Mohammad Jafar Tarokh

Developing an automatic system for detection, segmentation, and classification of skin lesions is very useful to aid well-timed diagnosis of skin diseases. Lesion segmentation is a crucial task for automated diagnosis of skin cancers, as it affects significantly the accuracy of the subsequent steps. Varieties in sizes and locations of lesions, and the lesions with low-contrast boundaries make this task very challenging. In this paper, a three-stage CNN-based method is presented for accurate segmentation of lesions from dermoscopic images. At the first step, normalization, approximate locations and sizes of lesions are estimated. Due to the importance of the normalization stage, three CNN-based networks (Mask R-CNN, RetinaNet, and YOLOv3) are used for the lesion detection. A convolutional network is presented and used to combine the results of the object detection networks with a novel approach. The output of the first stage is a normalized cropped image containing the detected lesion in the center. At the second stage, segmentation, a CNN in a DeepLab3+ structure, is used to extract the lesion from the normalized image. Finally, an active contour method is used as the postprocessing to enhance the boundary of the segmented lesion. The proposed method is evaluated on well-known datasets. Experiments show that the proposed method outperforms all the previous state-of-the-art methods.

The increasing data scales in today’s business sectors coupled with the necessity of risk management raise the importance of business intelligence tools as an integrated solution for the insurance industry. These tools have mostly been used to achieve effective risk management. Although methods of risk management in the insurance industry have been proposed many years ago, the research effort has primarily been focused on predictive analyses. This study aimed to investigate the role of business intelligence as a solution to illustrate its potential in risk management particularly for decision-makers in agricultural insurance. We hypothesized that this would make a preferable decision in uncertain conditions. Sample data from the online transaction process system of Iran agricultural insurance fund were preprocessed in SQL server. Multidimensional online analytical processing architecture was analyzed using Targit business intelligence tool. Our results identified financial risks that lead to a framework of controlling risk based on business intelligence in the agricultural insurance fund.

Automatic accurate skin lesion segmentation systems are very helpful for timely diagnosis and treatment of skin cancers. Recently, methods based on convolutional neural networks (CNN) have presented powerful performances and good results in biomedical applications. In the proposed method, a novel structure based on Mask RCNN, a proposed CNN, and a geodesic segmentation method is presented to improve the performance of the skin lesion segmentation. Lesions are detected and segmented by the Mask R‐CNN in the first stage. A multi‐atrous full convolutional neural network (MAFCNN) is proposed to combine outputs of the Mask RCNN and the input image to present more accurate segmentation results. To modify boundary of the lesion segmented by the MAFCNN, a geodesic segmentation method is used. Some parts of the segmentation result of the proposed CNN are utilized as labeled pixels for the geodesic method. Results demonstrate that using the proposed MAFCNN in a novel structure followed by the geodesic method significantly improves the mean Jaccard value. Experiments on five well‐known skin image datasets show that the proposed method outperforms other state‐of‐the‐art methods.

The intensive care units (ICUs) are among the most expensive and essential parts of all hospitals for extremely ill patients. This study aims to predict mortality and explore the crucial factors affecting it. Generally, in the health care systems, having a fast and precise ICU mortality prediction for patients plays a key role in care quality, resulting in reduced costs and improved survival chances of the patients. In this study, we used a medical dataset, including patients’ demographic details, underlying diseases, laboratory disorder, and LOS. Since accurate estimates are required to have optimal results, various data pre-processings as the initial steps are used here. Besides, machine learning models are employed to predict the risk of mortality ICU discharge. For AdaBoost model, these measures are considered AUC= 0.966, sensitivity (recall) = 87.88%, Kappa=0.859, F-measure = 89.23% making it, AdaBoost, accounts for the highest rate. Our model outperforms other comparison models by using various scenarios of data processing. The obtained results demonstrate that the high mortality can be caused by underlying diseases such as diabetes mellitus and high blood pressure, moderate Pulmonary Embolism Wells Score risk, platelet blood count less than 100000 (mcl), hypertension (HTN), high level of Bilirubin, smoking, and GCS level between 6 and 9.

Business conversation manager is a system which makes organization's communications possible on computer system platforms and also within defined processes and relationships. This research reviews the literature of BCM systems and introduces and classifies the most important factors for successful implementation by investigating various references. The aim is to identify critical success factors in order to avoid high risk probabilities and facilitate the implementation and finally getting better outcomes. Next, factors are extracted from experts' points of view and they depicted as a model to show critical factors.