Keynote Speakers:

Sudan Jha, Senior IEEE Member and ACM Member,

Professor, Department of Computer Science and Engineering, School of Engineering, Kathmandu University, Nepal.

Biography: 

Academic:

With a total 22+ years of teaching, research and industrial experience, Prof. Sudan Jha is a Senior IEEE member; ACM member, Editor-in-Chief, International book series editor, acclaimed Principal Scientist, International Keynote Speaker. He has delivered a number of Keynote Speeches / expert talks around the world. Presently working as a Professor in Department of Computer Science & Engineering, Kathmandu University, Nepal; bears experiences from top notch universities like KIIT University, Chandigarh University, Christ University, etc. Apart from these, he has working experiences as "technical director in Nepal television", "Principal in Nepal College of IT", "Individual Consultant in Nepal Telecom Authority". He is passionate about quality of higher education and is working extensively on smart platforms.

Research:

80+ accepted and published research papers, book chapters in reputed SCI, SCIE, indexed refereed journals and conferences.

Editor-in-Chief in an international journal; Guest Editors in SCIE/ESCI/SCOPUS indexed journals. Three patents in his name.

Authored / edited 5 books for recent advanced topics in IoT, 5G, AI for the publishers - Elsevier, CRC and AAP.

Accomplished two international funded projects.

In addition, he has been resource person in several national / international faculty development programs and Short term training programs for faculties and students.

Guest Editor in several SCIE and ESCI journals, reviewer/TPC member in various conferences and journals.

He is also:

an IBM certified Engineer on “Microservices Architecture And Implementation”; Certified Data Scientist with proficiency in Python;

NASSCOM and Ministry of Electronics and Information Technology, Govt. of India certified "Machine Learning - Linear Regression";

Certified in "Foundations of Artificial Intelligence" by SkillsUp.

His research area of interest includes Internet of Things, Artificial Intelligence, Machine Learning (Deep Learning), Neutrosophic theory and Neutrosophic Soft Set Systems.

Speech Title:

Achieving improved accuracy and explainability by combining ML for IoT network attack detection with Explainable Artificial Intelligence techniques

Abstract:

With the increasing number of devices connected to the internet, the potential for cyber-attacks has become a major concern. Traditional methods for detecting and mitigating attacks on IoT networks often lack transparency and explainability, making it difficult to understand the reasoning behind decisions and identify potential vulnerabilities. This talk will focus on various proposals that utilizes Explainable Artificial Intelligence techniques to enhance the interpretability of IoT network attack detection models. The talk will also focus on the lack of transparency in the decision-making process and the scarcity of attack data for training purposes, which are some of the significant challenges. The solutions to overcome these challenges will also be discussed.

Shamik Tiwari,

Professor & Cluster Head,  School of Computer Science, University of Petroleum & Energy Studies, India.

Biography: 

Dr. Shamik Tiwari is an experienced professor with a strong background of twenty years in computer vision, data science, predictive and statistical modeling, machine learning, and deep learning. He is also listed among world's 2 percent research scientists by Stanford University ranking-2023. Throughout his career, he has demonstrated a deep passion for understanding and solving complex problems in these fields, and he is committed to pushing the boundaries of knowledge through research and sharing his expertise with the next generation of students.

In the realm of computer vision, he has extensive knowledge of image processing techniques, object detection, recognition, and medical informatics algorithms. He has worked on various projects involving computer vision applications such as Alzheimer's disease detection, Monkeypox detection, Diabetic Retinopathy Screening, facial recognition, object segmentation, and scene understanding. His research contributions have been published in reputable conferences and journals, showcasing his ability to contribute to the advancement of this field.

Speech Title:

Unleashing the Power of Generative AI: Journey towards a Transformed Future

Abstract:

Generative Artificial Intelligence (AI) has emerged as a groundbreaking technology that is revolutionizing the boundaries of human creativity and innovation. This speech will take the audience on a captivating journey through the evolution, capabilities, and potential of generative AI, showcasing its transformative impact across various domains.

The keynote will commence by providing an overview of generative AI and its fundamental principles. We will explore how generative models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), enable machines to learn from vast amounts of data and generate novel and realistic outputs. By comprehending the underlying architecture and training methodologies, the audience will gain insight into the fascinating world of generative AI.

Invited Speakers:

Claudio Marche,

Assistant Professor, Department of Electrical and Electronic Engineering, Università degli Studi di Cagliari, Italy

Speech Title:

Leveraging Artificial Intelligence and Machine Learning for Service Discovery in Social Internet of Things

Abstract:

With the rapid proliferation of the Internet of Things (IoT) and its associated applications, there is an exponential increase in network traffic. Service discovery has become a vital mechanism that allows devices to search for required services. A novel paradigm, known as Social IoT, has been introduced recently. In this paradigm, devices autonomously establish social relationships based on the rules set by their owners. In this context, "things" opportunistically interact with their peers, reducing traffic volume. So, each node employs strategies to manage traffic diversity by selecting a suitable peer for discovery to provide composite services for human benefit. In this scenario, Artificial Intelligence (AI) and Machine Learning (ML) hold significant potential in managing this mechanism efficiently. Their capability to learn from data and make informed decisions can be harnessed to streamline the service discovery process. By analyzing interaction patterns and network dynamics, these models can identify the most suitable peers for service discovery, thereby reducing traffic volume and enhancing the overall system performance. Furthermore, these novelty techniques can adapt to changing network conditions and device behaviours, ensuring robust and dynamic service discovery. This speech explores the potential of AI and ML in managing service discovery in SIoT, aiming to encourage a more efficient and autonomous IoT environment.