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You searched for subject:(Hybrid Intelligent Methods). Showing records 1 – 2 of 2 total matches.

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Indian Institute of Science

1. Babu, T Ravindra. Large Data Clustering And Classification Schemes For Data Mining.

Degree: PhD, Faculty of Engineering, 2009, Indian Institute of Science

Data Mining deals with extracting valid, novel, easily understood by humans, potentially useful and general abstractions from large data. A data is large when number of patterns, number of features per pattern or both are large. Largeness of data is characterized by its size which is beyond the capacity of main memory of a computer. Data Mining is an interdisciplinary field involving database systems, statistics, machine learning, visualization and computational aspects. The focus of data mining algorithms is scalability and efficiency. Large data clustering and classification is an important activity in Data Mining. The clustering algorithms are predominantly iterative requiring multiple scans of dataset, which is very expensive when data is stored on the disk. In the current work we propose different schemes that have both theoretical validity and practical utility in dealing with such a large data. The schemes broadly encompass data compaction, classification, prototype selection, use of domain knowledge and hybrid intelligent systems. The proposed approaches can be broadly classified as (a) compressing the data by some means in a non-lossy manner; cluster as well as classify the patterns in their compressed form directly through a novel algorithm, (b) compressing the data in a lossy fashion such that a very high degree of compression and abstraction is obtained in terms of 'distinct subsequences'; classify the data in such compressed form to improve the prediction accuracy, (c) with the help of incremental clustering, a lossy compression scheme and rough set approach, obtain simultaneous prototype and feature selection, (d) demonstrate that prototype selection and data-dependent techniques can reduce number of comparisons in multiclass classification scenario using SVMs, and (e) by making use of domain knowledge of the problem and data under consideration, we show that we obtaina very high classification accuracy with less number of iterations with AdaBoost. The schemes have pragmatic utility. The prototype selection algorithm is incremental, requiring a single dataset scan and has linear time and space requirements. We provide results obtained with a large, high dimensional handwritten(hw) digit data. The compression algorithm is based on simple concepts, where we demonstrate that classification of the compressed data improves computation time required by a factor 5 with prediction accuracy with both compressed and original data being exactly the same as 92.47%. With the proposed lossy compression scheme and pruning methods, we demonstrate that even with a reduction of distinct sequences by a factor of 6 (690 to 106), the prediction accuracy improves. Specifically, with original data containing 690 distinct subsequences, the classification accuracy is 92.47% and with appropriate choice of parameters for pruning, the number of distinct subsequences reduces to 106 with corresponding classification accuracy as 92.92%. The best classification accuracy of 93.3% is obtained with 452 distinct subsequences. With the… Advisors/Committee Members: Murty, M Narasimha (advisor).

Subjects/Keywords: Data Mining; Data Classification; Image Processing; Data Clustering; Data Compaction; Data Mining - Algorithms; Hybrid Intelligent Systems; Data Reduction; Data Representation; Hybrid Schemes; Hybrid Intelligent Methods; Computer Science

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Babu, T. R. (2009). Large Data Clustering And Classification Schemes For Data Mining. (Doctoral Dissertation). Indian Institute of Science. Retrieved from http://etd.iisc.ac.in/handle/2005/440

Chicago Manual of Style (16th Edition):

Babu, T Ravindra. “Large Data Clustering And Classification Schemes For Data Mining.” 2009. Doctoral Dissertation, Indian Institute of Science. Accessed October 19, 2020. http://etd.iisc.ac.in/handle/2005/440.

MLA Handbook (7th Edition):

Babu, T Ravindra. “Large Data Clustering And Classification Schemes For Data Mining.” 2009. Web. 19 Oct 2020.

Vancouver:

Babu TR. Large Data Clustering And Classification Schemes For Data Mining. [Internet] [Doctoral dissertation]. Indian Institute of Science; 2009. [cited 2020 Oct 19]. Available from: http://etd.iisc.ac.in/handle/2005/440.

Council of Science Editors:

Babu TR. Large Data Clustering And Classification Schemes For Data Mining. [Doctoral Dissertation]. Indian Institute of Science; 2009. Available from: http://etd.iisc.ac.in/handle/2005/440

2. Park, Jaeyong. Safe Controller Design for Intelligent Transportation System Applications using Reachability Analysis.

Degree: MS, Electrical and Computer Engineering, 2013, The Ohio State University

Intelligent Transportation Systems (ITS) apply well-established technologies in communications, control, and computer hardware and software to increase safety and improve operational performance of the transportation network without expanding the current infrastructure. For many ITS applications, ensuring safety of the traffic participants, including drivers and pedestrians, is one of the most important research initiatives of the Intelligent Transportation Systems Society (ITSS). The ITS applications range from collision avoidance for autonomous or human-driven vehicles to cooperation of multiple vehicles to achieve common goals such as reduced fuel consumption or increased traffic throughput. The main challenges when designing controllers for such systems are the need to consider the close combination of, and coordination between, the system's computational and physical elements. Most of the vehicles nowadays are controlled by tens of or even hundreds of microcontrollers, which communicate via a CAN bus, for electric steering, braking, chassis and body control. Moreover, vehicles interact with other traffic participants including (semi) autonomous vehicles and human-driven cars and also with roadside units through a Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) communication, resulting in a large-scale Cyber-Physical System. Thus, traditional control theory that has been devoted to modeling continuous systems cannot adequately model such complex Cyber-Physical Systems, where both continuous (physical plant, e.g., vehicle) and discrete components (computing and communication) closely interacting each other.This thesis studies the design of continuous control laws that satisfy the safety property of the systems and their interfaces with discrete components that abstract human's high-level, decision making process. Our primary goals are to design continuous controllers for ITS applications that by design guarantee the safety property without further verification. First of all, hybrid systems, a class of modeling frameworks which form the foundation for a mathematical approach to Cyber-Physical Systems will be introduced. Then the reachability analysis techniques are developed to compute the exact reachable sets which will then be manipulated to design control laws that satisfy the safety property of the system.As a motivating application, we consider the Adaptive Cruise Control (ACC) system which becomes increasingly popular in commercial vehicles but lacks a fully-automated Collision Avoidance (CA) functionality, thus still leaving the responsibility to human drivers to apply proper braking force. Since the currently available ACC systems are developed mainly for providing drivers comfort and convenience riding, it cannot address the situation where safety should come first. Such situations may include sudden deceleration of a preceding vehicle and cut-in by a slower vehicle, where a rear-end collision is imminent or unavoidable. Thus, an active CA system needs to be developed and fully integrated into… Advisors/Committee Members: Ozguner, Umit (Advisor).

Subjects/Keywords: Electrical Engineering; Cyber-physical systems; intelligent transportation systems; hybrid systems; hybrid automata; reachability analysis; level set methods; hamilton-jacobi-isaacs equations; pursuit-evasion game; adaptive cruise control; collision avoidance

…dynamics. The hybrid automata have been proved useful for developing methods for hybrid control… …uner, Hybrid systems modeling and reachability-based J. Park, A. Kurt, and U. controller… …design methods for unmanned systems. Unmanned Systems. To be submitted. ¨ Ozg¨ ¨ uner, A game… …Intelligent Transportation Systems . . . . . 1.2.1 Adaptive Cruise Control . . . . . . . 1.2.2… …Systems in Nature . Why Hybrid Systems? . . . . . . . . . . . . Scope and Goals… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Park, J. (2013). Safe Controller Design for Intelligent Transportation System Applications using Reachability Analysis. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1366201401

Chicago Manual of Style (16th Edition):

Park, Jaeyong. “Safe Controller Design for Intelligent Transportation System Applications using Reachability Analysis.” 2013. Masters Thesis, The Ohio State University. Accessed October 19, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366201401.

MLA Handbook (7th Edition):

Park, Jaeyong. “Safe Controller Design for Intelligent Transportation System Applications using Reachability Analysis.” 2013. Web. 19 Oct 2020.

Vancouver:

Park J. Safe Controller Design for Intelligent Transportation System Applications using Reachability Analysis. [Internet] [Masters thesis]. The Ohio State University; 2013. [cited 2020 Oct 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1366201401.

Council of Science Editors:

Park J. Safe Controller Design for Intelligent Transportation System Applications using Reachability Analysis. [Masters Thesis]. The Ohio State University; 2013. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1366201401

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