Accepted Papers

Fast Searching on Cage Graph

Xiaoli Sun and Farong Zhong, Department of Computer Science, Zhejiang Normdal University, Jinhua, China.

ABSTRACT

In order to adapt the needs of actual pursuit-evasion problem and the diversity of the area of the pursuit-evasion problem, we investigate the fast searching on the cage graph. First, we study properties of cage graph to get lower bounds on the fast search number which is the minimum number of searchers needed to capture the intruder of cage graph. Then we apply lower bounds to get the fast search number of cage graph. We also provide an algorithm of fast searching on cage graph.

KEYWORDS

Graph searching, Fast searching, Cage graph.

Evaluating the Performance of Feature Extraction Techniques Using Classification Techniques

Harshit Mittal1,Maharaja Agrasen Institute of Technology, New Delhi, India.

ABSTRACT

Dimensionality reduction techniques are widely used in machine learning to reduce the computational complexity of the model and improve its performance by identifying the most relevant features. In this research paper, we compare various dimensionality reduction techniques, including Principal Component Analysis(PCA), Independent Component Analysis(ICA), Local Linear Embedding(LLE), Local Binary Patterns(LBP), and Simple Autoencoder, on the Olivetti dataset, which is a popular benchmark dataset in the field of face recognition. We evaluate the performance of these dimensionality reduction techniques using various classification algorithms, including Support Vector Classifier (SVC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The goal of this research is to determine which combination of dimensionality reduction technique and classification algorithm is the most effective for the Olivetti dataset. Our research provides insights into the performance of various dimensionality reduction techniques and classification algorithms on the Olivetti dataset. These results can be useful in improving the performance of face recognition systems and other applications that deal with high-dimensional data.

KEYWORDS

Principal Component Analysis(PCA); Independent Component Analysis(ICA); Local Linear Embedding(LLE); Local Binary Patterns(LBP); Simple Autoencoder; Support Vector Classifier (SVC); Linear Discriminant Analysis (LDA); Logistic Regression (LR); K-Nearest Neighbors (KNN); Support Vector Machine (SVM); Olivetti dataset.

Kidney Ct Image Analysis Using Cnn

Harshit Mittal1,Maharaja Agrasen Institute of Technology, New Delhi, India.

ABSTRACT

Medical image analysis is a vital component of modern medical practice, and the accuracy of such analysis is critical for accurate diagnosis and treatment. Computed tomography (CT) scans are commonly used to visualize the kidneys and identify abnormalities such as cysts, tumors, and stones. Manual interpretation of CT images can be time-consuming and subject to human error, leading to inaccurate diagnosis and treatment. Deep learning models based on Convolutional Neural Networks (CNNs) have shown promise in improving the accuracy and speed of medical image analysis. In this study, we present a CNN-based model to accurately classify CT images of the kidney into four categories: Normal, Cyst, Tumor, and Stone, using the CT KIDNEY DATASET. The proposed CNN model achieved an accuracy of 99.84% on the test set, with a precision of 0.9964, a recall of 0.9986, and a F1-score of 0.9975 for all categories. The model was able to accurately classify all images in the test set, indicating its high accuracy in identifying abnormalities in CT images of the kidney. The results of this study demonstrate the potential of deep learning models based on CNNs in accurately classifying CT images of the kidney, which could lead to improved diagnosis and treatment outcomes for patients. This study contributes to the growing body of literature on the use of deep learning models in medical image analysis, highlighting the potential of these models in improving the accuracy and efficiency of medical diagnosis.

KEYWORDS

Medical image analysis; Computed tomography (CT); Deep learning; Convolutional Neural Networks (CNNs); CT KIDNEY DATASET.