Sri Ramakrishna Engineering College

About: Sri Ramakrishna Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Control theory. The organization has 1030 authors who have published 843 publications receiving 3822 citations.

Predictive Analytics on Diabetes Data using Machine Learning Techniques

Abstract: Diabetes mellitus is an ongoing illness related with anomalous undeniable levels of the sugar glucose in the blood and it is a major public health problem. Diabetes has become the 4th driving reason for death in developed countries. Though several methodologies have been developed to predict this chronic disease, there is a need for innovative approaches which may aid in early prediction of diabetes and its complications. This research work aims at developing an effective diabetes prediction system by taking advantage of machine learning. In this study, distinctive AI methods are applied to perform predictive analytics over the diabetes data sets. The proposed system strives for better prevention, diagnosis and management of Type 2 diabetes and focuses on developing machine learning strategies for precision medicine. It provides a platform not only for early recognition of Type 2 diabetes but also prevention of hazardous complications. The proposed system allows the practitioners to predict more precisely which treatment and prevention systems for diabetes will work as the best for specified patients. Thus, the system will greatly contribute to the quality of healthcare and beneficial to a large extent of today’s community.

Investigations on Pipeline Optimized Adaptive Fir Filter Architecture for Audio De-noising

Abstract: This project proposes a high-speed retimed de-noising adaptive filter on different FPGA platforms using FIR filter. Retiming technique is the minimization of the clock period in a circuit. Using retiming, an adaptive filter can have a low critical path, low power consumption, and high throughput. In an adaptive filter, the least mean square (LMS) is the utmost familiar adaptive algorithm by virtue of its simple structure. LMS algorithm is easy to execute in the real-time systems; to improve its critical path, it is crucial to correct the LMS adaptive algorithm. The retimed VLSI architecture lifts up the operational speed of an adaptive filter through limiting the critical path using delay components and also has a faster convergence. A fine-grain pipelined LMS method is used to further increase the maximum operating speed which provides pipelining at a computational component level. The critical path delay in the Virtex-5 series FPGA platform for direct form implementation of retimed LMS is found to be 9.104 ns, whereas that of traditional un-retimed LMS structure is 24.283 ns, thereby minimization of 37% critical path delay is achieved. A fine-grain retimed adaptive filter on Virtex-5 series FPGA platform achieves 8% improvement in clock frequency compared to a retimed structure.

Image fusion method using non-subsampled shearlet transform and fuzzy and simple fuzzy neural network algorithms:

Abstract: The methodology of combining two or more relevant images into a single highly informative image is referred to as image fusion. A new fusion methodology is introduced for combining images obtained from multiple cameras using non-subsampled shearlet transform (NSST), fuzzy logic and a simple fuzzy neural network (SFNN). The shearlet transform combines the power of multi-scale methods with a unique ability to capture the geometry of multi-dimensional information and is efficient in representing images containing edges. The unique characteristic of shearlets is the utilization of shearing to control directional selectivity, as opposed to rotation utilized by curvelets. The shearlets are not tight edges and therefore it is necessary to perform the synthesis process by iterative methods. A new method, NSST, is introduced for multi-resolution decomposition of input images is introduced. The pixel-based fusion is performed by using fuzzy logic of NSST low-pass coefficients to generate superior quality. The regio...