Globally, breast cancer is one of the most significant causes of death among women. Early detection accompanied by prompt treatment can reduce the risk of death due to breast cancer. Currently, machine learning in cloud computing plays a pivotal role in disease diagnosis, but predominantly among the people living in remote areas where medical facilities are scarce. Diagnosis systems based on machine learning act as secondary readers and assist radiologists in the proper diagnosis of diseases, whereas cloud-based systems can support telehealth services and remote diagnostics. Techniques based on artificial neural networks (ANN) have attracted many researchers to explore their capability for disease diagnosis. Extreme learning machine (ELM) is one of the variants of ANN that has a huge potential for solving various classification problems. The framework proposed in this paper amalgamates three research domains: Firstly, ELM is applied for the diagnosis of breast cancer. Secondly, to eliminate insignificant features, the gain ratio feature selection method is employed. Lastly, a cloud computing-based system for remote diagnosis of breast cancer using ELM is proposed. The performance of the cloud-based ELM is compared with some state-of-the-art technologies for disease diagnosis. The results achieved on the Wisconsin Diagnostic Breast Cancer (WBCD) dataset indicate that the cloud-based ELM technique outperforms other results. The best performance results of ELM were found for both the standalone and cloud environments, which were compared. The important findings of the experimental results indicate that the accuracy achieved is 0.9868, the recall is 0.9130, the precision is 0.9054, and the F1-score is 0.8129.

In this manuscript a recognition system for offline handwritten Gurmukhi characters and numerals using gradient information as mode of feature extraction technique is proposed. Two ways of extracting features using gradient information are explained in this paper. Both methods operate by accumulating gradient information from an image by dividing it into sub-images (blocks) and finally concatenating the obtained gradient features obtained from each block to form a vector of feature values with dimensionality 200. The efficiency of this feature vector is tested on two separate handwritten databases of Gurmukhi characters and Gurmukhi numerals containing 7000 & 2000 sample binary images respectively. Recognition rates of 97.38% for database of Gurmukhi characters and 99.65% for Gurmukhi numerals are obtained. Work has also been extended to test the effectiveness of the Gradient feature extraction technique on dataset of Gurmukhi characters and numerals combined together