The proliferation of artificial intelligence (AI) technologies and chatbots has the potential to significantly reshape higher education. It is now imperative for stakeholders in this sector to grasp the fundamental aspects of AI technologies and understand their implications. This paper not only introduces basic AI concepts but also explains their specific applications and relevance in the higher education context. Moreover, it outlines the prospects of using AI technologies and chatbots to boost student engagement, presenting a synthesis of the opportunities available. Concurrently, we discuss the concerns and challenges associated with integrating AI into higher education settings. Several articles included in this special issue explore these opportunities and challenges from diverse viewpoints and within various contexts, across countries such as Australia, the United Kingdom, Vietnam, Cyprus, and GCC nations. Finally, we propose several avenues for future research aimed at enhancing student engagement through AI, charting a path forward for empirical evidence and practical application of AI and chatbots in enhancing student engagement.

The recent coronavirus pandemic has wreaked havoc on global economies, heightening interest in crisis management. As a result, it is critical to provide decision-makers with some assistance in improving their decision-making. As a research field, artificial intelligence (AI) has permeated nearly every facet of human endeavour, gradually displacing humans in tasks with promising outcomes. By combining these two fields of research, this chapter proposes ADDS: an artificial intelligence-based decision-making framework for crisis management. It proposes a decision-support framework. The development of such a framework can be beneficial for two reasons: (1) it can aid in advanced crisis preparedness, and (2) it can result in effective and productive communication during a crisis. It is worth noting that a thorough understanding of this can aid in planning, controlling, and managing the situation.

Short-term traffic parameter forecasting is critical to modern urban traffic management and control systems. Predictive accuracy in data-driven traffic models is reduced when exposed to non-recurring or non-routine traffic events, such as accidents, road closures, and extreme weather conditions. The analytical mining of data from social networks – specifically twitter – can improve urban traffic parameter prediction by complementing traffic data with data representing events capable of disrupting regular traffic patterns reported in social media posts. This paper proposes a deep learning urban traffic prediction model that combines information extracted from tweet messages with traffic and weather information. The predictive model adopts a deep Bi-directional Long Short-Term Memory (LSTM) stacked autoencoder (SAE) architecture for multi-step traffic flow prediction trained using tweets, traffic and weather datasets. The model is evaluated on an urban road network in Greater Manchester, United Kingdom. The findings from extensive empirical analysis using real-world data demonstrate the effectiveness of the approach in improving prediction accuracy when compared to other classical/statistical and machine learning (ML) state-of-the-art models. The improvement in predictive accuracy can lead to reduced frustration for road users, cost savings for businesses, and less harm to the environment.

This paper presents a review of multi-agent system architectures. The importance of these architectures is discussed first and a set of functional and non functional criteria are identified. Different architectures from the literature are presented and evaluated against this set of criteria.

This paper describes an Intuitionistic fuzzy relational repository, which covers the need for storing and querying imprecise information. The model uses the terms and operations defined in the Intuitionistic fuzzy sets theory in order to describe the relational algebra model with Intuitionistic fuzzy terms.

This paper presents a proactive model and tool for urban traffic analysis and management that integrates deep learning for traffic parameter prediction with traffic microsimulation, providing traffic analysts with the ability to visualise the traffic network state ahead of time, generate traffic control measures, and analyse the consequences of the applied traffic control measure(s). The model adopts an integrated assess-forecast-simulate approach in which traffic flow characteristics are applied on deep Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) stacked autoencoders to forecast traffic flow and speed, which are subsequently passed on to a traffic microsimulation tool - Simulation of Urban Mobility (SUMO) - where the predicted parameters are used to generate a traffic future state simulation. The model is evaluated using sensor-collected historical traffic and weather data from Stretford, Greater Manchester in the United Kingdom. The results show the promise of the proposed model when applied towards urban traffic management and control.

Traffic parameter forecasting is critical to effective traffic management but is a challenging task due to the stochasticity of traffic flow characteristics, especially in urban road networks. Traffic networks can be affected by external factors, such as weather, events, accidents, and road construction works. The impact of these factors can affect traffic flow parameters by influencing travel time, density, occupancy, and operating speed. Although deep neural networks (DNNs) have recently shown promising signs in traffic prediction using big data, there still exists the issue of maximizing the use of the model capabilities by using big data sources. This paper proposes an improved urban traffic speed prediction approach involving input-level data fusion and deep learning. Motivated by deep learning prediction methods, we propose a Long Short-Term Memory Neural Network (LSTM-NN) for traffic speed prediction that combines traffic and weather datasets on an urban road network in Greater Manchester, United Kingdom. The experimental results substantiate the value of the approach when compared to the use of traffic-only data sources for traffic speed prediction.

This paper focuses on quantifying the effect of rainfall and temperature intensities on urban traffic characteristics at peak and off-peak periods respectively using traffic data from Greater Manchester, UK, as case study. Three broader issues are addressed: (1) the impact of rainfall on urban traffic; (2) the impact of rainfall intensity on traffic flow parameters at peak and off-peak periods respectively; (3) the impact of atmospheric temperature level on peak and off-peak urban traffic. Our contribution arises both from the combination of factors included in the study as well as distinct analyses of peak and off-peak traffic data. This is the first study undertaken in a real urban environment with reduced operating speed (30 mph), and can provide urban traffic policymakers with crucial information that can be used to modify or develop traffic planning decisions in order to maximize traffic network utilization.

Modelling and understanding user interests are particularly important tasks for designing services and building systems for customized solutions in web personalization and recommender systems. User generated content (UGC) constitutes a significant source of information for capturing user interests. This paper, suggests an approach to user profiling that analyses the Term Frequency (TF) and the Inverse Document Frequency (IDF) of selected tourism services by utilising the Fuzzy set Qualitative Comparative Analysis (FsQCA). It analyses a sample of customer reviews that are collected from tourism web sites. This paper considers the amount of money that customers spent during their hotel stay, as the outcome set in the FsQCA analysis. The results produce causal combinations of services that are necessary and sufficient for building customer interests models that best lead to the outcome and argue for the applicability of the FsQCA in modelling user interests.

With the explosion of Web 2.0, customers are able to share their opinions and sentiments online. This has led to new opportunities for companies and organizations to understand people's opinions towards their products or services and can serve to improve their products or market strategy more effectively. However, the data on the Web is huge and unstructured, which makes it difficult to analyze automatically and in bulk. This paper proposes a novel (semantic-based) framework for fine-grained sentiment analysis. It also includes a practical implementation of the framework to analyse the sentiments expressed within customer reviews, aiming to provide accuracy of sentiment classification. The framework is able to deal with mixed-opinion reviews and handle contextual information via a sentiment lexicon containing multi-word expressions. Datasets across two domains (phone products and hotel services) were used to evaluate the proposed framework for its reliability and validity. A sizeable performance improvement was noted whereby the proposed methodology yielded a result of 91.3% accuracy in sentiment classification as compared to the baseline (SentiWordNet) which had a result of 71.0%.

Mobile banking as a kind of new combinative product of electronic currency with the advantage of handling a variety of financial businesses whenever and wherever, is rapidly becoming an alternative, efficient and relatively safe way to conduct banking services, compared to traditional banking. A lot of research has been conducted in this field. However, it can be seen that most researches are focusing on the influence factors of mobile banking adoption. Specific researches on the relationship among demographic characteristics and the mobile banking functions and marketing strategies are quite few. This article aims to offer a comprehensive analysis of the influence of demographic characteristic factors on the use of mobile banking functions via the use of questionnaires and big data analytics. Several cross analyses and decision trees are used in the process in order to provide banks with implications to find potential target clients groups according to the demographic characteristics, which can help them to improve certain products and services. Since the investigation includes both users and nonusers of mobile banking, Chinese domestic banks can plan, to not only find potential new customers, but also retain existing clients based on the results of the analysis.

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid detection of meat spoilage microorganisms during aerobic storage at chill and abuse temperatures, Fourier transform infrared spectroscopy with the aid of a neuro-fuzzy identification model has been considered in this research. Spectral information was obtained from the surface of beef samples during aerobic storage at various temperatures, while a microbiological analysis had identified the population of Total Viable Counts. The intelligent model constructs its initial rules by clustering while the final fuzzy rule base is determined by competitive learning. Results confirmed the advantage of the proposed scheme against the adaptive neuro-fuzzy inference system and multilayer perceptron in terms of prediction accuracy.

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid and non-destructive detection of meat spoilage, Fourier transform infrared (FTIR) spectroscopy with the aid of an intelligent decision system, was considered in this work. FTIR spectra were obtained from the surface of beef samples at various temperatures, while a microbiological analysis identified the population of total viable counts for each sample. An adaptive fuzzy logic system model that utilizes a prototype defuzzification scheme has been developed to classify beef samples in their respective quality class and to predict simultaneously their associated microbiological population directly from FTIR spectra. Results confirmed the superiority of the adopted methodology and indicated that FTIR spectral information in combination with an efficient choice of a modeling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage.

The use of vision technology for quality testing of food production has the obvious advantage of being able to continuously monitor a production using non-destructive methods thus increasing the quality and minimizing cost. The performance of a multispectral imaging system has been evaluated in monitoring the spoilage of minced beef stored either aerobically or under modified atmosphere packaging (MAP), at different storage temperatures (0, 5, 10, and 15 °C). The detection system explores both qualitative and quantitative information extracted from spectral data with the aid of an advanced neuro-fuzzy identification model. The proposed model constructs its initial rules by clustering while the final fuzzy rule base is determined by competitive learning. Results indicated that multispectral information could be considered as an alternative methodology for the accurate evaluation of meat spoilage.

This volume constitutes the proceedings of the 7th IFIP WG 8.1 Conference on the Practice of Enterprise Modeling held in November 2014 in Manchester, UK.The focus of the PoEM conference series is on advances in the practice of enterprise modeling through a forum for sharing knowledge and experiences between the academic community and practitioners from industry and the public sector.The 16 full and four short papers accepted were carefully reviewed and selected from 39 submissions. They reflect different topics of enterprise modeling including business process modeling, enterprise architecture, investigation of enterprise modeling methods, requirements engineering, and specific aspects of enterprise modeling.

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid determination of meat spoilage, Fourier transform infrared (FTIR) spectroscopy technique, with the help of advanced learning-based methods, was attempted in this work. FTIR spectra were obtained from the surface of beef samples during aerobic storage at various temperatures, while a microbiological analysis had identified the population of Total viable counts. A fuzzy principal component algorithm has been also developed to reduce the dimensionality of the spectral data. The results confirmed the superiority of the adopted scheme compared to the partial least squares technique, currently used in food microbiology.

Event representation models provide a framework in which we can reason about events so as to interpret the collective behaviour of objects over time and space domains. Many are context-specific and lack flexibility when faced with unstructured video. In the past many efforts have been made to define a comprehensive event description framework (EDF), which can provide a framework to develop ontologies for semantic annotation of video events. However, it is observed that there are some areas of event modelling that were not fully explored. Hence, we extended and modified the EDF and proposed the extended version of it (EDFE). Following are some of the major extensions we have proposed in EDFE. 1) EDFE extends the entity representation model of EDF by introducing three new entity classes: that of text entity, virtual entity and internal entity. II) EDFE introduces a new set of predicates for describing more complex event scenarios and facilitating the event detection process. It also introduces granularity as a feature of temporal predicates to capture the temporal association between sub-events. III) It introduces the event evidence feature to capture the full evidence for the detected events. IV) The data structure of EDF is extended and modified to capture the properties of EDFE and to store the results of the event detection process. V) We model complex events from real world surveillance videos using the proposed EDFE.

Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the Expectation Maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models.

The development of accurate models to describe and predict pressure inactivation kinetics of microorganisms is very beneficial to the food industry for optimization of process conditions. The need for methods to model highly nonlinear systems is long established. The architecture of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model is proposed. The objective of this research is to investigate the capabilities of the proposed scheme, in predicting the survival curves of Listeria monocytogenes inactivated by high hydrostatic pressure in UHT whole milk. The proposed model is obtained from the Takagi–Sugeno–Kang fuzzy system by replacing the THEN part of fuzzy rules with a “multiplication” wavelet neural network. Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzy rules. The performance of the proposed scheme has been compared against neural networks and partial least squares models usually used in food microbiology.