Aims & Scope

The Journal of AI & Knowledge Engineering (JAIKE) is a peer-reviewed and open access publication that disseminates the latest and most innovative research on cognitive processes related to knowledge mining, AI, knowledge discovery, knowledge acquisition, knowledge processing, and distribution in the context of the Internet of Things (IoT) and smart product development. The principal objective of JAIKE is to serve as a conduit between the academic and industrial communities, providing a unified platform for the dissemination of scientific findings in the fields of artificial intelligence and knowledge engineering. The journal publishes original, significant, and timely leading-edge theories and technologies of AI and Knowledge Engineering that utilize the cognitive processes of knowledge mining, knowledge acquisition, knowledge learning, knowledge discovery, knowledge reasoning, knowledge processing, and knowledge optimization.

 

JAIKE provides a forum for academics, scientists, researchers, practitioners, managers, industrial professionals, decision- and policy-makers of (non-)government organizations, technology solution developers, smart process developers, and knowledge-workers for smart products, IOTs, and systems to exchange new research findings and their application to improve business economics and thereby gain competitive advantages in both smart products and smart processes development. The journal's key research areas include, but are not limited to:

 

• Artificial Intelligence (AI): prototype applications; performance evaluation of large-scale or domain-specific knowledge bases or ontologies; development of domain or task-focused tools, techniques, or methods; assessment of AI tools, techniques, or methods for domain suitability; system architectures that work; scalability of techniques; integration of AI with other technologies; development methodologies; validation and verification; lessons learned; social and other technology transition issues.
• Knowledge Mining and Discovery (KMD): intelligent objects, knowledge elements, rules definition, rules management, Ontology, and Semantic web, capturing intellectual property, security, etc.
• Knowledge Acquisition (KA): knowledge capture, knowledge-based modeling, social network analysis & modeling, knowledge processing, human-computer interactions, learning and adaptation, and knowledge visualization,
• Knowledge Intelligence (KI): Innovative Computing, Intelligent Communication and Smart Electrical Systems, Recent Trends
• Knowledge-based Engineering (KBE): knowledge and data engineering, methods of capturing rules & heuristics, concepts of knowledge advisor, knowledge expert, and product knowledge templates, process knowledge templates, business knowledge templates, technological objects, and tools in product life cycle management.
• Knowledge-Based Systems (KBS): software architecture for developing general-purpose tools, progressive systems, Smart Part concepts, and product configurators. Use of demand-driven constructs in building dynamical systems.
• Knowledge Management (KM): knowledge sharing and warehousing, knowledge-base processing, intelligent information retrieval, knowledge distribution, and methods to exchange knowledge across intelligent objects. Computational techniques used in KM include soft computing (including neural nets, fuzzy logic, probabilistic reasoning, evolutionary computing, hybrid computing, agent architectures and systems, and genetic algorithms).
• Internet-of-Things (IoTs): The next generation of Internet Protocols (IP), a Global system of Internet Protocol (IP), Connected computer networks, sensors, actuators, machines, and devices, Sensors and actuators embedded in physical objects—from roadways to pacemakers, Wireless networks, networked interconnection of everyday objects, Ubiquitous intelligence. IoT interaction via embedded systems, a distributed network of devices communicating with human beings and other devices.
• Smart Products: At the core of the Internet of Things lays the smart product – equipped with RFID technology. Three typical elements of smart products. Physical components, smart components, and connectivity components. The physical component is the product’s mechanical and electrical parts. The smart components are the product’s software, sensors, data storage, and other similar features. The last component is the connectivity component. It consists of the protocols or ports that enable the connection between products or human beings.
• Generative AI: This can create something entirely new, including text, images, audio, synthetic data, and many more. Deep learning models, multi-modal AI technologies, real-time perception and response to content, AI comprises machine learning, natural language processing (NLP), image processing, and computer vision
• Knowledge-driven Automation (KDA): includes decision-based design, decision support systems, knowledge-based optimization, sales configurations, and Expert Systems: Neural networks-based applications, biomedical systems, geographical systems, enterprise software systems, and emerging applications (such as Internet technologies, search engines, and digital libraries)
• Knowledge Architecture & IOTs Platforms: high-performance computing systems, distributed intelligent systems, embedded systems, mobile systems, real-time systems, techniques to provide interoperability and knowledge re-usability across enterprise applications,
• AIKE Applications: We solicit submissions of original research, and AI/KBE/KBS/KM/KDA/IoTs application papers that address and utilize those topics and themes.