CA : A Cancer Journal for Clinicians

This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

/pdf/online-short-term-solar-power-forecasting-1ljgpbshkb.pdf

Online Short-term Solar Power Forecasting

Screening for breast cancer.

Statistical methods in medical research , Statistical methods in medical research , کتابخانه دیجیتال جندی شاپور اهواز

Statistical methods in medical research

Whale Optimization Algorithm (WOA) is an optimization algorithm developed by Mirjalili and Lewis in 2016. An overview of WOA is described in this paper, rooted from the bubble-net hunting strategy, besides an overview of WOA applications that are used to solve optimization problems in various categories. The best solution has been determined to make something as functional and effective as possible through the optimization process by minimizing or maximizing the parameters involved in the problems. Research and engineering attention have been paid to Meta-heuristics for purposes of decision-making given the growing complexity of models and the needs for quick decision making in the engineering. An updated review of research of WOA is provided in this paper for hybridization, improved, and variants. The categories included in the reviews are Engineering, Clustering, Classification, Robot Path, Image Processing, Networks, Task Scheduling, and other engineering applications. According to the reviewed literature, WOA is mostly used in the engineering area to solve optimization problems. Providing an overview and summarizing the review of WOA applications are the aims of this paper.

A comprehensive survey: Whale Optimization Algorithm and its applications

Analysis of the evolution of breast tumours using strain tensors.

Fibromyalgia syndrome in medical students

Left atrial (LA) and atrial scar segmentation from late gadolinium-enhanced magnetic resonance imaging (LGE MRI) is an important task in clinical practice. Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of LA scars provide important information on the pathophysiology and progression of atrial fibrillation (AF). LAScarQS 2022: Left Atrial and Scar Quantification & Segmentation Challenge provided the dataset to evaluate the segmentation model to segment the LA and scars. In this paper, we have developed a semi-supervised segmentation approach using the pseudo labeling approach. We have trained two different models for LA segmentation. In the first model, we have trained 3DResUnet with deep supervision techniques to get the pseudo label using training and validation datasets and in the second model, we have trained the nnUNet model that uses the pseudo segmentation labels of the first model with true labels for LA segmentation. The proposed solution provides optimal performance for the LA segmentation task and achieved a 0.88 Dice score on the validation dataset. The source code will be publicly available at https://github.com/RespectKnowledge/Semi-supervised_Segmentation_LAS-carQS-2022-Challenge . 

Automatic Semi-supervised Left Atrial Segmentation Using Deep-Supervision 3DResUnet with Pseudo Labeling Approach for LAScarQS 2022 Challenge

Image registration is an important operation in many aerospace imaging applications, so that, in this paper a promising approach is proposed for satellite images registration. In this approach, we propose to use the Lorentzian norm as a fitness function with an artificial immune system optimization (AIS) technique to reduce the registration outlier and avoid trapping into local minima. To demonstrate the effectiveness of the proposed approach it is compared with the state-of-the-art Lukas-Kanade registration approach for many data sets.

http://ieeexplore.ieee.org/iel7/6542274/6550724/06550796.pdf

Mohamed Abdel-Nasser

Papers

Analysis of the evolution of breast tumours using strain tensors.

Fibromyalgia syndrome in medical students

Automatic Semi-supervised Left Atrial Segmentation Using Deep-Supervision 3DResUnet with Pseudo Labeling Approach for LAScarQS 2022 Challenge

Satellite images registration approach using Lorentzian norm with an artificial immune system

Gamma Effect in Face Detection Methods