Prof. Dr. Ir. Lailil Muflikhah

Universitas Brawijaya, Indonesia

Prof. Dr. Ir. Lailil Muflikhah, S.Kom., M.Sc. is a Professor of Machine Learning at the Faculty of Computer Science, Universitas Brawijaya, Indonesia. She earned her bachelor’s degree in Informatics from Institut Teknologi Sepuluh Nopember (ITS), a master’s degree in Computer and Information Systems from Universiti Teknologi Petronas, Malaysia, and a doctorate in Bioinformatics from Universitas Brawijaya.

Her research interests center on Artificial Intelligence in healthcare, with a focus on biomedical informatics, bioinformatics, and medical imaging. She has led numerous funded projects on disease detection and biomarker identification using machine learning and deep learning methods, covering areas such as dengue shock syndrome, Alzheimer’s disease, lung cancer, hypertension risk, and liver cancer.

She has published extensively in international journals, authored several academic books on data mining, artificial intelligence, and bioinformatics, and holds multiple intellectual property rights in AI-based healthcare applications.

Topic: AI-Driven Personalized Healthcare: From Multimodal Data Integration to Precision Innovation

Abstract: The Personalized Medicine through the Integration of Clinical, Genetic, and Medical Imaging Data based on Machine Learning Model represents a strategic innovation designed to address the challenges of diagnosing and treating complex diseases in the digital era. This approach leverages Artificial Intelligence (AI), specifically advanced machine learning (ML) and deep learning, to integrate diverse medical modalities, including clinical data, genetic profiles (SNPs, DNA sequences), and medical imaging (MRI, PET, Histopathology). This manuscript synthesizes innovations across feature engineering, optimization, and multimodal fusion to enhance diagnostic accuracy and enable precision healthcare. Key innovations include the use of XGBoost for highly accurate SNP-based hypertension risk prediction (98% accuracy), the development of a novel attention-enhanced dual-autoencoder framework for robust histopathology feature clustering (lowest Davies-Bouldin Index of 3.633 with GMM), and the demonstrated superiority of multimodal models (e.g., 93% accuracy for Alzheimer’s diagnosis using MRI + SNP data). By utilizing fusion strategies like intermediate and late fusion, The Model successfully provides a holistic view of patient conditions, moving away from limited unimodal approaches. This work underscores the critical role of AI-driven multimodal analysis in achieving clinical-grade reliability and paving the way for scalable, personalized healthcare interventions, even in resource-constrained environments via mobile deployment (TensorFlow Lite).

Telkom University, Indonesia

Seorang dosen berpengalaman dengan keahlian dalam Big Data & Data Analytics, Ekonomi Digital, dan Blockchain. Beliau memiliki latar belakang yang kuat dalam penelitian dan aplikasi praktis di bidang-bidang ini, yang membuatnya menjadi salah satu peneliti terpercaya di RC DBE. Beliau dikenal karena dedikasinya dalam mengajar dan kemampuannya mengintegrasikan teori dengan praktik nyata, mempersiapkan mahasiswa untuk sukses dalam dunia teknologi yang terus berkembang. Komitmennya terhadap pendidikan dan inovasi telah memberikan dampak signifikan dalam komunitas akademis dan industri.

Topic: Shaping a Sustainable Digital Future: Al, Blockchain, and Beyond

Abstract: The integration of Industry 5.0 and Society 5.0 marks a paradigm shift toward sustainable, human-centered innovation driven by advanced technologies such as Artificial Intelligence (AI), blockchain, and data analytics. While Industry 5.0 focuses on human–machine collaboration to enhance productivity and environmental responsibility, Society 5.0 envisions a technology-enabled society that promotes inclusivity and resilience. This keynote discusses how digital business strategies can bridge these paradigms by harnessing AI and data analytics for mass personalization—offering customized, efficient, and sustainable solutions that cater to diverse societal needs. Blockchain’s decentralized and transparent features further empower token-based economies that encourage eco-friendly behavior, advance circular economies, and build trust across global networks.

At the core of this transformation lies the importance of platform strategies that connect industries and communities through AI-driven insights. Such platforms facilitate scalable innovation ecosystems, expanding equitable access and supporting sustainability objectives. The keynote also explores the synergistic use of IoT, AI, and blockchain to enable seamless data and service integration, propelling Industry 5.0 advancements while realizing the social and environmental ideals of Society 5.0. Finally, it outlines practical approaches for organizations to adopt these innovations, demonstrating how digital transformation can simultaneously tackle global challenges and promote inclusive economic and human development—redefining technology’s role in shaping a sustainable digital era.