http://localhost:8080/xmlui/handle/123456789/246 Tue, 23 Jun 2026 07:37:53 GMT 2026-06-23T07:37:53Z http://localhost:8080/xmlui/handle/123456789/3540 Title: Fuzzy Based Adaptive Two Phase Traffic Signal Controller Abstract: An adaptive traffic signal controller computes the phase timings based on real-time data of traffic and it generally results in better performance for various traffic situations. In general the fuzzy traffic signal controllers are proved to be better as compared to the traditional fixed-time controller. However the decisions were made on traffic conditions and the significance of phase sequence was suppressed. This research work dealt with the new fuzzy traffic signal controller for a full single intersection. The proposed fuzzy controller includes two main phases: fuzzy phase selection mechanism and fuzzy green time decision. The first phase will decides the next phase to be green and second phase decides the green timings for the corresponding phase. The proposed fully approach is compared with the traditional fixed-timed control system (FTC) and experimental results shows the significance reduction in average waiting time at the intersection and average queue length over fixed timed control. Mon, 09 Jul 2018 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/3540 2018-07-09T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3539 Title: Traffic Congestion Index and Level Estimation using Two Phase Fuzzy Controller Authors: Pimple, Prachiti; Babhulkar, Gajanan; Masram, Bhumesh; Jadhav, Payal Abstract: Many measures have been proposed to represent the status of traffic conditions on arterial roadways in urban areas. Traffic congestion is rising nowadays and to understand its nature, a systematic mechanism is required. A new approach is presented in this research work to measure the congestion index first and then the congestion level. In this research work, a two phase fuzzy controller is applied wherein in first phase the traffic congestion index is measured by using travel speed rate and very-low speed rate followed by congestion level measurement by using density state and congestion index in next phase. The application of the proposed approach is demonstrated using real world data of small area segment of Nagpur city, India. The outcome was a single congestion index value between 0 and 1, where 0 is the best condition and 1 is the worst condition. Wed, 15 Jul 2020 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/3539 2020-07-15T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3536 Title: A survey on Intelligent Data Mining Techniques used in Heart Disease Prediction Authors: Rane, Archana L. Abstract: Imminent need of turning huge amount of available health data into useful information and knowledge attracts data mining techniques in medical diagnosis process. Data mining is a procedure of distinguishing and extracting valuable data and setting up connection between attributes in substantial datasets. Existing heart disease prediction models use one or multiple data mining techniques. This paper surveys heart disease prediction systems systematically wherein techniques are compiled, tabulated and analyzed based on hybrid techniques categorization. In this paper, the techniques are classified into two main categories: Discrete and Integrated, which are further classified as supervised, unsupervised, hybrid and miscellaneous. It is revealed from this survey, even though usage of one data mining technique performs well, hybrid data mining techniques yield promising outcomes in the determination of coronary illness. Mon, 12 Nov 2018 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/3536 2018-11-12T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3520 Title: A REVIEW ON INSTANCE AND FEATURE SELECTION IN BIG DATA ENVIRONMENT Abstract: Instance and feature selection has become an effective approach due to the enormous data which is continuously being produced in the field of research. It is difficult to process such large datasets by many systems. Though the traditional techniques are useful for large datasets, the numbers when in hundreds, thousands or millions face scaling problems. The proposed work focuses on, scalable instance and feature selection in big data environment. Locality-sensitive hashing instance selection F (LSH-IS-F) is a two pass method used to find similar instances along with Pearson correlation coefficient for feature selection. Hash function family is used which is a general method of reducing the size of a set; this is achieved by reindexing the elements into buckets. This process find similar instance and features in same bucket, hence instance/features can be reduced. The work aims at improving the performance of locality sensitive hashing by storing extra statistics of the instances and features that is assigned to each class in the bucket and also to improve accuracy of instance and feature selection algorithm by prototype generation. Thu, 01 Jun 2017 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/3520 2017-06-01T00:00:00Z