The quality of the wood is determined by the number of defects and its distribution. In a piece of timber, the most common type of imperfection is called knot that decreases the strength of the wood. Manual selection and classification process of knots is tedious and time consuming job. An automatic sensing machine is able to inspect wood automatically and correctly identify the defects it possess, and its effect on the quality of the final product. In this paper, it is proposed to detect and classify the knots in timber boards. The image of knots is pre processed using Hilbert transform and Gabor filters. The features obtained from pre processing, is classified using data mining techniques and compared with bagging technique.

Predicting the student performance is playing vital role in educational sector so that the analysis of student's status helps to improve for better performance. Applying data mining concepts and algorithms in the field of education is Educational Data Mining. In recent days, Machine learning algorithms are very much useful in almost all the fields. Many researchers used machine learning algorithms only. In this paper we proposed the student performance prediction system using Deep Neural Network. We trained the model and tested with Kaggle dataset using different algorithms such as Decision Tree (C5.0), Naï ve Bayes, Random Forest, Support Vector Machine, K-Nearest Neighbor and Deep neural network in R Programming and compared the accuracy of all other algorithms. Among six algorithms Deep Neural Network outperformed with 84% as accuracy.

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Diabetic Retinopathy (DR) is an eye disease, which may cause blindness by the upsurge of insulin in blood. The major cause of visual loss in diabetic patient is macular edema. To diagnose and follow up Diabetic Macular Edema (DME), a powerful Optical Coherence Tomography (OCT) technique is used for the clinical assessment. Many existing methods found out the DME affected patients by estimating the fovea thickness. These methods have the issues of lower accuracy and higher time complexity. In order to overwhelm the above limitations, a hybrid approaches based DR detection is introduced in the proposed work. At first, the input image is preprocessed using green channel extraction and median filter. Subsequently, the features are extracted by gradient-based features like Histogram of Oriented Gradient (HOG) with Complete Local Binary Pattern (CLBP). The texture features are concentrated with various rotations to calculate the edges. We present a hybrid feature selection that combines the Particle Swarm Optimization (PSO) and Differential Evolution Feature Selection (DEFS) for minimizing the time complexity. A binary Support Vector Machine (SVM) classifier categorizes the 13 normal and 75 abnormal images from 60 patients. Finally, the patients affected by DR are further classified by Multi-Layer Perceptron (MLP). The experimental results exhibit better performance of accuracy, sensitivity, and specificity than the existing methods.

The widespread applications of Mobile Ad hoc Networks (MANETs) have lead to the development of many protocols in this field. Routing protocols for ad hoc networks have generally ignored channel fading. This paper proposes a routing protocol which calculates the channels non-fading duration for routing which attempts to minimize packet loss due to fading and also reuse the path with some security mechanisms to increase the throughput. Specifically, in the proposed work the faded paths can be reused when they become available again, rather than being discarded, also the loads are balanced on the link. The Channel Aware -Ad hoc On-demand Multipath Distance Vector (CA-AOMDV) used for channel average non-fading duration as the routing metric. The Load Based Channel Aware -Ad hoc On-demand Multipath Distance Vector (LBCA-AOMDV) is used for increasing throughput and packet delivery ratio. The NS-2 is used to perform both the simulation and evaluation of the performance of proposed protocol and to compare it with existing protocols. The simulation result demonstrates improvement in the throughput, packet delivery ratio, security and reduction of packet loss on routing.