Multi-spectral images classification based on intelligent water drops algorithm

Mosul's city land covers soil, cultivated land, stony, pastoral land, water, and ploughed agricultural land. We have classified multispectral images captured by the sensor (TM) carried on the Landsat satellite. Integrated approach of intelligent water drops (IWDs) algorithm is used to identify natural terrain. In this research, IWDs have been suggested to find the best results for multispectral image classification. The purpose of using an algorithm, give accurate and fast results by comparing the IWD algorithm with the K-mean algorithm. The IWD algorithm is programmed using the Matlab2017b software environment to demonstrate the proposed methodology's effectiveness. The proposed integrated concept has been applied to satellite images of Mosul city in Iraq. By comparing the IWD with the K-mean, we found clear time superiority of the IWD algorithm, equal 1.4122 with (K-mean) time equal 18.9475. Furthermore, the water drop algorithm's classification accuracy is 95%, while the K-mean classification accuracy is 83.3%. Based on the analysis and results, we conclude the IWD is a robust promising and approach to detecting remote sensing image changes and multispectral image classification.

Two way Threshold Based Intelligent Water Drops Feature Selection Algorithm for Accurate Detection of Breast Cancer

Breast cancer is one of the common reasons for deaths of women over the globe. It has been found that a Computer- Aided Diagnosis (CAD) system can be designed using X-ray mammograms for early-stage detection of breast cancer, which can decrease the death rate to a large extend. This paper work proposes a novel 2-way threshold based Intelligent water drops (IWD) algorithm for feature selection to design an effective and efficient CAD system that can detect breast cancer in early stage. This approach first extracts the Local Binary Patterns (LBP) in wavelet domain from mammograms and then apply our introduced 2-way threshold based (IWD) algorithm to extract most important subset of features from the extracted features set. 2-way thresholding is a technique to find a lower bound (LB) and an upper bound (UB) on the number of features to be selected in the optimal subset. So, using these threshold values IWD is capable of producing multiple optimal subsets of features rather than producing a single optimal subset of features. The best subset among the above subsets is then used train and deploy Support Vector Machine (SVM) to classify new mammograms. The results have shown that the proposed model outperforms many of the existing CAD systems. Further we have compared our introduced feature selection technique with other meta heuristic features selection techniques such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Genetic Algorithm (GA), Gravitational Search Algorithm (GSA), Inclined Planes System Optimization (IPO) and Grey Wolf Optimization Algorithm (GWO) and found that it outperforms the others. The accuracy, precision, recall, specificity and F1-score of our proposed framework are measured as 99%, 98.7% ,98.123%, 96.2% and 98.4% respectively.