Smart computing and IoT (Internet of Things) are two distinct but related concepts. Smart computing refers to the use of intelligent algorithms and technologies to enable machines and systems to perform tasks that typically require human intelligence. This can include things like natural language processing, computer vision, machine learning, and other forms of artificial intelligence. Smart computing can help systems make better decisions, automate tasks, and improve efficiency.

Machine learning, a core component of smart computing, involves the development of algorithms that allow systems to learn from and make predictions based on data. In the context of this conference, the scope of machine learning encompasses a wide range of applications, including but not limited to, predictive analytics, anomaly detection, natural language processing, image and video analysis, and autonomous systems. The emphasis is on innovative approaches to machine learning that enhance the capabilities of IoT devices, improve data-driven decision-making processes, and create smarter, more adaptive systems.

On the other hand, IoT refers to the network of physical devices, vehicles, home appliances, and other items that are embedded with sensors, software, and network connectivity, allowing them to collect and exchange data. IoT aims to create a more connected world where devices can communicate with each other and with people, creating new possibilities for automation and optimization.

SIML 2024 is going to be a hybrid conference that combines both online participation through Zoom Meetings and in-person gatherings at Edutorium UMS in Surakarta, Central Java, Indonesia. We invite all academicians and professionals from both international and domestic institutions to participate in this conference. Accepted and presented papers will be submitted for inclusion into IEEE Xplore subject to meeting IEEE Xplore’s scope and quality requirements.

Access the proceedings from the 2024 conference here :
https://ieeexplore.ieee.org/xpl/conhome/10577996/proceeding