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| 1 | Local correlation be tween Monte-Carlo dose and radiation-induced fibrosis in lung cancer pantients显示文摘 | Stroian G Martens C Souham I L | 2008 | Int J Radiat Oncol Biol Phys2008，70，3： | 1 |
| 2 | Weekly cisplatin plus external beam radiotherapy and high dose rate brachtherapy in patient with locally advanced carcinoma of cervix显示文摘 | Souham IL Seymour R Roman TN | | 0，，04： | 1 |
| 3 | Chemotherapy ver sus supportive care in advanced non-small-cell lung cancer in proved survival without detrim ent quality of life 显示文摘 | Spiro S G Rudd R M Souham I R L | 2004 | Thorax2004，59，10： | 1 |
| 4 | Robust ACO-Based Landmark Matching and Maxillofacial Anomalies Classification显示文摘Imagery assessment is an efficient method for detecting craniofacial anomalies.A cephalometric landmark matching approach may help in orthodontic diagnosis,craniofacial growth assessment and treatment planning.Automatic landmark matching and anomalies detection helps face the manual labelling lim-itations and optimize preoperative planning of maxillofacial surgery.The aim of this study was to develop an accurate Cephalometric Landmark Matching method as well as an automatic system for anatomical anomalies classification.First,the Active Appearance Model(AAM)was used for the matching process.This pro-cess was achieved by the Ant Colony Optimization(ACO)algorithm enriched with proximity information.Then,the maxillofacial anomalies were classified using the Support Vector Machine(SVM).The experiments were conducted on X-ray cephalograms of 400 patients where the ground truth was produced by two experts.The frameworks achieved a landmark matching error(LE)of 0.50±1.04 and a successful landmark matching of 89.47%in the 2 mm and 3 mm range and of 100%in the 4 mm range.The classification of anomalies achieved an accuracy of 98.75%.Compared to previous work,the proposed approach is simpler and has a comparable range of acceptable matching cost and anomaly classification.Results have also shown that it outperformed the K-nearest neigh-bors(KNN)classifier. | Dalel Ben Ismail Hela Elmannai Souham Meshoul Mohamed Saber Naceur | 2023 | Intelligent Automation & Soft Computing2023，，2： | 0 |
| 5 | Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging,Laboratory VNIR Spectroscopy and Their Combination显示文摘Pedo-spectroscopy has the potential to provide valuable information about soil physical,chemical,and biological properties.Nowadays,wemay predict soil properties usingVNIRfield imaging spectra(IS)such as Prisma satellite data or laboratory spectra(LS).The primary goal of this study is to investigate machine learning models namely Partial Least Squares Regression(PLSR)and Support Vector Regression(SVR)for the prediction of several soil properties,including clay,sand,silt,organic matter,nitrate NO3-,and calcium carbonate CaCO_(3),using five VNIR spectra dataset combinations(%IS,%LS)as follows:C1(0%IS,100%LS),C2(20%IS,80%LS),C3(50%IS,50%LS),C4(80%IS,20%LS)and C5(100%IS,0%LS).Soil samples were collected at bare soils and at the upper(0–30 cm)layer.The data set has been split into a training dataset 80%of the collected data(n=248)and a validation dataset 20%of the collected data(n=61).The proposed PLSR and SVR models were trained then tested for each dataset combination.According to our results,SVR outperforms PLSR for both:C1(0%IS,100%LS)and C5(100%IS,0%LS).For Soil Organic Matter(SOM)prediction,it achieves(R^(2)=0.79%,RMSE=1.42%)and(R^(2)=0.76%,RMSE=1.3%),respectively.The data fusion has improved the soil property prediction.The highest improvement was obtained for the SOM property(R^(2)=0.80%,RMSE=1.39)when using the SVR model and applying the second Combination C2(20% of IS and 80%LS). | Emna Karray Hela Elmannai Elyes Toumi Mohamed Hedi Gharbia Souham Meshoul Hamouda Aichi Zouhaier Ben Rabah | 2023 | Computer Modeling in Engineering & Sciences2023，，8： | 0 |
| 6 | Explainable Heart Disease Prediction Using Ensemble-Quantum Machine Learning Approach显示文摘Nowadays,quantum machine learning is attracting great interest in a wide range offields due to its potential superior performance and capabilities.The massive increase in computational capacity and speed of quantum computers can lead to a quantum leap in the healthcarefield.Heart disease seriously threa-tens human health since it is the leading cause of death worldwide.Quantum machine learning methods can propose effective solutions to predict heart disease and aid in early diagnosis.In this study,an ensemble machine learning model based on quantum machine learning classifiers is proposed to predict the risk of heart disease.The proposed model is a bagging ensemble learning model where a quantum support vector classifier was used as a base classifier.Further-more,in order to make the model’s outcomes more explainable,the importance of every single feature in the prediction is computed and visualized using SHapley Additive exPlanations(SHAP)framework.In the experimental study,other stand-alone quantum classifiers,namely,Quantum Support Vector Classifier(QSVC),Quantum Neural Network(QNN),and Variational Quantum Classifier(VQC)are applied and compared with classical machine learning classifiers such as Sup-port Vector Machine(SVM),and Artificial Neural Network(ANN).The experi-mental results on the Cleveland dataset reveal the superiority of QSVC compared to the others,which explains its use in the proposed bagging model.The Bagging-QSVC model outperforms all aforementioned classifiers with an accuracy of 90.16%while showing great competitiveness compared to some state-of-the-art models using the same dataset.The results of the study indicate that quantum machine learning classifiers perform better than classical machine learning classi-fiers in predicting heart disease.In addition,the study reveals that the bagging ensemble learning technique is effective in improving the prediction accuracy of quantum classifiers. | Ghada Abdulsalam Souham Meshoul Hadil Shaiba | 2023 | Intelligent Automation & Soft Computing2023，，4： | 0 |
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