19c96fd517d854497e8-3854 20220131051905096 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 01 30 2022 11 3 Associate Professor & Dean, Department of Lords School of Computer Applications & IT, Lords University, Alwar, Rajasthan, India. Dr. Gurjeet Singh The paper focuses on predicting the Nifty 50 Index by using 8 Supervised Machine Learning Models. The techniques used for empirical study are Adaptive Boost (AdaBoost), k-Nearest Neighbors (kNN), Linear Regression (LR), Artificial Neural Network (ANN), Random Forest (RF), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM) and Decision Trees (DT). Experiments are based on historical data of Nifty 50 Index of Indian Stock Market from 22nd April, 1996 to 16th April, 2021, which is time series data of around 25 years. During the period there were 6220 trading days excluding all the non trading days. The entire trading dataset was divided into 4 subsets of different size-25% of entire data, 50% of entire data, 75% of entire data and entire data. Each subset was further divided into 2 parts-training data and testing data. After applying 3 tests- Test on Training Data, Test on Testing Data and Cross Validation Test on each subset, the prediction performance of the used models were compared and after comparison, very interesting results were found. The evaluation results indicate that Adaptive Boost, k- Nearest Neighbors, Random Forest and Decision Trees under performed with increase in the size of data set. Linear Regression and Artificial Neural Network shown almost similar prediction results among all the models but Artificial Neural Network took more time in training and validating the model. Thereafter Support Vector Machine performed better among rest of the models but with increase in the size of data set, Stochastic Gradient Descent performed better than Support Vector Machine. 01 30 2022 18 28 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C9733.0111322 https://www.ijitee.org/portfolio-item/c97330111322/ Department of Information and Communication Technology, Comilla University, Cumilla, Bangladesh. Md. Rakib Hasan Ismath Ara Itu Department of Management Information Systems, University of Dhaka, Dhaka, Bangladesh. Nowadays, social media platforms have evolved into an esoteric method for audiences to consume information. News spreading through the social network are also a great source of information as well. For the advancement of Internet access, the information consumption pattern is dramatically changing. As a consequence of those, fake news has become one of the prime concerns because of its potentiality to endanger a society in different perspectives as well as has a political and social impact. Because false news causes so much confusion among people, we will train a model to identify all types of fake news in response to public demand. For this respective research, we collect real data from many reliable and reputed online news portals and fake news from unreliable resources. For converting text to vector format Bag of Words is used. Besides TF-IDF is used for extracting the feature and then CNN is used for classification. With an 83.14% accuracy our model can efficiently detect fake and real news. This work paves a path for an easy automatic fake news detection system which will be very helpful for us to prevent spreading the false information and helps to find the truth. 01 30 2022 29 35 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C9640.0111322 https://www.ijitee.org/portfolio-item/c97400111322/ Department of Mathematics, Assam University, Assam, India. Geeti Gogoi In this present study we examine analytically the MHD flow problem close by a stagnation point on a stretching sheet which is nonlinear. Similarity transformation is used to modify the fluid flow governing equations into a system of ordinary differential equations .The modified equations are resolved with the assist of MATLAB bvp4c. The influence of viscosity parameter, thermal conductivity parameter, suction parameter and magnetic parameter on velocity and temperature are computed and presented through graphs. 01 30 2022 36 41 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C9743.0111322 https://www.ijitee.org/portfolio-item/c97430111322/