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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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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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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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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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Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

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Computer modeling in Engineering & Sciences 2024年第8期140卷 1539-1562页

作者： Samar m.Alqhtani Toufique A.Soomro Faisal Bin Ubaid Ahmed Ali muhammad Irfan Abdullah A.asiri Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Electronic Engineering The University of LarkanoLarkanaSindh75660Pakistan Computer Science Department Sukkur IBA UniversitySukkurSindh65200Pakistan Eletrical Engineering Department Sukkur IBA UniversitySukkurSindh65200Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various *** resonance imaging(mRI)and computed tomography(CT)are utilized to capture brain *** plays a crucial role in the diagnosis of brain tumors and the examination of other brain ***,manual assessment of mRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely ***,early diagnosis of brain tumors is intricate,necessitating the use of computerized *** research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain *** proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-means(FCm)clustering technique in the third *** final step involves classification using the Support Vector machine(SVm)*** classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced magnetic Resonance Imaging(CE-mRI)*** proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of ***,this method exhibits a shorter processing time of 0.44 s compared to existing *** performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and *** enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classi

关键词： Brain tumor magnetic resonance imaging principal component analysis fuzzy c-clustering support vector machine

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PHOTOCHROmIC PROPERTIES OF (E)-DICYCLOPROPYLmETHYLENE-(2,5-DImETHYL-3-FURYLETHYLIDENE)-SUCCINIC ANHYDRIDE DOPED IN POLYSTYRENE THIN FILmS

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Chinese Journal of Polymer Science 2008年第4期26卷 433-441页

作者： Abood A.Bahajaj Abdullah m.asiri Chemistry Department Faculty of ScienceHadhramout University of Science & TechnologyP.O.Box 50512MukallaRepublic of Yemen Chemistry Department Faculty of ScienceKing Abdul Aziz UniversityJeddah 21413P.O.Box 80203Saudi Arabia

Fulgide 1-E doped in polystyrene polymer films was heated at various annealing *** irradiation with UV light(366 nm),fulgide 1-E undergoes a conrotatory ring closure to the pink colored closed form *** later color was switched back to the original color when the films were irradiated with white *** kinetics of photocoloration and photobleaching processes were followed spectrophotometrically by monitoring the absorbance of the ring closed product 1-C at itsλ_(max) of 525 *** first-...

关键词： Fulgides Photochromism Polymer film Photobleaching Photoreaction kinetics Fatigue resistant

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Synthesis of Dy_2O_3 nanoparticles via hydroxide precipitation: effect of calcination temperature

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Journal of Rare Earths 2014年第3期32卷 259-264页

作者： Bahaa m.Abu-Zied Abdullah m.asiri Chemistry Department Faculty of Science King Abdulaziz University Center of Excellence for Advanced materials Research (CEAmR) King Abdulaziz University Chemistry Department Faculty of Science Assiut University

This work described the preparation of dysprosium oxide, Dy203, nanoparticles using the homogeneous precipitation method. Dy3＋ ions were precipitated using NaOH solution. The obtained product was filtered, dried, and then calcined for 1 h at the temperature range of 300-700 ℃ in static air. The calcination temperature of the Dy-precttrsor was chosen based on its decomposi- tion as indicated by the TGA analysis. The crystalline structure and surface morphology of the calcined solids were studied by X-ray diffraction （XRD）, field emission scanning electron microscopy （FE-SEm）, transmission electron microscopy （TEm） and X-ray pho- toelectron spectroscopy （XPS）. The obtained results revealed that Dy203 with crystallites size of 11-21 nm was formed at 500 ℃. Such value increased to 25-37 nm for the sample calcined at 700 ℃.

关键词： nanocrystalline dysprosium oxide Dy203 precipitation method Dy203 nanoparticles rare earths

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Genesis of nanocrystalline Ho_2O_3 via thermal decomposition of holmium acetate: Structure evolution and electrical conductivity properties

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Journal of Rare Earths 2019年第2期37卷 185-192页

作者： Bahaa m.Abu-Zied Abdullah m.asiri Center of Excellence for Advanced materials Research (CEAmR) King Abdulaziz University Chemistry Department Faculty of Science King Abdulaziz University Chemistry Department Faculty of Science Assiut University

This study focuses on the preparation of nanostructured holmium oxide via the decomposition of holmium acetate precursor utilizing the non-isothermal strategy. Thermogravimetric analysis(TGA) was used to follow up the various thermal events involved in the decomposition process. Dehydration completes approximately at 150℃, which is followed by the decomposition of the anhydrous acetate leading to the formation of holmium oxide. Based on the TGA results the acetate precursor was heated non-isothermally at the temperature range of 150 e700℃. The obtained solids were characterized using powder X-ray diffraction(XRD), X-ray photoelectron spectroscopy(XPS), Fourier transform infrared spectroscopy(FT-IR), field-emission scanning electron microscopy(FE-SEm) and transmission electron microscopy(TEm). It is found that nanocrystalline Ho_2 O_3 starts to form at 500℃ and presents the only phase detected at the 500 e700℃ range. The electrical conductivity of the solids that form at the temperature range of 300 e700℃ was investigated. The obtained values were correlated with the observed structural modifications accompanying the heat treatment. The electrical conductivity of the Ho_2 O_3 samples prepared at 500, 600 and 700℃ reaches the values of 1.92 × 10^(-7), 1.61 × 10^(-7) and 8.33 × 10^(-8) Ω^(-1)cm^(-1) at a measuring temperature of 500℃, respectively. These values are potentially advantageous for high-resistivity devices.

关键词： Nanocrystalline Ho2O3 Thermal decomposition Ho2O3 Holmium oxide Rare earth oxides Nanomaterials

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Interaction of antipsychotic drug with novel surfactants: micellization and binding studies

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Chinese Journal of Chemical Engineering 2018年第3期26卷 566-573页

作者： Naved Azum malik Abdul Rub Abdullah m.asiri Center of Excellence for Advanced materials Research King Abdulaziz University Chemistry Department Faculty of Science King Abdulaziz University

The interaction of cationic gemini surfactants(alkanediyl-α,ω-bis(alkyl dimethylammonium bromide)) with an antipsychotic drug(chlorpromazine hydrochloride(CPZ)) has been investigated. Various micellar and interfacial parameters have been deliberated by surface tension measurement to report the nature of interactions between drug and novel surfactant mixtures. The behavior of mixed systems, their compositions and activities of components have been analyzed in the light of Rubingh's theory. The results indicate synergism in the binary *** binding study between CPZ and surfactants has been done by spectroscopic techniques such as UV–visible and fluorescence. The results are discussed in the light of the use of gemini surfactants as promising drug delivery agents for phenothiazine drugs, and hence, improve their bioavailability.

关键词： Gemini surfactants Antipsychotic drug Chlorpromazine hydrochloride mixed micellization

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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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