Practical 1  (HPC 1): Design and implement Parallel Breadth First Search and Depth First Search based on existing algorithms using OpenMP. Use a Tree or an undirected graph for BFS and DFS 

(HPC 2): Write a program to implement Parallel Bubble Sort  and Merge Sort using OpenMP. Use existing algorithms and measure the performance of sequential and parallel algorithms

(HPC 3): Implement Min, Max, Sum and Average operations using Parallel Reduction

(HPC 4):Write a CUDA Program For: 1.Addition of Two Large Vectors 2.Matrix Multiplication Using CUDA C

(HPC 5 Any One)

1. Evaluate Performance Enhancement of Parallel Quick Sort Algorithm Using

MPI.

2. Implement Huffman Encoding on GPU.

3. Implement Parallelization of Database Query Optimization

4. Implement Non-Serial Polyadic Dynamic Programming With GPU

Parallelization

(DL1): Linear regression by using Deep Neural network: Implement Boston

housing price prediction problem by Linear regression using Deep Neural

network. Use Boston House price prediction dataset.

(DL 2 Any One)

1.  Classification using Deep neural network: Multiclass classification using

Deep Neural Networks: Example: Use the OCR letter recognition dataset

https://archive.ics.uci.edu/ml/datasets/letter+recognition

2. Binary classification using Deep Neural Networks Example: Classify movie

reviews into positive" reviews and "negative" reviews, just based on the text

content of the reviews. Use IMDB dataset

( DL 3 Any One)

1.Convolutional neural network (CNN): Use MNIST Fashion Dataset and

create a classifier to classify fashion clothing into categories.

2.Convolutional neural network (CNN): Use any dataset of plant disease and

design a plant disease detection system using CNN.

(DL 4 Any One)

1.Project:Human Face recognition

1. 
   import cv2

# Use a valid cascade file path

face_cap = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

video = cv2.VideoCapture(0)

while True:

    ret, frame = video.read()

    if not ret:

        break

    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    faces = face_cap.detectMultiScale(

        gray_frame,

        scaleFactor=1.1,

        minNeighbors=5,

        minSize=(30, 30),

        flags=cv2.CASCADE_SCALE_IMAGE

    )

    for (x, y, w, h) in faces:

        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

    cv2.imshow("Real-Time Face Detection", frame)

    if cv2.waitKey(1) & 0xFF == ord('t'):

        break

video.release()

cv2.destroyAllWindows()

2.Projcet:Gender and Age Detection:Predict If a person is a male and female

and also their Age.

3.Colorizing Old  B&W Images :Color Old Black & White Images To

Colourful Images.