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| 1 | 3H Dendrimer Nanoparticle Organ/Tumor Distribution显示文摘 | Shraddha S. Nigavekar Lok Yun Sung Mikel Llanes Areej El-Jawahri Theodore S. Lawrence Christopher W. Becker Lajos Balogh Mohamed K. Khan | 2004 | Pharmaceutical Research2004，，3： | 2 |
| 2 | Antioxidant and urease inhibitory C-glycosylflavonoids from Celtis africana显示文摘 | Shagufta Perveen AzzaMuhammed El-Shafae Areej Al-Taweel GhadaAhmed Fawzy Abdul Malik Nighat Afza Mehreen Latif Lubna Iqbal | 2011 | Journal of Asian Natural Products Research2011，，9： | 1 |
| 3 | Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques显示文摘The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity.Vision-based target detection and object classification have been improved due to the development of deep learning algorithms.Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise,well-engineered,and complete detection of objects,scene or events.The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection.In this study we examined to solve these problems described by(1)extracting region-of-interest in the images(2)vehicle detection based on instance segmentation,and(3)building deep learning model based on the key features obtained from input parking images.We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces.Image augmentation techniques were performed using edge detection,cropping,refined by rotating,thresholding,resizing,or color augment to predict the region of bounding boxes.A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling,training,validating and testing on parking video frames through video-camera.The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6%than previous reported methodologies.Moreover,this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection.The results are verified using Python,TensorFlow,OpenCV computer simulation frameworks. | Kashif Iqbal Sagheer Abbas Muhammad Adnan Khan Atifa Ather Muhammad Saleem Khan Areej Fatima Gulzar Ahmad | 2021 | Computers, Materials & Continua2021，，2： | 1 |
| 4 | 基于Cattaneo-Christov热通量模型的倾斜磁驱动Casson纳米流体在径向拉伸板上的流动显示文摘本文以二维倾斜磁驱动Casson纳米流体为对象,研究了其在驻点附近流过径向拉伸板时的传导、热辐射和放热/吸热现象。为了研究传热特性,采用了Cattaneo-Christov热通量模型。利用Buongiorno模型描述纳米流体流动问题方程,以探讨布朗运动、热泳、热滑移和质量滑移条件的影响。采用一组相似变换将研究模型的高阶非线性偏微分方程转化为常微分方程组。利用射击法的效率优势,对所提出的流动问题方程进行了数值管理。通过图表讨论了流动参数对流体速度、浓度和温度的影响。随着Casson参数的增大,流体速度加快,表面摩擦因数也增大;随着磁参数增大,流体的速度减慢。 | Areej FATIMA Muhammad SAGHEER Shafqat HUSSAIN | 2023 | Journal of Central South University2023，30，11： | 1 |
| 5 | Prigerson update on bereavement research:Evidence-based guidelines for the diagnosis and treatment of complicated bereavement显示文摘 | Baohui Zhang Areej MS Eljawhri BS | 2006 | Journal of Palliative Medicine2006，5，5： | 1 |
| 6 | Probing the architecture of the Mycobacterium marinum arylamine N-acetyltransferase active site显示文摘Treatment of latent tuberculosis infection remains an important goal of global TB eradication.To this end,targets that are essential for intracellular survival of Mycobacterium tuberculosis are particularly attractive.Arylamine N-acetyltransferase(NAT)represents such a target as it is,along with the enzymes encoded by the associated gene cluster,essential for mycobacterial survival inside macrophages and involved in cholesterol degradation.Cholesterol is likely to be the fuel for M.tuberculosis inside macrophages.Deleting the nat gene and inhibiting the NAT enzyme prevents survival of the microorganism in macrophages and induces cell wall alterations,rendering the mycobacterium sensitive to antibiotics to which it is normally resistant.To date,NAT from M.marinum(MMNAT)is considered the best available model for NAT from M.tuberculosis(TBNAT).The enzyme catalyses the acetylation and propionylation of arylamines and hydrazines.Hydralazine is a good acetyl and propionyl acceptor for both MMNAT and TBNAT.The MMNAT structure has been solved to 2.1Åresolution following crystallisation in the presence of hydralazine and is compared to available NAT structures.From the mode of ligand binding,features of the binding pocket can be identified,which point to a novel mechanism for the acetylation reaction that results in a 3-methyltriazolo[3,4-a]phthalazine ring compound as product. | Areej M.Abuhammad Edward D.Lowe Elizabeth Fullam Martin Noble Elspeth F.Garman Edith Sim | 2010 | Protein & Cell2010，1，4： | 1 |
| 7 | Aberrant Expression of CD13 Identifies a Subgroup of Standard-Risk Adult Acute Lymphoblastic Leukemia With Inferior Survival显示文摘 | Bakul I. Dalal Areej Al Mugairi Steven Pi Soo Yeon Lee Nikisha S. Khare Jason Pal Adam Bryant Alok P. Vakil Sally Lau Yasser R. Abou Mourad | 2014 | Clinical Lymphoma Myeloma and Leukemia2014，，3： | 1 |
| 8 | Raoultella planticola, a central venous line exit site infection 显示文摘 | Nada B Areej M | 2014 | J Taibah Univ Med Sci2014，9，2： | 1 |
| 9 | A cephalometric study of sella turcica size and morphology among young Iraqi normal population in comparison to patients with maxillary malposed canine显示文摘 | Areej A Lamia A | 2011 | J Bagh College Dentistry2011，23，4： | 1 |
| 10 | Convolutional Neural Network Based Intelligent Handwritten Document Recognition显示文摘This paper presents a handwritten document recognition system based on the convolutional neural network technique.In today’s world,handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users.This technology is also helpful for the automatic data entry system.In the proposed systemprepared a dataset of English language handwritten character images.The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents.In this research,multiple experiments get very worthy recognition results.The proposed systemwill first performimage pre-processing stages to prepare data for training using a convolutional neural network.After this processing,the input document is segmented using line,word and character segmentation.The proposed system get the accuracy during the character segmentation up to 86%.Then these segmented characters are sent to a convolutional neural network for their recognition.The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset.The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%,and for validation that accuracy slightly decreases with 90.42%. | Sagheer Abbas Yousef Alhwaiti Areej Fatima Muhammad A.Khan Muhammad Adnan Khan Taher M.Ghazal Asma Kanwal Munir Ahmad Nouh Sabri Elmitwally | 2022 | Computers, Materials & Continua2022，，3： | 1 |
| 11 | A Trailblazing Framework of Security Assessment for Traffic Data Management显示文摘Connected and autonomous vehicles are seeing their dawn at this moment.They provide numerous benefits to vehicle owners,manufacturers,vehicle service providers,insurance companies,etc.These vehicles generate a large amount of data,which makes privacy and security a major challenge to their success.The complicated machine-led mechanics of connected and autonomous vehicles increase the risks of privacy invasion and cyber security violations for their users by making them more susceptible to data exploitation and vulnerable to cyber-attacks than any of their predecessors.This could have a negative impact on how well-liked CAVs are with the general public,give them a poor name at this early stage of their development,put obstacles in the way of their adoption and expanded use,and complicate the economic models for their future operations.On the other hand,congestion is still a bottleneck for traffic management and planning.This research paper presents a blockchain-based framework that protects the privacy of vehicle owners and provides data security by storing vehicular data on the blockchain,which will be used further for congestion detection and mitigation.Numerous devices placed along the road are used to communicate with passing cars and collect their data.The collected data will be compiled periodically to find the average travel time of vehicles and traffic density on a particular road segment.Furthermore,this data will be stored in the memory pool,where other devices will also store their data.After a predetermined amount of time,the memory pool will be mined,and data will be uploaded to the blockchain in the form of blocks that will be used to store traffic statistics.The information is then used in two different ways.First,the blockchain’s final block will provide real-time traffic data,triggering an intelligent traffic signal system to reduce congestion.Secondly,the data stored on the blockchain will provide historical,statistical data that can facilitate the analysis of traffic conditions according to past behavior. | Abdulaziz Attaallah Khalil al-Sulbi Areej Alasiry Mehrez Marzougui Neha Yadav Syed Anas Ansar Pawan Kumar Chaurasia Alka Agrawal | 2023 | Intelligent Automation & Soft Computing2023，37，8： | 0 |
| 12 | Modelling Intelligent Driving Behaviour Using Machine Learning显示文摘In vehicular systems,driving is considered to be the most complex task,involving many aspects of external sensory skills as well as cognitive intelligence.External skills include the estimation of distance and speed,time perception,visual and auditory perception,attention,the capability to drive safely and action-reaction time.Cognitive intelligence works as an internal mechanism that manages and holds the overall driver’s intelligent system.These cognitive capacities constitute the frontiers for generating adaptive behaviour for dynamic environments.The parameters for understanding intelligent behaviour are knowledge,reasoning,decision making,habit and cognitive skill.Modelling intelligent behaviour reveals that many of these parameters operate simultaneously to enable drivers to react to current situations.Environmental changes prompt the parameter values to change,a process which continues unless and until all processes are completed.This paper model intelligent behaviour by using a‘driver behaviour model’to obtain accurate intelligent driving behaviour patterns.This model works on layering patterns in which hierarchy and coherence are maintained to transfer the data with accuracy from one module to another.These patterns constitute the outcome of different modules that collaborate to generate appropriate values.In this case,accurate patterns were acquired using ANN static and dynamic non-linear autoregressive approach was used and for further accuracy validation,time-series dynamic backpropagation artificial neural network,multilayer perceptron and random sub-space on real-world data were also applied. | Qura-Tul-Ain Khan Sagheer Abbas Muhammad Adnan Khan Areej Fatima Saad Alanazi Nouh Sabri Elmitwally | 2021 | Computers, Materials & Continua2021，，9： | 0 |
| 13 | Optimal Deep Learning Model Enabled Secure UAV Classification for Industry 4.0显示文摘Emerging technologies such as edge computing,Internet of Things(IoT),5G networks,big data,Artificial Intelligence(AI),and Unmanned Aerial Vehicles(UAVs)empower,Industry 4.0,with a progressive production methodology that shows attention to the interaction between machine and human beings.In the literature,various authors have focused on resolving security problems in UAV communication to provide safety for vital applications.The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification(CSODL-SUAVC)model for Industry 4.0 environment.The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image classification.Primarily,the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation(ML-DWT),CSO-related Optimal Pixel Selection(CSO-OPS),and signcryption-based encryption.The proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover images.The secret images,encrypted by signcryption technique,are embedded into cover images.Besides,the image classification process includes three components namely,Super-Resolution using Convolution Neural Network(SRCNN),Adam optimizer,and softmax classifier.The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV communication.The proposed CSODLSUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct aspects.The simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches. | Khalid A.Alissa Mohammed Maray Areej A.Malibari Sana Alazwari Hamed Alqahtani Mohamed K.Nour Marwa Obbaya Mohamed A.Shamseldin Mesfer Al Duhayyim | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 14 | A Secure Key Agreement Scheme for Unmanned Aerial Vehicles-Based Crowd Monitoring System显示文摘Unmanned aerial vehicles(UAVs)have recently attractedwidespread attention in civil and commercial applications.For example,UAVs(or drone)technology is increasingly used in crowd monitoring solutions due to its wider air footprint and the ability to capture data in real time.However,due to the open atmosphere,drones can easily be lost or captured by attackers when reporting information to the crowd management center.In addition,the attackers may initiate malicious detection to disrupt the crowd-sensing communication network.Therefore,security and privacy are one of the most significant challenges faced by drones or the Internet of Drones(IoD)that supports the Internet of Things(IoT).In the literature,we can find some authenticated key agreement(AKA)schemes to protect access control between entities involved in the IoD environment.However,the AKA scheme involves many vulnerabilities in terms of security and privacy.In this paper,we propose an enhancedAKAsolution for crowdmonitoring applications that require secure communication between drones and controlling entities.Our scheme supports key security features,including anti-forgery attacks,and confirms user privacy.The security characteristics of our scheme are analyzed byNS2 simulation and verified by a random oracle model.Our simulation results and proofs show that the proposed scheme sufficiently guarantees the security of crowd-aware communication. | Bander Alzahrani Ahmed Barnawi Azeem Irshad Areej Alhothali Reem Alotaibi Muhammad Shafiq | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 15 | Securing 3D Point and Mesh Fog Data Using Novel Chaotic Cat Map显示文摘With the rapid evolution of Internet technology,fog computing has taken a major role in managing large amounts of data.The major concerns in this domain are security and privacy.Therefore,attaining a reliable level of confidentiality in the fog computing environment is a pivotal task.Among different types of data stored in the fog,the 3D point and mesh fog data are increasingly popular in recent days,due to the growth of 3D modelling and 3D printing technologies.Hence,in this research,we propose a novel scheme for preserving the privacy of 3D point and mesh fog data.Chaotic Cat mapbased data encryption is a recently trending research area due to its unique properties like pseudo-randomness,deterministic nature,sensitivity to initial conditions,ergodicity,etc.To boost encryption efficiency significantly,in this work,we propose a novel Chaotic Cat map.The sequence generated by this map is used to transform the coordinates of the fog data.The improved range of the proposed map is depicted using bifurcation analysis.The quality of the proposed Chaotic Cat map is also analyzed using metrics like Lyapunov exponent and approximate entropy.We also demonstrate the performance of the proposed encryption framework using attacks like brute-force attack and statistical attack.The experimental results clearly depict that the proposed framework produces the best results compared to the previous works in the literature. | K.Priyadarsini Arun Kumar Sivaraman Abdul Quadir Md Areej Malibari | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 16 | Limb Salvage Surgery for Bone Tumors around the Knee: The Oncological and Functional Outcome---King Hussein Medical Center Experience显示文摘 | Raed Al-Zaben Mohamed Alturk Abdullah Alkhawaldah Areej Al-Zaben Jamal Rahaymeh | 2015 | Journal of US-China Medical Science2015，12，3： | 0 |
| 17 | A Lightweight Driver Drowsiness Detection System Using 3DCNN With LSTM显示文摘Today,fatalities,physical injuries,and significant economic losses occur due to car accidents.Among the leading causes of car accidents is drowsiness behind the wheel,which can affect any driver.Drowsiness and sleepiness often have associated indicators that researchers can use to identify and promptly warn drowsy drivers to avoid potential accidents.This paper proposes a spatiotemporal model for monitoring drowsiness visual indicators from videos.This model depends on integrating a 3D convolutional neural network(3D-CNN)and long short-term memory(LSTM).The 3DCNN-LSTM can analyze long sequences by applying the 3D-CNN to extract spatiotemporal features within adjacent frames.The learned features are then used as the input of the LSTM component for modeling high-level temporal features.In addition,we investigate how the training of the proposed model can be affected by changing the position of the batch normalization(BN)layers in the 3D-CNN units.The BN layer is examined in two different placement settings:before the non-linear activation function and after the non-linear activation function.The study was conducted on two publicly available drowsy drivers datasets named 3MDAD and YawDD.3MDAD is mainly composed of two synchronized datasets recorded from the frontal and side views of the drivers.We show that the position of the BN layers increases the convergence speed and reduces overfitting on one dataset but not the other.As a result,the model achieves a test detection accuracy of 96%,93%,and 90%on YawDD,Side-3MDAD,and Front-3MDAD,respectively. | Sara A.Alameen Areej M.Alhothali | 2023 | Computer Systems Science & Engineering2023，44，1： | 0 |
| 18 | Fuzzy Logic Inference System for Managing Intensive Care Unit Resources Based on Knowledge Graph显示文摘With the rapid growth in the availability of digital health-related data,there is a great demand for the utilization of intelligent information systems within the healthcare sector.These systems can manage and manipulate this massive amount of health-related data and encourage different decision-making tasks.They can also provide various sustainable health services such as medical error reduction,diagnosis acceleration,and clinical services quality improvement.The intensive care unit(ICU)is one of the most important hospital units.However,there are limited rooms and resources in most hospitals.During times of seasonal diseases and pandemics,ICUs face high admission demand.In line with this increasing number of admissions,determining health risk levels has become an essential and imperative task.It creates a heightened demand for the implementation of an expert decision support system,enabling doctors to accurately and swiftly determine the risk level of patients.Therefore,this study proposes a fuzzy logic inference system built on domain-specific knowledge graphs,as a proof-of-concept,for tackling this healthcare-related issue.The system employs a combination of two sets of fuzzy input parameters to classify health risk levels of new admissions to hospitals.The proposed system implemented utilizes MATLAB Fuzzy Logic Toolbox via several experiments showing the validity of the proposed system. | Ahmad F Subahi Areej Athama | 2023 | Computers, Materials & Continua2023，77，12： | 0 |
| 19 | Lean body mass index is a marker of advanced tumor features in patients with hepatocellular carcinoma显示文摘BACKGROUND Obesity is an independent risk factor for the development of hepatocellular carcinoma(HCC)and may influence its outcomes.However,after diagnosis of HCC,like other malignancies,the obesity paradox may exist where higher body mass index(BMI)may in fact confer a survival benefit.This is frequently observed in patients with advanced HCC and cirrhosis,who often present late with advanced tumor features and cancer related weight loss.AIM To explore the relationship between BMI and survival in patients with cirrhosis and HCC.METHODS This is a retrospective cohort study of over 2500 patients diagnosed with HCC between 2009-2019 at two United States academic medical centers.Patient and tumor characteristics were extracted manually from medical records of each institutions'cancer registries.Patients were stratified according to BMI classes:<25 kg/m^(2)(lean),25-29.9 kg/m^(2)(overweight),and>30 kg/m^(2)(obese).Patient and tumor characteristics were compared according to BMI classification.We performed an overall survival analysis using Kaplan Meier by the three BMI classes and after adjusting for Milan criteria.A multivariable Cox regression model was then used to assess known risk factors for survival in patients with cirrhosis and HCC.RESULTS A total of 2548 patients with HCC were included in the analysis of which 11.2%(n=286)were classified as noncirrhotic.The three main BMI categories:Lean(n=754),overweight(n=861),and obese(n=933)represented 29.6%,33.8%,and 36.6%of the total population overall.Within each BMI class,the non-cirrhotic patients accounted for 15%(n=100),12%(n=94),and 11%(n=92),respectively.Underweight patients with a BMI<18.5 kg/m^(2)(n=52)were included in the lean cohort.Of the obese cohort,42%(n=396)had a BMI≥35 kg/m^(2).Out of 2262 patients with cirrhosis and HCC,654(29%)were lean,767(34%)were overweight,and 841(37%)were obese.The three BMI classes did not differ by age,MELD,or Child-Pugh class.Chronic hepatitis C was the dominant etiology in lean compared to the overweight and obese patients(71%,62%,49%,P<0.001).Lean patients had significantly larger tumors compared to the other two BMI classes(5.1 vs 4.2 vs 4.2 cm,P<0.001),were more likely outside Milan(56%vs 48%vs 47%,P<0.001),and less likely to undergo transplantation(9%vs 18%vs 18%,P<0.001).While both tumor size(P<0.0001)and elevated alpha fetoprotein(P<0.0001)were associated with worse survival by regression analysis,lean BMI was not(P=0.36).CONCLUSION Lean patients with cirrhosis and HCC present with larger tumors and are more often outside Milan criteria,reflecting cancer related cachexia from delayed diagnosis.Access to care for hepatitis C virus therapy and liver transplantation confer a survival benefit,but not overweight or obese BMI classifications. | Andrew Scott deLemos Jing Zhao Milin Patel Banks Kooken Karan Mathur Hieu Minh Nguyen Areej Mazhar Maggie McCarter Heather Burney Carla Kettler Naga Chalasani Samer Gawrieh | 2024 | World Journal of Hepatology2024，16，3： | 0 |
| 20 | IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19显示文摘The prediction of human diseases,particularly COVID-19,is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment.To deal with the prediction and diagnosis of COVID-19,we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System(IoMTSM-HMFIS).The proposed system determines the various factors like fever,cough,complete blood count,respiratory rate,Ct-chest,Erythrocyte sedimentation rate and C-reactive protein,family history,and antibody detection(lgG)that are directly involved in COVID-19.The expert system has two input variables in layer 1,and seven input variables in layer 2.In layer 1,the initial identification for COVID-19 is considered,whereas in layer 2,the different factors involved are studied.Finally,advanced lab tests are conducted to identify the actual current status of the disease.The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19;the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining.MATLAB-2019a tool is used to conduct the simulation.The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%.Finally,to achieve improved performance,the analysis results of the system were shared with experts of the Lahore General Hospital,Lahore,Pakistan. | Tahir Abbas Khan Sagheer Abbas Allah Ditta Muhammad Adnan Khan Hani Alquhayz Areej Fatima Muhammad Farhan Khan | 2020 | Computers, Materials & Continua2020，，12： | 0 |
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