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ICISDM2024 Keynote & Invited Speakers

 

 

Prof. Anu Gokhale

St. Augustine’s University, USA

Biography:Dr. Anu A. Gokhale is currently a Professor and Chair of the Department of Computer Information Systems at Saint Augustine’s University. She has been selected as a 2023-24 Convergence Fellow by the Association of American Colleges & Universities. Gokhale visited Cairo University in Egypt in August 2022 as Fulbright Specialist in Data Analytics. Formerly, she was a Distinguished Professor and Coordinator of the Computer Systems Technology program at Illinois State University (ISU). Gokhale has completed thirty years as faculty and has received several College and University research, teaching and service awards. Having earned certifications in online delivery, she was recruited to mentor colleagues in online teaching beginning March 2020. Gokhale was named Fulbright Distinguished Chair in STEM+C at the University of Pernambuco, Brazil, 2016-17; was a Faculty Fellow in Israel and Fulbright Specialist in Cybersecurity at Gujarat Technological University, India in summer 2017. As a Visiting Professor in College of Business at Shandong University in Jinan, China in spring 2017, her focus was on e-commerce. Her achievements encompass extensively cited refereed publications; groundbreaking externally funded research supported by a continuous 20-year stream of grants from state and federal agencies including the National Science Foundation; and elevation of the ISU student experience through excellence in teaching, mentorship, and the creation of opportunities for students to get involved in research. Originally from India, she has a master’s in physics‒electronics from the College of William & Mary, and a doctorate from Iowa State University. Dr. Gokhale authored a second edition of her book Introduction to Telecommunications, which has an international edition in Chinese. She continues to be an invited keynote speaker at various conferences. As an active volunteer in IEEE, she has served as R4 Educational Activities Chair, Women in Engineering Coordinator, and MGA representative to the Educational Activities Board. She was honored with the IEEE Third Millennium Medal and 2019 Region 4 Outstanding Professional Award. She consults for business and industry to increase productivity using data analytics and business intelligence while leveraging e-technologies. She has delivered multiple workshops focusing on hybrid teaching & learning, inclusion & diversity, as well as on algorithms and data analytics.

Title of Speech:AI in Information Systems: Algorithm Design and Applications

Title of Speech:Enterprise information systems combined with latest developments in data mining strategies have created unprecedented opportunities for enhancing competitive advantage. Executives seek to leverage Artificial Intelligence (AI), the biggest driver of technological change, to inform decision-making. Corporate data environments include both structured and unstructured information and there exists tremendous potential to glean key insights for business advantage from the vast data that is available today and new data that is being constantly generated. Algorithms used in analyzing big data vary significantly based on the problem of study and its goals and objectives. The talk will address the issues and processes associated with analyzing big data in business information systems, applicable algorithms to enhance functionality and predictive analytics, and discuss how data-driven decisions support product/service innovation.

Assoc. Prof. Mohamed Zakaria Kurdi

University of Lynchburg in Virginia, USA

Biography: Dr. Kurdi is an Associate Professor of Computer Science at the University of Lynchburg in Virginia, USA. In addition to his Ph.D. in CS, he has an interdisciplinary background in Software Engineering, Cognitive Science, and Linguistics. Before joining the University of Lynchburg, he worked in several institutions in North America and Europe. His research interests are in text and data mining and their applications to areas like intelligent computer-assisted language education, authorship attribution, bioinformatics, and Social Network Analysis (SNA). He authored a two-volume textbook about Natural Language Processing (NLP) that was published in French and English. His recent work on text mining won two best paper awards and a nomination from three different international conferences.

Title of Speech: Automatic Diagnosis of Alzheimer’s Disease using Lexical Features extracted from Language Samples 

Abstract: Background: this study has a twofold goal. First, it aims to improve the understanding of the impact of Dementia of type Alzheimer’s Disease (AD) on different aspects of the lexicon. Second, it aims to demonstrate that such aspects of the lexicon, when used as features of a machine learning classifier, can help achieve state-of-the-art performance in automatically identifying language samples produced by patients with AD.
Methods: the main dataset used is derived from the ADDreSS challenge, which is a part of the DementiaBank data set. This dataset consists of transcripts of descriptions of the Cookie Theft picture, produced by 54 subjects in the training part and 24 subjects in the test part. The number of narrative samples is 108 in the training set and 48 in the test set. First, the impact of AD on 99 selected lexical features is studied using both the training and testing parts of the main dataset. Then some machine learning experiments were conducted on the task of classifying transcribed speech samples with text samples that were produced by people with AD from those produced by normal subjects. Several experiments were conducted to compare the different areas of lexical complexity, identify the subset of features that help achieve optimal performance, and study the impact of the size of the input on the classification. To evaluate the generalization of the models built on narrative speech to new tasks two generalization tests were conducted using written data from two British authors, Iris Murdoch and Agatha Christie, and the transcription of some speeches by former President Ronald Reagan.
Results: using lexical features only, state-of-the-art classification, F1 and accuracies of over 91% were achieved in categorizing language samples produced by individuals with AD from the ones produced by healthy control subjects. This confirms the substantial impact of AD on lexicon processing. The task generalization tests show that the system scales well to a new task.
Conclusions: AD has a substantial impact on the lexicon. Hence, with lexical information only, one can achieve state of the art classification of language samples produced by AD patients vs normal control patients.
Keywords: Alzheimer’s Disease automatic diagnosis, lexical complexity, lexical diversity, lexical density, lexical sophistication  

Dr. Mohammad Hossain

University of Minnesota Crookston, USA

Biography: Mohammad Hossain is an Assistant Professor of Software Engineering and IT Management at the University of Minnesota Crookston, where he teaches various software engineering courses. He earned his Ph.D. from North Dakota State University in 2016. His Ph.D. dissertation title was “Foundational Algorithms Underlying Horizontal Processing of Vertically Structured Big Data Using pTrees.” His research interest includes Data mining, Machine Learning, Software Engineering, Cybersecurity, Algorithm, etc.