Breast cancer has been recently considered as one of the broadly spread diseases that causes death among women. Early disease diagnosis is a critical aim in building the treatment policies and is extremely related to safety of patient. Therefore, there is a necessity for computer aided detection (CAD) in order to provide accurate and rapid diagnosis for breast cancer. Recently, many classification models utilizing machine learning approaches have been adopted and modified to diagnose breast cancer disease. Moreover, the performance of each model depends on different compositions such as the number and type of data features and the parameters of model. In order to enhance the performance of classification model, this research proposes a model using modified K-means algorithm to create a new training dataset of breast cancer which can highly improve the performance of support vector machine model. A modified K-means algorithm is also proposed to build a high quality training dataset that contributes significantly to reduce the training time of classifiers, and improve the performance of classifier. The proposed model handles the noise and irregularity in data and produce high quality dataset which represents all the cases of disease. The two recognized datasets Wisconsin Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) have been used to examine and appraise the performance of the proposed model. The experimental results show that the proposed model has a significant performance compared to other previous works and with accuracy level of 98.067%, sensitivity of 100%, specificity of 94.811%, precision of 97.011% and finally with area under the curve related to the receiver operating characteristic of 97.406%.

Cell Formation (CF) problem considers as the most important issue in the CellularManufacturing (CM) system particularly the design step. CF deals with the creation ofmachine cells (MCs) and part families (PFs). Numerous methods, algorithms andmathematical models were proposed and used in the literature for solving the CF problem.The current paper used a heuristic method based on the hamming distance to form MCs&PFs, this proposed method calculates the hamming distance for the parts, firstly thenrearranges them based on the results to shape the PFs. Afterward, the hamming distance wascalculated for machines, then the machines rearranged based on the results to form the MCs.Three datasets from the literature were utilized to validate the proposed method. Fiveperformance measures were used for comparison and evaluation, these measures areExceptional Elements EE, Percent of Exceptional elements PE, Voids, Grouping EfficiencyGE and Machine Utilization MU. The results referred to the outperforms of the hammingdistance based method comparing with the best known results in the literature. Among thetotal 20 performance indexes: three are better than, twelve are equal to and five are almostequivalent to the best known results. On the other hand, the proposed hamming distancebased method is effectual particularly in terms of the number of machine cells and PE.