Diagnosis of Brain Tumors in MR Images Using Metaheuristic Optimization Algorithms - Braik, Malik; Sheta, Alaa; Aljahdali, Sultan (Springer International Publishing, 2020)
Image clustering presents a hot topic that researchers have chased extensively. There is always a need to a promising clustering technique due to its vital role in further image processing steps. This paper presents a compelling clustering approach for brain tumors and breast cancer in Magnetic Resonance Imaging (MRI). Driven by the superiority of nature-inspired algorithms in providing computational tools to deal with optimization problems, we propose Flower Pollination Algorithm (FPA) and Crow Search Algorithm (CSA) to present a clustering method for brain tumors and breast cancer. Evaluation clustering results of CSA and FPA were judged using two apposite criteria and compared with results of K-means, fuzzy c-means and other metaheuristics when applied to cluster the same benchmark datasets. The clustering method-based CSA and FPA yielded encouraging results, significantly outperforming those obtained by K-means and fuzzy c-means and slightly surpassed those of other metaheuristic algorithms.
Keywords: Clustering; Crow Search Algorithm; Flower Pollination Algorithm; Fuzzy C-mean; K-means; dept.csc

River Flow Forecasting: A Comparison Between Feedforward and Layered Recurrent Neural Network - Aljahdali, Sultan; Sheta, Alaa; Turabieh, Hamza (Springer International Publishing, 2020)
Forecasting the daily flows of rivers is a challenging task that have a significant impact on the environment, agriculture, and people life. This paper investigates the river flow forecasting problem using two types of Deep Neural Networks (DNN) structures, Long Short-Term Memory (LSTM) and Layered Recurrent Neural Networks (L-RNN) for two rivers in the USA, Black and Gila rivers. The data sets collected for a period of seven years for Black river (six years for training and one year for testing) and four years for Gila river (three years for training and one year for testing) were used for our experiments. An order selection method based partial auto-correlation sequence was employed to determine the appropriate order for the proposed models in both cases. Mean square errors (MSE), Root mean square errors (RMSE) and Variance (VAF) were used to evaluate to developed models. The obtained results show that the proposed LSTM is able to produce an excellent model in each case study.
Keywords: Forecasting; Long short-term memory; River Flow; dept.csc

Pavement crack detection using a lightweight convolutional neural network - Sheta, Alaa; Turabieh, Hamza; Aljahdali, Sultan; Alangari, Abdulaziz (2020)
Keywords: Convolution; Convolutional neural networks; Crack detection; Data division; Different sizes; Network architecture; New York city; Number of samples; Pavement crack detection; Pavement cracks; Pavements; Road cracks; Training and testing; dept.csc; ⛔ No DOI found