Advantages And Disadvantages Of Cnn Models

The objective of the neural network is to transform the input to meaningful output. Neural networks are often used for statistical analysis and data modeling. Neural network has many uses in data processing, robotics, and medical diagnosis [2]. From the starting of the neural network there are various types found, but each and every types has some advantages and disadvantages. Deep learning and -neural network software are the categories of artificial neural network. The parallel process also allows ANNs to process the large amount of data very efficiently. The artificial neural network is built with a systematic

For standard backpropagation algorithm, an approximate steepest descent rule will be used. The performance index is approximated by equation

Table 2 illustrates that the second, third and fourth kernel parameter gives the highest accuracy. The performance of the classifier can be evaluated by using Accuracy. The equation (1) can be used to calculate the accuracy. Any of the kernel parameters can be used for further classification to

Thus our proposed optimal feature subset selection based on multi-level feature subset selection produced better results based on number of subset feature produced and classifier performance. The future scope of the work is to use these features to annotate the image regions, so that the image retrieval system can retrieve relevant images based on image semantics.

Training an artificial Neural Network involves choosing and allowing models for models which there are several associated algorithms.

The training data contained both labeled data D_la={〖x_i,y_i}〗_(i=1)^kl and unlabeled data D_un= {〖x_j}〗_(j=kl+1)^(kl+u) where x_(i ) is the feature descriptor of image I and y_i={1,…,k} is its label .k is the number of categories. l is the number of labeled data in each category, and u is the number of unlabeled data. Our method aims to learn a high-level image representation S by exploiting the few labeled data D_land great quantities of unlabeled ones, which is then fed into different classifiers to obtain final classification results. The procedure of semisupervised feature learning by SSEP is shown in Fig. 1. First, a new sampling algorithm based on GNA [19] is proposed to produce T WT sets P^t={(〖s_i^t,c_i^t)}〗_(i=1)^kp , t ∈{1,…..,T}

We have used support vector machine (SVM) for classification task. We have used RBF kernel for training the classifier. 10 fold cross-validation is used for determining cost parameter C and best kernel width for RBF kernel function. If we perform classification without any feature selection or feature extraction then the accuracy is 48.99% and 65.82% for AVIRIS and HYDICE image respectively which is very poor and it highly motivates us to apply feature reduction technique. In table II we have shown the classification accuracy for each of the pair of class for PCA, MI and PCA-QMI.

This algorithm was simulated with Matlab. These datasets and the mentioned characteristics are considered and the algorithm of each dataset with different slopes for the activation fumcion of interest were evaluated so that the best slope can be obtained. After running the program for several times and computing the average to obtain the best result, the optimum slope was evaluated for each dataset and the best slopes for Breast Cancer, Diabetes, Bupa, and

Dr. Ahrendt noted the huge advancements that have been made over the last decade, but made sure to note that the math behind AI and machine learning is quite old mathematics. “Now that we can compute things so quickly… we can see the bloom of AI and machine learning.”

Even though we had a lot of success in our study, we ran into several limitations. Our first limitation was our choice of software. We chose KNIME as our software because anyone can access it without any real difficulty, and it is free and downloadable on any computer. With that being said, we first ran into the issue of having too much data. The software was incapable and too slow to use the majority of the data we had. The other limitation we had with KNIME was being unable to run our initial deep learning model, which was already discussed in the previous

The calculated RSS for this model was 594.9, which is significantly lower than the original model. This shows that the training of the model was effective and improved the overall accuracy of the model.

Abstract— Nowadays, with the increase in the dreadful diseases, huge amount of database is produced in hospitals and are exponentially increasing day by day. Utilizing these medical images after efficient classification plays a major role in case based reasoning and supports in clinical decision making. Therefore, it is important to classify these images and access them accurately. A modality classifier helps to classify the medical images based on the modality. In our study, we analyzed spatial and spectral features of Magnetic Resonance (MR) images and Computer Tomography (CT) scan and also the fusion of these features is performed. It is found that these modalities have different characteristics which help in classification. Initially, these images are preprocessing using the Median filter and spatial and spectral features were extracted and feature fusion is performed. The

The interview session has been done by include an open-ended and closed-ended question which are related to the implemented project. Next, sampling technique is executed by system analyst who does evaluation to the current system or prototype. These processes give feedback in evaluation form that filled after tested the system. Lastly, observation is performed by using questionnaire form. According to Burch (1992), the questionnaire is analyzed and transform into structured form that easy to understand. After all information has been collected, structuring of system requirement takes place. It focused on development process modeling which perform “graphically representing the process, or actions, that capture, manipulate, store, and distribute data between a system and environment” (Hoffer, George, & Valacich, 2012, p. 182). In this step, Data flow diagram (DFD) is structured by system analyst using special tools and techniques to create a decision table. According to Hoffer, George and Valacich (2012), decision table is a “diagram of process logic where the logic is reasonably complicated” (p. 200). This table is useful to help system analyst to make a decision toward the project. Then all information’s gained from this phase are documented in System Analysis Report (SAR) that acts as a guideline or reference to the future system development project (Burch, 1992).

To construct an optimal hyperplane, SVM employs an iterative training algorithm, which is used to minimize an error function. According to the form of the error function, SVM models can be classified into four distinct groups: