Scopus Research — Mohammed Maitham Mohammed

Due to climate change, it is important to study the relationship between floods and sea-level rise in coastal city resilience. In this research sea surface temperature (SST) from MODIS, wind speed, precipitation, and sea-level rise from satellite altimetry are investigated for dynamic sea-level variability. An annual SST increase of 0.1C° is observed around the Gothenburg coast. Also in the middle of the North Sea, an annual increase of about 0.2C° is evident. The annual sea surface height (SSH) trend is 3 mm on the Gothenburg coast. We have a strong positive spatial correlation between SST and SSH near the Gothenburg coast. In the next step, dynamic sea-level variability is predicted with a convolution neural network and long short term memory. Root mean square error of wind speed, precipitation, SST, and mean sea-level forecasts are ±0.84 m/s, ±48.75 mm, ±3.48C° and ±24 mm, respectively. The 5-year trends of mean seal level show a significant increase from 28 mm/year to 46 mm/year in the last 5 year periods and the rate of increase has doubled. In the final step, the water rise of 5–10 m in Gothenburg city was simulated, and in the worst scenario, more than 50 % of the city will be damaged. © 2022 The Authors

Medicinal plants have always been studied and considered due to their high importance for preserving human health. However, identifying medicinal plants is very time-consuming, tedious and requires an experienced specialist. Hence, a vision-based system can support researchers and ordinary people in recognising herb plants quickly and accurately. Thus, this study proposes an intelligent vision-based system to identify herb plants by developing an automatic Convolutional Neural Network (CNN). The proposed Deep Learning (DL) model consists of a CNN block for feature extraction and a classifier block for classifying the extracted features. The classifier block includes a Global Average Pooling (GAP) layer, a dense layer, a dropout layer, and a softmax layer. The solution has been tested on 3 levels of definitions (64 × 64, 128 × 128 and 256 × 256 pixel) of images for leaf recognition of five different medicinal plants. As a result, the vision-based system achieved more than 99.3% accuracy for all the image definitions. Hence, the proposed method effectively identifies medicinal plants in real-time and is capable of replacing traditional methods. © 2022 by the authors.

Predicting the compressive strength of concrete (CSoC) is of high significance in civil engineering. The CSoC is a highly dependent and non-linear parameter that requires powerful models for its simulation. In this work, two novel optimization techniques, namely evaporation rate-based water cycle algorithm (ER-WCA) and equilibrium optimizer (EO) are employed for optimally finding the parameters of a multi-layer perceptron (MLP) neural processor. The efficiency of these techniques is examined by comparing the results of the ensembles to a conventionally trained MLP. It was observed that the ER-WCA and EO optimizers can enhance the training accuracy of the MLP by 11.18 and 3.12% (in terms of reducing the root mean square error), respectively. Also, the correlation of the testing results climbed from 78.80% to 82.59 and 80.71%. From there, it can be deduced that both ER-WCA-MLP and EO-MLP can be promising alternatives to the traditional approaches. Moreover, although the ER-WCA enjoys a larger accuracy, the EO was more efficient in terms of complexity, and consequently, time-effectiveness. Copyright © 2022 Techno-Press, Ltd.