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Comprehensive review into the challenges of gastrointestinal tumors in the Gulf and Levant countries

World Journal of Clinical Cases 2020年第3期8卷 487-503页

作者： Rare Tumors GI Group Fadi Farhat Abdulaziz Al Farsi Ahmed Mohieldin Bassim Al Bahrani Eman Sbaity hassan jaffar Joseph Kattan Kakil Rasul Khairallah Saad Tarek Assi Waleed El Morsi Rafid A Abood 不详 Hammoud Hospital UMC Saida PO Box 652Lebanon Medical Oncology Department Royal HospitalMuscat PO Box 1331Oman Medical Oncology Department Kuwait Cancer Control CenterKuwait PO Box 42262Kuwait Division of General Surgery American University of BeirutBeirut 11072180Lebanon Oncology Department Tawam HospitalAl Ain PO Box 15258United Arab Emirates Hemato-oncology Department Hotel Dieu de FranceBeirutLebanon Hemato-oncology Department National Center for Cancer Care and ResearchDohaQatar Pathology Department Institute National de PathologicBeirutLebanon Oncology Department Faculty of MedicineSaint-Joseph UniversityBeirutLebanon Pfizer Oncology-Emerging Markets Dubai Media CityDubaiUnited Arab Emirates Oncology Department Basra College of MedicineBasraIraq

Although gastrointestinal stromal tumors(GISTs)are rare,with an incidence of 1/100000 per year,they are the most common sarcomas in the peritoneal cavity.Despite considerable progress in the diagnosis and treatment of GIST,about half of all patients are estimated to experience recurrence.With only two drugs,sunitinib and regorafenib,approved by the Food and Drug Administration,selecting treatment options after imatinib failure and coordinating multidisciplinary care remain challenging.In addition,physicians across the Middle East face some additional and unique challenges such as lack of published local data from clinical trials,national disease registries and regional scientific research,limited access to treatment,lack of standardization of care,and limited access to mutational analysis.Although global guidelines set a framework for the management of GIST,there are no standard local guidelines to guide clinical practice in a resource-limited environment.Therefore,a group of 11 experienced medical oncologists from across the Gulf and Levant region,part of the Rare Tumors Gastrointestinal Group,met over a period of one year to conduct a narrative review of the management of GIST and to describe regional challenges and gaps in patient management as an essential step to proposing local clinical practice recommendations.

关键词： Gastrointestinal stromal tumors Diagnosis Disease management Treatment Challenges Middle East

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Personal Review:Sources of sulfide in waste streams and current biotechnologies for its removal

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2007年第7期8卷 1126-1140页

作者： MAHMOOD Qaisar ZHENG Ping CAI Jing HAYAT Yousaf hassan Muhammad jaffar WU Dong-lei HU Bao-lan Department of Environmental Engmeermg Zhejiang University Hangzhou 310029 China College of Agriculture and Biotechnology Zhejiang University Hangzhou 310029 China

Sulfide-containing waste streams are generated by a number of industries. It is emitted into the environment as dis- solved sulfide (S2- and HS-) in wastewaters and as H2S in waste gases. Due to its corrosive nature, biological hydrogen sulfide removal processes are being investigated to overcome the chemical and disposal costs associated with existing chemically based removal processes. The nitrogen and sulfur metabolism interacts at various levels of the wastewater treatment process. Hence, the sulfur cycle offers possibilities to integrate nitrogen removal in the treatment process, which needs to be further optimized by appropriate design of the reactor configuration, optimization of performance parameters, retention of biomass and optimization of biomass growth. The present paper reviews the biotechnological advances to remove sulfides from various environments.

关键词：氢化硫微生物硫磺代谢作用

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Decision Aid Model for Private-owned Electric Vehicles Participating in Frequency Regulation Ancillary Service Market

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 621-629页

作者： Liwei Wang Yingyun Sun Haotian Wang Pengfei Zhao Muhammad Safwan jaffar School of Electrical and Electronic Engineering North China Electric Power UniversityBeijing 102206China

To reduce the difficulty and enhance the enthusiasm of private-owned electric vehicles(EVs) in participating in frequency regulation ancillary service market(FRASM), a decision aid model(DAM) is proposed. This paper presents three options for EV participating in FRASM, i. e., the base mode(BM), unidirectional charging mode(UCM), and bidirectional charging/discharging mode(BCDM), based on a reasonable simplification of users' participating willingness. In BM, individual EVs will not be involved in FRASM, and DAM will assist users to set the optimal charging schemes based on travel plans under the time-of-use(TOU) price. UCM and BCDM are two modes in which EVs can take part in FRASM. DAM can assist EV users to create their quotation plan, which includes hourly upper and lower reserve capabilities and regulation market mileage prices. In UCM and BCDM, the difference is that only the charging rate can be adjusted in the UCM, and the EVs in BCDM can not only charge but also discharge if necessary. DAM can estimate the expected revenue of all three modes, and EV users can make the final decision based on their preferences. Simulation results indicate that all the three modes of DAM can reduce the cost, while BCDM can get the maximum expected revenue.

关键词： Electric vehicle(EV) frequency regulation decision aid model(DAM) utility maximization battery wear cost

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Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

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Computers, Materials & Continua 2024年第3期78卷 3377-3390页

作者： Umar Rashid Arfan jaffar Muhammad Rashid Mohammed S.Alshuhri Sheeraz Akram Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54600Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Department of Radiological Sciences and Medical Imaging College of AppliedMedical SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

关键词： Pulmonary nodules segmentation histogram thresholding

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Weber Law Based Approach for Multi-Class Image Forgery Detection

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Computers, Materials & Continua 2024年第1期78卷 145-166页

作者： Arslan Akram Javed Rashid Arfan jaffar Fahima Hajjej Waseem Iqbal Nadeem Sarwar Department of Computer Science Superior UniversityLahore54000Pakistan Information Technology Services University of OkaraOkara56300Pakistan Departmet of Computer Science MLC LabOkara56300Pakistan Department of CS&SE International Islamic UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science Bahria UniversityLahore CampusLahore54600Pakistan

Today’s forensic science introduces a new research area for digital image analysis formultimedia security.So,Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or createmisleading publicity by using tempered images.Exiting forgery detectionmethods can classify only one of the most widely used Copy-Move and splicing forgeries.However,an image can contain one or more types of forgeries.This study has proposed a hybridmethod for classifying Copy-Move and splicing images using texture information of images in the spatial domain.Firstly,images are divided into equal blocks to get scale-invariant features.Weber law has been used for getting texture features,and finally,XGBOOST is used to classify both Copy-Move and splicing forgery.The proposed method classified three types of forgeries,i.e.,splicing,Copy-Move,and healthy.Benchmarked(CASIA 2.0,MICCF200)and RCMFD datasets are used for training and testing.On average,the proposed method achieved 97.3% accuracy on benchmarked datasets and 98.3% on RCMFD datasets by applying 10-fold cross-validation,which is far better than existing methods.

关键词： Copy-Move and splicing non-overlapping block division texture features weber law spatial domain xgboost

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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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Computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like Pakistan.This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context.The research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate schedulers.In addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the tool.The findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation approach.Specifically,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test images.To validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD detection.This research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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镉胁迫对不同水稻基因型植株生长和抗氧化酶系统的影响

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中国水稻科学 2004年第3期18卷 239-244页

作者：邵国胜 MUHAMMAD jaffar hassan 章秀福张国平浙江大学农业与生物技术学院浙江杭州310029 中国水稻研究所浙江杭州310006

以籽粒镉积累水平不同的两种品种 (丙 972 5 2 ,低积累型 ;秀水 6 3,高积累型 )为材料 ,研究了镉胁迫对水稻植株生长和抗氧化酶系统的影响。采用水培试验 ,镉处理设 0 .0、0 .1、1.0和 5 .0 μmol/L 4个水平。结果表明 ,镉胁迫抑制植株... 详细信息

以籽粒镉积累水平不同的两种品种 (丙 972 5 2 ,低积累型 ;秀水 6 3,高积累型 )为材料 ,研究了镉胁迫对水稻植株生长和抗氧化酶系统的影响。采用水培试验 ,镉处理设 0 .0、0 .1、1.0和 5 .0 μmol/L 4个水平。结果表明 ,镉胁迫抑制植株生长和叶绿素合成 ,改变植株丙二醛 (MDA)含量和超氧物歧化酶 (SOD)、过氧化氢酶 (CAT)、过氧化物酶 (POD)活性。在抗氧化酶活性上 ,根和地上部对镉胁迫的反应存在着差异。总体上 ,SOD、CAT和POD活性随镉水平的提高而减少 ,而MDA含量则表现相反。根和地上部MDA含量随着培养液中镉浓度提高而增加 ,且增加幅度秀水 6 3明显大于丙 972 5 2。与对照相比 ,生长在 5 .0 μmol/LCd处理下的植株 ,SOD活性在孕穗期下降 4 6 %～ 5 2 % ,在分蘖期仅下降 13%～ 19%。高浓度镉胁迫下 ,两品种在MDA含量的增加幅度和叶绿素含量的降低幅度上表现不同 ,显示出它们对镉的耐性存在着差异。

关键词：镉胁迫水稻基因型植株生长抗氧化酶耐性浓度

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Prediction of Anoxic Sulfide Biooxidation Under Various HRTs Using Artificial Neural Networks

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Biomedical and Environmental Sciences 2007年第5期20卷 398-403页

作者： QAISAR MAHMOOD PING ZHENG DONG-LEI WU XU-SHENG WANG HAYAT YOUSAF EJAZ UL-ISLAM MUHAMMAD jaffar hassan GHULAM JILANI MUHAMMAD RASHID AZIM Department of Environmental Engineering College of Environment and Resource Science Zhejiang University Hangzhou 310029 Zhejiang China Institute of Statistical Genetics Zhejiang University Hangzhou 310029 Zhejiang China tUniversity of Arid Agriculture Rawalpindi Pakistan University of Arid Agriculture Rawalpindi Pakistan Federal Government Post-graduate College Sector H-8 Islamabad Pakistan

Objective During present investigation the data of a laboratory-scale anoxic sulfide oxidizing （ASO） reactor were used in a neural network system to predict its performance. Methods Five uncorrelated components of the influent wastewater were used as the artificial neural network model input to predict the output of the effluent using back-propagation and general regression algorithms. The best prediction performance is achieved when the data are preprocessed using principal components analysis （PCA） before they are fed to a back propagated neural network. Results Within the range of experimental conditions tested, it was concluded that the ANN model gave predictable results for nitrite removal from wastewater through ASO process. The model did not predict the formation of sulfate to an acceptable manner. Conclusion Apart from experimentation, ANN model can help to simulate the results of such experiments in finding the best optimal choice for ASO based denitrification. Together with wastewater collection and the use of improved treatment systems and new technologies, better control of wastewater treatment plant （WTP） can lead to more effective maneuvers by its operators and, as a consequence, better effluent quality.

关键词： Artificial neural networks Effluent sulfide prediction Effluent nitrite prediction Principal components analysis Wastewater treatment ASO reactor

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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Computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnosis increases the patient’s chances of recovery.However,issues like overfitting and inconsistent accuracy across datasets remain challenges.In a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib detection.The aim was to create a robust detection mechanism that consistently performs well.Metrics such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for evaluation.The findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated categories.It demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib detection.These insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG signals.The comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib detection.Moreover,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

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Computers, Materials & Continua 2024年第6期79卷 4963-4977页

作者： Iftikhar Naseer Tehreem Masood Sheeraz Akram Zulfiqar Ali Awais Ahmad Shafiq Ur Rehman Arfan jaffar Faculty of Computer Sciences&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Information System Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Lung cancer is a leading cause of global mortality rates.Early detection of pulmonary tumors can significantly enhance the survival rate of patients.Recently,various Computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high accuracy.Nevertheless,the existing method-ologies cannot obtain a high level of specificity and sensitivity.The present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet architecture.The LCSC model comprises two distinct stages.The first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung lobes.Subsequently,an improved AlexNet architecture is employed to classify lung cancer.During the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate nodules.The suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or malignant.The proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

关键词： Lung cancer segmentation AlexNet U-Net classification

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