Category: MATLAB Projects

A system which provides ​prediction of the consumption of electricity for rural region ​ by considering the climatic conditions.

The input parameters of which will be :

Temperature
Precipitation
Relative Humidity
Wind speed
Max Temperature
Min Temperature
Sunshine hours
Air pressure
Rainfall
evaporation

The output will be the electricity consumption in kWh for the region (graphical form and numeric value)
Using ANFIS algorithm ​will be applied on the datasets to obtain estimates of the consumption which will give an estimate of the energy requirements of the area

Platform to be used: MATLAB

The main requirements of prediction of the consumption of electricity for rural regionwill be:
1.​ ​Complete training of the data by applying the neuro-fuzzy model
2.​ ​The gui with user side page showing options for selecting the month ,year ,region which will
then produce a graph showing the estimated values of electricity consumption along with
comparison with actual values to show accuracy
3.​ ​The fuzzy rule set and neural model code. (we have been told by our mentor to avoid to use
any inbuilt functions and code the rule set use in Fuzzy logic and neural network algorithms and
rule sets.)
4.​ ​The user side should be able to login to the system so the gui should also include login page
after which it asks the user to enter the region for which consumption is to be known
5. The application should be able to predict at the electrical load one week and month ahead
6.The coding should be done for feed forward network or bpn for neural part for the given data.
7. Minimum rule sets required for fuzzification of the data for clustering.
8. Output required in charts and numeric value
9.
The basic outline for the neural-fuzzy network.Neural training takes place in the input layer.
10.Minimum tuples required 500-600 for training

Problem Statement

The objective of this work is to develop a system for detection of Alzheimer’s disease from MRI scan of a patient using machine learning approach.

The system will take MRI scan of a patient and will attempt to detect Alzheimer’s based on pre-determined set of features. It will mainly focus on physical characteristics such as texture, area and shape of the hippocampus region which is ill affected in the early stages of the Alzheimer’s.

The output of the system will be a binary classification label suggesting either positive or negative case of Alzheimer’s.

It is basically categorized under neural network matlab based project

Proposed Approach

The proposed approach can be divided into two phases: i) training phase and ii) classification phase

1. Training phase: A set of labelled MRI scans is pre-processed for noise reduction, normalization, segmentation and ROI extraction. The training phase can be summarized as follows:
2. Extract features such as texture, mean, skewness, variance, standard deviation, area, perimeter, etc. from the pre-processed MRI scans.
3. Train an ANN classifier using this feature set.

The output of the training phase is a trained classifier capable of predicting binary classification label based on features of MRI scan.

The performance of the trained classifier can be evaluated using measures like accuracy, sensitivity and specificity.

1. Classification: This phase can be summarized as follows:
2. Take as input, MRI scan of a patient.
3. Pre-process the MRI scan.
4. Extract the required features from the patient’s MRI scan.
5. Use the trained classifier to predict the classification label for the patient’s MRI scan.

The output of this phase is a binary classification label suggesting either positive or negative classification label for the patient’s MRI scan.

The metrics used to determine performance of the trained classifier can be used to determine performance of the proposed approach and its comparison with existing methods.

The flowchart in figure 2 depicts the working of the proposed approach.

 

Abstract

Object detection is the process of finding instances of real world objects such as faces, vehicles and buildings in images or videos. Face Detection and Pedestrian Detection comes under the Object detection.

Vehicle detection is a part of Object detection. Vehicle detection mainly focus on detecting the vehicle. Automated Vehicle detection will be done by first obtaining the images or videos of vehicles in traffic areas under surveillance. This can be done by using Image Sensors. After the Image or video being captured the vehicles in the image or video have to detected. Finally,after detecting the vehicles the vehicles have to be counted. Depending on the count the traffic volume will be detected.