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 1. IEEE Access

https://ieeeaccess.ieee.org/about-ieee-access/learn-more-about-ieee-access/

Format                                   : Open Access

Frequency                             : Continuous

Submission to publication : 4-6 weeks (typical)

Topics                                    : All topics in IEEE

Model                                    : Binary Peer Review

Indexing Information         :IET Inspec, Journal Citation Reports/Science Edition (Clarivate Analytics), Science Citation Index Expanded (Clarivate Analytics), Scopus, Web of Science (Clarivate Analytics)

Article processing charge    : US $1,750 (paid)

 2.     Informacije MIDEM Journal of Microelectronics, Electronic Components and Materials

http://journal.midem-drustvo.si/Home.aspx

Format                                   : Open Access

Frequency                             : quarterly

Submission to publication : 6 – 8 weeks

Topics                                    : Electrical and electronics

Model                                    : Blind peer review

Indexing Information         : SCOPUS, Scimago

 Article processing charge   : 200 € (paid)

 

 3.       Advances in Electrical and Computer Engineering

http://www.aece.ro/about.php

Format                                   : open access,

Frequency                             : quarterly

Submission to publication : 53 days

Topics                                    : various fields of science and technology

Model                                    : peer-reviewed

Indexing Information         : Science Citation Index Expanded, SCOPUS

Article processing charge    : 300 EUR

 4.       The International Journal of Electrical and Computer Engineering Systems

https://ijeces.ferit.hr/index.php/ijeces

Format                                   : not open access

Frequency                             : Semi - Annual

Submission to publication : 5-6 months

Topics                                    : electrical and computer engineering

Model                                    : peer-reviewed

Indexing Information         : Scopus , clarivate analytics

Article processing charge    : - (unpaid)

 

5.       International Journal of All Research Education & Scientific Methods

http://www.ijaresm.com/

Format                                   : open access

Frequency                             : monthly

Submission to publication : 1- 2 months

Topics                                    : all the research fields

Model                                    : peer-reviewed

Indexing Information         : UGC Approved List of Journals

Article processing charge    : 1500 Rs / Indian authors, USD 40 foreign authors

check this link for more information

https://youtu.be/FNtEK-a_N6U

Brain Tumor Detection Using CNN with Python Keras and Tensorflow

 Brain tumor

Brain Tumor is a very rare condition that very few people usually affected by it. Of any aging brain, tumors can occur, It usually occurs in fifth or sixth decades of the patients i.e., after fifty years. To improve human judgement in diagnosis advent of new technology into health care can be witnessed. An entrance of computer vision into diagnosis would reduce human error in judgment. The MRI scans are capable of identifying even the smallest aberrations in the human body.  

Brain Tumor Types 

In the brain, you can get two types of tumors. They are

1) Benign Tumors:

Benign tumor is completely removed from the brain, can be completely removed. Usually, do not have any problem in their future life.

2) Malignant Tumors:

Malignant tumor can be removed or it can cause recurrence of the brain, but not spread outside of the brain. It recurs into the brain.

Brain Tumor Diagnosis –

Diagnosis of Brain Tumor can be done by either doing the CT scan of the brain or MRI of the Brain. MRI usually cannot miss any brain tumor, CT scan sometimes misses brain tumors.

Deep learning

Here we train a model to specifically identify these tiny aberrations from MRIs and predict presence of a tumor with high accuracy. Convolutional Neural Network (CNN) is one of the most effective techniques for this problem statement. Thus using image preprocessing and deep learning using keras and tensorflow, we built a highly reliant and robust model to solve this problem. 

The source code for Convolutional Neural Network is 

Initially the modeules such as tensorflow, keras, numpy and matplotlib are imported.

1. import tensorflow as tf
2. import numpy as np
3. from keras.preprocessing import image
4. from keras.preprocessing.image import ImageDataGenerator
5. from matplotlib import pyplot as plt
6. tf.__version__

Then Data Preprocessing is done with the image training dataset

1. train_datagen = ImageDataGenerator(rescale = 1./255, shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True)
2. training_set = train_datagen.flow_from_directory('dataset/training_set',  target_size = (64, 64), batch_size = 32, class_mode = 'binary')

Alternatively the preprocessing is done with the image test dataset

1. test_datagen = ImageDataGenerator(rescale = 1./255)
2. test_set = test_datagen.flow_from_directory('dataset/test_set', target_size = (64, 64),                batch_size = 32, class_mode = 'binary')

Followed by training and testing the CNN is build with differnt cnn layers such as the convolution layer, maxpool layer, flatten layer, connection layer and the output layer. 

1. cnn = tf.keras.models.Sequential()
2. cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu', input_shape=[64, 64, 3]))
3. cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
4. cnn.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation='relu'))
5. cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
6. cnn.add(tf.keras.layers.Flatten())
7. cnn.add(tf.keras.layers.Dense(units=128, activation='relu'))
8. cnn.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))

Once all the layers of CNN are added training is done. For training the optimizer used is 'adam' and 'accuracy' is used as an metrics.

1. cnn.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
2. cnn.fit(x = training_set, validation_data = test_set, epochs = 15)

Finally after training CNN, testing is done with an brain tumor image. This final testing code is meant to predict whether the given MRI image is affected y brain tumor or not.

1. test_image = image.load_img('dataset/2.jpg', target_size = (64, 64))
2. plt.imshow(test_image)
3. plt.title('Test Brain Image'), plt.xticks([]), plt.yticks([])
4. plt.show()
5. test_image = image.img_to_array(test_image)
6. test_image = np.expand_dims(test_image, axis = 0)
7. result = cnn.predict(test_image)
8. training_set.class_indices
9. if result[0][0] == 1:
10.     prediction = 'brain tumor present'
11. else:
12.     prediction = 'no brain tumor'
13. print(prediction)

The output for this code is:

Input test image

Predicted Image

The demo of this code is given in the following link:

https://youtu.be/E1G6IlzCigY

For queries contact : researchscholarguidance@gmail.com

Machine Learning techniques to detect and track tropical cyclones

•  In this work a Satellite tropical cyclone images is taken as an input. These images not only demonstrate a storm's position but also can be employed to estimate its intensity since certain cloud patterns are feature of particular wind speeds.
• Alternatively, the median filter which is a non-linear digital filtering method is used to remove noise from the input satellite image. This is a pre-processing step to develop the results of later processing.
• This is followed by image segmentation to segment the exact cloud pattern of the tropical cyclone.
• This is followed by K-means Nearest Neighbour (KNN) image classification algorithm. This is responsible for splitting the four different labels of given tropical cyclone images. The k-NN algorithm is suited because it is perhaps the simplest of the machine learning (ML) algorithms.
• After classification of the different labels of cyclone a particular image is chosen to detect the cyclone center or centroid. By choosing the exact cyclone center location the effects of the cyclone is studied.

The demo of this work is given in the following link

https://youtu.be/zu81OBmG4UI

 

 Input: Set of four different labels of cyclone images

Output: classified output, cyclone center or centroid detection.

Canny Edge Detection Opencv Python Source code

The Canny edge detector is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in images.

Canny edge detection is a technique to extract useful structural information from different vision objects and dramatically reduce the amount of data to be processed.

·         It has been widely applied in various computer vision systems.

·         Canny has found that the requirements for the application of edge detection on diverse vision systems are relatively similar.

·         Thus, an edge detection solution to address these requirements can be implemented in a wide range of situations.

Full Demo video on YouTube in the below link. 

Please don't forget to like, share and subscribe to the channel for related videos.

https://youtu.be/z2TkXY50ZLw

Full Source Code

# -*- coding: utf-8 -*-

"""

Created on Fri Jan 29 15:38:12 2021

@author: rarekindphdsolutions

"""

import cv2

import numpy as np

from matplotlib import pyplot as plt

img = cv2.imread('sample.jpg',0)

edges = cv2.Canny(img,100,200)

plt.imshow(img,cmap = 'gray')

plt.title('Gray Image'), plt.xticks([]), plt.yticks([])

plt.show()

plt.imshow(edges,cmap = 'gray')

plt.title('Edge Image'), plt.xticks([]), plt.yticks([])

plt.show()

 

Python program to display restaurant menu

Food Ordering System in python

 

 Hello all

Welcome to my blog

Here i am going to upload some of my codes associated to my YouTube channel.