19c96fd51791d8d23b97615 20211127053231298 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 11 30 2021 10 4 10.35940/ijrte.10.4 https://www.ijrte.org/download/volume-10-issue-4/ M.Tech. Scholar, Department of Civil Engineering, Rajasthan Technical University, Kota (Rajasthan), India. Swati Jitendra Khatti PhD Fellow, Department of Civil Engineering, Rajasthan Technical University, Kota (Rajasthan), India. Kamaldeep Singh Grover Professor, Department of Civil Engineering, Rajasthan Technical University, Kota (Rajasthan), India. Concrete is a composite material formed by cement, water, and aggregate. Concrete is an important material for any Civil Engineering project. Several concretes are produced as per the functional requirements using waste materials or by-products. Many researchers reported that these waste materials or by-products enhance the concrete properties, but the laboratory procedures for determining the concrete properties are time-consuming. Therefore, numerous researchers used statistical and artificial intelligence methods for predicting concrete properties. In the present research work, the compressive strength of GGBS mixed concrete is computed using AI technologies, namely Regression Analysis (RA), Support Vector Machine (SVM), Decision Tree (DT), and Artificial Neural Networks (ANNs). The cement content (CC), C/F ratio, w/c ratio, GGBS (in Kg & %), admixture, and age (days) are selected as input parameters to construct the RA, SVM, DT, ANNs models for computing the compressive strength of GGBS mixed concrete. The CS_MLR, Link_CS_SVM, 20LF_CS_DT, and GDM_CS_ANN models are identified as the best architectural AI models based on the performance of AI models. The performance of the best architectural AI models is compared to determine the optimum performance model. The correlation coefficient is computed for input and output variables. The compressive strength of GGBS mixed concrete is highly influenced by age (curing days). Comparing the performance of optimum performance AI models and models available in the literature study shows that the optimum performance AI model outperformed the published models. 11 30 2021 241 250 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.D6631.1110421 https://www.ijrte.org/wp-content/uploads/papers/v10i4/D66311110421.pdf Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India. B.Meena Preethi R. Gowtham PG Scholar, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India. S. Aishvarya PG Scholar, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India. S. Karthick PG Scholar, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India. D.G. Sabareesh PG Scholar, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India. The project entitled as “Rainfall Prediction using Machine Learning & Deep Learning Algorithms” is a research project which is developed in Python Language and dataset is stored in Microsoft Excel. This prediction uses various machine learning and deep learning algorithms to find which algorithm predicts with most accurately. Rainfall prediction can be achieved by using binary classification under Data Mining. Predicting the rainfall is very important in several aspects of one’s country and can help from preventing serious natural disasters. For this prediction, Artificial Neural Network using Forward and Backward Propagation, Ada Boost, Gradient Boosting and XGBoost algorithms are used in this model for predicting the rainfall. There are totally five modules used in this project. The Data Analysis Module will analyse the datasets and finding the missing values in the dataset. The Data Pre-processing includes Data Cleaning which is the process of filling the missing values in the dataset. The Feature Transformation Module is used to modify the features of the dataset. The Data Mining Module is used to train the dataset to models using any algorithm for learning the pattern. The Model Evaluation Module is used to measure the performance of the model and finalize the overall best accuracy for the prediction. Dataset used in this prediction is for the country Australia. This main aim of the project is to compare the various boosting algorithms with the neural network and find the best algorithm among them. This prediction can be major advantage to the farmers in order to plant the types of crops according to the needy of water. Overall, we analyse the algorithm which is feasible for qualitatively predicting the rainfall. 11 30 2021 251 254 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.D6611.1110421 https://www.ijrte.org/wp-content/uploads/papers/v10i4/D66111110421.pdf