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btn-border text-white btn-sm text-nowrap overflow-hidden">   07-08 November 2024</button> </div> <div class="col-md-6 col-sm-6 col-6 contact-info d-flex align-items-center justify-content-end text-nowrap overflow-hidden"> <a href="./contacts?_action=login" class="btn btn-sm">Login</a> <a href="./contacts?_action=signup" class="btn btn-sm"> Register</a> </div> </div> </div> </section>  <header id="header" class="d-flex align-items-center"> <div class="container d-flex align-items-center justify-content-between"> <h1 class="logo"><a href="./" ><img class="logo" src="" onerror="this.style.display='none'" ></a></h1>  <nav id="navbar" class="navbar"> <ul> <li id="li_home" ><a class="nav-link scrollto" href="././">Home </a> </li> <li id="li_menu_999" class="dropdown"><a class="nav-link scrollto" href="javascript:void(0)">Conference Structure </a> <ul> <li id="li_menu_1000"><a href="./page_229.html">Conference Organizational Structure</a></li> <li id="li_menu_1003"><a 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href="./">Home</a></li> <li> Workshops</li> </ol> </div> </div> </section><main id="main" data-aos="fade-up"> <section class="static-page padding-top-20"> <div class="container"> <div id="u_content" class="usrr_cntt"><h1 style="text-align: center;">Workshops</h1>   If you are interested in leading a workshop at ODSIE 2024, we invite you to submit your proposal. For this purpose, please email your suggested workshop title, your short bio, and your CV to <a href="/cdn-cgi/l/email-protection#cda2a9bea4a88dbfa8abaea2a3abe3aea2a0e3">[email protected]. </a>We will review your submission and get in touch with you promptly upon receipt.     <hr /> Embracing Artificial Intelligence in Modern Education: Opportunities & Challenges <img style="border-style: groove;" src="data/cnf1704867816/uploads/Dr-_Meenakshi_Kaushik.png" alt="" width="256" height="261" /> Lecturer: Dr. Meenakshi Kaushik Professor, Dean (Research), TIIPS ,GGSIPU (Guru Gobind Singh University), India   Short Bio: Dr. Meenakshi has more than 15 years of experience in teaching, training, research and corporate in the area of HR & OB, Values & Ethics, and as a Motivational and PDP Trainer. Presently she is working as a professor in Trinity Institute of innovations in Professional studies, Guru Gobind Singh Indraprastha University. Her Ph.D. thesis is entitled "A Study of Leadership Effectiveness in women executives in Context of Delhi Based business organizations” has brought relevant theoretical and empirical imperatives on leadership development strategies among women leaders and executives. She has also rendered education as a professor with several reputed Institutions, colleges and universities. A currently she is also supervising PhD candidates of Aligarh Muslim university (AMU, Central university). She has authored and edited a book entitled “Digital Transformation: Recent Trends & practices” in 2022 available at Amazon, Kindle, Himalaya publication website. She is continuously been invited as a Keynote speaker, Session Chair and as a reviewer in various national and international conferences and summits. she is continuously involved in research and publication work and written many research papers in Scopus, Elsevier, UGC listed journals and has got several citations in  her research papers. She is continuously in a process to contribute to the society in terms of her teaching, training, publications and research activities. She has recently received LREA 2024 award, from London School of Digital Business (LSDB) for her contribution in Education Industry.   Abstract: Artificial intelligence or AI is the technology enabling computers and machines to simulate human intelligence and problem-solving capabilities. Artificial Intelligence along with other technologies like sensors, robotics and geo-locations perform various tasks with lot of efficiency and efficacy at a very faster pace. GPS guidance, generative AI tools are the best examples of artificial intelligence, used in education industry. The integration of AI in modern education plays a very pivotal role in modern education and it has transformed all the learning processes, teaching methods, administrative processes through automation, developing student’s skills, knowledge and wisdom and training of future professionals In this information technology era the integration of AI is not only considered as a necessity but also a strategic direction for the development of modern educational is performing various roles in modern education like personalization of learning experiences, intelligent tutoring system, improving accessibility and efficiency of education, developing future leaders, innovators and professionals competent in this dynamic market. In this workshop, few key areas related to Artificial Intelligence (AI) shall be discussed along with various chat bots and AI models used in teaching training and research.   <hr />   From Data to Decisions: Hybrid Deep Learning Models in the Field of Health <img style="border-style: groove; display: block; margin-left: auto; margin-right: auto;" src="data/cnf1704867816/uploads/Dr-_Ezgi.png" alt="" width="256" height="261" /> Lecturer: Dr. Ezgi Özer Assistant Professor, Piri Reis University, Department of Computer Engineering, Turkey    Short Bio: Dr. Özer gives courses in optimization, artificial intelligence, and robotics, as theoretical and practical in many years. Her Ph.D thesis topic is early diagnosis of epileptic seizures over EEG signals using deep learning approach. She is one of the research group members at ASPI (Advanced Signal Processing Initiative) which has been associated Nova Lisbon University, School of Science and Technology since 2018. As part of her doctoral thesis, she has spent two years on signal processing and deep learning methods as a researcher at Nova Lisbon University the Department of Electrical and Computer Engineering. Her research interest covers the different feature selection/extraction methods and machine learning/deep learning algorithms, new forecasting models, signal processing, image processing, interpreting large datasets, and data management.   Abstract: In today’s data-driven work stands at the forefront of innovation and problem-solving, in the field of health. It has essential role in extracting meaningful information from vast amounts and complex datasets, at the intersection of computer science, statistics, and data. The transition from data to actionable decisions is increasingly powered by hybrid deep learning models. These models integrate various machine learning and deep learning techniques with different feature extract and select methods to improve decision-making processes and optimize outcomes. This workshop covers the journey of deep learning from theoretical foundations to practical applications in the medical domain. It will begin by examining the core principles of learning, highlighting key algorithms and architectures that underpin its success. Additionally, it will address the challenges and ethical considerations inherent in integrating deep learning into medical practice, including data privacy, interpretability, and the need for robust validation.   <hr />   Integrating Data Science and Optimization for Efficient Engineering Management <img style="border-style: groove; display: block; margin-left: auto; margin-right: auto;" src="data/cnf1704867816/uploads/Vuppulapati%C2%A0Chandra_Sekhar_Naidu%C2%A0.png" alt="" width="256" height="261" /> Lecturer: Vuppulapati Chandra Sekhar Naidu   Computer Science (AI/ML)- Coforge    Short Bio: Mr. Vuppulapati Chandra Sekhar Naidu is an IT professional with over 23 years of experience in Computer Science and Information Technology, having worked with prestigious organizations such as IBM, EXL, Virtusa, UST Global, and Polaris in Principal Architecture and Leadership roles. His expertise spans Big Data, AI/ML, Cloud, Cybersecurity, and Data Governance. He has published articles in these fields, developed advanced analytical models, and designed cutting-edge solutions. Mr. Vuppulapati also served as a reviewer for various university journals and was recently selected as a GLOBEE Judge in Cybersecurity, where he assessed submissions from over 50 companies. He has been invited to speak at several international conferences on communication, computational intelligence, and computing technologies, and he regularly reviews research-oriented papers from leading organizations, universities, international seminar/workshops offering innovative ideas that can make a meaningful impact. His experience includes managing diverse global clients across industries such as banking, finance, healthcare, retail, insurance, manufacturing, logistics, and telecom. As a Senior Member of IEEE, IET and Fellow of IETE, he currently leads Data, AI/ML, Cloud, and Big Data projects at COFORGE.COM, as a Senior Practice Leader -AI/ML where he drives strategic engagements and provide thought leadership to expand the data analytics practice.   Abstract: In the context of today’s dynamic and technology-driven industrial environment, the convergence of advanced optimization techniques and data science has become essential for solving intricate challenges in engineering management. This paper delves into the application of mathematical optimization models, including linear programming, mixed-integer programming, and metaheuristic algorithms, alongside cutting-edge data science methodologies such as machine learning, predictive analytics, and big data analytics. Together, these tools are reshaping decision-making processes in key engineering domains such as supply chain optimization, production scheduling, energy management, quality control, and risk mitigation. By leveraging real-time data and algorithmic strategies, engineering managers can not only optimize operational efficiency but also drive substantial cost reductions and enhance strategic foresight under conditions of uncertainty. The analysis presented is bolstered by case studies demonstrating the practical implementation of these data-driven models, revealing their impact on improving system performance and decision accuracy. This interdisciplinary approach provides a robust framework for the future of intelligent and sustainable engineering management, offering transformative insights that are critical for global industrial competitiveness.   <hr />   Statistics vs. Machine Learning: Approaches, Applications, and Decision-Making <img style="border-style: groove; display: block; margin-left: auto; margin-right: auto;" src="data/cnf1704867816/uploads/Govindaraj.png" alt="" width="256" height="261" /> Lecturer: Vasanthi Govindaraj  Senior Software Engineer, National General (An Allstate Company)    Short Bio: Ms. Vasanthi Govindaraj is an accomplished IT professional with over 18 years of experience specializing in Artificial Intelligence, mainframe modernization, and the development of data-driven solutions. She has successfully led projects such as migrating 10 million loan records to the Microsoft Azure Cloud, achieving a 40% increase in processing speed and $1.2 million in annual savings. Vasanthi’s expertise spans AI, machine learning, and deep learning, with her research paper on deep learning recently published in a prominent international journal. A dedicated mentor on ADP List, she also serves as an expert judge for the Claro Awards and was nominated by the WomenTech Network for the Women in Tech Global Awards 2024. As a Senior Member of IEEE and Fellow of IETE, she has been invited to speak at several international conferences and has reviewed AI conference papers globally.   Abstract:  In the era of big data and artificial intelligence, making sense of complex data is more crucial than ever. Both Statistics and Machine Learning (ML) are key players in this process, but they approach data analysis from distinct perspectives. Statistics, rooted in mathematics, emphasizes interpretability, hypothesis testing, and inference, while Machine Learning, grounded in computer science, focuses on predictive accuracy, automation, and handling large datasets. This workshop will delve into the key differences and similarities between these fields, with practical applications in industries like finance, healthcare, and manufacturing. Participants will engage in hands-on exercises to explore when to use statistical methods versus ML techniques such as neural networks and decision trees. By the end, attendees will have a clear understanding of how to choose the best approach for different data challenges and improve decision-making in a data-driven world.   <hr />   Innovation labs An approach to innovation process digitalization   <img style="border-style: groove; display: block; margin-left: auto; margin-right: auto;" src="data/cnf1704867816/uploads/Dr-_Ali_Bagheri.png" alt="" width="256" height="261" /> Lecturer: Dr. Ali Bagheri  Dr. Ali Bagheri PhD.  In Technology and Innovation management Co-Author of Innovation Lab Book University Lecturer (SRBIU),Tehran, Iran    Short Bio: Dr. Ali Bagheri, born in 1981, holds a Ph.D. in Technology Management with a specialization in Innovation Management and a DBA in Business Administration with a focus on Digital Transformation. He also has a Master’s degree in Strategic Management. Dr. Ali Bagheri, as a professional business coach and a member of the International Coaching Federation, helps businesses grow and transform. He is a university lecturer at the Science and Research Branch and is a researcher in the field of innovation, contributing to research and development. Additionally, he is a board member and CEO of several companies with over 30 years of industry experience. He has served as a judge and a central committee member in startup events like SAKOU at Islamic Azad University, Science and Research Branch. Currently, he works as a coach, mentor, and consultant in the field of innovation with many startups and knowledge-based companies both domestically and internationally. He is also a member of various scientific associations, including the Iranian Technology Management and Innovation Association and the Futurists Association, and has been active in the specialized working groups of these associations. By leveraging his knowledge and experience, he contributes to the development and progress in various areas of management and innovation and is always seeking innovative solutions to complex business problems. In addition, Dr. Ali Bagheri, as a lecturer, has conducted various workshops in the field of innovation and technology management, sharing his knowledge and experiences with others, contributing to the growth and development of these areas. He is also a member of the Iran-Switzerland Chamber of Commerce and the UAE-Switzerland Chamber of Commerce and has been active in their specialized committees, such as Artificial Intelligence and Business Innovation.     Abstract: The ideal form for an innovation process, whether in its general form from the stage of idea formation to entering the market and commercialization, or in each of the parts separately, should be in such a way as to have the highest productivity. (combined efficiency and effectiveness) during the process. Moving in this direction will reduce financial, time and human costs. One of the concepts that covers the innovation process from the beginning to the end is the innovation laboratory concept. By using these laboratories, organizations, commercial companies, accelerators and growth centers have been able to obtain a comprehensive view of this process, and secondly, by making changes in each part of it, they can adjust the outputs according to the micro goals. and guide their parents. According to Clayton Christensen, 95% of product innovation processes fail. According to PWC's 2017 report, 54% of innovative organizations face a major gap in transforming their innovation strategy into a big business strategy (Brown et al., 2017). In this regard, as factors of innovation failure, BCG reports in 2016 that 42% of failed innovation projects had a very long development time. 32% did not choose the right idea and 31% did not have a risk-taking culture. 25% lacked the ability to collaborate, 22% did not have good enough ideas, and 20% were lacking in marketing innovations (Michael Ringel, 2016). In the search for ideas, all the efforts of these laboratories are to involve the customer more at the beginning of the innovation process so that they can minimize the possible costs of not welcoming the idea in the market. Another thing is that under the selection process, how to act in screening among the flood of ideas in such a way that the highest chance can be imagined for the commercialization of that idea. Some people consider the use of experts and the opinions of experienced experts to be helpful, but in practice and in the real world, this does not happen due to organizations being locked in their current routines, and in the continuation of innovation, it suffers a systemic failure. in addition to routines, the limited rationality of humans during the search and selection process can also be mentioned. Another issue is that, after the stage of choosing ideas during the process of implementation and exploitation, the requirement of prototyping, pilot productions and testing the beta form of products are all costly and time-consuming processes. The involvement of human tastes, even in the form of experts, adds to the complexity of this phase and stages of managing innovation projects. Finally, in the last part, i.e. the maximum use of innovation achievements, the limitations of the life cycle of a product or technology, as well as the dependencies of the previous path of an organization, as well as knowledge spillovers due to the human-centeredness of decision-making processes, this can be making the sector face a serious challenge. The consequences of the continuation of this form of decision-making in innovation management increase the probability of failure of the whole process and include the reduction of effectiveness and the achievement of goals, i.e. the successful commercialization of ideas in the market, on the one hand, and on the other hand, the reduction of efficiency, which means the loss of financial resources and Sometimes due to the existence of human decisions, it is affected by limited rationality and the overall productivity of innovation decreases. Among the possible influencing factors in increasing the productivity of an innovation process, it is possible to reduce the time interval from idea generation to entering the market, moving from path dependencies to path creation strategy, lean screening and selection of ideas. In an optimal way, he pointed out in capturing the opinions of customers along the way and absorbing and adapting their ideas through the strategy of open innovation and optimizing the prototyping phase in the implementation of the project. In all of these solutions, according to the obtained literature, traces of human decisions can be seen both individually and in the form of organizational routines created over time. The main issue in this research is how to digitize the innovation process in the form of designing a model of an innovation laboratory. From a practical point of view, it is to present the model of an innovation laboratory that can solve this gap and systemic failure caused by the human mind in decision-making by using intelligence.   <hr />   A Soft Computing Framework for Strategic Decision Making   <img style="border-style: groove; display: block; margin-left: auto; margin-right: auto;" src="data/cnf1704867816/uploads/Dr-_Sanjib_Biswas.png" alt="" width="256" height="261" /> Lecturer: Dr. Sanjib Biswas Assistant Professor, Amity Business School, Amity University Kolkata Short Bio: Dr. Sanjib Biswas presently teaches at Amity University, Kolkata in the area of Decision Sciences and Operations Management. He did his Ph.D. under the guidance of Prof. Gautam Bandyopadhyay in Management Studies from NIT, Durgapur. He has two decades of experience working in Industry and Academics. He has been a visiting faculty and external examiner for MBA dissertation projects and viva voce for Indian Institute of Foreign Trades (IIFT) Kolkata for the last 4 years. He has published/presented more than 100 papers including papers in Scopus/ SCI indexed journals and proceedings such as Information Sciences, Annals of Operations Research, FUSME, DMAME and others. He is a regular reviewer and editorial board member of 20+ Scopus/SCI indexed journals including Applied Soft Computing, Technology Forecasting and Social Change, Computers and Industrial Engineering, Expert Systems with Applications, OPSEARCH, Reports in Mechanical Engineering and many others. He is among the top 2% researchers in the world 2024 (Stanford University) and received several best paper awards in National and International Conferences. He holds three patents to his credit. He has delivered several invited talks as a resource person, trainer and expert. His research interests include Supply Chain Management, MCDM, Soft Computing, Technology Management and Sustainability.   Abstract: The world has been experiencing the transformational growth in technological development in the era of Industry 4.0 since 2011. The Universe is embracing the power of Metaverse. In the realm of Industry 4.0, the hospitality sector has been undergoing a phenomenal change by leveraging metaverse technologies like blockchain, augmented and virtual reality, digital twin, avatars and 3D models to create a memorable immersive journey for the customers. In this paper, we aim to carry out a strategic analysis of the business environment of the hotel industries in India for adoption of metaverse technologies. We propose a p, q Quasirung Orthopair Fuzzy number (p, q – QOFN) based soft computing framework for carrying out a SWOT analysis of the hotel industries. We use a novel integrated framework of COBRAC and WENSLO for conducting the multi-criteria group decision analysis. The sensitivity analysis and comparison with other models have been carried out to examine the reliability of the result.  </div> </div> </section> </main>  <footer id="footer"> <div class="footer-top"> <div class="container"> <div class="row"> <div class="col-lg-3 col-md-6 footer-links"> <h4>Quick Link</h4> <ul> <li class="nomargin"> <a href="page_263.html">PhD Thesis Competition</a></li> </ul> </div> <div class="col-lg-3 col-md-6 footer-contact"> <a style="color: #ffffff;" href="../../../../../../../../cnf/contact.us">Contact Us</a> <a style="color: #ffffff;" href="../../../../../../../../p_symposiumproposal">Symposium Proposal</a> <a style="color: #ffffff;" href="../../../../../../../../p_panelproposal">Panel Proposal</a> </div> <div class="col-lg-3 col-md-6 footer-links"> </div> <div class="col-lg-3 col-md-6 footer-links"> <div class="footer-newsletter"> <div class="padding-3"> <div class="row"> <h4>Newsletter Subscription</h4> <div class="nopadding">Subscribe to our email newsletter for useful tips and valuable resources.</div> <form action="" method="post"> <input type="email" name="email"><input type="submit" value="Subscribe"> </form> </div> </div> </div> <div class="social-links mt-3"> <a href="" class="facebook" title="Facebook"></a><a href="" class="x-twitter" title="X-twitter"></a><a href="https://www.linkedin.com/company/refconf/?viewAsMember=true" class="linkedin" target="_blank" title="Linkedin"></a> </div> </div> </div> </div> </div> <div class="container py-4"> <div class="copyright"> Conference Management System. 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