No abstract

Each year number of deaths is increasing extremely because of breast cancer. It is the most frequent type of all cancers and the major cause of death in women worldwide. Any development for prediction and diagnosis of cancer disease is capital important for a healthy life. Consequently, high accuracy in cancer prediction is important to update the treatment aspect and the survivability standard of patients. Machine learning techniques can bring a large contribute on the process of prediction and early diagnosis of breast cancer, became a research hotspot and has been proved as astrong technique. In this study, we applied five machine learning algorithms: Support Vector Machine (SVM), RandomForest, Logistic Regression, Decision tree (C4.5) and K-Nearest Neighbours (KNN) on the Breast Cancer WisconsinDiagnostic dataset, after obtaining the results, a performance evaluation and comparison is carried out between these different classifiers. The main objective of thisresearch paperisto predict and diagnosis breast cancer, using machine- learning algorithms, and find out the most effective whit respect to confusion matrix, accuracy and precision. It is observed that Support vector Machine outperformed all other classifiers and achieved the highest accuracy (97.2%). All the work is done in the Anaconda environment based on python programming language and Scikit-learn library. Keywords: Decision Tree, KNN, SVM, Malignant, Benign , Logistic Regression

Infrastructural development in agriculture will directly help achieve sustainable development goals (SDGs) in the least developed countries (LDCs) as the majority of the population in these regions depend on agriculture. This study presents the case of Nepal, one of the LDCs and suggests the establishment of a urea manufacturing plant for improving agriculture productivity and fulfilling the SDGs of zero hunger, no poverty and decent work, and economic growth. Herein, in the context of Nepal, we have reviewed: (i) the status of SDGs of Nepal, (ii) agricultural productivity associated with usage and supply of urea, (iii) technologies associated with urea production, (iv) the feasibility of establishing a urea plant based on the raw material availability and sustainability and (v) the opportunity for economic and technological development. The hydropower-powered electrolysis and CO2 capture from cement industry flue gas were determined to be the strategically feasible and sustainable pathway for urea production and consequently, the fulfillment of SDGs in the context of Nepal. A detailed project study on the economics of the electrolysis-based urea manufacturing process is recommended to foster a sustainable development national plan for Nepal. Although this report highlights the various aspects of urea production in Nepal, this study can be useful for other LDCs dependent on agriculture to achieve SDGs.