The importance of education can not be denied as it plays the vital role in the development of an individual and ultimate progress of a nation is absolutely depends on the quality of education that is planned for young generation. There are different strategies, methods and techniques which make the teaching learning more effective the rules and guidance provided by pedagogy helps in achieving the aims, goals and objectives of education. The main objectives of this study were (a) to find out the relative effects of multiple pedagogical cycles in classroom teaching on students' learning at secondary level. (b) To investigate whether the students can retain the learning for a longer time when taught through pedagogical cycle. The population of the study was the students of secondary level in the schools of Khyber Pakhtunkhwa. The study was delimited to the students of 10 th class of Urdu medium schools .Only one Section of 10 th class of Government Girls High School K.D.A. Kohat was taken as sample of the study. Sample students were further divided into two groups, i.e. experimental and controlled groups on the basis of their pre-test scores. Experimental group was taught by pedagogical cycle while the Control group was taught by traditional method for a period of six weeks. At the end of the treatment, a post-test was administered and scores of pre-test, post-test were served as data of the study. T-test and analysis of variance were applied to know the significance of difference between the scores of groups at 0.05 level.

Breast cancer is categorized as an aggressive disease, and it is one of the leading causes of death. Accurate survival predictions for both long-term and short-term survivors, when delivered on time, can help physicians make effective treatment decisions for their patients. Therefore, there is a dire need to design an efficient and rapid computational model for breast cancer prognosis. In this study, we propose an ensemble model for breast cancer survivability prediction (EBCSP) that utilizes multi-modal data and stacks the output of multiple neural networks. Specifically, we design a convolutional neural network (CNN) for clinical modalities, a deep neural network (DNN) for copy number variations (CNV), and a long short-term memory (LSTM) architecture for gene expression modalities to effectively handle multi-dimensional data. The independent models’ results are then used for binary classification (long term > 5 years and short term < 5 years) based on survivability using the random forest method. The EBCSP model’s successful application outperforms models that utilize a single data modality for prediction and existing benchmarks.

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