DINT

Distributed Intelligence Research Group

About Us

This interdisciplinary research group, formally established on December 24, 2024, investigates the fundamental principles and cutting-edge applications of Distributed Intelligence. Our research spans a wide range of domains, including the development of robust and scalable distributed systems for data management, high-performance computing, and edge computing. We leverage advanced AI and machine learning techniques to address complex challenges in areas such as anomaly detection, predictive modeling, and decision-making under uncertainty. Our research methodologies encompass a diverse set of approaches, including data mining, natural language processing, computer vision, and social network analysis. We strive to create innovative solutions that have a significant impact on various sectors, including healthcare, finance, transportation, and smart cities, by advancing the frontiers of Distributed Intelligence.

Mission

To advance the frontiers of Distributed Intelligence by developing and deploying innovative solutions that leverage the power of decentralized systems, artificial intelligence, and data science to address critical challenges across diverse domains.


Aim

Our research group aims to foster interdisciplinary collaboration among computer scientists, engineers, social scientists, and other relevant fields to develop cutting-edge Distributed Intelligence technologies. We will conduct fundamental and applied research in areas like distributed systems, machine learning, and data analytics, focusing on scalability, robustness, and fairness. Our research will address critical societal needs in healthcare, sustainability, education, and other domains, while ensuring the responsible and ethical development and deployment of these technologies. We will actively disseminate our research findings through publications, open-source contributions, and collaborations with industry and academia to accelerate the adoption and impact of Distributed Intelligence for the benefit of society.

Advancing the Frontiers of Distributed Intelligence
Unlocking the Power of Decentralized Intelligence for a Smarter Future
Welcome to the official website of the Distributed Intelligence Research Group!

Our interdisciplinary research group is at the forefront of exploring the fundamental principles and pioneering the cutting-edge applications of Distributed Intelligence. We are a team of dedicated researchers passionate about unraveling the complexities and harnessing the immense potential of systems where intelligence is distributed across multiple interconnected entities.

Our Core Research Areas:

Our research endeavors span a diverse and impactful range of domains within Distributed Intelligence, focusing on creating robust, scalable, and intelligent solutions for real-world challenges. Key areas of our investigation include:

Robust and Scalable Distributed Systems: We are developing next-generation architectures for data management, ensuring efficient storage, retrieval, and processing of vast datasets across distributed environments. Our work also focuses on high-performance computing (HPC), leveraging distributed resources to tackle computationally intensive tasks, and edge computing, bringing intelligence closer to the data source for real-time processing and decision-making.
Keywords: distributed systems, scalable systems, data management, high-performance computing, HPC, edge computing, distributed architecture, cloud computing, fog computing.

We are leveraging the power of artificial intelligence (AI) and machine learning (ML) to address complex challenges inherent in distributed systems. Our research explores novel techniques for:

Anomaly Detection: Identifying unusual patterns and outliers in distributed data streams to ensure system security and operational integrity.
Keywords: anomaly detection, fraud detection, fault diagnosis, outlier detection, distributed monitoring.

Predictive Modeling: Developing intelligent models to forecast future trends and behaviors based on distributed data, enabling proactive decision-making.
Keywords: predictive modeling, forecasting, time series analysis, distributed prediction, demand forecasting.

Decision-Making Under Uncertainty: Designing intelligent agents and algorithms that can make optimal decisions in complex, dynamic, and uncertain distributed environments.
Keywords: decision-making, reinforcement learning, multi-agent systems, distributed optimization, uncertainty quantification.

Our interdisciplinary approach employs a rich set of methodologies to extract insights and build intelligent systems from distributed data sources:

Data Mining: Discovering hidden patterns, correlations, and valuable knowledge from large-scale distributed datasets.
Keywords: data mining, big data analytics, knowledge discovery, pattern recognition, distributed data analysis.

Natural Language Processing (NLP): Enabling distributed systems to understand, interpret, and generate human language for improved communication and interaction.
Keywords: natural language processing, NLP, text mining, sentiment analysis, distributed language models.

Computer Vision: Developing distributed vision systems that can analyze and interpret visual information from multiple sources.
Keywords: computer vision, image processing, video analytics, distributed vision systems, object detection.

Social Network Analysis (SNA): Investigating the structure and dynamics of distributed social networks to understand information flow, influence, and community behavior.
Keywords: social network analysis, SNA, network science, community detection, influence maximization, distributed social sensing.
Our Vision: Impacting Key Sectors Through Distributed Intelligence

We are committed to translating our research breakthroughs into innovative solutions that address critical challenges and create significant impact across various sectors, including:

Healthcare: Developing intelligent distributed systems for remote patient monitoring, disease prediction, and personalized medicine.
Keywords: distributed healthcare, telemedicine, remote monitoring, medical diagnosis, personalized medicine.

Finance: Building robust and secure distributed platforms for fraud detection, algorithmic trading, and risk management.
Keywords: distributed finance, fintech, algorithmic trading, fraud prevention, risk management, blockchain.

Transportation: Creating intelligent transportation systems for autonomous vehicles, traffic optimization, and smart logistics.
Keywords: distributed transportation, autonomous vehicles, smart traffic management, logistics optimization, connected vehicles.

Smart Cities: Developing interconnected and intelligent urban environments for efficient resource management, public safety, and improved quality of life.
Keywords: smart cities, urban computing, IoT, smart infrastructure, distributed sensing, environmental monitoring.

We invite you to explore our website to learn more about our ongoing research projects, publications, team members, and opportunities for collaboration.
Join us in shaping the future of Distributed Intelligence!


Members

Dr. Zulkifli Tahir

Distribution System and Web Programming

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Dr. Zulkifli Tahir is an Assistant Professor in the Department of Informatics, Faculty of Engineering, Universitas Hasanuddin, Indonesia. His research interests lie in the areas of Web Technologies including progressive web apps, web server load balancing, and digital transformation for small and medium industries; Distributed System, Fog Computing and Internet of Things (IoT), with a focus on smart homes, solar panel monitoring, and resource allocation; and Machine Learning and Artificial Neural Networks for applications in maintenance decision support systems, industrial machine fault detection, and image processing.

Dr. Wardi

Telecomunication

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Dr. Wardi is Associate Professor in the Department of Electrical Engineering, Faculty of Engineering, Universitas Hasanuddin and is renowned for his groundbreaking contributions in the field of telecommunications technology. The primary focus of his study is on many facets of wireless communications, namely the capacity and coverage planning of LTE-A (4.5G) at a frequency of 2300 MHz. His investigations on the performance of routing protocols such as OLSR and BATMAN in Multi-hop Ad Hoc Networks and Mesh Ad Hoc Networks, using Raspberry Pi as a platform, have resulted in noteworthy contributions to the field. Dr. Wardi has created a portable IP-based communication system that utilizes Raspberry Pi as an exchange. This demonstrates his inventive approach to practical telecoms solutions

Tyanita Puti Marindah W, M.Inf.

Social media and Data Analytics, Social Informatics, and Artificial Intelligence

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Lecturer at Department of Informatics, Universitas Hasanuddin, Indonesia. Master of Informatics graduated from Kyoto University, majoring in Social Informatics. Varied academic experience in social media in disaster relief. My research interest includes disaster risk management, data analysis, and artificial intelligence. Looking to utilize the knowledge gained during my research and personal skill endeavors to pursue a professional lecturer career.

Anugrayani Bustamin, M.T.

Language Processing, Artificial Intelligence, Data Mining, Multimedia Analysis​

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Anugrayani Bustamin is a dedicated researcher at Hasanuddin University with a strong focus on leveraging computational intelligence for various applications. Her diverse research interests span areas such as audio data analysis for emotion classification, intelligent waste management systems utilizing transfer learning, and innovative solutions for livestock identification. Additionally, she has contributed to the field through investigations into digital learning for educators and the application of data mining techniques for disease clustering. Her work demonstrates a commitment to addressing real-world challenges through the development and implementation of intelligent systems.

Iqra Aswad, M.T.

Web Programming

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Iqra Aswad is an Assistant Professor at Hasanuddin University with expertise in Web Programming, affiliated with the Distributed Intelligence Research Group. His research focuses on web-based real-time communication technologies. His research explores the limitations of WebRTC in supporting a large number of clients in video conferencing applications and proposes an infrastructure utilizing media servers and a load-balancing algorithm to address these challenges within the Hasanuddin University environment. The study resulted in the development of a WebRTC-based video conferencing application incorporating this algorithm and a media server-based infrastructure model.

Muhammad Alief Fahdal Imran Oemar, M.Sc.

Software Engineering, Web Engineering, Animation

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Muhammad Alief Fahdal Imran Oemar is a dedicated IT Developer and lecturer at Hasanuddin University, specializing in Java and JavaScript programming for backend systems and dynamic web solutions. He holds a Master of Science in Software Engineering. His academic and research activities encompass a range of topics, including the application of information technology in various sectors and the impact of technology on performance and development. Evidencing his commitment to community engagement and the application of his expertise, he has contributed to publications focusing on the digitalization of business for breeders and the use of information systems for village management to enhance regional economies.

Arliyanti Nurdin, M.T.

Intelligence System and Data Analytics

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Arliyanti Nurdin is a researcher affiliated with Hasanuddin University, demonstrating expertise in the field of computer science, particularly in areas related to intelligent systems and machine learning. Her research interests are reflected in her contributions to the analysis of traffic collision detection using advanced techniques like YOLOV4 Deepsort and Artificial Neural Networks. Furthermore, her work extends to the application of machine learning in financial analysis, specifically in the context of credit scoring for tuition fee payments. Additionally, Arliyanti Nurdin has explored the performance of various word embedding techniques, including Word2Vec, Glove, and FastText, for text classification tasks. Her earlier research also includes the development of methods for information extraction from textual data, such as the 5W1H framework, utilizing deep learning architectures like CNN and bidirectional LSTM.

A. Ais Prayogi Alimuddin, S.T., M.Eng.

Artificial Intelligence, Data Management, Mobile Programming,

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A. Ais Prayogi Alimuddin, S.T., M.Eng. is a lecturer in the Department of Informatics at Hasanuddin University. His research interests focus on the fields of Artificial Intelligence, Data Management, and Mobile Programming. With a strong academic background and a passion for technology, he has contributed significantly to the advancement of knowledge in these areas, guiding students and conducting research that bridges theoretical concepts with practical applications.

Ts. Dr. Wan Mohd Ya'akob Wan Bejuri

Intellegent Computing and Analysis

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Wan Mohd Yaakob Wan Bejuri is a dedicated researcher at Universiti Teknikal Malaysia Melaka, actively contributing to the field of intelligent systems and human-computer interaction. His diverse research portfolio encompasses areas such as real-time systems, computer vision, and mobile computing, with a strong emphasis on leveraging advanced technologies like convolutional neural networks. This is evident in his recent work exploring hand gesture detection for in-vehicle infotainment systems. Furthermore, his scholarly contributions extend to broader discussions on the impact and future of artificial intelligence, as reflected in his publication in Dewan Kosmik.