College of Engineering, Pune

About: College of Engineering, Pune is a based out in . It is known for research contribution in the topics: Sliding mode control & Control theory. The organization has 4264 authors who have published 3492 publications receiving 19371 citations. The organization is also known as: COEP.

Trust Management Model based on Fuzzy Approach for Ubiquitous Computing

Abstract: The current state of ubiquitous computing has been greatly influenced by emerging networking developments like Internet of Things (IoT), Future Internet etc. Adequate trust management is crucial to provide security. The entities involved in communication must be trusted for specific purposes depending on their role. Using trust model, devices can run trust computations and guide their behaviors. To this effect, a method is needed to evaluate the level of trust between devices. Trust models investigated so far discusses that devices face problems when communicating as transforming trust relationships from real to virtual world requires the negotiation of trust based on the security properties of devices. However, these models are developed in limited devices. This paper proposes a distributed trust model for device-to-device communication in ubiquitous computing. Mathematical model based on fuzzy rules to establish trust is presented. Fuzzy simulation of the model is presented to validate the findings. Simulation results show that proposed model calculates fuzzy trust values reliably.

Detection of Diabetic Retinopathy in Retinal Images Using MLP Classifier

Abstract: The rising situation in the developing world suggests diabetic retinopathy may soon be a major problem in the clinical world [1]. Hence, detection of diabetic retinopathy is important. This paper focuses on Multi Layer Perception Neural Network (MLPNN) to detect diabetic retinopathy in retinal images. In this paper the MLPNN classifier is presented to classify retinal images as normal and abnormal. A feature vector is formed with 64-point Discrete Cosine Transform (DCT) with different 09 statistical parameters namely Entropy, mean, standard deviation, average, Euler number, contrast, correlation, energy and homogeneity. The Train N Times method was used to train the MLPNN to find best feature subset. The training and cross validation rates by the MLP NN are 100% for detection of normal and abnormal retinal images.

Deep Learning Based Representation for Face Recognition

Abstract: Face Recognition is one of the challenging process due to huge amount of wild datasets. Deep learning has been provided good solution in terms of recognition performance, as day by day this have been dominating the field of biometric. In this paper our goal is to study deep learning based face representation under several different conditions like lower and upper face occlusions, misalignment, different angles of head poses, changing illuminations, flawed facial feature localization using deep learning approaches. For extraction of face representation two different popular models of Deep learning based called Lightened CNN and VGG-Face and have reflected in this paper. As both of this model show that deep learning model is robust to different types of misalignment and can tolerate localizations error of the intraocular distance.