Dr. Yashu Swami
Aditya Engineering College, India

Biography: Dr. Yashu Swami in Aditya Engineering College, India. He is influential managerial aptitude, impressive public dealing, enthusiastic leadership traits, complex problem solving, efficient teamwork, knowledge dissemination, quick learning ability, design, and creative ingenuity is a natural instinct to me. I consider myself fortunate enough to possess it. It’s my pleasure and passion to share my academic, administrative, and interpersonal talents to those who cherish these talents and its intervention. I prefer working with professionals and students from all creative fields and passionate about my orientation towards academics as well as administration. Adaptability is one of the major strengths which reciprocate my versatility and creative ideation.

Title: Analytical Comparison of Threshold Voltage Modeling, Simulation and Extraction methods for Nano MOSFETs

Abstract: Threshold voltage (VTH) is the most evocative aspect of MOSFET operation. It is the crucial device constraint to model on-off transition characteristics. Precise VTH value of the device is extracted and evaluated by several estimation techniques. However, these assessed values of VTH diverge from the exact values due to various short channel effects (SCEs) and non-idealities present in the nano device. Numerous prevalent VTH extraction methods are discussed. All the results are verified by extensive 2-D TCAD simulation and confirmed through analytical results at 10-nm technology node along with various sub 45-nm technology nodes. Application of the threshold extraction methods to implement noise analysis is briefly presented to infer the most appropriate extraction method at nanometer technology nodes.

Keywords: Threshold Voltage, Constant Current Source Technique, Linear Extrapolation, Technique, Threshold Voltage Estimation Techniques, Short Channel Effects, Drift Diffusion Model, Resistive Load Inverter, Noise Margin Analysis

Dr. Shashi Mehrotra
KL University, India

Biography: Dr. Shashi Mehrotra working as an Associate Professor in the Department of Computer Science and Engineering, KL University, Vaddeswaram since December 2017. She possesses a work experience of around 15 years in teaching undergraduate and post graduate level, research, and academic administration. She completed her Ph. D in Technology from Birla Institute of Technology, Mesra, Ranchi, India. MTech in Computer Engineering from ITM Gurgaon, MCA, and M.phil from Madurai Kamaraj University. Her Area of Interest include artificial Intelligence, Machine Learning, Nature-inspired approaches. She contributed research papers in International journals / National journals / International conferences/ National conferences of repute in India and other countries. She has participated invited talks in conferences, session chair, and organized many professional activities like a special session in conferences/FDPs, Workshops, expert lectures. Committee member or a reviewer for several international journals and conferences. She is a life member of professional bodies like Computer Society of India (CSI) and member of ACM.

Title: Intelligent Information Retrieval Framework

Abstract: Information searching is becoming very common and important in the current digital era and the availability of substantial digital information of various domains. A search engine returns a list of related documents against a user query. Most of the results retrieved by the search engines may not be relevant to the user. Due to the problem of polysemy, the results may belong to more than one meaning related to the user query. The polysemy queries cause ambiguities in the search result and affect the relevancy of document retrieval.
Polysemy is many possible meanings of a word. A user may not be interested in all the documents returned in response to a search and may want to focus on a coherent group of similar documents. Identifying the relationship of interest among web data is the main challenge. Various approaches have been used for search result clustering in the past few years. The session presents our framework that utilizes the capabilities of the traditional and evolutionary clustering approach for efficient and convenient web search and directions worthy of future research.

Keywords: Clustering, Information Retrieval, Evolutionary