Fuzzy document classification using ontology based approach for term weighting

With the surge in web corpus, document classification is a vital issue in information retrieval. Term weighting increases the accuracy of classification for documents represented in the vector space model. This paper proposes an ontoTf-idf term weighting method based on the assessment of semantic similarity between the group label and the term. In this paper, a comparative analysis of the performance of the traditional Term Frequency-Inverse Document Frequency (Tf-idf) method and ontoTf-idf method is carried on the WebKB and Reuters-21578 benchmark datasets. The efficiency of ontoTf-idf method is validated with kNN (k nearest neighbor) and Fuzzy kNN classifier on the WebKB and Reuters-21578 datasets. The experimental results obtained with the proposed ontoTf-idf method outperform the Tf-idf method. In the proposed work, distance metrics like Euclidean distance, Cosine similarity, Manhattan distance, and Jaccard co-efficient are applied with Fuzzy kNN classifier on the WebKB and Reuters-21578 dataset

Cluster based Routing Algorithm to Minimize Energy Consumption in WSN

Low energy consumption and increasing the network lifetime are key factors in proposing a wireless sensor network protocol. Clustering or hierarchical methods have been proven to be effective in decreasing the energy consumption. Clustering methods make use of cluster-heads in order to achieve the efficiency in wireless sensor networks. Gridding approach is used in clustering that divides the network layout into grid with nodes falling into the each grid. Cluster-head provides a hierarchy in the network, one between the nodes and cluster-head and second between the cluster-head and the base station. Cluster-heads aims at aggregating the data from sensor nodes and decrease the controlling data in the network. This idea increases the lifetime of the network by decreasing the energy consumed by the nodes. In cluster based routing algorithm with mass-centre approach to minimize energy consumption in WSN, election of the cluster-heads is carried out based on two parameters, one is energy and the other is mass-centre of the sensor nodes. Threshold energy is selected and all the nodes that have the energy levels higher than the threshold energy qualify to be cluster-head. The second factor is the mass-centre of the nodes in which the weight of the node that lies near the centre of the grid is chosen to be the cluster-head for the current round. DOI: 10.17762/ijritcc2321-8169.150510

Detection of scoliosis in human spine using Cobb angle

BACKGROUND: The human backbone is the central support structure of the body. It connects different parts of the body. The spine helps in doing various daily activities such as sitting, walking, standing, and bending. Any impairment in the spine causes spinal disease. Scoliosis is an abnormality in the spinal curve. It consists of a lateral curve. The severity of scoliosis is based on the curvature of the spine, which is calculated based on end plates. AIMS AND OBJECTIVES: Scoliosis is measured using the Cobb angle. The most challenging task is to automate the calculation of scoliosis, considering X-rays of the spine as the input image. Once the end plates are detected, the calculation of the Cobb angle becomes easy, and the calculation can be automated. MATERIALS AND METHODS: The proposed method considers X-ray images of the spine. X-ray images of patients who are defective with scoliosis are used to calculate the Cobb angle using the computerized method. The X-ray images of patients include the Cobb angle ranging with different degrees. Here, the Cobb angle with a degree up to 10 is considered normal and above 10° is considered not normal/affected with scoliosis. In the computerized method, Python 3.7.4 software is used for quantifying the Cobb angle of scoliosis. The main objective of finding the Cobb angle using the computerized method is to reduce human intervention while calculating the Cobb angle. Before the processing begins, the most tilted extreme and inferior vertebrae were taken for cropping the region of interest. As X-ray images are prone to noise, a median filter has been used for image smoothing. For the given X-ray to determine horizontal edge detection, a Gaussian derivative with the suitable thresholding and edge extraction structure has been used. The horizontal edge detection method is considered more suitable compared to vertical detection for detecting end plates of the spinal curve in the case of scoliosis and Cobb angle detection. After horizontal edge detection, the Hough transform is used for the detection of vertebrae slopes. After calculating the slopes of vertebrae, the Cobb angle of scoliosis has been calculated. RESULTS: The proposed Hough transform method increases the efficiency of the existing system accuracy from 75%–80% to 80%–85% by making the Cobb angle calculation automated, thereby reducing manual intervention. Further, depending on the severity of the curvature of the spine in detecting scoliosis, treatment may be suggested to the patients. CONCLUSION: The proposed method is used to calculate the Cobb angle using the image processing technique. The X-ray images of the human spine are taken as input images. By cropping the region of interest in the spinal image from end vertebral plates, the Cobb angle is calculated with minimal human intervention. By adjusting the parameters of the above-mentioned technique, more accurate results are obtained. This technique helps in diagnosis and treatment of scoliosis by evaluation of automated Cobb angle