Mohammad Zahaby; Mostafa Boroumandzadeh; Iman Makhdoom
Abstract
Breast cancer is one of the most prevalent cancers among women and remains a leading cause of cancer-related mortality. Mammography is the primary imaging modality for the early detection of breast tumors. Providing timely and highly accurate diagnoses is a top priority for physicians ...
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Breast cancer is one of the most prevalent cancers among women and remains a leading cause of cancer-related mortality. Mammography is the primary imaging modality for the early detection of breast tumors. Providing timely and highly accurate diagnoses is a top priority for physicians and healthcare providers in the management of critical illnesses. This paper presents a Medical Decision Support System (MDSS) that utilizes Yager’s rule of combination to classify and diagnose breast cancer patients by integrating information from multiple data sources. Medical text reports (MTR) and key feature vectors extracted from electronic health records (EHR) were reduced using Principal Component Analysis (PCA) and then classified using Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), and Support Vector Machines (SVM). Medical images were preprocessed and classified using a U-Net model. A novel decision fusion algorithm, called weighted Yager, was introduced to determine the Breast Imaging-Reporting and Data System (BI-RADS) categories, taking into account the accuracy of each class in each classifier as evidence. The performance of the proposed system was evaluated based on standard metrics including accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. The proposed system achieved the highest accuracy of 96.23\%, outperforming individual classifiers (CNN: 86.37%, MLP: 92.11%, SVM: 87.92%, U-Net: 92.97%, and Yager: 93.49%). The weighted Yager fusion method yielded the best performance with an accuracy of 96.23%, sensitivity of 98.80%, specificity of 85.90%, PPV of 86.21%, NPV of 97.82%, and F1-score of 85.87%. These findings demonstrate that integrating decisions from multiple classifiers significantly improves diagnostic accuracy and robustness.
Soghra Mikaeyl Nejad
Abstract
Gene expression signatures reflect the response of cell tissues to diseases, genetic disorders, and drug treatments, containing hidden patterns that can provide valuable insights for biological research and cancer diagnostics. This studyproposes a hybrid ...
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Gene expression signatures reflect the response of cell tissues to diseases, genetic disorders, and drug treatments, containing hidden patterns that can provide valuable insights for biological research and cancer diagnostics. This studyproposes a hybrid deep learning approach combining convolutional neural networks (CNNs) and support vector machines (SVMs) to classify cancer types using unstructured gene expression data. We applied three hybrid CNN-SVM models to a dataset of 10,340 samples spanning 33 cancer types from the Cancer Genome Atlas. The CNN component extracted latent features from the gene expression data, while the SVM replaced the softmax layer to enhance classification robustness. Among the proposed models, the Hybrid-CNN-SVM model achieved superior performance, demonstrating excellent prediction accuracy and outperforming other models. This study highlights the potential of hybrid deep learning frameworks for cancer type prediction and underscores their applicability to high-dimensional genomic datasets.
Seyed Mohsen Izadyar; Mohammad Eshaghnezhad; Hossein Davoodi Yeganeh
Abstract
This study presents a model of a quantum dot laser with a planar cavity, employing numerical methods and artificial neural networks for simulation purposes. The investigation focuses on the influence of critical parameters, including the injection current into the active layer of the quantum dot ...
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This study presents a model of a quantum dot laser with a planar cavity, employing numerical methods and artificial neural networks for simulation purposes. The investigation focuses on the influence of critical parameters, including the injection current into the active layer of the quantum dot laser and the carrier relaxation time to a lower energy state level. The model delves into the intricate carrier and photon dynamics within the laser, solving a system of coupled equations that describe these interactions. The fourth-order Runge-Kutta method is utilized to solve these equations numerically. The results indicate that increased pumping power enhances the stable power levels and the peak power output of the laser. Additionally, analysis of the power versus intensity of current ($P-I$) characteristic curve reveals that a longer carrier relaxation time to a lower energy state leads to a higher threshold current and a reduction in the quantum efficiency of the device. The study also examines the laser switch-on time against the injection current. Finally, the deterioration in the quality of quantum dots and quantum wells is scrutinized. To gain deeper insights into the effect of increased pumping current on laser switch-on time, the study complements numerical findings with the application of artificial neural networks, yielding significant results.
Ghasem Ahmadi; Mohammad Teshnehlab; Fahimeh Soltanian
Abstract
o enhance the performances of rough-neural networks (R-NNs) in the system identification, on the base of emotional learning, a new stable learning algorithm is developed for them. This algorithm facilitates the error convergence by increasing the memory depth of R-NNs. ...
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o enhance the performances of rough-neural networks (R-NNs) in the system identification, on the base of emotional learning, a new stable learning algorithm is developed for them. This algorithm facilitates the error convergence by increasing the memory depth of R-NNs. To this end, an emotional signal as a linear combination of identification error and its differences is used to achieve the learning laws. In addition, the error convergence and the boundedness of predictions and parameters of the model are proved. To illustrate the efficiency of proposed algorithm, some nonlinear systems including the cement rotary kiln are identified using this method and the results are compared with some other models.
Azhdar Soleymanpour Bakefayat; Sima Karamseraji
Volume 2, Issue 1 , April 2017, , Pages 43-63
Abstract
The method of triangular functions (TF) could be a generalization form of the functions of block-pulse (Bp). The solution of second kind integral equations by using the concept of TF would lead to a nonlinear equations system. In this article, the obtained nonlinear system ...
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The method of triangular functions (TF) could be a generalization form of the functions of block-pulse (Bp). The solution of second kind integral equations by using the concept of TF would lead to a nonlinear equations system. In this article, the obtained nonlinear system has been solved as a dynamical system. The solution of the obtained nonlinear system by the dynamical system through the Newton numerical method has got a particular priority, in that, in this method, the number of the unknowns could be more than the number of equations. Besides, the point of departure of the system could be an infeasible point. It has been proved that the obtained dynamical system is stable, and the response of this system can be achieved by using of the fourth order Runge-Kutta. The results of this method is comparable with the similar numerical methods; in most of the cases, the obtained results by the presented method are more efficient than those obtained by other numerical methods. The efficiency of the new method will be investigated through examples.
