CT liver tumor segmentation hybrid approach using neutrosophic sets, fast fuzzy c-means and adaptive watershed algorithm

Liver tumor segmentation from computed tomography (CT) images is a critical and challenging task. Due to the fuzziness in the liver pixel range, the neighboring organs of the liver with the same intensity, high noise and large variance of tumors. The segmentation process is necessary for the detection, identification, and measurement of objects in CT images. We perform an extensive review of the CT liver segmentation literature

2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation: executive summary.

S

2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation: executive summary.

S

withdrawn 2017 hrs ehra ecas aphrs solaece expert consensus statement on catheter and surgical ablation of atrial fibrillation

n/

Case Study of a Solar Chimney in Mansoura, Egypt

 A solar chimney system was established in Mansoura University which was designed as a square collector with an area of 100m2, the side length 10 m, height 8 m and the diameter 0.3 m. It’s floor was painted with black bitumen and covered with plastic having a thickness of 150 microns. The height of air inlet collector (periphery) was 0.15 m to investigate the effect of environmental temperature on the performance of solar chimney (SCS). Tests were carried out at a wide range of ambient temperatures and solar radiation. In this study, conducted on September 20 and 21, ambient temperature, air inlet temperature, the temperature under collector, the temperature under the chimney, air velocity and solar radiation during the two days were recorded with time. Experimental measurements indicate that as long as the collector air temperature increases, the velocity of air inside the chimney increases

A novel machine learning-based feature extraction method for classifying intracranial hemorrhage computed tomography images

One of the most serious forms of brain stroke is intracranial hemorrhage (ICH). When an artery bursts, the brain and the tissue around the artery start bleeding. This study proposes a joint feature selection strategy to classify computed tomography (CT) images of intracranial hemorrhage. The joint feature set is composed of transform and texture features. Joint features are constructed from a combination of grey level co-occurrence matrix (GLCM) features, discrete wavelet features (DWT), and discrete cosine features (DCT). Brain hemorrhage CT image classification uses ensemble-based machine learning (ML) techniques. On the training dataset, a Synthetic Minority Over-Sampling Technique (SMOTE) is applied to treat the problem of oversampling by adding fresh data. Additionally, the sequential forward feature selection technique is used to obtain feature subsets. The classification accuracy is further examined for varied feature vector sizes. Confusion matrix, precision, and recall in categorization are employed as performance evaluation measurements. The ML-based ensemble classifiers can produce highly accurate results with the aid of the proposed novel feature extraction mechanism. When taking into consideration a crucial feature set consisting of six features, it can be seen that Random Forest obtained the greatest accuracy, which is 87.22%

Role of some selected Bifidobacterium strains in modulating immunosenescence of aged albino rats

Probiotic administration has been associated with enhanced immune function in elderly subjects. However, approaches for selection of an “ideal” strain of bifidobacteria are still difficult. The aim of the present study is to investigate the possible modulatory effects of three strains of Bifidobacterium species (Bifidobacterium adolescentis ATCC 15704, Bifidobacterium breve ATCC 15700 and Bifidobacterium longum ATCC 15707) on haematological and immunological parameters of aged albino rats corresponding to normal adult ones. The animals were divided into six groups; three groups of aged rats were fed yoghurt inoculated with one of the Bifidobacterium strains; one group of aged rats was fed yoghurt alone (control aged); two groups of adult and aged rats were provided with normal diet and assigned as normal groups. The total leucocyte count was significantly increased in the three bifidobacteria-treated aged groups as compared with both normal and control aged rats. Serum IgA level was considerably increased in all treated rats. On the contrary, serum IgE level was significantly decreased in rats supplemented with yoghurt inoculated with B. adolescentis or B. breve. Both B. adolescentis and B. breve groups showed significant enhanced production of TNF-α. Furthermore, the production of cytokine IL-8 was significantly increased in the B. adolescentis group. Interestingly, it was apparent that only B. adolescentis had the most pronounced effect on aged rats to regain nearly normal values as measured in normal adult rats. Conclusively, the present work indicates that dietary consumption of selected bifidobacteria strains may have a particular application in the elderly especially in terms of immunomodulation

Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model

Feature selection is a well-known prepossessing procedure, and it is considered a challenging problem in many domains, such as data mining, text mining, medicine, biology, public health, image processing, data clustering, and others. This paper proposes a novel feature selection method, called AOAGA, using an improved metaheuristic optimization method that combines the conventional Arithmetic Optimization Algorithm (AOA) with the Genetic Algorithm (GA) operators. The AOA is a recently proposed optimizer; it has been employed to solve several benchmark and engineering problems and has shown a promising performance. The main aim behind the modification of the AOA is to enhance its search strategies. The conventional version suffers from weaknesses, the local search strategy, and the trade-off between the search strategies. Therefore, the operators of the GA can overcome the shortcomings of the conventional AOA. The proposed AOAGA was evaluated with several well-known benchmark datasets, using several standard evaluation criteria, namely accuracy, number of selected features, and fitness function. Finally, the results were compared with the state-of-the-art techniques to prove the performance of the proposed AOAGA method. Moreover, to further assess the performance of the proposed AOAGA method, two real-world problems containing gene datasets were used. The findings of this paper illustrated that the proposed AOAGA method finds new best solutions for several test cases, and it got promising results compared to other comparative methods published in the literature