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Rhabdomyosarcoma in Children: About 10 Cases

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Open Journal of Pediatrics 2024年第3期14卷 522-530页

作者： Ayad Ghanam Manal Azizi hind Zahiri houssain Benhaddou Imane Kamaoui amal Bennani Aziza El Ouali Abdeladim Babakhouya Maria Rkain Noufissa Benajiba Department of Pediatrics Mohammed VI University Hospital Faculty of Medicine and Pharmacy Mohammed First University Oujda Morocco Department of Pediatric Surgery Mohammed VI University Hospital Faculty of Medicine and Pharmacy Mohammed First University Oujda Morocco Department of Radiology Mohammed VI University Hospital Faculty of Medicine and Pharmacy Mohammed First University Oujda Morocco Department of Anatomopathology Mohammed VI University Hospital Faculty of Medicine and Pharmacy Mohammed First University Oujda Morocco

Introduction: Rhabdomyosarcoma (RMS) is a malignant soft-tissue tumor arising from striated muscle cells. It accounts for 60% - 70% of malignant mesenchymal tumors and 5% of pediatric cancers. Two-thirds of these cancers are diagnosed in children under 6 years of age, with a slight male predominance. Materials and Methods: This is a retrospective descriptive study of 10 cases of RMS collected in the pediatric hematology and oncology department of the Oujda university hospital, over a 5-year period, running from January 2018 to December 2022. Results: The median age at diagnosis was 3 years, with a sex ratio of 1. The mean time to diagnosis was 2 months. The most common site was the head and neck (50%), followed by the genitourinary tract (20%), the extremities (20%) and finally the abdomen (10%). The most frequent mode of discovery was a mass or swelling found in 90% of patients (all sites included), followed by exophthalmos in 30% of cases. At the diagnostic stage, CT scans were performed in 70% of cases and MRI in 5 patients (50%). histological diagnosis was determined by immunohistochemical pathology in all our patients, with a predominance of embryonal (70%) versus alveolar (20%) and spindle cell types (10%). all patients underwent an extension workup, and a cervico-thoraco-abdominopelvic CT was performed in all patients (100%);MRI was performed in 2 patients (20%);lymph node involvement was present in 5 patients (50%). Metastases at the time of diagnosis were noted in only 1 patient (10%), who simultaneously presented with two metastatic sites;testicular and abdominal wall. Sixty percent of patients presented with advanced disease (high risk) and 40% with standard risk. Chemotherapy was used in all patients (100%), with upfront tumor resection performed in 40%. Fifty percent of patients received radiotherapy at a mean dose of 43 Gy, with the orbit the most frequently irradiated area (30%). all patients underwent CTscan and/or MRI and/or ultrasound surveillance.

关键词： Rhabdomyosarcoma Child Chemotherapy Surgery Radiotherapy

Decomposition of biomass gasification tar model compounds over waste tire pyrolysis char

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Waste Disposal and Sustainable Energy 2022年第2期4卷 75-89页

作者： amal S.al-Rahbi Paul T.Williams Present address:Applied Sciences Department Higher College of TechnologyUniversity of Technology and Applied SciencesMuscatSultanate of Oman School of Chemical and Process Engineering University of LeedsLeedsLS29JTUK

Gasification of biomass produces a syngas containing trace amounts of viscous hydrocarbon tar,which causes serious problems in downstream pipelines,valves and processing *** study focuses on the use of tire-derived pyrolysis char for tar conversion using biomass tar model compounds representative of *** catalytic decomposition of tar model compounds,including methylnaphthalene,furfural,phenol,and toluene,over tire char was investigated using a fixed bed reactor at a bed temperature of 700℃and 60 min time on *** influence of temperature,reaction time,porous texture,and acidity of the tire char was investigated with the use of methylnaphthalene as the tar model *** tar model compounds were found to have higher conversion than those containing a single or multi-aromatic *** reactivity of tar compounds followed the order of furfural>phenol>toluene>*** conversion of the model compounds in the presence of the tire char was much higher than tar thermal *** production increased dramatically with the introduction of tire *** h_(2)potential for the studied tar model compounds was found to be in the range of 40%–50%.The activity of tire char for naphthalene removal was compared with two commercial activated carbons possessing a very well-developed porous *** results suggest that the influence of Brunauer-Emmett-Teller surface area of the carbon on tar cracking is negligible compared with the mineral content in the carbon samples.

关键词： Biomass Gasification Syngas Tar Tire Char

Robust Image Watermarking Using LWT and Stochastic Gradient Firefly algorithm

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Computers, Materials & Continua 2023年第4期75卷 393-407页

作者： Sachin Sharma Meena Malik Chander Prabha amal al-Rasheed Mona alduailij Sultan almakdi Department of CSE Koneru Lakshmaiah Education FoundationGunturAndhra Pradesh522302India Deparmtent of CSE Chandigarh UniversityMohaliPunjab140413India Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran55461Saudi Arabia

Watermarking of digital images is required in diversified applicationsranging from medical imaging to commercial images used over the ***, the copyright information is embossed over the image in the form ofa logo at the corner or diagonal text in the background. however, this formof visible watermarking is not suitable for a large class of applications. In allsuch cases, a hidden watermark is embedded inside the original image as proofof ownership. A large number of techniques and algorithms are proposedby researchers for invisible watermarking. In this paper, we focus on issuesthat are critical for security aspects in the most common domains like digitalphotography copyrighting, online image stores, etc. The requirements of thisclass of application include robustness (resistance to attack), blindness (directextraction without original image), high embedding capacity, high Peak Signalto Noise Ratio (PSNR), and high Structural Similarity Matrix (SSIM). Mostof these requirements are conflicting, which means that an attempt to maximizeone requirement harms the other. In this paper, a blind type of imagewatermarking scheme is proposed using Lifting Wavelet Transform (LWT)as the baseline. Using this technique, custom binary watermarks in the formof a binary string can be embedded. hu’s Invariant moments’ coefficientsare used as a key to extract the watermark. A Stochastic variant of theFirefly algorithm (FA) is used for the optimization of the technique. Undera prespecified size of embedding data, high PSNR and SSIM are obtainedusing the Stochastic Gradient variant of the Firefly technique. The simulationis done using Matrix Laboratory (MATLAB) tool and it is shown that theproposed technique outperforms the benchmark techniques of watermarkingconsidering PSNR and SSIM as quality metrics.

关键词： Image watermarking lifting wavelet transform discrete wavelet transform(DWT) firefly technique invariant moments

Deep Learning Prediction Model for NiCralY Diffusion Barrier Thickness for Tungsten Wires

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Computers, Materials & Continua 2022年第12期73卷 5755-5769页

作者： amal h.alharbi hanan A.hosni Mahmoud Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In the last decades,technology has used Copper for IC interconnect and it has been the best material used in the wire ***,Copper is now showing inefficiency as downscaling is getting *** research starts to show Tungsten(W)as a possible replacement,for its better downsizing *** scaling-down of interconnects dimension has to be augmented with thin diffusion *** is crucial to subdue tungsten diffusion in the nickel-based thermal spray Flexicord(NiCralY)coating ***,diffusion barriers with thicknesses less than 4.3 nm do not to execute *** the introduction of two dimensional layers,hexagonal boron has been recommended as a substitute for Tungsten diffusion barrier layers with thicknesses less than 1.5 Nano meters(nm).Nevertheless,vacancies flaws may develop into a Tungsten dissemination path,which is a problematic issue in the manufacturing of diffusion *** energy layer density,of Tungsten atom diffusion via a di-vacancy in NiCralY,is computed by density functions *** has complex energy barrier which is thicker than other materials such as *** is due to the sturdier contact and charge variance of NI and Cr in ***,we utilize the energy barriers of several vacancy constructions and produce a dataset to be employed in the proposed 3-imensional deep learning model(3D-DNN).Our trained deep learning neural model can predict the energy barrier of Tungsten diffusion through arbitrarily configured NiCralY with accuracy greater than 98.4%in 5×5 *** results generate directors on selecting barriers through energy computation.

关键词： Deep learning tungsten NiCralY thickness barrier

A Deep Real-Time Fire Prediction Parallel D-CNN Model on UDOO BOLT V8

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Computers, Materials & Continua 2022年第12期73卷 6237-6252页

作者： amal h.alharbi hanan A.hosni Mahmoud Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

hazardous incidences have significant influences on human life,and fire is one of the foremost causes of such hazard in most *** prediction and classification model from a set of fire images can decrease the risk of losing human lives and *** promotion of fire emergency can be of great ***,construction of these prediction models is relevant and *** article proposes an operative fire prediction model that depends on a prediction unit embedded in the processor UDOO BOLT V8 hardware to predict fires in real time.A fire image database is improved to enhance the images quality prior to classify them as either fire or *** proposed deep learning-based Very Deep Convolutional Networks Visual Geometry Group(VGG-16)model(Parallel VGG-16)is an enhanced version of the VGG-16 model,by incorporating parallel convolution layers and a fusion module for better *** experimental results validate the performance of the Parallel VGG-16 which achieves an accuracy of 97%,compared to the compared state-of-the-art ***,we integrate the prediction module into a UDOO BOLT V8 computer,which precisely controlled the fire alarm so that it can cautious people from fire in real *** this paper we propose a complete fire prediction model using a camera and the UDOO BOLT V8 embedded *** experiments validate the effectiveness and applicability of the proposed fire model.

关键词： Fire prediction UDOO BOLT V8 deep learning

MIoT Based Skin Cancer Detection Using Bregman Recurrent Deep Learning

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Computers, Materials & Continua 2022年第12期73卷 6253-6267页

作者： Nithya Rekha Sivakumar Sara Abdelwahab Ghorashi Faten Khalid Karim Eatedal alabdulkreem amal al-Rasheed Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Mobile clouds are the most common medium for aggregating,storing,and analyzing data from the medical Internet of Things(MIoT).It is employed to monitor a patient’s essential h.alth signs for earlier disease diagnosis and *** the various disease,skin cancer was the wide variety of cancer,as well as enhances the endurance *** recent years,many skin cancer classification systems using machine and deep learning models have been developed for classifying skin tumors,including malignant melanoma(MM)and other skin ***,accurate cancer detection was not performed with minimum time *** order to address these existing problems,a novel Multidimensional Bregman Divergencive Feature Scaling Based Cophenetic Piecewise Regression Recurrent Deep Learning Classification(MBDFS-CPRRDLC)technique is introduced for detecting cancer at an earlier *** MBDFS-CPRRDLC performs skin cancer detection using different layers such as input,hidden,and output for feature selection and *** patient information is composed of *** patient information was stored in mobile clouds server for performing predictive *** collected data are sent to the recurrent deep learning *** the first hidden layer,the feature selection process is carried out using the Multidimensional Bregman Divergencive Feature Scaling technique to find the significant features for disease identification resulting in decreases time *** by,the disease classification is carried out in the second hidden layer using cophenetic correlative piecewise regression for analyzing the testing and training *** process is repeatedly performed until the error gets *** this way,disease classification is accurately performed with higher *** evaluation is carried out for factors namely Accuracy,precision,recall,F-measure,as well as cancer detection time,by the amount of patient *** observed result confirms that the pr

关键词： MIoT skin cancer detection recurrent deep learning classification multidimensional bregman divergencive scaling cophenetic correlative piecewise regression

Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

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Computers, Materials & Continua 2023年第2期74卷 2725-2738页

作者： Mohammad alamgeer amal al-Rasheed Ahmad alhindi Manar Ahmed hamza Abdelwahed Motwakel Mohamed I.Eldesouki Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Computing and Information SystemMecca24382Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia

Data mining process involves a number of steps fromdata collection to visualization to identify useful data from massive data *** same time,the recent advances of machine learning(ML)and deep learning(DL)models can be utilized for effectual rainfall *** this motivation,this article develops a novel comprehensive oppositionalmoth flame optimization with deep learning for rainfall prediction(COMFO-DLRP)*** proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental ***,data pre-processing and correlation matrix(CM)based feature selection processes are carried *** addition,deep belief network(DBN)model is applied for the effective prediction of rainfall ***,COMFO algorithm was derived by integrating the concepts of comprehensive oppositional based learning(COBL)with traditional MFO ***,the COMFO algorithm is employed for the optimal hyperparameter selection of the DBN *** demonstrating the improved outcomes of the COMFO-DLRP approach,a sequence of simulations were carried out and the outcomes are assessed under distinct *** simulation outcome highlighted the enhanced outcomes of the COMFO-DLRP method on the other techniques.

关键词： Data mining rainfall prediction deep learning correlation matrix hyperparameter tuning metaheuristics

Deep Learning Based License Plate Number Recognition for Smart Cities

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Computers, Materials & Continua 2022年第1期70卷 2049-2064页

作者： T.Vetriselvi E.Laxmi Lydia Sachi Nandan Mohanty Eatedal alabdulkreem Shaha al-Otaibi amal al-Rasheed Romany F.Mansour Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTrichy620002India Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science&Engineering Vardhaman College of Engineering(Autonomous)Hyderabad501218India Department of Informatics and Computing Singidunum UniversitySerbia Computer Science Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Information Systems Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended *** controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city *** the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic *** VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number *** current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license *** proposed model involves two main stages namely,license plate detection and Tesseract-based character *** detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 ***,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)*** performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods.

关键词： Deep learning smart city tesseract computer vision vehicle license plate recognition

Text Augmentation-Based Model for Emotion Recognition Using Transformers

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Computers, Materials & Continua 2023年第9期76卷 3523-3547页

作者： Fida Mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal amal al-Rasheed Department of Computer Science The University of HaripurHaripur22620Pakistan Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and Technology PeshawarPeshawar25120Pakistan Department of Electronics Engineering Technology University of TechnologyNowshera24100Pakistan Department of Computer Science Shaheed Benazir BhuttoWomen UniversityPeshawar25000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

关键词： Emotion recognition in conversation graph-based network text augmentation-basedmodel multimodal emotion lines dataset bidirectional encoder representation for transformer