Special Sessions
  • 1
Scope: Machine Learning
Title: Machine Learning: Powering Solutions Across Industries
Keywords: Machine learning, deep learning, reinforcement learning, edge AI, blockchain, edge computing, computation offloading, resource management, energy efficient
Summary: Machine Learning (ML) is transforming problem-solving across diverse domains. At its core, ML enables computers to learn autonomously, analyze vast datasets, identify patterns, and make informed decisions. In healthcare, ML algorithms aid in disease diagnosis and treatment planning, leveraging data to enhance precision medicine. Financial sectors utilize ML for fraud detection and risk management, while e-commerce platforms optimize user experiences through personalized recommendations. In manufacturing, ML optimizes production processes, and in transportation, it contributes to the development of autonomous vehicles. ML's versatility extends to natural language processing, powering voice assistants and language translation. It plays a pivotal role in image and speech recognition, impacting fields such as security and accessibility. As organizations worldwide integrate ML into their workflows, its capacity to streamline operations, make predictions, and uncover insights continues to reshape the landscape of problem-solving across diverse industries. The current era is marked by the endless possibilities unlocked by machine learning, propelling us towards a future where intelligent systems contribute to innovative solutions and unprecedented advancements.
  • 2
Scope: Machine Learning
Title: Artificial Intelligence in Industries
Keywords: Intelligent sensing, data modelling, data mining, cooperative control, monitoring, decision-making
Summary: Artificial Intelligence exists in industries broadly and promotes the efficiency and yields of intelligent production greatly. The development of AI can increase prediction accuracy and measurement accuracy, handle uncertainties in industrial process, and so on.  Artificial Intelligence in industries should present the concerns as below. 1.AI techniques for intelligent sensing in industrial process. 2.AI techniques for industrial data modelling in industrial process. 3.AI techniques for industrial big data analysis and data mining. 4.AI techniques for cooperative control, autonomous control, operation optimization, and so on. 5.AI techniques for abnormal remote monitoring, performance monitoring and assessment, fault diagnosis and prediction, automatic  fault recovery, life prediction, and so on. 6.AI techniques for automatic parameter measurement. 7. AI techniques for intelligent decision-making.
  • 3
Scope: Machine Learning
Title: Modeling, optimization and control of robotic and unmanned systems based on reinforcement learning
Keywords: Reinforcement Learning, Robotics, Unmanned Systems, System Modeling, System Optimization, Intelligent Control
Summary: With the continuous advancement of industry 4.0, the development and advancement of technology of robot and unmanned system industry has become an important  breakthrough point in the transformation of manufacturing industry from "labor intensive" to "technology intensive", which undertakes the important mission of " corner overtaking" via national high scientific and technical strength, and plays a decisive role  in country's economic system transformation as well as the development of cutting-edge manufacturing industry. Furthermore, with the development of modern control theory and industrial systems, the controlled objects have gradually expanded from simple mechanical equipment to more complex systems such as robots, unmanned system, aerospace, power systems, and chemical processes, etc. Optimal Control, as one of the main methods of modern control system design, aims to find the optimal control strategy to maximize or minimize the performance index of the control system. It is a new research field after the classical automatic control theory. While ensuring the stability of the system, it is of great significance to improve the control effect and reduce the loss of resources, etc., and has been more widely used in the fields of industrial control, financial investment and management decision-making. As the hottest technology in the 21st century, artificial intelligence cannot be developed without machine learning. Reinforcement learning, as the core of artificial intelligence and machine learning, is the foundation of computer intelligence. Reinforcement learning emphasizes the interaction between the environment and the agent, with a focus on the long-term interaction to change its policies. Through its interactions with the environment, the agent can modify future actions or control policies based on the response to its stimulating actions. Therefore, we expect that to implement the modeling, optimization and control of robotic and unmanned systems based on reinforcement learning.
  • 4
Scope: Machine Learning
Title: Machine Learning for Future Wireless Communications
Keywords: Machine Learning, Wireless Communications
Summary: Wireless communication is rapidly evolving to meet the growing demands of emerging technologies and applications, such as the Internet of Things (IoT), autonomous vehicles, smart cities, and virtual/augmented reality. To address the challenges posed by these evolving demands, there is a need for innovative techniques that can enhance the performance, efficiency, and reliability of future wireless communication systems. Machine learning (ML) has emerged as a promising approach to tackle these challenges and unlock new capabilities in wireless communications. ML algorithms can analyze massive amounts of data, extract patterns, and make intelligent decisions to optimize various aspects of wireless systems. By leveraging ML, future wireless communication networks can adapt to dynamic environments, optimize resource allocation, mitigate interference, improve spectrum efficiency, enhance security, and enable intelligent data-driven decision-making. This workshop aims to explore the latest advancements and applications of ML in the context of future wireless communications. Topics of interest for paper submissions include, but are not limited to: lML-enabled intelligent resource allocation and management in wireless networks lML-enabled beamforming and massive MIMO techniques for enhanced capacity and coverage lML-driven interference mitigation and management in dense wireless networks lML-based mobility management and handoff optimization for dynamic network scenarios lML techniques for energy-efficient and sustainable wireless communications lML applications in Internet of Things (IoT) and machine-to-machine (M2M) communications lFederated learning and distributed ML for privacy-preserving wireless networks Selected paper will be recommended to MDPI Electronics for potential fast-track publications.
  • 5
Scope: Cloud Computing
Title: The Future of Cloud Computing: Trends, Technologies, and Practical Insights
Keywords: Cloud Computing, Edge Computing, Optimization Algorithms
Summary: As the Internet of Everything expands and artificial intelligence (AI) algorithms make significant breakthroughs, it has spawned a large number of cloud applications. The hosting and services of these applications require strong computing power. To meet the rising demand for ubiquitous computing power, cloud computing and edge computing have emerged as crucial computing paradigms, offering diverse solutions. In this rapidly advancing technological landscape, it is vital for organizations to stay informed about the future of cloud computing. The purpose of this conference is to provide a platform for researchers, academics, and practitioners to share and explore original contributions related to the trends that will shape the cloud computing landscape. During this conference, we will delve into emerging trends and cutting-edge technologies that are revolutionizing the field of cloud computing. We cordially invite researchers, academics, and practitioners to submit original contributions to the International Conference MLCCIM 2024 on The Future of Cloud Computing: Trends, Technologies, and Practical Insights. This conference presents an excellent opportunity to connect with leading experts in the field, exchange ideas, and collaborate on innovative solutions. The conference will encompass a broad spectrum of topics, including, but not limited to: Cloud computing systems and architecture Multi-access edge cloud networks Cloud-centric network architecture Computing force networks Data center networks Computation offloading algorithms Social and mobile cloud applications Cloud security and privacy
  • 6
Scope: Machine Learning
Title: Native AI: Network-Empowered Generative Artificial Intelligence
Keywords: Generative Artificial Intelligence, Native AI Network, 6G, AI Computing
Summary: Generative Artificial Intelligence (AI) stands as a groundbreaking technology, granting computers the capacity to replicate human creativity and independently craft fresh content. By harnessing deep learning algorithms, it sifts through extensive datasets to produce text, images, music, and even videos. Employing methodologies like Generative Adversarial Networks (GANs) and Transformers, generative AI drives innovation across diverse sectors. For the vision of 6G ubiquitous intelligence, the native AI network has been proposed to support the deep integration of connection, computing, data, and AI models and supports on-demand AI lifecycle management for guaranteed quality of AI service. Thus, native AI networks can provide the required real-time, trust-worthy, and green intelligent services. Essentially, employing native AI network to generative AI yields numerous advantages: enhanced data security, reduced power consumption, decreased latency, improved reliability, optimized data utilization, and lowered data processing expenses. Consequently, network-empowered generative artificial intelligence holds immense potential for transformative impact. This workshop focuses on gathering papers that explore the utilization of generative artificial intelligence within native AI networks, aiming to advance relevant domains. Relevant topics for this workshop encompass, but are not restricted to:Applications of generative AI in Native AI scenarios. ŸResponsible, explainable, and interpretable generative AI in Native AI Networks. ŸEnergy efficient and low-latency generative AI in Native AI Networks with resource constraints. ŸSecurity risk concerns of generative AI in Native AI Networks. ŸGenerative AI for explosive data processing in Native AI Networks. ŸPrivacy-preserving and security frameworks for generative AI in Native AI Networks. ŸFundamental trade-offs between privacy and generative AI training efficiency in Native AI Networks. ŸIntegration of generative AI with emerging technologies in Native AI Networks, e.g., blockchain. Deterministic and probabilistic design methodologies to improve the performance of generative AI over Native AI Networks.
  • 7
Scope: Intelligent Mining
Title: Application and Trend of Intelligent Mining in Manufacturing Industry
Keywords: Data Mining; Intelligent Manufacturing; Quality Control; Application Research; Development Trend
Summary: Industrial big data covers the whole process data from product design, raw material procurement, manufacturing, logistics, sales and service. These data include not only structured data, such as product specifications, production plans, but also large number of unstructured data, such as equipment operating status, environmental parameters, and so on. With the depth promotion of Made in China 2025 strategy, industrial big data has become a new source of mining the value of production and manufacturing. By making full use of intelligent mining, manufacturing enterprises can better analyze various data in the production process, better optimize the production process, improve product quality, reduce costs and enhance decision-making ability.
  • 8
Scope: Cloud Computing
Title: Mega Satellite Constellations Access for 6G Era
Keywords: Satellite-Ground Integrated Networks, Satellite Cloud Computing, Computation Offloading, Privacy Protection.
Summary: It has attracted great interests for mega satellite constellations access for 6G era, which can not only provide pervasive intelligence services in support of terrestrial users, but also achieves global coverage and seamless access. The distinct difference between traditional satellite systems and mega constellations is high-speed inter-satellite links and massive satellite units. Thus, the mega satellite constellations access needs to be automatic, resilient, self-organized and self-controlled to reduce the system execution energy consumption and latency. Fortunately, artificial intelligence (AI) technologies will help mega constellations networking at all levels, including multi-domain satellites, ground controllers, inter-satellite links and satellite-ground links. Finally, the mega satellite constellations aceess will create unprecedented opportunities once following challenges are addressed well. Topics of Interest: We consider completely original and unpublished works not under experiencing peer review by any other journals/magazines/conferences/surveys The interest of topics include, but not limited to: 1.Development, design and analysis of mega constellations access. 2.Mobility and handover management 3.Novel user cases for satellite-ground integrated networks 4.Satellite cloud computation for massive terrestrial users 5.Edge access and caching for satellite networkings 6.Privacy protection and authentication for satellite networks 7.Testbed developments for satellite networks 8.The integration of sensing, computation and communication for satellite networks 9.AI for satellite networkings Free space optical communication for satellite networks
  • 9
Scope: Machine Learning, Cloud Computing, Intelligent Mining
Title: Swarm intelligence and its applications in cloud computing and intelligent mining
Keywords: Swarm intelligence; design optimization; cloud computing; intelligent mining
Summary: The rapid and deep development of modern science and technology leads to a vast explosion of dimensionality in mathematics, physics, and engineering. The analytical solutions could be no longer formulated now. Thanks to the associated development of modern computation methods and computer hardware, the nature-inspired algorithms (NIAs) especially the swarm intelligence were proposed and gave us an opportunity to solve the modern problems with simulations of nature evolutions. Swarm intelligence had been designed and applied in almost every aspect of our modern world, either in feature selection or automatic control, power dispatching, parameter estimation, shop scheduling, and so on. The design optimization is also an important part in cloud computing to increase the device usage or minimizing the energy cost, in intelligent mining to increase the mining speed and accuracy.