College of Engineering, Pune

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

Biomedical image indexing and retrieval based on new efficient hybrid approach using directional decomposition and a novel local directional frequency encoded pattern: a post feature descriptor

Abstract: In this paper, a new efficient hybrid approach based on directional decomposition and post local feature extraction is proposed for biomedical image indexing and retrieval. Initially, triplet half-band filter bank (THFB) is modified and used in directional filter bank (DFB) for directional decomposition of images. DFB decomposes image into directional frequency sub-bands. To acquire local information in each directional sub-band of images, a novel local directional frequency encoded pattern (LDFEP) feature descriptor is proposed as post feature descriptor. The LDFEP is based on establishing the relationship between 00, 450, 900, and 1350 directional frequency components. The deliberation of directional as well as local information composes hybrid approach which is more efficient than the existing descriptors for biomedical image retrieval. Manhattan distance is selected to compute analogy between the query feature vector and the feature vector of images from the database. The efficacy of the proposed approach in terms of precision and recall has been evaluated by conducting the experiments on three well-known biomedical databases: Open access series of imaging studies (OASIS)-MRI, EXACT 09-CT and NEMA-CT. The experimental results confirmed the superiority of the proposed approach in comparison with the state-of-the-art feature descriptors for biomedical image retrieval.

Multi technique amalgamation for enhanced information identification with content based image data

Abstract: Image data has emerged as a resourceful foundation for information with proliferation of image capturing devices and social media. Diverse applications of images in areas including biomedicine, military, commerce, education have resulted in huge image repositories. Semantically analogous images can be fruitfully recognized by means of content based image identification. However, the success of the technique has been largely dependent on extraction of robust feature vectors from the image content. The paper has introduced three different techniques of content based feature extraction based on image binarization, image transform and morphological operator respectively. The techniques were tested with four public datasets namely, Wang Dataset, Oliva Torralba (OT Scene) Dataset, Corel Dataset and Caltech Dataset. The multi technique feature extraction process was further integrated for decision fusion of image identification to boost up the recognition rate. Classification result with the proposed technique has shown an average increase of 14.5 % in Precision compared to the existing techniques and the retrieval result with the introduced technique has shown an average increase of 6.54 % in Precision over state-of-the art techniques.

Sales forecasting using data warehouse and Naïve Bayesian classifier

Abstract: Organizations need to analyze their day to day sales information in order to forecast the sales of their products and services. This forecasting can be used to increase the production of products to meet the demand or can be used to take corrective measures to increase the sales. This paper presents a novel method of sales forecasting using fuzzy logic, data warehouse and Naive Bayesian classifier. Experiments are performed using sales data of five years collected from many shops located in different cities to prove the efficiency of proposed mechanism.

Implementation of CFD–FSI Technique Coupled with Response Surface Optimization method for Analysis of Three-Lobe Hydrodynamic Journal Bearing

Abstract: In the work presented here, numerical simulations were carried out using computational fluid dynamics and fluid–structure interactions for three-lobe journal bearing. ANSYS Workbench® software was used for the study. The elastic deformations were also considered for the analysis. The fluid pressure forces and displacements were transferred through inbuilt transfer interface available in the software. The optimized journal bearing position was achieved using a response surface optimization technique. The methodology was validated by comparing numerical results obtained with experimental results available in the literature, and a good agreement was found. The proposed numerical method was implemented to study the pressure distribution in three-lobe journal bearing considered for study at three eccentricity ratios 0.25, 0.6 and 0.75 for various speeds ranging from 1000 to 4000 RPM. Preload factor of 0.5 was considered for the study. The results were compared with a set of experimental data obtained on a test rig developed by the authors.

Representation of recommender system in IoT using cyber physical techniques

Abstract: The Internet of Things (IoT) is changing the way we perceive information. It has inspired solutions for a variety of everyday problems. With the advent of IoT, the internet will house several “intelligent “objects capable of making their own decisions and communicate with each other in an efficient manner. SOA services coupled with decision making capability helps in efficient processing of information. Cyber-Physical Systems (CPSs) represent a new paradigm of future intelligent systems. Cyber Physical System consist of loosely coupled subsystems which interact with mechanisms of Service oriented Architecture (SOA). Web service is a very important candidate technology to achieve SOA requirements that allows the service providers to publish their services to many service consumers. The proposed paper aims at providing the basic architecture of IoT and CPSs and a system which provides notification (as a future work) through email or SMS and recommendation services to the end users. A recommendation system is basically used for predicting users preferences and interests. Recommender Systems, these days, are no longer personal recommender systems, rather they are group recommender systems which list out recommendations for a group of users.