Survival of the Fittest in PlayerUnknown BattleGround

The goal of this paper was to predict the placement in the multiplayer game PUBG (playerunknown battleground). In the game, up to one hundred players parachutes onto an island and scavenge for weapons and equipment to kill others, while avoiding getting killed themselves. The available safe area of the game map decreases in size over time, directing surviving players into tighter areas to force encounters. The last player or team standing wins the round. In this paper specifically, we have tried to predict the placement of the player in the ultimate survival test. The data set has been taken from Kaggle. Entire dataset has 29 attributes which are categories to 1 label(winPlacePerc), training set has 4.5 million instances and testing set has 1.9 million. winPlacePerc is continuous category, which makes it harder to predict the survival of the fittest. To overcome this problem, we have applied multiple machine learning models to find the optimum prediction. Model consists of LightGBM Regression (Light Gradient Boosting Machine Regression), MultiLayer Perceptron, M5P (improvement on C4.5) and Random Forest. To measure the error rate, Mean Absolute Error has been used. With the final prediction we have achieved MAE of 0.02047, 0.065, 0.0592 and 0634 respectively.

Origami Inspired Solar Panel Design

The goal of this paper was to take a flat solar panel and make cuts on the panel to make smaller, but still viable solar panels. These smaller solar panels could then be arranged in a tree-like design. The hope was that by having solar panels faced in different directions in 3-dimensional space, the tree system would be able to pick up more sunlight than a flat solar panel. The results were promising, but this project did not take every factor into account. Specifically, optimum shape, temperature and the resistance of system, reflection of sun-rays were not explored in this project. This paper will take an approach from origami paper folding to create the optimum arrangement that will allow the overall system to absorb the maximum energy. Since the system stays stationary throughout the day, it can reduce the maintenance cost and excess energy use because it does not require solar tracking. In this project we have implemented a variety of Evolutionary Algorithms to find the most efficient way to cut a flat solar panel and arrange the resulting smaller panels. Each solution in the population will be tested by computing the amount of solar energy that is absorbed at particular times of the day. The EA will be exploring different combinations of angles and heights of the smaller panels on the tree such that the system can produce the maximum amount of power throughout the day. The performance of our Evolutionary algorithms are comparable to the performance of flat solar panels. Keywords: - Evolutionary Programming, Evolution Strategy, Genetic Algorithm, Solar Panel Optimization.

Skin Cancer Recognition using Deep Residual Network

The advances in technology have enabled people to access internet from every part of the world. But to date, access to healthcare in remote areas is sparse. This proposed solution aims to bridge the gap between specialist doctors and patients. This prototype will be able to detect skin cancer from an image captured by the phone or any other camera. The network is deployed on cloud server-side processing for an even more accurate result. The Deep Residual learning model has been used for predicting the probability of cancer for server side The ResNet has three parametric layers. Each layer has Convolutional Neural Network, Batch Normalization, Maxpool and ReLU. Currently the model achieves an accuracy of 77% on the ISIC - 2017 challenge.

American Sign Language Alphabet Recognition using Deep Learning

Tremendous headway has been made in the field of 3D hand pose estimation but the 3D depth cameras are usually inaccessible. We propose a model to recognize American Sign Language alphabet from RGB images. Images for the training were resized and pre-processed before training the Deep Neural Network. The model was trained on a squeezenet architecture to make it capable of running on mobile devices with an accuracy of 83.29%.