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Journal of Water Resource and Protection 2015年第7期7卷 588-596页

作者： majed Ibrahim Barbara Koch Geographic Information System and Remote Sensing Department Al al-Bayt University Mafraq Jordan Remote Sensing and Landscape Department (Felis) Freiburg University Freiburg Germany

Groundwater is the main source of water supply in Jordan. Due to lower precipitation rates in recent years, the surface water is increasingly limited. Research on groundwater vulnerability helps protect this main source of water supply and assists in the development of plans to confront the deterioration and contamination of aquifers from wastewater and agricultural activities in order to protect groundwater for future generations. The important factors to determine groundwater vulnerability are environmental conditions;hence the spatial conditions in arid to semi-arid areas must be taken into account when applying different models. The aquifer vulnerability has been assessed in Jordan by using the DRASTIC method;remote sensing and Geographic Information System (GIS) data were used to derive and process data. The DRASTIC index was used with seven parameters to describe physical characteristics of the aquifers. It is concluded that about 34% of the area was considered to be of moderate vulnerability, but the share increased to 60% after modifying the index. While high vulnerability was at 25% of the total area, it decreased to 6.3% with the modified index;therefore, urgent pollution prevention measures should be taken for every kind of relevant activity within the whole basin.

关键词： Land Use Groundwater DRASTIC Index GIS SAR

Brain Tumor Detection and Classification Using PSO and Convolutional Neural Network

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Computers, Materials & Continua 2022年第12期73卷 4501-4518页

作者： Muhammad Ali Jamal Hussain Shah Muhammad Attique Khan majed Alhaisoni Usman Tariq Tallha Akram Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadWah CampusPakistan Departmnt of Computer Science HITEC University TaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of EE COMSATS University IslamabadWah CampusPakistan Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Tumor detection has been an active research topic in recent years due to the high mortality *** vision(CV)and image processing techniques have recently become popular for detecting tumors inMRI *** automated detection process is simpler and takes less time than manual *** addition,the difference in the expanding shape of brain tumor tissues complicates and complicates tumor detection for *** proposed a newframework for tumor detection aswell as tumor classification into relevant categories in this *** tumor segmentation,the proposed framework employs the Particle Swarm Optimization(PSO)algorithm,and for classification,the convolutional neural network(CNN)*** preprocessing techniques such as noise removal,image sharpening,and skull stripping are used at the start of the segmentation ***,PSO-based segmentation is *** the classification step,two pre-trained CNN models,alexnet and inception-V3,are used and trained using transfer *** a serial approach,features are extracted from both trained models and fused features for final *** classification,a variety of machine learning classifiers are *** dice values on datasets BRATS-2018 and BRATS-2017 are 98.11 percent and 98.25 percent,respectively,whereas average jaccard values are 96.30 percent and 96.57%(Segmentation Results).The results were extended on the same datasets for classification and achieved 99.0%accuracy,sensitivity of 0.99,specificity of 0.99,and precision of ***,the proposed method is compared to state-of-the-art existingmethods and outperforms them.

关键词： Magnetic resonance imaging(MRI) tumor segmentation deep learning features extraction classification

Deep Learning and Improved Particle Swarm Optimization Based Multimodal Brain Tumor Classicatio

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Computers, Materials & Continua 2021年第7期68卷 1099-1116页

作者： Ayesha Bin T.Tahir Muhamamd Attique Khan majed Alhaisoni Junaid Ali Khan Yunyoung Nam Shui-Hua Wang Kashif Javed Department of Computer Science HITEC UniversityTaxila47040Pakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea School of Architecture Building and Civil Engineering Loughborough UniversityLoughboroughLE113TUUK Department of Robotics SMME NUSTIslamabadPakista

Background:A brain tumor reects abnormal cell ***:Surgery,radiation therapy,and chemotherapy are used to treat brain tumors,but these procedures are painful and *** resonance imaging(MRI)is a non-invasive modality for diagnosing tumors,but scans must be interpretated by an expert ***:We used deep learning and improved particle swarm optimization(IPSO)to automate brain tumor *** scan contrast is enhanced by ant colony optimization(ACO);the scans are then used to further train a pretrained deep learning model,via transfer learning(TL),and to extract features from two dense *** fused the features of both layers into a single,more informative *** IPSO algorithm selected the optimal features,which were classied using a support vector ***:We analyzed high-and low-grade glioma images from the BRATS 2018 dataset;the identication accuracies were 99.9%and 99.3%,***:The accuracy of our method is signicantly higher than existing techniques;thus,it will help radiologists to make diagnoses,by providing a“second opinion.”

关键词： Brain tumor contrast enhancement deep learning feature selection classication

Synthesis of new two-dimensional titanium carbonitride Ti_(2)C_(0.5)N_(0.5)T_(x)MXene and its performance as an electrode material for sodium-ion battery

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InfoMat 2021年第12期3卷 1422-1430页

作者： Kun Liang Anika Tabassum Ahmad majed Chaochao Dun Feipeng Yang Jinghua Guo Kaitlyn Prenger Jeffrey JUrban Michael Naguib Department of Physics and Engineering Physics Tulane UniversityNew OrleansLouisianaUSA The Molecular Foundry Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Advanced Light Source Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA

Two-dimensional(2D)layered transition metal carbides/nitrides,called MXenes,are attractive alternative electrode materials for electrochemical energy *** to their metallic electrical conductivity and low ion dif-fusion barrier,MXenes are promising anode materials for sodium-ion batteries(SIBs).Herein,we report on a new 2D carbonitride MXene,viz.,Ti_(2)C_(0.5)N_(0.5)T_(x)(T_(x) stands for surface terminations),and the only second carbonitride after Ti_(3) CNT_(x) so far.A new type of in situ HF(HCl/KF)etching condition was employed to synthesize multilayer Ti_(2)C_(0.5)N_(0.5)T_(x) powders from Ti_(2)AlC_(0.5)N_(0.5).Spontaneous intercalation of tetramethylammonium followed by sonication in water allowed for large-scale delamination of this new titanium carbonitride into 2D *** Ti_(2)C_(0.5)N_(0.5)T_(x) powders showed higher specific capac-ities and larger electroactive surface area than those of Ti_(2)CT_(x) ***-layer Ti_(2)C_(0.5)N_(0.5)T_(x) powders show a specific capacity of 182 mAh g^(-1) at 20 mA g^(-1),the highest among all reported MXene electrodes as SIBs with excellent cycling stability.

关键词： MXene sodium-ion battery titanium carbonitride two-dimensional

Integrating Lithostratigraphic Units and GIS-Analysis Techniques to Modified Surface Water Quality Index

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Journal of Environmental Protection 2016年第8期7卷 1104-1112页

作者： majed Ibrahim Habes Al-Mashakbeh Geographic Information System and Remote Sensing Department Al-Albayt University Mafraq Jordan Applied of Earth and Environmental Sciences Department Al-Albayt University Mafraq Jordan

The purpose of this study is to investigate the effect of using Bicarbonate and Calcium parameters as alternatives to the lithostratigraphic units covered the catchment area, on water quality index (WQI) values that have been implemented with GIS technique at Wadi Al-Arab Dam. The analyzed results (by WQI method) have been used to depict water quality for the two approaches. Based on physico-chemical parameters, the calculated values for WQI over the 3-year for study period were 169, 168, and 157, respectively. While the WQI values were 184, 183 and 172, respectively, as a result of incorporated Bicarbonate and Calcium parameters in WQI calculations that significantly contributed to increasing the WQI. The elevated values may be attributed to the influence of carbonate stone dissolution and mechanical erosion under weathering conditions that are prevalent during winter season in the catchment area. As a consequence of lithostratigraphic unites product and GIS technique integration and normalization processes, most of water quality ranks are good and only autumn season has poor water quality in the 2012 and 2013, while in 2014 it has good water quality in the same season. The WQI values increase in general trend from winter to autumn seasons during the study period that may be referred to outflow by daily consumption, evaporation rising, and seepage water. The analysis shows that the modified water quality values of the Wadi Al-Arab Dam Reservoir (WADR) vary after using Bicarbonate and Calcium parameters by constant value. Generally, the results signify that the WADR is not polluted based on the physical and chemical characteristics of water.

关键词： GIS Lithostratigraphic Units Water Quality Index Surface Water Reservoir Seasonality Irbid Wadi Al-Arab Dam Jordan

Giant two-photon absorption of anatase TiO_(2) in Au/TiO_(2) core-shell nanoparticles

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Photonics Research 2023年第7期11卷 1303-1313页

作者： LIJIE WANG TSZ HIM CHOW MALTE OPPERMANN JIANFANG WANG majed CHERGUI Laboratory of Ultrafast Spectroscopy ISIC and Lausanne Centre for Ultrafast Science(LACUS)École Polytechnique Fédérale de Lausanne(EPFL)CH-1015 LausanneSwitzerland Department of Physics The Chinese University of Hong KongShatinHong Kong SARChina Division of Physical Science and Engineering King Abdullah University of Science and Technology(KAUST)ThuwalKingdom of Saudi Arabia Chemistry Department University of Basel4001 BaselSwitzerland

We report on deep-to-near-UV transient absorption spectra of core-shell Au∕SiO_(2) and Au∕TiO_(2) nanoparticles(NPs) excited at the surface plasmon resonance of the Au core, and of UV-excited bare anatase TiO_(2) NPs. The bleaching of the first excitonic transition of anatase TiO_(2) at ~3.8 e V is a signature of the presence of electrons/holes in the conduction band(CB)/valence band(VB) of the material. We find that while in bare anatase TiO_(2) NPs, two-photon excitation does not occur up to the highest used fluences(1.34 mJ∕cm^(2)), it takes place in the TiO_(2) shell at moderate fluences(0.18 mJ∕cm^(2)) in Au∕TiO_(2) core-shell NPs, as a result of an enhancement due to the plasmon resonance. We estimate the enhancement factor to be of the order of ~10~8–10~9. Remarkably, we observe that the bleach of the 3.8 eV band of TiO_(2) lives significantly longer than in bare TiO_(2), suggesting that the excess electrons/holes in the conduction/valence band are stored longer in this material.

关键词： material absorption excited

A Hybrid Attention-Based Residual Unet for Semantic Segmentation of Brain Tumor

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Computers, Materials & Continua 2023年第7期76卷 647-664页

作者： Wajiha Rahim Khan Tahir Mustafa Madni Uzair Iqbal Janjua Umer Javed Muhammad Attique Khan majed Alhaisoni Usman Tariq Jae-Hyuk Cha Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadWah CampusPakistan Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Management Information Systems CoBAPrince Sattam Bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

Segmenting brain tumors in Magnetic Resonance Imaging(MRI)volumes is challenging due to their diffuse and irregular ***,2D and 3D deep neural networks have become famous for medical image segmentation because of the availability of labelled ***,3D networks can be computationally expensive and require significant training *** research proposes a 3D deep learning model for brain tumor segmentation that uses lightweight feature extraction modules to improve performance without compromising contextual information or *** proposed model,called Hybrid Attention-Based Residual Unet(HA-RUnet),is based on the Unet architecture and utilizes residual blocks to extract low-and high-level features from MRI *** and Squeeze-Excitation(SE)modules are also integrated at different levels to learn attention-aware features adaptively within local and global receptive *** proposed model was trained on the BraTS-2020 dataset and achieved a dice score of 0.867,0.813,and 0.787,as well as a sensitivity of 0.93,0.88,and 0.83 for Whole Tumor,Tumor Core,and Enhancing Tumor,on test dataset *** results show that the proposed HA-RUnet model outperforms the ResUnet and AResUnet base models while having a smaller number of parameters than other state-of-the-art ***,the proposed HA-RUnet model can improve brain tumor segmentation accuracy and facilitate appropriate diagnosis and treatment planning for medical practitioners.

关键词： MRI volumes residual Unet BraTs-2020 squeeze-excitation(SE)

GaitDONet: Gait Recognition Using Deep Features Optimization and Neural Network

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Computers, Materials & Continua 2023年第6期75卷 5087-5103页

作者： Muhammad Attique Khan Awais Khan majed Alhaisoni Abdullah Alqahtani Ammar Armghan Sara A.Althubiti Fayadh Alenezi Senghour Mey Yunyoung Nam Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakaka 72388Saudi Arabia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of ICT Convergence Soonchunhyang UniversityKorea

Human gait recognition(HGR)is the process of identifying a sub-ject(human)based on their walking *** subject is a unique walking pattern and cannot be simulated by other ***,gait recognition is not easy and makes the system difficult if any object is carried by a subject,such as a bag or *** article proposes an automated architecture based on deep features optimization for *** our knowledge,it is the first architecture in which features are fused using multiset canonical correlation analysis(MCCA).In the proposed method,original video frames are processed for all 11 selected angles of the CASIA B dataset and utilized to train two fine-tuned deep learning models such as Squeezenet and *** transfer learning was used to train both fine-tuned models on selected angles,yielding two new targeted models that were later used for feature *** are extracted from the deep layer of both fine-tuned models and fused into one vector using *** improved manta ray foraging optimization algorithm is also proposed to select the best features from the fused feature matrix and classified using a narrow neural network *** experimental process was conducted on all 11 angles of the large multi-view gait dataset(CASIA B)dataset and obtained improved accuracy than the state-of-the-art ***,a detailed confidence interval based analysis also shows the effectiveness of the proposed architecture for HGR.

关键词： Human gait recognition biometric deep learning features fusion optimization neural network

Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization

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Computers, Materials & Continua 2023年第6期75卷 5379-5393页

作者： Abida Sharif Imran Sharif Muhammad Asim Saleem Muhammad Attique Khan majed Alhaisoni Marriam Nawaz Abdullah Alqahtani Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadPakistan Faculty of Computing Riphah International UniversityFaisalabadPakistan Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science UET TaxilaTaxilaPakistan Department of Software Engineering UET TaxilaTaxilaPakistan Software Engineering Department College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942KSA Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a *** a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid ***,optimal path selection to route traffic between the origin and destination is *** research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network ***,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal *** the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal *** model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective ***,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles.

关键词： Internet of vehicles internet of things fuzzy logic optimization path planning

Dataset of Large Gathering Images for Person Identification and Tracking

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Computers, Materials & Continua 2023年第3期74卷 6065-6080页

作者： Adnan Nadeem Amir Mehmood Kashif Rizwan Muhammad Ashraf Nauman Qadeer Ali Alzahrani Qammer H.Abbasi Fazal Noor majed Alhaisoni Nadeem Mahmood Faculty of Computer and Information System Islamic University of MadinahMadinah42351Saudi Arabia Department of Computer Science and Information Technology Sir Syed University of Engineering and TechnologyKarachi75300Pakistan Department of Computer Science Federal Urdu University of ArtsScience&TechnologyIslamabad45570Pakistan Department of Physics Federal Urdu University of ArtsScience&TechnologyKarachi75300Pakistan James Watt School of Engineering University of GlasgowGlasgowG128QQUK Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science University of KarachiKarachi75270Pakistan

This paper presents a large gathering dataset of images extracted from publicly filmed videos by 24 cameras installed on the premises of Masjid Al-Nabvi,Madinah,Saudi *** dataset consists of raw and processed images reflecting a highly challenging and unconstraint *** methodology for building the dataset consists of four core phases;that include acquisition of videos,extraction of frames,localization of face regions,and cropping and resizing of detected face *** raw images in the dataset consist of a total of 4613 frames obtained fromvideo *** processed images in the dataset consist of the face regions of 250 persons extracted from raw data images to ensure the authenticity of the presented *** dataset further consists of 8 images corresponding to each of the 250 subjects(persons)for a total of 2000 *** portrays a highly unconstrained and challenging environment with human faces of varying sizes and pixel quality(resolution).Since the face regions in video sequences are severely degraded due to various unavoidable factors,it can be used as a benchmark to test and evaluate face detection and recognition algorithms for research *** have also gathered and displayed records of the presence of subjects who appear in presented frames;in a temporal *** can also be used as a temporal benchmark for tracking,finding persons,activity monitoring,and crowd counting in large crowd scenarios.

关键词： Large crowd gatherings a dataset of large crowd images highly uncontrolled environment tracking missing persons face recognition activity monitoring