BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Asia/Kolkata BEGIN:STANDARD DTSTART:19451014T230000 TZOFFSETFROM:+0630 TZOFFSETTO:+0530 TZNAME:IST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20150208T124547Z UID:F141B7E0-E5B6-11E7-833E-0050568D7F66 DTSTART;TZID=Asia/Kolkata:20150207T110000 DTEND;TZID=Asia/Kolkata:20150207T123000 DESCRIPTION:Abstract:\n\nDecision making from incomplete and high dimension al datasets is a challenging task\, especially in the areas of medicine an d public health management. In particular\, the recent spread of infectiou s diseases such as Ebola\, Swine flu and Dengue has showcased the inadequa cies in evolving effective methods to contain them. The complexity lies in handling large number of people in short time\, similarities of clinical symptoms and non-availability of resources (laboratoties and test equipmen t) that would cater to large population. A machine aided diagnosis based o n clinical and laboratory features would be of great help in disease manag ement. In this talk\, we discuss new techniques based on statistics and co mputational intelligence to predict true positives cases more effectively [1]\, [2]\, [3]\, [4]\, [5]. Also\, we showcase its performance with the s tate-of-the-art methods being employed in this area[6]\, [7]\, [8]. In add ition\, we present the challenges and approaches being employed by us in i dentifying influential features from these databases.\n\nREFERENCES [1] M. Naresh Kumar Alternating Decision trees for early diagnosis of dengue fev er\, arXiv preprint\, arXiv:1305.7331\, 2013. [2] V. Sree Hari Rao and M. Naresh Kumar New Intelligence-Based Approach for Computer-Aided Diagnosis of Dengue Fever\, IEEE Transactions on Information Technology in Biomedici ne\, 16(1):112-118\, 2012. [3] V. Sree Hari Rao and M. Naresh Kumar Estima tion of the parameters of an infectious disease model using neural network s\, Nonlinear Analysis: Real World Applications\, 11 (3): 1810-1818\, 2010 . [4] V. Sree Hari Rao and M. Naresh Kumar Predictive Dynamics: Modeling f or Virological Surveillance and Clinical Management of Dengue\, Dynamic Mo dels of Infectious Diseases\, Volume 1\, Springer\, New York\, USA\, 2013. [5] V. Sree Hari Rao and M. Naresh Kumar\, Control of Infectious Diseases : Dynamics and Informatics\, Dynamic Models of Infectious Diseases\, Volum e 2\, Springer\, New York\, USA\, 2013. [6] D. Chadwick\, B. Arch\, A. Wil der-Smith\, and N. Paton\, Distinguishing dengue fever from other infectio ns on the basis of simple clinical and laboratory features: application of logistic regression analysis\, J Clinical Virolology\, 35(2):147153\, 200 6. [7] M. M. Ramos\, K. M. Tomashek\, D. F. Arguello\, C. Luxemburger\, L. Quiones\, J. Lang\, and J. L. Muoz-Jordan\, Early clinical features of de ngue infection in puerto rico\, Transactions of the Royal Society of Tropi cal Medicine and Hygiene\, 103 (9):878884\, 2009. [8] L. Tanner\, M. Schre iber\, J. Low\, A. Ong\, and T. Tolfvenstam\, Decision tree algorithms pre dict the diagnosis and outcome of dengue fever in the early phase of illne ss PLoS Neglected Tropical Diseases\, 2 (3): 19\, 2008.\n\nCo-sponsored by : Prof. Maniza Hijab\, IT Department MJCET Hyderabad\n\nSpeaker(s): M Nare sh Kumar\, \n\nHyderabad\, Andhra Pradesh\, India LOCATION:Hyderabad\, Andhra Pradesh\, India ORGANIZER:mjitdfaculty@gmail.com SEQUENCE:0 SUMMARY:[Legacy Report] (Technical Lecture) Advances in Intelligent Computa tional Methods for Decision Support URL;VALUE=URI:https://events.vtools.ieee.org/m/113892 X-ALT-DESC:Description: <br /><p><strong>Abstract: </strong></p>\n<p>Decisi on making from incomplete and high dimensional datasets is a challenging t ask\, especially in the areas of medicine and public health management. In particular\, the recent spread of infectious diseases such as Ebola\, Swi ne flu and Dengue has showcased the inadequacies in evolving effective met hods to contain them. The complexity lies in handling large number of peop le in short time\, similarities of clinical symptoms and non-availability of resources (laboratoties and test equipment) that would cater to large p opulation. A machine aided diagnosis based on clinical and laboratory feat ures would be of great help in disease management. In this talk\, we discu ss new techniques based on statistics and computational intelligence to pr edict true positives cases more effectively [1]\, [2]\, [3]\, [4]\, [5]. A lso\, we showcase its performance with the state-of-the-art methods being employed in this area[6]\, [7]\, [8]. In addition\, we present the challen ges and approaches being employed by us in identifying influential feature s from these databases.</p>\n<p>&nbsp\;</p>\n<p>REFERENCES [1] M. Naresh K umar Alternating Decision trees for early diagnosis of dengue fever\, arXi v preprint\, arXiv:1305.7331\, 2013. [2] V. Sree Hari Rao and M. Naresh Ku mar New Intelligence-Based Approach for Computer-Aided Diagnosis of Dengue Fever\, IEEE Transactions on Information Technology in Biomedicine\, 16(1 ):112-118\, 2012. [3] V. Sree Hari Rao and M. Naresh Kumar Estimation of t he parameters of an infectious disease model using neural networks\, Nonli near Analysis: Real World Applications\, 11 (3): 1810-1818\, 2010. [4] V. Sree Hari Rao and M. Naresh Kumar Predictive Dynamics: Modeling for Virolo gical Surveillance and Clinical Management of Dengue\, Dynamic Models of I nfectious Diseases\, Volume 1\, Springer\, New York\, USA\, 2013. [5] V. S ree Hari Rao and M. Naresh Kumar\, Control of Infectious Diseases: Dynamic s and Informatics\, Dynamic Models of Infectious Diseases\, Volume 2\, Spr inger\, New York\, USA\, 2013. [6] D. Chadwick\, B. Arch\, A. Wilder-Smith \, and N. Paton\, Distinguishing dengue fever from other infections on the basis of simple clinical and laboratory features: application of logistic regression analysis\, J Clinical Virolology\, 35(2):147153\, 2006. [7] M. M. Ramos\, K. M. Tomashek\, D. F. Arguello\, C. Luxemburger\, L. Quiones\ , J. Lang\, and J. L. Muoz-Jordan\, Early clinical features of dengue infe ction in puerto rico\, Transactions of the Royal Society of Tropical Medic ine and Hygiene\, 103 (9):878884\, 2009. [8] L. Tanner\, M. Schreiber\, J. Low\, A. Ong\, and T. Tolfvenstam\, Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness PLoS N eglected Tropical Diseases\, 2 (3): 19\, 2008.</p> END:VEVENT END:VCALENDAR