Abstract-The technological growth generates the massive data in all the fields. Classifying these highdimensional data is a challenging task among the researchers. The high-dimensionality is reduced by a technique is known as attribute reduction or feature selection. This paper proposes a genetic algorithm (GA)-based features selection to improve the accuracy of medical data classification. The main purpose of the proposed method is to select the significant feature subset which gives the higher classification accuracy with the different classifiers. The proposed genetic algorithmbased feature selection removes the irrelevant features and selects the relevant features from original dataset in order to improve the performance of the classifiers in terms of time to build the model, reduced dimension and increased accuracy. The proposed method is implemented using MATLAB and tested using the medical dataset with various classifiers namely Naï ve Bayes, J48, and k-NN and it is evident that the proposed method outperforms other methods compared.
In scientific world, Face recognition becomes an important research topic. The face identification system is an application capable of verifying a human face from a live videos or digital images. One of the best methods is to compare the particular facial attributes of a person with the images and its database. It is widely used in biometrics and security systems. Back in old days, face identification was a challenging concept. Because of the variations in viewpoint and facial expression, the deep learning neural network came into the technology stack it’s been very easy to detect and recognize the faces. The efficiency has increased dramatically. In this paper, ORL database is about the ten images of forty people helps to evaluate our methodology. We use the concept of Back Propagation Neural Network (BPNN) in deep learning model is to recognize the faces and increase the efficiency of the model compared to previously existing face recognition models.
An intelligent monitoring device is an application which is developed for the security purpose and less storage convenience. The proposed system is applicable in any of the areas, which are needed to be monitored all the time using MAP-V Secucam. By combining the application and MAP-V Secucam we can use this system as an intelligent monitoring device. The MAP-V Secucam is used to catch the live images of the area in which it is being implemented, if any motion is detected, the MAP-V Secucam will be turned to recording mode. The recorded footages are stored in a particular folder. As the device detects the motion, it sends the alerts using web service. In this way the system will provide the security against any misdeed.
The frequent traffic jams at major intersections calls for an effective management system. This paper suggests implementing a smart traffic controller by image processing using Raspberry pi module. A camera will be installed on the four sides to capture image sequences. The captured images will then be analyzed using digital image processing for vehicles intensity, and based on this intensity on the road, traffic light can be controlled. It has been customized to control the traffic system by giving sufficient time for each side, depending on the number of cars on each direction to give out a way for ITS [Intelligent Transport System].
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