Shape1 Shape2

Thumbnail
Dr. Amir Hussain Speaker

Towards Trustworthy and Responsible AI: Real-world Use Cases, Challenges and Opportunities

Edinburgh Napier University, UK

Abstract: World-leading multi-disciplinary research at Edinburgh Napier University in Scotland is pioneering the collaborative development of trustworthy and responsible artificial intelligence (AI) technologies to engineer the next-generation of smart industrial and healthcare systems.

This talk will provide an introduction to these emerging technologies and outline a number of real-world use cases including future research directions and challenges.

Bio:
Amir Hussain obtained his B.Eng (1st Class Honours with distinction) and Ph.D from the University of Strathclyde in Glasgow, UK, in 1992 and 1997 respectively. Following an UK EPSRC funded Postdoctoral Fellowship (1996-98) and Research Lectureship at the University of Dundee, UK (2018-20), he joined the University of Stirling, UK, in 2000 where he was appointed to a Personal Chair in Cognitive Computing in 2012. Since 2018, he has been Director of the Centre of AI and Robotics at Edinburgh Napier University, UK. His research and innovation interests are cross-disciplinary and industry-led, aimed at developing cognitive data science and responsible AI technologies to engineer the smart healthcare and industrial systems of tomorrow. He has co-authored nearly 600 papers including around 300 journal papers (h-index: 71, 22,000+ citations) and 20 Books, and supervised over 40 PhD students. He has led major national and international projects, including as Principal Investigator of the current multi-million pound COG-MHEAR programme (funded under the UK EPSRC Transformative Healthcare Technologies 2050 Call) that aims to develop truly personalised assistive hearing and communication technologies. He is founding Chief Editor of (Springer's) Cognitive Computation journal and Editorial Board member for (Elsevier’s) Information Fusion and various IEEE Transactions. Amongst other distinguished roles, he is an executive committee member of the UK Computing Research Committee (the national expert panel of the IET and BCS for UK computing research). He served as General Chair of the 2020 IEEE WCCI (the world’s largest IEEE technical event on computational intelligence, comprising the flagship IJCNN, IEEE CEC and FUZZ-IEEE) and the 2023 IEEE Smart World Congress (featuring six co-located IEEE Conferences).

Thumbnail
Dr. Shahid Mumtaz Speaker

6G: Vision, Requirements, Technical Challenges, Standardization & Implementations

Nottingham Trent University, UK

Abstract: 6G is the next step in the evolution of mobile communication and will be a key component of the Networked Society. In particular, 5G will accelerate the development of the Virtual world.

To enable connectivity for a wide range of applications and use cases, the capabilities of 6G wireless access must extend far beyond those of previous generations of mobile communications. Examples of these capabilities include ultra high data rates, ultra low latency, ultra-high reliability, energy efficiency and extreme device densities, and will be realized by the development of 5G in combination with new radio-access technologies. Therefore, this talk explains the different key technology components of 6G and from implementation to standardization.

Bio:
Shahid Mumtaz is an IET Fellow, IEEE ComSoc and ACM Distinguished speaker, recipient of IEEE ComSoC Young Researcher Award (2020), IEEE Senior member, founder and EiC of IET “Journal of Quantum communication”, Vice-Chair: Europe/Africa Region- IEEE ComSoc: Green Communications & Computing society and Vice-chair for IEEE standard on P1932.1: Standard for Licensed/Unlicensed Spectrum Interoperability in Wireless Mobile Networks. He has more than 15 years of wireless industry/academic experience. He has received his Master's and Ph.D. degrees in Electrical & Electronic Engineering from Blekinge Institute of Technology, Sweden, and University of Aveiro, Portugal in 2006 and 2011, respectively. From 2002 to 2003, he worked for for Ericsson and Huawei at Research Labs in Sweden. He has been with Instituto de Telecomunicações and Full Professor at NTU. He is the author of 4 technical books, 12 book chapters, 300+ technical papers (180+ Journal/transaction, 100+ conference, 2 IEEE best paper award- in the area of mobile communications with H-index of 65. He had/has supervised/co-supervising several Ph.D. and Master Students. He uses mathematical and system-level tools to model and analyze emerging wireless communication architectures, leading to innovative Master's theoretically optimal new communication techniques. He is working closely with leading R&D groups in the industry to transition these ideas to practice. He secures the funding of around 6M Euro.

Thumbnail
Prof. Lunchakorn Wuttisittikulkij Speaker

Developing an Immersive Virtual University Campus: A Practical Case Study of UCPverse & Intaniaverse.

Department of Electrical Engineering, Chulalongkorn University, Thailand

Abstract: The phrase "metaverse" refers to a virtual environment where people communicate, collaborate, learn, and live via digital avatars.

The metaverse is frequently portrayed as the Internet's next step in development, the place where people would connect socially, enjoy entertainment, and conduct business in ways that are impossible in the real world. Recent developments in augmented reality, mixed reality, and virtual reality are among the primary technologies that offer genuinely immersive experiences in 3D virtual space. Despite its potential, the metaverse is still in its infancy, and in the not too distant future, no one platform will rule all industries. This tutorial explains how academic institutions can create virtual campuses and the advantages the metaverse can offer the academic community. As a real-world study case, the website UCPverse and intaniaverse.com, a model of a virtual university campus, will be used. This guide will be helpful for individuals who want to build their own metaverse.

Bio:
Seminar Summary: The phrase "metaverse" refers to a virtual environment where people communicate, collaborate, learn, and live via digital avatars. The metaverse is frequently portrayed as the Internet's next step in development, the place where people would connect socially, enjoy entertainment, and conduct business in ways that are impossible in the real world. Recent developments in augmented reality, mixed reality, and virtual reality are among the primary technologies that offer genuinely immersive experiences in 3D virtual space. Despite its potential, the metaverse is still in its infancy, and in the not too distant future, no one platform will rule all industries. This tutorial explains how academic institutions can create virtual campuses and the advantages the metaverse can offer the academic community. As a real-world study case, the website UCPverse and intaniaverse.com, a model of a virtual university campus, will be used. This guide will be helpful for individuals who want to build their own metaverse.

Thumbnail
Prof. Piya Kovintavewat Speaker

Evolution of Signal Detection and Decoding for Future Magnetic Recording Systems.

Telecommunications Engineering Program, Nakhon Pathom Rajabhat University (NPRU), Thailand

Abstract: A current hard disk drive utilizing a perpendicular recording technique is now reaching its storage capacity at 1 tera bits per square inch (Tb/in2) because of the super-paramagnetic effect.

To get around this limitation, several approaches have been proposed, such as bit-patterned magnetic recording (BPMR), two-dimensional magnetic recording (TDMR), and multi-layer magnetic recording (MLMR). But since BPMR is currently commercially accessible and has a storage capacity of up to 4 Tb/in2, this session will concentrate on it. Nonetheless, this talk concentrates on BPMR because it can now be commercially available and has a storage capacity of up to 4 Tb/in2. This talk will provide a brief overview of the sophisticated signal detection and decoding techniques that can be employed in BPMR, such as 2D modulation coding, multi-head multi-track detection, and Al-based data detection.

Bio:
Dr. Piya Kovintavewat received the B.Eng. summa cum laude from Thammasat University, Thailand (1994), the M.S. degree from Chalmers University of Technology, Sweden (1998), and the Ph.D. degree from Georgia Institute of Technology (2004), all in Electrical Engineering. His thesis work titled "Timing Recovery Based on Per-Survivor Processing" was awarded second prize for new Ph.D. thesis in the field of information technology by the National Research Council of Thailand in 2005. He is currently a Professor in Telecommunication program, Faculty of Science and Technology, Nakhon Pathom Rajabhat University (NPRU), Nakhon Pathom, Thailand. His main research interests include coding and signal processing as applied to digital data storage systems. Prior to working at NPRU, he worked as an engineer at Thai Telephone and Telecommunication company (1994-1997), and as a research assistant at National Electronics and Computer Technology Center (1999), both in Thailand. He also had work experiences with Seagate Technology, Pennsylvania, USA (summers 2001, 2002, and 2004).

Thumbnail
Prof.Dr. Andrew Ware Speaker

Virtual Reality: A Catalyst for Upskilling Medical Staff in Developing Economies.

University of South Wales

Abstract: In the realm of healthcare, the demand for skilled medical professionals in developing economies is at an all-time high, with limited resources and access to quality training programs. This talk explores the transformative potential of Virtual Reality (VR) technology as a powerful tool to bridge the skills gap and upskill medical staff in these resource-constrained settings. The presentation will delve into the multifaceted ways in which VR can revolutionize medical training and education in developing economies.

It will highlight the following key points: 1. Immersive Learning Environments: VR creates realistic, immersive simulations that allow medical professionals to practice surgical procedures, diagnose illnesses, and respond to medical emergencies in a safe and controlled virtual environment. This hands-on experience enhances their clinical skills and decision-making abilities. 2. Accessible and Cost-effective Training: Traditional medical training often requires costly equipment and infrastructure. VR provides an accessible and cost-effective alternative, making it easier for healthcare institutions in developing economies to provide high-quality training to their staff. 3. Continuous Learning and Skill Maintenance: Healthcare is a dynamic field with constantly evolving practices and technologies. VR offers a platform for continuous learning and skill maintenance, enabling medical staff to stay updated with the latest advancements in medicine. 4. Addressing Healthcare Disparities: In regions with limited access to healthcare facilities, VR can extend the reach of medical expertise. Telemedicine and remote consultations facilitated by VR can help provide healthcare services to underserved populations. 5. Assessing Competency: VR allows for the objective assessment of medical staff's competency and skills, ensuring that they meet the necessary standards and certifications. 6. Collaborative Learning: VR fosters collaborative learning experiences, enabling medical professionals to engage in virtual team-based training exercises and share knowledge across geographical boundaries. By harnessing the power of VR technology, developing economies can uplift their healthcare systems and empower medical staff with the skills and knowledge required to meet the healthcare needs of their communities. This talk will explore case studies and examples from around the world to showcase the real-world impact of VR in upskilling medical staff, ultimately contributing to improved healthcare outcomes in developing economies. To get around this limitation, several approaches have been proposed, such as bit-patterned magnetic recording (BPMR), two-dimensional magnetic recording (TDMR), and multi-layer magnetic recording (MLMR). But since BPMR is currently commercially accessible and has a storage capacity of up to 4 Tb/in2, this session will concentrate on it. Nonetheless, this talk concentrates on BPMR because it can now be commercially available and has a storage capacity of up to 4 Tb/in2. This talk will provide a brief overview of the sophisticated signal detection and decoding techniques that can be employed in BPMR, such as 2D modulation coding, multi-head multi-track detection, and Al-based data detection.

Bio:
Dr. Piya Kovintavewat received the B.Eng. summa cum laude from Thammasat University, Thailand (1994), the M.S. degree from Chalmers University of Technology, Sweden (1998), and the Ph.D. degree from Georgia Institute of Technology (2004), all in Electrical Engineering. His thesis work titled "Timing Recovery Based on Per-Survivor Processing" was awarded second prize for new Ph.D. thesis in the field of information technology by the National Research Council of Thailand in 2005. He is currently a Professor in Telecommunication program, Faculty of Science and Technology, Nakhon Pathom Rajabhat University (NPRU), Nakhon Pathom, Thailand. His main research interests include coding and signal processing as applied to digital data storage systems. Prior to working at NPRU, he worked as an engineer at Thai Telephone and Telecommunication company (1994-1997), and as a research assistant at National Electronics and Computer Technology Center (1999), both in Thailand. He also had work experiences with Seagate Technology, Pennsylvania, USA (summers 2001, 2002, and 2004).

Thumbnail
Dr. Dang (Kevin) Nguyen Speaker

Harnessing Novel Sensors and Advanced Machine Learning for Real-time Respiratory Diagnostics

Massachusetts General Hospital and Harvard Medical School, Massachusetts, USA

Abstract: The global health landscape has been dramatically reshaped by the COVID-19 pandemic. For effective responses in public health, swift and dependable diagnostic instruments are essential. The urgency of the situation necessitates the development of noninvasive, contactless, and cost-effective diagnostic platforms that can cater to diverse healthcare settings.

This research presents a novel platform that leverages the combined potential of Magnetic Respiratory Sensing Technology (MRST) and Machine Learning (ML) to facilitate real-time monitoring and diagnosis of COVID-19, along with other respiratory-related diseases. Specifically, the MRST is adept at capturing and monitoring breathing patterns and respiratory rates, utilizing three distinct breath testing protocols: standard breath, breath-holding, and profound inhalation. To transition from mere data collection to valuable diagnostic conclusions, a range of ML algorithms—including support vector machines, random forest, ensemble models, and deep learning paradigms—were trained and validated using datasets from COVID-19 patients and healthy individuals. This multi-model approach not only bolstered diagnostic robustness and reliability but also ensured adaptability to various healthcare conditions and computational resources. Demonstrating remarkable efficacy, the diagnostic system identified respiratory anomalies linked to COVID-19 with an accuracy rate surpassing 90%. A detailed comparison of the ML models' performance further elucidates the distinct advantages and characteristics of each in the diagnostic procedure.

Bio:
Dang Nguyen, B.S.B.E., received his Bachelor of Science with Honors from the Department of Medical Engineering at the University of South Florida. He currently works as a Clinical Researcher at the Corrigan Minehan Heart Center within Massachusetts General Hospital while participating in the Postgraduate Medical Education Program at Harvard Medical School. He has membership in the Tau Beta Pi engineering honor society, American College of Cardiology, and European Society of Cardiology. His research endeavors cover areas such as sensing technologies, wearable medical devices, telemedicine, and advanced computational approaches including machine learning and deep learning. At The Laboratory for Advanced Materials and Sensors, his role is pivotal in the development of Magnetic Respiratory Sensing Technology (MRST), aiming to provide real-time tracking and diagnostics for COVID-19 and other respiratory diseases. Dang Nguyen has authored over 20 publications and 6 patents, with his work featured in The Lancet, PNAS, and BMJ Oncology, and served as a member of the editorial board and reviewer for numerous peer-reviewed journals.

Thumbnail
Dr. Sam Khoze Speaker

AI Virtual Humans: Bridging the Gap Between Science Fiction and Reality.

aiLIFE.ai

Abstract: A journey into the realm of Exploring the uncharted territory of AI Virtual Humans, where we delve deep into the forefront of Bio-Inspired Communicative AI and the seamless integration of Large Language Models (LLMs) with advanced Computer Vision. Immerse in the multifaceted applications of AI Virtual Humans and underscore the paramount importance of a human-centered approach while dissecting the profound societal implications and transformative potential of this innovative field.

Dr. Sam Khoze is an AI technologist, roboticist, and researcher. He holds a Doctor of Medicine degree, a Postgraduate in Marketing Analysis from MIT, and a Master’s in Artificial Intelligence. He is known for his cutting-edge research, innovative approaches, and focus on Bio-Inspired Communicative AI. He has developed and designed one of the most intelligent social Robot series in association with Professor Hiroshi Ishiguro’s Department of Systems Innovation in the Graduate School of Engineering Science at Osaka University, and Dr. Firouz Naderi, former director of Solar System Exploration at NASA/JPL. Through his collaboration with the Advanced Telecommunications Research Institute (ATR), Dr. Khoze represents some of the world’s most advanced and intelligent robots, Erica and Alter. He has achieved the distinction of being the first to cast and train Hollywood’s first fully autonomous artificially intelligent entity, in a Motion Picture, as featured by The New York Times.

Thumbnail
Dr. Noha Hazzazi Speaker

Software Reliability and Healthcare

Howard University

Abstract: Software reliability has emerged as a critical concern in healthcare systems due to the increasing dependence on technology for patient care, data management, and medical research. Unreliable software can result in not only financial losses but also in risks to patient safety and well-being.

This research aims to examine the relationship between software reliability and healthcare outcomes, focusing on critical applications such as Electronic Health Records (EHR), telemedicine, medical imaging, and life-support systems. We discuss the methodologies employed for ensuring software reliability, including formal verification, fault tolerance, and extensive testing regimes, within the context of healthcare compliance standards like HIPAA and FDA guidelines. Our findings indicate that higher levels of software reliability correlate with improved patient outcomes and reduced medical errors. However, this research also highlights ongoing challenges such as the complexity of healthcare systems, rapid technological advancements, and human-software interaction issues. The study concludes with recommendations for improving software reliability in healthcare, encompassing both technical and policy aspects. Digital twins are extensively used in industrial applications. Businesses increase their productivity by converting their physical assets and processes into digital twin. Digital twin is evolving increasingly promising to accelerate digital transformation at a moderate cost.
Bio:
Noha Hazzazi, Ph.D. is an Assistant Professor in the Department of Electrical Engineering and Computer Science at Howard University’s College of Engineering and Architecture. She is also Co-Director of the Appropriate Technology Center. Broadly, her methodological research focuses on process optimization in healthcare systems, software reliability, software safety and security. Dr. Hazzazi is currently leading a joint effort with Boston University under the auspices of the public interest technology university network (PIT-UN) to advocate public interest considerations in technology policy. She is a member of the Golden Key International Honor Society and Phi Beta Delta honor society and is a recipient of a six-sigma certificate during an internship at Raytheon. Dr. Hazzazi received her Ph.D. in Information Technology from George Mason University in Fairfax, Virginia. She is a member of an underrepresented group and is committed to increasing diversity in the computer sciences. She has been a faculty adviser for the Upsilon Pi Epsilon honor society.

Thumbnail
Prof. Dr Ayyaz Hussain Speaker

Deep Learning – Past, Present and Future

Quaid-i-Azam University Islamabad

Abstract: Deep learning is widely used in numerous applications ranging from speech recognition, natural language understanding and computer vision etc. No single algorithm paradigm has even been so broadly and successfully used as deep nets. Invited talk will focus on history of deep learning, its applications and research trends.


Bio:
Ayyaz Hussain is currently working as Professor of Computer Science at Quaid-i-Azam University Islamabad. From 2010 to 2020 he was Associate Professor and Chair of the Department of Computer Science & Software Engineering at International Islamic University Islamabad. He worked as Research Professor at Gwangju Institute of Science and Technology South Korea from 2013-2014. During 2000 to 2010 he served as Software Engineer at National Engineering and Scientific Commission. Dr. Hussain received his PhD from National University of Computer and Emerging Sciences (NUCES-FAST) in 2009. His research interests include Image processing, computer vision, machine learning and deep learning. He has published more than 40 journal articles along with number of conference papers. He has supervised more than 10 PhD and 25 MS students.

Thumbnail
Dr. Sajid Anwar Khan Speaker

Synergy between Machine Learning and Software Engineering

IM Sciences, Peshawar

Abstract: Emergence of Machine Learning (ML) as the epic center of computational research in last decade has now widened to all phases of system development life cycle from requirements to maintenance and from planning to continuous improvement.

This widening of scope for ML has led to extended and improved development and application of intelligent tools for automatic extraction of information from different documents, identification of functional and non-functional requirements, and test suites etc. With the swift progressions in ML and artificial intelligence, use of ML-based techniques and methodologies for software engineering are introduced and further optimized for greater efficiency of software engineers, processes and the product. From requirements to test cases, and from architecture to documentation, the ML artifacts and tools are now being employed.
Bio:
Dr. Sajid Anwar is Professor at the Centre of Excellence in Information Technology Institute of Management Sciences (IMSciences), Peshawar, Pakistan. He received his MS (Computer Science, 2007) and Ph.D degrees (Software Engineering, 2011) from NUCES-FAST, Islamabad. Previously, he was head of Undergraduate Program in Software Engineering at IMSciences.

Thumbnail
Dr. Wajahat Qazi Speaker

Cognitive Approach Machines with Subjectivity and Awareness

COMSATS University Islamabad, Lahore Campus

Abstract: : Advancement in artificial intelligence has played a catalyst role in the implementation of complex decision-making autonomous systems. These systems b design is cognitive in nature. The design rationale used in their build makes them to operate in objective manner. In recent years the question of making this system to make objective decisions is under consideration along with their capability to possess certain level of awareness.

We have been trying to address and answer this question by the development of a cognitive agent. By design this agent is made capable of possessing certain level of consciousness, imagination, and subjectivity under control conditions as of now. This presentation will introduce our effort in this regard covering the design and implementation of strategies used. The results and progress is encouraging as compare to the current state-of-the-art solutions.

Bio:
Dr. Wajahat Qazi is a distinguished AI and robotics expert, currently serving as an Assistant Professor and In-charge of the BS (AI) Program at COMSATS University Islamabad, Lahore Campus. Alongside his academic role, he contributes significantly to industry advancements as a Consultant in Computer Vision at Services Syndicate Pvt Ltd and in AI & Robotics at Arctan Engineering Solutions Pvt Ltd. Dr. Qazi is the Founding Vice Chair of the IEEE Robotics and Automation Society Joint Section Chapter, showcasing his leadership in fostering collaboration within the robotics community. His expertise extends to advising on intelligent machines and robotics, bridging the gap between academia and industry. With a passion for shaping the future of AI, Dr. Wajahat Qazi continues to inspire and contribute to the transformative potential of artificial intelligence.

Thumbnail
Dr. Zahoor Jan Speaker

AI and Economic Growth

Islamia College Peshawar

Abstract: : The recent emergence of generative artificial intelligence (AI) raises the question whether we are on the brink of a rapid acceleration in task automation that will drive labor cost savings and raise productivity. Despite significant uncertainty around the potential use of generative AI, its ability to generate content that is indistinguishable from human-created output and to break down communication barriers between humans and machines reflects a major advancement with potentially large macroeconomic effects.

If generative AI delivers on its promised capabilities, the labor market could face significant disruption. Using data on occupational tasks in both the US and Europe, we find that roughly two-thirds of current jobs are exposed to some degree of AI automation, and that generative AI could substitute up to one-fourth of current work. Extrapolating our estimates globally suggests that generative AI could expose the equivalent of 300mn full-time jobs to automation. The boost to global labor productivity could also be economically significant, and we estimate that AI could eventually increase annual global GDP by 7%. Although the impact of AI will ultimately depend on its capability and adoption timeline, this estimate highlights the enormous economic potential of generative AI.

Bio:
Zahoor Jan is currently holding the rank of an associate professor in computer science at Islamia College Peshawar, Pakistan and Vice- Chancellor University of Dir. He received his M.S. and Ph.D. degree from FAST University Islamabad in 2007 and 2011 respectively. His areas of interests include Artificial Intelligence, Machine Learning and Data analytics. He has published 70 research papers in high reputed international journals and conferences. His impact factor is more than 80 and has more than 3000 citations. He has produced 5 PhDs and 45 MS/MPhil.

Thumbnail
Dr. Hadeed Ahmed Sher Speaker

Development of fault finding algorithms for Photovoltaic systems

Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Pakistan

Abstract: : Photovoltaic systems are one of the most promising solutions of clean energy. The scale of PV plants ranges from the domestic to commercial applications. Like every other power system, they are prone to various kind of faults. In this talk, I will give an overview of the major faults in a PV plants.

In addition, emphasis will be given to electrical faults i.e. , line to line (L-L) and line to ground (L-G) faults. PV array fault occurs gradually due to the slow deterioration of PV cable or panel insulation. As a result, the insulation impedance, slowly disappears from the high value to 0. Most of the literature methods analyze and detect faults at zero fault impedance . However, for a finite fault resistance the detection, classification and localization of the fault becomes a challenging problem. The state-of-the-art algorithms in this regard will be discussed in detail.

Bio:
Hadeed Ahmed Sher (Senior Member, IEEE) received the B.Sc. degree in electrical engineering from Bahauddin Zakariya University, Multan, Pakistan, in 2005, the M.Sc. degree in electrical engineering from the University of Engineering and Technology, Lahore, Pakistan, in 2008, and the Ph.D. degree in electrical engineering from King Saud University (KSU), Riyadh, Saudi Arabia, in 2016. He has more than 10 year work experience in industry and academia. He is currently an Associate Professor with the Faculty of Electrical Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan. His research interests include grid connected solar photovoltaic systems, maximum power point tracking, fault analysis of PV systems and power electronics. He has authored and coauthored more than 50 publications in IEEE transactions, journals, and conferences. He was the recipient of the Research Excellence Award from the KSU College of Engineering in the year 2012 and 2015. He is an Associate Editor for the IET Renewable Power Generation

Thumbnail
Dr. Rehan Akbar Speaker

Embrace the Digital Technology – DIGITAL TWIN.

Florida International University, FL

Abstract: A digital twin is a virtual representation of an object or system that crosses its lifecycle, functions on real-time data, and run simulations, machine learning and logic to help decision-making. A digital twin is a virtual model designed to accurately replicate a physical object.

The world’s economies are digitally driven now. It is critical for digital economies to take the initiatives in key areas such as digital adoption, digital entrepreneurship, digital innovations, and above all, digital twin. The Digital Twin requires an ecosystem of IT infrastructure, digital computing technologies, and simulations in an organization. Digital twins are extensively used in industrial applications. Businesses increase their productivity by converting their physical assets and processes into digital twin. Digital twin is evolving increasingly promising to accelerate digital transformation at a moderate cost.
Bio:
Dr. Rehan is working as Associate Professor at the Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Malaysia. Previously, He was working as Associate Professor and Head of Department at the Department of Information Systems, Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman, Malaysia. Having more than 22 years of experience in the field of IT, he has established a strong industry, academic, and research profile. His research papers have been published in high-impact research journals and conferences. He has secured many national and international research grants and worked in collaboration on international projects. He is also serving as a member of the editorial board and reviewer of recognized and reputed journals and conferences.

Thumbnail
Dr Muhammad Adnan Khan Speaker

Applications of Weighted Federated Machine Learning

Skyline University College, Sharjah, UAE

Abstract: Federated learning is a way to train AI models without anyone seeing or touching your data. Federated learning (also known as collaborative learning) is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them.

Federated learning (FL) overcomes the preceding difficulties by utilizing a centralized aggregate server to disseminate a global learning model. Today we will discussed multiple applications of Weighted Federated Machine Learning e.g.; Hydrogen Storage Prediction & healthcare. 5.0. Hydrogen Storage Prediction: Proposed Weighted Federated Machine Learning based Hydrogen storage system achieved 96.40% and 3.60% accuracy and miss rate, respectively. Therefore, the proposed HSPS-WFML is an efficient model for hydrogen storage prediction. Healthcare 5.0: The proposed system demonstrates that the approach is optimized effectively for healthcare monitoring. In contrast, the proposed healthcare 5.0 system entangled with FL Approach achieves 93.22% accuracy for disease prediction, and the proposed Weighted Federated Machine Learning-based secure healthcare
Bio:
MUHAMMAD ADNAN KHAN (Senior Member IEEE) is currently working as an Associate Professor at the School of Computing, Skyline University College, Sharjah, UAE & Riphah School of Computing and Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, Pakistan, and Assistant Professor at the Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si, Republic of Korea. I completed my Ph.D. from ISRA University, Islamabad, Pakistan, by obtaining a scholarship award from the Higher Education Commission, Islamabad, Pakistan, in 2016. I also completed my M.Phil. & BS degrees from the International Islamic University, Islamabad, Pakistan by obtaining a scholarship award from the Punjab Information & Technology Board, Govt. of Punjab, Pakistan in 2010 and 2007 respectively. Before joining the above-mentioned Universities, Khan worked in various academic and industrial roles in Pakistan and the Republic of Korea. I have been teaching graduate and undergraduate students in computer science and engineering for the past 15 years. So far, I have successfully supervised 11 Ph.D. students, 40+ M.Phil students & 70+ undergraduate students. Presently, I am guiding 05 Ph.D. scholars and 03 M.Phil. Scholars. I have published more than 240 research articles with Cumulative JCR-IF 640+ in reputed International Journals as well as International Conferences. My research interests primarily include Machine Learning, Image Processing & Medical Diagnosis, Computational Intelligence, Artificial Intelligence, etc.

Thumbnail
Prof. Dr. Usman Qamar Speaker

Leveraging the Power of Al & Data Science

National University of Sciences and Technology (NUST), Pakistan

Abstract: In recent years, AI & Data Science has gained a truly remarkable visibility and has attracted a great deal of interest coming from academia and industry. We have been witnessing a wealth of architectures, paradigm, and learning methods materialized through concepts of deep learning cutting across challenging areas of computer vision and natural language processing, among others. With an omnipresence of floods of data, Data Science has impacted almost all endevours of human life.

With the rapidly evolving area and waves of new AI algorithms, there is a genuine and timely need for a systematic exposure of the AI and Data Science to help the audience to build a coherent and systematic picture of the discipline and facilitate sifting through the meanders of current technology and understand the essence and applicability of the developed methods.
Bio:
Dr Usman Qamar is Professor of Data Science and Head of Department, Computer & Software Engineering, National University of Sciences and Technology (NUST), Pakistan. He has over 15 years of experience in data science both in academia and industry having spent nearly 10 years in the UK. He has a Masters in Computer Systems Design from the University of Manchester Institute of Science and Technology (UMIST), UK. His MPhil and PhD are from University of Manchester, UK. He has published extensively in the domain of AI and Data Science which include 27 Book Chapters, 50+ Impact Factor Journal Publications with a combined impact factor of over 200 (Clarivate Analytics Impact Factor) and over 100 Conference Publications. He has also written 4 books which have all been published by Springer & Co including two textbooks on Data Science. The textbook titled “Data Science Concepts and Techniques with Applications” with Springer & Co has been written as an accessible textbook with the aim of presenting an introduction as well as advanced concepts of the emerging and interdisciplinary field of data science. Recently, the textbook was awarded the top publications at Springer for the United Nations Sustainable Development Goal, SDG4: Quality Education. He has also written the second edition of the textbook which includes an additional 5 chapters as well as over 300 exercise questions. He is also an Associate Editor of some of the most prestigious journals in AI and Data Science including Information Sciences, Applied Soft Computing, Engineering Applications in AI, Applied Intelligence, AI and Ethics, Computers in Biology and Medicine, ACM Transactions on Asian & Low-Resource Language Information Processing, Neural Computing and Applications and PLOS One. He has successfully supervised 6 PhD students and over 100 master students. He has also established Digital Pakistan Lab at a cost of PKR 88 Million which was funded by the Planning Commission, Pakistan under the umbrella of National Centre for Big Data and Cloud Computing (NCBC). The lab has a vision of transforming the industry to the latest standards in digital automation. He is also National Member of the Curriculum Development Committee (CDC), Pakistan for the “Computer Engineering Technology”, an expert committee member of Engineering & Technology for the evaluation/recognition of national research journals for the Higher Education Commission (HEC), Pakistan and Vice-Chairman for implementation of “Think Future” Initiative of Govt of Pakistan. He has received multiple research awards, including Best Book Award 2017/18 by Higher Education Commission (HEC), Pakistan and Best Researcher of Pakistan 2015/16 by Higher Education Commission (HEC), Pakistan.

Thumbnail
Dr. Muhammad Asif Speaker

Underwater Fish Detection and Visual Analysis using Deep Learning

Lahore Garrison University

Abstract: Fish detection and visual analysis of their habitats are essential components of the scientific process for understanding how fish communities interact in their environment. This paper presents a technique for underwater fish detection and their habitat recognition using RESNet-50 deep learning architecture.

To develop a technique, “DeepFish” dataset is used which is taken from a Github repository that consists of 40,000 images. Experimental analysis reveals that the proposed system achieved 99.87% accuracy for fish detection and 100% for visual analysis with respect to habitat recognition.
Bio:
MUHAMMAD ASIF received the PhD degree in Digital Image Processing from Capital University of Science and Technology (CUST), Islamabad, Pakistan, in 2016. Currently he is an Associate Professor and Dean Faculty of Computer Sciences at Lahore Garrison University (LGU), Lahore, Pakistan. His research domains include digital image and video processing, computer vision, machine learning, parallel processing, embedded system optimization and network security. He has contributed more than 55 research papers.

Thumbnail
Dr. Muhammad Usman Akram Speaker

An Intelligent Dual Contrastive Learning and Transformers based Framework for Analysis of Unstained Skin Tissue Samples

National University of Sciences and Technology

Abstract: Histopathology, an essential field in medical research, involves the examination of cells and tissues to understand various diseases and conditions. To aid in this study, tissue samples are traditionally stained to enhance color and contrast, enabling detailed analysis. Staining is a crucial step in histopathology that prepares tissue sections for microscopic examination.

Hematoxylin and eosin (H&E) staining, also known as basic or routine staining, is used in 80% of histopathology slides worldwide. To enhance the histopathology workflow, recent research has focused on integrating generative artificial intelligence and deep learning models. These models have the potential to improve staining accuracy, reduce staining time, and minimize the use of hazardous chemicals, making histopathology a safer and more efficient field. However, staining procedures are costly, time-consuming, and prone to introducing inconsistencies. To address these challenges, we present an innovative cloud-based application that leverages deep learning techniques to analyze skin tissues and provide valuable insights to dermo-pathologists. Our solution revolves around the concept of virtual staining and analysis, achieved through the utilization of generative networks and incremental semantic segmentation. We introduce a novel three-stage, dual contrastive learning-based, image-to-image generative (DCLGAN) model for virtually applying an "H&E stain" to unstained skin tissue images. The proposed model utilizes a unique learning setting comprising two pairs of generators and discriminators. By employing contrastive learning, our model maximizes the mutual information between traditional H&E-stained and virtually stained H&E patches.


Bio:
Dr. Muhammad Usman Akram is currently serving as Tenured Professor in Department of Computer & Software Engineering at NUST College of EME. Dr. Usman is the co-founder and director of the Biomedical Image/Signals Analysis Lab and NUST-spinoff, RISETech Pvt Ltd. Dr. Usman received multiple national and international awards where some of the most honorable mentions are Salim-uz-Zaman Siddiqui Prize in Applied Science-2020 by the Pakistan Academy of Sciences, Best Researcher and Best University Teacher-Awards by HEC. Considering his extra-ordinary services for science, engineering and technology and impact of his innovations on society, Dr. Usman has recently conferred Tamgha-e-Imtiaz in the filed of Engineering by the Govt. of Pakistan.

Thumbnail
Dr. Waqas Haider Khan Bangyal Speaker

Artificial Intelligence for Healthcare

Kohsar University Murree, Punjab, Pakistan

Abstract: Artificial Intelligence has a great impact on the healthcare field and will continue to transform health systems radically. Every healthcare professional should arm themselves with the knowledge to face these changes.

Given advice and warnings from some of the top minds like Elon Mush and the late Steven Hawkings, it seems inevitable that AI is going into a fast-pace development in the next few years and likely to impact every aspect of our lives very soon. This talk will describe some of the most important big data applications in healthcare, namely, quality and patient safety, early detection of diseases and individualized prevention. We will also discuss how AI will go hand-in-hand in the future of health care for all the stakeholders, in terms of high-performance healthcare and precision medicine. On average, a patient generates 80 megabytes of imaging each year. For a healthcare organization, this trove of data from patients has an obvious clinical, financial, and operational value. However, the value of AI in health care is only realized when this raw information is converted into knowledge that changes the practice. The next generation of healthcare delivery requires a team effort from data science, delivery science, and implementation science to ensure that the right patient receives the right care from the right provider at the right time and the right place. In this talk, It will discuss opportunities and challenges faced when bringing big data for healthcare illustrated through some of our ongoing efforts.

Bio:
Dr. Waqas Haider Khan Bangyal received his Ph.D. in Computer Science. Dr. Bangyal is currently an Associate Professor/Chairperson Department of Computer Science, Kohsar University, Murree, Punjab, Pakistan. Dr. Bangyal has over 80 publications, including book chapters, journal and conference papers. His publications have been published in the top venue, such as IEEE, Elsevier, and Springer. Dr. Bangyal is a Senior Member of IEEE SMC and an active Member of ACM and vice president of the INNS Pakistan Chapter. Dr. Bangyal has 20+ years of global experience in Computer Science, Computer Vision, Artificial Intelligence, IoT, Big Data, and block chain. He has also been a facilitator, mentor, and coach for various programs. His area of expertise is technology and innovation and he offers advice on how to promote and convert ideas into successful products/ solutions as well as build strategies with a focus on personal and career development. Dr. Bangyal is serving as an editorial board member for various journals including Elsevier, IEEE , Wiley, IEEE Access, IEEE IoT and Springer Journals. He has served as the TPC co-chair, publicity co-chair, organization chair, and TPC member for many international conferences. He has been a reviewer for prestigious journals of Elsevier, Springer, and Wiley publications, and evolved in holding a number of events as a member of the executive or technical committee. His research interests include Optimization, Machine Learning & Meta-heuristics, Image Processing, and Pattern Recognition. He is a member of ACM and IEEE.

Thumbnail
Dr. Allah Ditta Speaker

SECURITY ENHANCEMENT USING S-BOX BASED DATA ENCRYPTION AND LSB IMAGE STEGANOGRAPHY

University of Education, Lahore

Abstract: In this world of growing technologies, we have adopted many new approaches to sending, receiving, or storing data. Further on communication has also grown with the adaptation of 5g, cloud computing, and blockchain technologies.

But still, we are lacking in securing our data from being stolen or hacked by any unauthorized user. Keeping in view all these things we are purposing a system that will help to achieve security for the secret information with two layers of security added to it. A substitution Box (S-box) is used to encrypt the secret message and then traditional least significant bit (LSB) image steganography is used to hide that encrypted message in an image. The substitution box adds complexity to the crypto-system by using different structures based on the Galois field and the Galois ring. We will describe a designed algorithm for the Implementation of S-boxes and these S-Boxes are used to encrypt the secret information. Moreover, in the second layer, LSB image steganography will be implemented and size, resolution, contrast, PSNR value, etc. will also be kept in view to mitigate the changes in these regarding the image so that no one can find a difference in the original image and Steg image. Overall, our proposed system combines the strength of S-box encryption and LSB image steganography to provide a robust and reliable security solution for protecting sensitive information. By employing two layers of security, we have named our proposed methodology CSSL.

Bio:
Dr. Allah Ditta received his M.Sc. and Ph.D. degrees in Computer Science and Technology from the Quaid-i-Azam University (Q.A.U) Islamabad, Pakistan and the College of Computer Science, Beijing University of Technology, China, in 2012 and 2017, respectively. In 2017, he joined the Department of Information Sciences, Division of Science and Technology and working as an Assistant Professor at the University of Education, Lahore, Pakistan. His research interests include Machine Learning, Information Security, Digital Steganography, Cryptography, Network security and Wireless sensor networks. Dr. Allah Ditta has authored more than 30 Impact Factor Journal papers in well recognized journals such as Journal of King Saud University, Computer and Information Sciences, IEEE Access, Sensors, CMC and Wireless Communications and Mobile Computing.

Thumbnail
Dr. Muhammad Adnan Siddique Speaker

Remote-SOS4EEZ: Remote Sensing for Oil Spill Detection in Pakistan’s Exclusive Economic Zone

Information Technology University of the Punjab (ITU).

Abstract: Oil spill is one of the major threats to marine ecosystems. It is caused by ship accidents, illegal ship discharge (bilge dumping) and natural oil seeps. An accurate monitoring and detection framework is necessary to classify the potential oil spills. Historically, synthetic aperture radar (SAR) data has been used efficiently for classification of oil spills due to its operational capability in all-weather conditions.

Oil spill appears as a dark stretch in SAR imagery. Similarly, low wind areas and algae blooms etc. have a similar dark signature, resulting in false positive detections. Research has been conducted on using deep learning techniques for classification of oil spills and look-alikes in SAR imagery, but still it remains a challenging task for researchers. This project is focused on using deep learning techniques and SAR imagery for classification of potential oil spills. We are investigating the deep learning based semantic segmentation of SAR images for classification of oil spills from look-alikes. Additionally, we are considering detection of coastal areas and ships, because the dark stretch detected near a ship has a greater probability of being an oil spill due to bilge dumping or ship accident. Primarily, our focus is Pakistan territorial waters in the Arabian Sea. Due to lack of any operational framework, the oil spill events in Pakistan Exclusive Economic Zone (EEZ) remain undetected until oil reaches the coastal regions. We are working on developing an operational framework to act to complement early warning systems.

Bio:
Biography: Dr. Muhammad Adnan Siddique is currently an Assistant Professor & Director of the Remote Sensing & Spatial Analytics Lab, at Information Technology University of the Punjab (ITU), Lahore, Pakistan. His research generally revolves around signal processing and machine learning for various geoscience applications, such as monitoring land surface deformation using space-borne radar imaging, observing glacier dynamics with active or passive sensors, detection of marine pollution, etc. He is assimilating pattern recognition & learning inspired approaches with traditional remote sensing. He also works in geospatial analytics. Dr. Siddique received the B.E. degree in electronics engineering from the National University of Sciences and Technology (NUST), Pakistan in 2006. He obtained the European Master of Research in Information and Communications Technology (MERIT) joint degree from Karlsruhe Institute of Technology (KIT), Germany and Universitat Politecnica de Cataluyna (UPC), Spain, in 2010. He did doctorate/post-doc at the Chair of Earth Observation & Remote Sensing, Swiss Federal Institute of Technology, ETH Zurich, Switzerland, from 2013 – 2018. Thereafter, he joined the National University of Computer and Emerging Sciences (NUCES) in 2019, and spent two semesters there as an Assistant Professor before moving to ITU in June 2020. In the long run, his aim is to contribute towards environmental protection by enabling the right technology-based interventions!

Thumbnail
Dr. Azhar Imran Mudassir Speaker

Artificial Intelligence in Healthcare: The Hope, The Hype and Real World Impact

Air University Islamabad

Abstract: Artificial intelligence in healthcare is an overarching term used to describe the use of machine-learning algorithms and software, or artificial intelligence (AI), to mimic human cognition in the analysis, presentation, and comprehension of complex medical and health care data, or to exceed human capabilities by providing new ways to diagnose, treat, or prevent disease. Specifically, AI is the ability of computer algorithms to approximate conclusions based solely on input data.

With the introduction of more innovative and new generation AI tools, healthcare is more advanced in the sense of more awareness, efficiency in delivering care, identification of developing complications, accurate diagnosis of diseases ahead of time, and most recent approaches for interventions.
Bio:
Dr. Azhar Imnran Mudasir is an Assistant Professor at the Department of Creative Technologies, Faculty of Computing & Artificial Intelligence, Air University, Islamabad, Pakistan. He has completed his doctoral degree in Software Engineering from Beijing University of Technology, China and master degree in Computer Science from University of Sargodha, Pakistan. He has worked as a Senior Lecturer at Department of Computer Science, University of Sargodha, Pakistan from 2012 to 2017. He is a renowned expert in Image Processing, Healthcare Informatics and Social Media Analysis. He is a regular member of IEEE, contributed with 60+ research articles in well-reputed international journals and conferences. He is the editorial member and reviewer of various journals including IEEE Access, MDPI Cancers, Applied Sciences, Mathematics, Springer Visual Computer, Talyor and Francis: Biomedical Imaging and Visualization, Multimedia Media Tools & Applications, IGI Global and Journal of Imaging, etc. Dr. Azhar has over 11 years of national as well as international academic experience as a Full-Time Faculty, teaching courses in Software Engineering, and core computing. Dr. Azhar has delivered guest talks, conducted seminar and trainings at numerous national and international forums in past. He has contributed in multiple international conferences in diverse roles (keynote speaker, technical/ committee member, registration, speaker, etc.). His research interests include Image Processing, Social Media Analysis, Medical Image Diagnosis, Machine Learning, and Data Mining. He aims to contribute to interdisciplinary research of computer science and human-related disciplines.