Integrating temporal data into static knowledge graphs Author Amanda Kau (ORCID: 0009–0004–4949–9284 ) Introduction Knowledge graphs (KGs) have proven to be an effective method of data representation that is increasingly popular. In KGs, entities and concepts are represented as nodes, while the relationships between nodes are depicted as edges.

Author Amir Aryani: (ORCID: 0000-0002-4259-9774) Definition A research collaboration network is a group of researchers, and practitioners, or both, working together on joint research activities. These networks often span across disciplines, geographic boundaries, and sectors, enabling participants to share resources, expertise, and data to address common research goals more effectively than they could individually.

Understanding Sequential Data Modelling with Keras for Time Series Prediction Author Wenyi Pi ( ORCID : 0009–0002–2884–2771) Introduction Recurrent Neural Networks (RNNs) are a special type of neural networks that are suitable for learning representations of sequential data like text in Natural Language Processing (NLP). We will walk through a complete example of using RNNs for time series prediction, covering

Recent Advances in Using Machine Learning with Graphs — Part 2 Latest findings in multiple research directions for handling graph construction and network security issues Author · Xuzeng He ( ORCID: 0009–0005–7317–7426) Introduction A graph, in short, is a description of items linked by relations, where the items of a graph are called nodes (or vertices) and their relations are called edges (or links). Examples of

Bridging Human Perception and AI’s Future: The Convergence of Visual Understanding and Semantic Networks Author · Vaibhav Khobragade ( ORCID: 0009–0009–8807–5982) Introduction The fusion of Vision-Language Models ( VLMs ), Generative Models, and Knowledge Graphs ( KGs ) is reshaping how artificial intelligence (AI) understands and interacts with the world.

Understanding the Power and Applications of Natural Language Processing Author Dhruv Gupta ( ORCID: 0009–0004–7109–5403) Introduction We are living in the era of generative AI. In an era where you can ask AI models almost anything, they will most certainly have an answer to the query. With the increased computational power and the amount of textual data, these models are bound to improve their performance.

Incorporating Knowledge Graphs to explain reasoning processes Author Amanda Kau ( ORCID: 0009–0004–4949–9284) Introduction Large language models (LLMs) like GPT-4 possess remarkable language abilities, allowing them to function as chatbots, translators, and much more.

Exploring the Boundaries of Creativity and Responsibility in the Age of AI-Driven Media Author Vaibhav Khobragade ( ORCID: 0009–0009–8807–5982) Introduction In 2024, the discipline of Generative AI takes a big step forward with the launch of revolutionary models that convert text into dynamic films, altering the landscape of digital content creation.

Prompt Engineering — Part 2 Using intelligence to use artificial Intelligence: A deep dive into Prompt Engineering Author Dhruv Gupta (ORCID: 0009–0004–7109–5403 ) Introduction In the previous article we discussed what prompt engineering and some of the techniques used for prompt engineering.

Understanding how RNNs work and its applications Author Wenyi Pi ( ORCID : 0009–0002–2884–2771) Introduction In the ever-evolving landscape of artificial intelligence (AI), bridging the gap between humans and machines has seen remarkable progress. Researchers and enthusiasts alike have tirelessly worked across numerous aspects of this field, bringing about amazing advancements.

Solutions to Enhance LLM Performance in Long Contexts Author · Qingqin Fang ( ORCID: 0009–0003–5348–4264) Introduction In the era of AI breakthroughs, large language models (LLMs) are not just advancements; they are revolutions, transforming how we interact with technology, from casual conversations with chatbots to the intricate mechanisms behind sophisticated data analysis tools.