt/mTOR signaling cascade

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BIOCELL 2021年第1期45卷 89-101页

作者： ATTALLA EL-KOTT EmAN ELBEALY FAHmY ELSAID HAITHAm EL-mEKKAWY ABD-EL-KARIm ABD-LATEIF ABDULALI TAWEEL HEBA KHALIFA AHmAD KANDEEL KAREEm mORSY ESSAm IBRAHIm mashael mOHAmmED BIN-mEFERIJ Biology Department College of ScienceKing Khalid UniversityAbha61413Saudi Arabia Zoology Department College of ScienceDamanhour UniversityDamanhour22511Egypt Biology Department College of Science for GirlsKing Khalid UniversityAbha61413Saudi Arabia Zoology Department College of ScienceMansoura UniversityMansoura35511Egypt Zoology Department College of ScienceFayoum UniversityFayoum63511Egypt Zoology Department Faculty of ScienceAl-Zawia UniversityAl-ZawiaLibya Biology Department Faculty of ScienceCairo UniversityCairo12611Egypt Research Center for Advanced materials Science(RCAmS) King Khalid UniversityAbha61413Saudi Arabia Blood Products Quality Control and Research Department National Organization for Research and Control of BiologicalsCairo12611Egypt Biology Department College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

This study investigates if the anti-tumor effect of Pterostilbene in the SKOV3 ovarian cancer(OC)cell line involves inhibition of cell metabolism and tested in this effect involves modulating AmPK and Akt-induced regulation of ***,SKOV3 cells were cultured in the humidified conditions in DmEm media for 24 h with or without increasing concentration of ***,the cells were incubated with Pterostilbene(IC_(50)=50μm)under similar conditions with or without pre-incubation with Dorsomorphin,an AmPK *** a dose-dependent manner,Pterostilbene inhibited SKOV3 cell survival and increased their lysate levels of lactate dehydrogenase(LDH)and single-stranded DNA(ssDNA).When SKOV3 cells were treated with 50μm Pterostilbene,Pterostilbene significantly suppressed cell migration and invasion,reduced lysate levels of lactic acid and the optical density of Oil Red O staining,and increased lysate glucose *** also increased levels of malondialdehyde(mDA),reactive oxygen species(ROS),and induced intrinsic cell apoptosis by upregulating protein levels of Bax and cleaved caspase-3 and reducing protein levels of ***,Pterostilbene reduced mRNA levels of sterol regulatory element-binding protein 1(SREBP-1),fatty acid synthase(FAS),acetyl CoA carboxylase-1(ACC-1),and AmP-activated protein kinase(AmPK).Furthermore,Pterostilbene increased the protein levels of p-AmPK,p-p53,p-raptor,p-TSC-2,but significantly decreased protein levels of p-Akt,p-TSC-2,p-mTOR,p-S6K1,and *** with Dorsomorphin(CC)abolished all the anti-tumorigenesis effects afforded by Pterostilbene and prevented Pterostilbene-induced phosphorylation of Akt,p53,and *** conclusion,the tumorsuppressive effect of Pterostilbene in SKOV3 cells involves the induction of ROS and inhibition of dysregulation cell metabolism mainly due to AmPK-induced Akt-dependent or independent suppression of mTOR.

关键词： Pterostilbene Ovarian cancer Lipogenesis Apoptosis AmPK

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managements of Special Health Care Needs Patients from Dental Assistant Point

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International Journal of Clinical medicine 2023年第2期14卷 59-64页

作者： Abdullah m. G. Al Enazi Dalal Khalaf Al Enazi Haya Nasser Al Duaida Ashwag Bander Al Bakheet mashael muteb Al Ghobain Abeer Salem Al Harbi Department of Laboratory and Pathology King Abdulaziz Medical City Riyadh Saudi Arabia Department of Dentistry King Fahad Medical City Riyadh Saudi Arabia

The main role of this article is to describe dental assistants and the dental department’s role in the dental delivery system for Special health care needs (SCHN) by creating a structure to educate the dental assisting profession and project factors that have a significant impact on the dental assistant. The dental assisting management of SCHN patient’s profession including dental Assistance general responsibilities, regulation credentialing, and education. The SCHN patient’s management creates a challenge in all area of the dental department. Handling patient with a SCHN is underexplored throughout the dental staff especially when there is not enough theoretical foundation, education and training to deal with this category of patients. SCHN patients are defined as individuals who have abnormal behavioral impairments, mental conditions or/and chronic disease or abnormal laboratory results. Thus, the aim of this article is to guide all dental assistance staff in the best dental management choice for SCHN patients. All health care teams are part of patient care in most medical settings. A work environment supportive ensures positive outcomes for patient care. This article was considered as those categories of patients required more care and special way to deal with, moreover language, age, gender and education level of the patient are also considered significant parries. Not many studies were found to support the care of SCHN patients in dentistry department. Taking experience from other area help to design a system to handle the SCHN even if it requires hiring a Dentist who is specialized in that filed.

关键词： Dental management Dental Assistant Dentist Special Needs Special Health Care Need

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