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您的检索式：作者名="Muhammad Hammad Aziz"
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| 1 | Second cancer risk after 3D-CRT, IMRT and VMAT for breast cancer显示文摘 | Yasser Abo-Madyan Muhammad Hammad Aziz Moamen M.O.M. Aly Frank Schneider Elena Sperk Sven Clausen Frank A. Giordano Carsten Herskind Volker Steil Frederik Wenz Gerhard Glatting | 2013 | Radiotherapy and Oncology2013，，： | 1 |
| 2 | Second cancer risk after 3D-CRT,IMRT and VMAT for breast cancer显示文摘 | Yasser Abo-Madyan Muhammad Hammad Aziz | 2014 | Radiother Oncol2014，110，： | 1 |
| 3 | Gamma irradiation-induced effects on the properties of TiO_2 on fluorine-doped tin oxide prepared by atomic layer deposition显示文摘The effect of gamma irradiation with different doses(25–75 kGy) on TiO_2 thin films deposited by atomic layer deposition has been studied and characterized by X-ray diffraction(XRD),photoluminescence measurements,ultraviolet–visible(UV–Vis) spectroscopy,and impedance measurements.The XRD results for the TiO_2 films indicate an enhancement of crystallization after irradiation,which can be clearly observed from the increase in the peak intensities upon increasing the gamma irradiation doses.The UV–Vis spectra demonstrate a decrease in transmittance,and the band gap of the TiO_2 thin films decreases with an increase in the gamma irradiation doses.The Nyquist plots reveal that the overall charge-transfer resistance increases upon increasing the gamma irradiation doses.The equivalent circuit,series resistance,contact resistance,and interface capacitance are measured by simulation using Z-view software.The present work demonstrates that gamma irradiation-induced defects play a major role in the modification of thestructural,electrical,and optical properties of the TiO_2 thin films. | Syed Mansoor Ali M.S.Algarawi Turki S.ALKhuraiji S.S.Alghamdi Muhammad Hammad Aziz M.Isa | 2018 | Nuclear Science and Techniques2018，29，7： | 0 |
| 4 | Reinforcing Artificial Neural Networks through Traditional Machine Learning Algorithms for Robust Classification of Cancer显示文摘Machine Learning(ML)-based prediction and classification systems employ data and learning algorithms to forecast target values.However,improving predictive accuracy is a crucial step for informed decision-making.In the healthcare domain,data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or diagnosis.Among ML algorithms,Artificial Neural Networks(ANNs)are considered the most suitable framework for many classification tasks.The network weights and the activation functions are the two crucial elements in the learning process of an ANN.These weights affect the prediction ability and the convergence efficiency of the network.In traditional settings,ANNs assign random weights to the inputs.This research aims to develop a learning system for reliable cancer prediction by initializing more realistic weights computed using a supervised setting instead of random weights.The proposed learning system uses hybrid and traditional machine learning techniques such as Support Vector Machine(SVM),Linear Discriminant Analysis(LDA),Random Forest(RF),k-Nearest Neighbour(kNN),and ANN to achieve better accuracy in colon and breast cancer classification.This system computes the confusion matrix-based metrics for traditional and proposed frameworks.The proposed framework attains the highest accuracy of 89.24 percent using the colon cancer dataset and 72.20 percent using the breast cancer dataset,which outperforms the other models.The results show that the proposed learning system has higher predictive accuracies than conventional classifiers for each dataset,overcoming previous research limitations.Moreover,the proposed framework is of use to predict and classify cancer patients accurately.Consequently,this will facilitate the effective management of cancer patients. | Muhammad Hammad Waseem Malik Sajjad Ahmed Nadeem Ishtiaq Rasool Khan Seong-O-Shim Wajid Aziz Usman Habib | 2023 | Computers, Materials & Continua2023，，5： | 0 |
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