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The main objective of this paper is to segment the disease affected portion of a plant leaf and extract the hybrid features for better classification of different disease patterns. A new approach named as Particle Swarm Optimization (PSO) is proposed for image segmentation. PSO is an automatic unsupervised efficient algorithm which is used for better segmentation and better feature extraction. Features extracted after segmentation are important for disease classification so that the hybrid feature extraction components controls the accuracy of classification for different diseases. The approach named as Hybrid Feature Extraction (HFE), which has three components namely color, texture and shape based features. The performance of the preprocessing result was compared and the best result was taken for image segmentation using PSO. Then the hybrid feature parameters were extracted from the gray level co-occurrence matrices of different leaves. The proposed method was tested on different images of disease affected leaves, and the experimental results exhibit its effectiveness.

Automated detection of retinal hemorrhages in fundus image [2] is crucial step towards early detection or screening is difficult among large population. A novel splat feature classification method is introduced to detect retinal hemorrhages. Classification is been achieved through supervised learning approaches. The performance of sensitivity and specificity is been improved while processing with retinal hemorrhages than with lesions. An area under receiver operating characteristics curve (ROC) of 0.96 can be achieved at splat level and 0.87 at image level. General Terms : Supervised Classification

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Reliability of power system is very essential for every nation to generate and transmit power without interruption. Power transformer is one of the most significant electrical apparatus and hence it must be kept in good health. Identification and classification of faults in power transformer is a major research area. Conventional method of fault classification in transformer uses gas concentrations data and interprets them using international standards. These standards are not able to classify the faults correctly under certain conditions. To overcome this limitation, several soft computing tools namely artificial neural network (ANN), Support Vector Machine (SVM) etc. are used to automate the process of classification of faults in transformers. However, there is a scope exists to improve the classification accuracy. Hence, this research work focuses to design Extreme Learning Machine (ELM) method for classifying fault very accurately using enthalpy of dissolved gas content in transformer oil as an input feature. The ELM method is tested with two databases: one based on IEC TC10 database (DB1) and the other one based on data collected from utilities in India (DB2). The application of ELM to Power Transformer fault classification based on enthalpy as input feature outperforms over the conventional classification based on gas concentration as input feature.