Coding Club Of Competitive Programmers

Objective :-   Application of Convolution Neural Network in disease detection such as pneumonia/covid detection through Chest X-ray/ heart beat classification etc. Abstract :   CNNs are powerful image processing, artificial intelligence (AI) that use deep learning to perform both generative and descriptive tasks, often using machine vison that includes image and video recognition, along with recommender systems and natural language processing (NLP). A CNN uses a system much like a multilayer   perceptron   that has been designed for reduced processing requirements. The layers of a CNN consist of an input layer, an output layer and a hidden layer that includes multiple convolutional layers, pooling layers, fully connected layers and normalization layers. The removal of limitations and increase in efficiency for image processing results in a system that is far more effective, simpler to trains limited for image processing and natural language processing. a).Application...

Objective: Classify the objects using deep learning techniques. Theory: Image classification involves assigning a class label to an image, whereas object localization involves drawing a bounding box around one or more objects in an image. Object detection is more challenging and combines these two tasks and draws a bounding box around each object of interest in the image and assigns them a class label. Together, all of these problems are referred to as object recognition. Image Classification: Predict the type or class of an object in an image. Input: An image with a single object, such as a photograph. Output: A class label (e.g. one or more integers that are mapped to class labels). Object Localization: Locate the presence of objects in an image and indicate their location with a bounding box. Input: An image with one or more objects, such as a photograph. Output: One or more bounding boxes (e.g. defined by a point, width, and height). Object Detection: Locate the presence of objects...

Objective: Application of Multi-Layer Perceptron on classification Problem Theory: Multi-layer perceptron (MLP) is a supplement of a feed-forward neural network. It consists of three types of layers—the input layer, output layer, and hidden layer, as shown in Fig. below. The input layer receives the input signal to be processed. The required task such as prediction and classification is performed by the output layer. An arbitrary number of hidden layers that are placed in between the input and output layer are the true computational engine of the MLP. Similar to a feed-forward network in an MLP the data flows in the forward direction from input to output layer. The neurons in the MLP are trained with the backpropagation learning algorithm. MLPs are designed to approximate any continuous function and can solve problems that are not linearly separable. The major use cases of MLP are pattern classification, recognition, prediction, and approximation. The computations taking place at every...

Objective: Solve the problem of human recognition from their faces using machine learning techniques. Theory: Let us introduce a new benchmark data set of face images with variable makeup, hairstyles and occlusions, named BookClub artistic makeup data, and then examine the performance of the ANNs under different conditions. Makeup and other occlusions can be used not only to disguise a person's identity from the ANN algorithms, but also to spoof a wrong identification. ANN Algorithm: Artificial Neural Network (ANN) are capable of learning patterns of interest from data in the presence of variations. An Artificial Neural Network in the field of Artificial intelligence where it attempts to mimic the network of neurons makes up a human brain so that computers will have an option to understand things and make decisions in a human-like manner. The artificial neural network is designed by programming computers to behave simply like interconnected brain cells. Artificial Neural Network p...

  Experiment-2 Objective: Solve the weather problem to predict the possibility of a rain happening under known parameters for e.g., temperature, humidity, wind flow, sunny or cloudy etc. using Bayesian Learning. Theory: The basic idea of Bayesian networks (BNs) (BNs) is to reproduce the most important dependencies and independencies among a set of variables in a graphical form (a directed acyclic graph) which is easy to understand and interpret. Let us consider the subset of climatic stations shown in the graph in Fıgure, where the variables (rainfall) are represented pictorially by a set of nodes; one node for each variable (for clarity of exposition, the set of nodes is denoted {y1, yn}). These nodes are connected by arrows, which represent a cause and effect relationship. That is, if there is an arrow from node yi to node yj , we say that yi is the cause of yj , or equivalently, yj is the effect of yi. Another popular terminology of this is to say that yi is a parent of yj or y...

Objective: For a given network of cities, find an optimal path to reach from a given source city to any other destination city using an admissible heuristic. Theory: Heuristics: The heuristic function h(n) tells A* an estimate of the minimum cost from any vertex n to the goal. It’s important to choose a good heuristic function. The heuristic can be used to control A*’s behavior. At one extreme, if h(n) is 0, then only g(n) plays a role, and A* turns into Dijkstra’s Algorithm, which is guaranteed to find a shortest path. If h(n) is always lower than (or equal to) the cost of moving from n to the goal, then A* is guaranteed to find a shortest path. The lower h(n) is, the more node A* expands, making it slower. If h(n) is exactly equal to the cost of moving from n to the goal, then A* will only follow the best path and never expand anything else, making it very fast. Although you can’t make this happen in all cases, you can make it exact in some special cases. It’s nice to know that given...