Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Towards expert systems for improved customer services using ChatGPT as an inference engine.

By harnessing both implicit and explicit customer data, companies can develop a more comprehensive understanding of their consumers, leading to better customer engagement and experience, and improved loyalty. As a result, businesses have embraced many AI technologies, including chatbots, sentiment analysis, voice assistants, predictive analytics, and natural language processing, within customer services and e-commerce. The arrival of ChatGPT, a state-of-the-art deep learning model trained with general knowledge in mind, has brought about a paradigm shift in how companies approach AI applications. However, given that most business problems are bespoke and require specialised domain expertise, ChatGPT needs to be aligned with the requisite task-oriented ability to solve these issues. This paper presents an iterative procedure that incorporates expert system development process models and prompt engineering, in the design of descriptive knowledge and few-shot prompts, as are necessary for ChatGPT-powered expert systems applications within customer services. Furthermore, this paper explores potential application areas for ChatGPT-powered expert systems in customer services, presenting opportunities for their effective utilisation in the business sector

User centered neuro-fuzzy energy management through semantic-based optimization

This paper presents a cloud-based building energy management system, underpinned by semantic middleware, that integrates an enhanced sensor network with advanced analytics, accessible through an intuitive Web-based user interface. The proposed solution is described in terms of its three key layers: 1) user interface; 2) intelligence; and 3) interoperability. The system’s intelligence is derived from simulation-based optimized rules, historical sensor data mining, and a fuzzy reasoner. The solution enables interoperability through a semantic knowledge base, which also contributes intelligence through reasoning and inference abilities, and which are enhanced through intelligent rules. Finally, building energy performance monitoring is delivered alongside optimized rule suggestions and a negotiation process in a 3-D Web-based interface using WebGL. The solution has been validated in a real pilot building to illustrate the strength of the approach, where it has shown over 25% energy savings. The relevance of this paper in the field is discussed, and it is argued that the proposed solution is mature enough for testing across further buildings

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.Comment: 11 PAGE

Affect and strategy use in independent language learning

Affect is about emotions and feelings, moods and attitudes, anxiety, tolerance of ambiguity and motivation. For some it is also connected with dispositions and preferences (Oatley & Jenkins, 1996). It is generally accepted that the affective domain encompasses a wide range of elements which reflect the human side of being, and play a part in conditioning behaviour and influencing learning. We are becoming more knowledgeable about the importance of attention to affective factors, but there is still a huge gap in terms of our knowledge of the affective strategies that students use or could use to promote more effective language learning. Moreover, the research that has been carried out into affect over several years has largely concentrated on language learning in the classroom (Arnold, 1999; Ehrman, 1996; MacIntyre, 1999; Young, 1999) with very few studies devoted to independent learning settings. Independent language learners, whether learning through self-access, distance or other modes, are a fast-growing group, and we need to know more about them, in particular the ways in which their affective needs differ from those of classroom learners (Harris, 2003; Hurd, 2002; White, 2003). This chapter investigates affect and strategy use in independent settings. It looks first at the concept of affect and its interrelationships with other domains, continues with an exploration of strategy definitions and classification schemes in relation to affect, and concludes with a study carried out with a small group of distance language learners using think-aloud verbal protocols

Principles and Applications of Data Science

Data science is an emerging multidisciplinary field which lies at the intersection of computer science, statistics, and mathematics, with different applications and related to data mining, deep learning, and big data. This Special Issue on “Principles and Applications of Data Science” focuses on the latest developments in the theories, techniques, and applications of data science. The topics include data cleansing, data mining, machine learning, deep learning, and the applications of medical and healthcare, as well as social media