# Smart Classification Paradigms for Protein Samples from 1-D Electrophoresis Gel +
## 2 State of Art in Electrophoresis and Image Retrieval
In the case of the work previously carried out by the same authors of this research published in [14], it should be noted that only a visual recovery system with a 93% recovery was carried out. In this new research proposal, four intelligent molecular weight measurement paradigms are implemented and compared between them, improving the measurement percentage in 96.7% through a back propagation neural network. Molecular Weight Measurement System Using Intelligent Classification in One-Dimensional Sample Electrophoresis  
This work carries out an application of intelligent classification paradigms, the first is by means of the wavelet transform, obtaining the approximation coefficients and generating five parametric statistics with which the input variables will be, in the first case, using the Euclidean metric and generating the matrix and classifying the molecular weight of the samples through four ranges based solely on their minimum value. In the ANN application, we take two types, back propagation and self-organization maps (SOM) where five approximation values are provided as input, in the same way as in the decision tree using algorithm J48. This research work is divided into three phases (See Figure 1), the first phase focuses on the creation and obtainment of electrophoresis samples, starting from a chemical process in the electrophoresis chamber to generate the polyacrylamide gel. Each gel contains 10 lanes, these lanes are scanned and each process contains protein information. Once the gel is scanned, each lane is separated. The RGB color map is used. From it, the gray levels are obtained. The second phase of the investigation consists of the application of the wavelet transform in the daubechies variant, decomposing the image of each sample up to level 3. This phase contains two interfaces, the first to load an electrophoresis sample and present its visual information: the minimum, maximum, mean, median, and standard deviation values. These values are obtained using the approximation coefficients, likewise, the detected molecular weight is presented. The second interface retrieves the five electrophoresis samples that have the most similarity to the query sample in the first interface. In the third phase we apply classification paradigms, with the four detection of molecular weight groups generated, the first paradigm is a CBIR system that uses a Euclidean distance to generate a matrix, followed by ordering and searching for the five similar values of molecular weights. The second paradigm is the neural networks, where five input data are fed to it, and four classification groups, as well as the decision tree. Figure 1: Diagram of the system, divided into three phases: First phase (in green square) is about electrophoresis process. Second phase (in blue square) is about feature extraction with wavelet transform. Third phase (in purple square) is about the three intelligent paradigms tested: (**a**) Content Based Image retrieval System, (**b**) Neural Networks (Backpropagation and Self Organization Maps) and (**c**) J48 Decision Tree. To determine the classification groups, we know the operation of a molecular marker, four classification groups were defined to determine the molecular weight of the sample. For the CBIR-WT model, we observed that only the minimum value of each sample helps to classify and calculate the weight, for the back propagation, SOM, and J48, the five characteristics previously obtained were used. The four groups are classified as follows, taking the minimum value as the main characteristic. Thus, it is clear that the second interface inside the system allows us to classify the electrophoresis samples from our database and we are able to recover similar samples with a similar molecular weight.