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: Exploration and Analysis of Conventional Threshold Voltage Estimation Methods for Advanced 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
Dr Hari Mohan Pandey
Edge Hill University, United Kingdom
Biography: Hari is with the department of computer science at Edge Hill University, UK. He is specialized in Computer Science & Engineering. His research area includes artificial intelligence, soft computing techniques, natural language processing, language acquisition and machine learning algorithms. He is author of various books in computer science engineering (algorithms, programming, and evolutionary algorithms). He has published over 100 scientific papers in reputed journals and conferences, served as session chair, leading guest editor, and delivered keynotes. He has been given the prestigious award “The Global Award for the Best Computer Science Faculty of the Year 2015” (http://asdf.international/hari-mohan-pandey-asdf-global-awards-2015/), award for completing INDO-US project “GENTLE”, award (Certificate of Exceptionalism) from the Prime Minister of India and award for developing innovative teaching and learning models for higher-education. Previously, He was working as a research fellow machine learning at Middlesex University, London where He worked on a European Commission project- DREAM4CAR. His role was to research and develop advanced machine learning techniques relevant to the project goals and to evaluate these on both project & reference data sets. To lead and manage relevant work packages in support of Project DREAMS4CARS -H2020 (https://www.dfki.de/en/web/research/projects-and-publications/projects-overview/projekt/dream4cars/), ensuring appropriate interfacing with partners. To lead a Work Package and to work with those of research partners; to carry out individual and collaborative research relevant to the project. To contribute to the development of software according to project specifications. To produce research reports and deliverables related to the project. To undertake work package leadership and general administrative tasks to ensure the smooth running of the project. To coordinate with research partners and stakeholders related to the project with immediate responsibility for the work package. Hari is visiting researcher at Middlesex University London, external examiner, and scientist for FONDECYT-CHILE Ministerio de Ciencia, Tecnología Conocimiento e Innovación Gobierno de Chile. Hari is action editor and associate editor of various reputed journals and serve as technical program member for different renowned conferences. Hari is also a fellow of teaching and learning in higher education of British standard, professional member of British Computer Society.
Title: Metaheuristic Algorithms for solving Engineering Optimization Problems.
Abstract: Metaheuristic algorithms are popular for solving search and optimization problems. The applications of metaheuristic algorithms are wide and cover several areas such as optimization, pattern recognition, learning, scheduling, economics, bioinformatics, natural language processing, image, and video processing etc. Objective (single or multi) function is used to measure the effectiveness of a metaheuristic algorithm, distributed randomly in the population. Typically, a population start dominating as the searching evolves. During the searching, objective function value shows no improvement as the population converges to a local optimum, this leads to the problems of the premature convergence and slow finishing. There are several factors involves for the success of a metahuristic algorithm. In this talk, I will cover (a) the factors which needs special attention before implementing a metahuristic algorithm for engineering optimization problems; (b) premature convergence and slow finishing; (c) approaches for alleviating premature convergence; and (d) a case study to explain the methodology of implementing a metahuristic algorithm for solving a real-world optimization problem.