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As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.

Fish school feeding behavior analysis based on images can provide important information for aquaculture managers to make effective feeding decision. However, it is a challenging task due to intra-class variation, cross-occlusion, and unbalanced image categories in real high-density industrial farming. At present, most of the existing works on fish school feeding behavior are limited because they seem to ignored the spatial relationship between the region of interest in fish feeding images. To address this research gap, we propose a dual attention network with Efficientnet-B2 for fine-grained short-term feeding behavior analysis of fish school. The algorithm includes EfficientNet-B2 network and two parallel attention modules, which focus on the feature extraction of the feeding region. In addition, several training strategies, such as mish activation function, ranger optimizer, label smoothing, and cosine annealing, are employed to improve the algorithm performance. Especially, label smoothing technique is used to address the problem of image class imbalance. To evaluate the effectiveness of our method, performance of proposed algorithm is analyzed on fish school feeding behavior dataset and it is also compared with benchmark Convolutional Neural Networks (CNNs) including AlexNet, VGG, Inception, ResNet, Densenet, SENet, and MobileNet. Comprehensive experimental results show that proposed algorithm achieves very good results in terms of the accuracy (the test accuracy is 89.56% on datasets), precision, parameters and floating point operations per second (FLOPS), compared with the benchmark classification algorithm. Therefore, we proposed method can be integrated into aquacultual vision system to guide farmers to plan their feeding strategy.

Artificial intelligence (AI) has been in existence for over six decades and has experienced AI winters and springs. The rise of super computing power and Big Data technologies appear to have empowered AI in recent years. The new generation of AI is rapidly expanding and has again become an attractive topic for research. This paper aims to identify the challenges associated with the use and impact of revitalised AI based systems for decision making and offer a set of research propositions for information systems (IS) researchers. The paper first provides a view of the history of AI through the relevant papers published in the International Journal of Information Management (IJIM). It then discusses AI for decision making in general and the specific issues regarding the interaction and integration of AI to support or replace human decision makers in particular. To advance research on the use of AI for decision making in the era of Big Data, the paper offers twelve research propositions for IS researchers in terms of conceptual and theoretical development, AI technology-human interaction, and AI implementation.

Classification simulation using SVM, fuzzy SVM, and FKP-MCOC classifiers. An improved FKP-MCO classifier based on fuzzification method, kernel technique, and penalty factors is proposed and is used for predicting protein-protein interaction hot spots.A fuzzy contribution of each input point is introduced to MCO classifier for soft separation.The penalty factors are used to trade-off overfitting for the majority and underfitting for the minority in dataset.FKP-MCOC avoids solving the quadratic programming problem so as to gain efficiency.FKP-MCOC obtains better performance of predicting active compounds in bioassay and protein interaction hot spots than MCOC and other classifiers in stability, separation and generalization. In order to understand the patterns of various biological processes and discover the principles of protein-protein interactions (PPI), it is important to develop effective methods for identifying and predicting PPI and their hot spots accurately. As for multi-criteria optimization classifier (MCOC), it can learn a decision function from different classes of training data and use it to predict the class labels of unknown samples. In many real world applications, owing to noises, outliers, imbalanced class distribution, nonlinearly separable problems, and other uncertainties, the predictive performance of MCOC degenerates rapidly. In this paper, we introduce a fuzzy contribution to each instance of training data, the unequal penalty factors to the samples in imbalanced classes, and kernel method to nonlinearly separable dataset, then a novel multi-criteria optimization classifier with fuzzification, kernel and penalty factors (FKP-MCOC) is constructed so as to reduce the effects of anomalies, improve the class imbalanced performance, and nonlinear separability in classification. The experimental results of predicting active compounds and protein interaction hot spots and comparison with MCOC, support vector machines (SVM) and fuzzy SVM, the conclusion shows that FKP-MCOC significantly increases the efficiency of classification, the partition of active and inactive compounds in bioassay, the separation of hot spot residues and energetically unimportant residues in protein interactions, and the generalization of predicting active compounds and hot spot residues in new instances.

According to Simon's (1977) decision making theory, intelligence is the first and most important phase in the decision making process. With the escalation of information resources available to business executives, it is becoming imperative to explore the potential and challenges of using agent-based systems to support the intelligence phase of decision-making. This research examines UK executives' perceptions of using agent-based support systems and the criteria for design and development of their ''ideal'' intelligent software agents. The study adopted an inductive approach using focus groups to generate a preliminary set of design criteria of ''ideal'' agents. It then followed a deductive approach using semi-structured interviews to validate and enhance the criteria. This qualitative research has generated unique insights into executives' perceptions of the design and use of agent-based support systems. The systematic content analysis of qualitative data led to the proposal and validation of design criteria at three levels. The findings revealed the most desirable criteria for agent based support systems from the end users' point view. The design criteria can be used not only to guide intelligent agent system design but also system evaluation.

Using intelligent agent-based systems to support information processing for executives has not been significantly advanced in both theory and practice. Research into this field tends to focus more on technical aspects than on social perspective. When executives are faced with increasing information availability and uncertainty in the business environment, using intelligent agent-based systems to enhance executives' information processing capability appears both an opportunity and a necessity. This study examines UK executives' perceptions of intelligent agent-based systems for information scanning, filtering, interpretation and alerting. The study follows a deductive research design, i.e. hypothesis formulation and testing from the user's perspective. Qualitative data was collected through focus groups and interviews with executives in the UK. The study produces rich evidence that challenges preconceptions of using agent-based information processing system by executives. The findings develop insight into executives' behavior in information processing, which has implications for intelligent system developers and organizational information processing practice.

Convergence of technologies in the Internet and the field of expert systems have offered new ways of sharing and distributing knowledge. However, there has been a general lack of research in the area of web-based expert systems (ES). This paper addresses the issues associated with the design, development, and use of web-based ES from a standpoint of the benefits and challenges of developing and using them. The original theory and concepts in conventional ES were reviewed and a knowledge engineering framework for developing them was revisited. The study considered three web-based ES: WITS-advisor -- for e-business strategy development, Fish-Expert -- for fish disease diagnosis, and IMIS -- to promote intelligent interviews. The benefits and challenges in developing and using ES are discussed by comparing them with traditional standalone systems from development and application perspectives.

This paper presents a research attempt to develop and evaluate a Web-based tele-diagnosis system for fish diseases. Fish disease diagnosis is a complicated process and requires high level of expertise. A tele-diagnosis system T-VET is being developed and tested in China. T-VET is the key sub system of a Web-based expert system for fish disease diagnosis. This paper looks at why a tele-diagnosis system is needed and what are the advantages and difficulties in developing and using such a system. The system's architecture and components are described. A user-centered approach was adopted throughout the development process to ensure the system would meet users' needs and will be accepted. As a result, the system functions were emerged and improved by users' requirements. The experience and feedback from testing and evaluating the system with potential users are discussed. Some valuable insights into the use of the Internet as a tele-diagnosis tool in aquaculture are highlighted.

A hybrid approach for integrating group Delphi, fuzzy logic and expert systems for developing marketing strategies is proposed in this paper. Within this approach, the group Delphi method is employed to help groups of managers undertake SWOT analysis. Fuzzy logic is applied to fuzzify the results of SWOT analysis. Expert systems are utilised to formulate marketing strategies based upon the fuzzified strategic inputs. In addition, guidelines are also provided to help users link the hybrid approach with managerial judgement and intuition. The effectiveness of the hybrid approach has been validated with MBA and MA marketing students. It is concluded that the hybrid approach is more effective in terms of decision confidence, group consensus, helping to understand strategic factors, helping strategic thinking, and coupling analysis with judgement, etc.

This paper looks at expert systems in management, by using a business game as an experimental vehicle. An expert system called EXGAME was developed to play a business game, which is normally played by students, with minimal human intervention. This paper concentrates on the effectiveness of EXGAME as compared with human players for tasks at different levels. EXGAME was able to replace human players in decision making at the operational level, and indeed outperform them. However, it proved to be impractical to replace human input at the strategic level. The paper also sheds some light on the problems of trying to build an expert system when there is no real expert. A combination of a modular knowledge-base structure and a process of ‘learning by experimentation’ was effective in this case; it is suggested that this may be an appropriate development strategy in other similar situations. © 1998 John Wiley & Sons, Ltd.

Formal planning can yield many benefits for all types of companies but many companies are experiencing difficulties with marketing planning. It has been recognized that expert systems would bring benefits to companies by helping them make better strategic plans, but the development of expert marketing systems is difficult and time consuming. Discusses some important issues related to the development of expert marketing systems. These issues are associated with understanding the domain, identifying users’ problems, knowing the limitation of expert systems and designing user‐friendly interface. These issues seem to have not been addressed adequately from expert system developers. It is hoped that discussions herein would help others to understand these issues better and to develop more successful expert marketing systems.

Strategic marketing planning is a complex task which involves many expert judgements. Expert systems would have a tremendous impact in this field, but the development of marketing expert systems has proven to be difficult. Describes a hybrid expert system, HYMS, for strategic marketing planning. HYMS aims to provide a structured marketing planning process, guide a user through this process, offer expert advice at key stages and finally make recommendations for users in setting objectives and strategies. It combines the advantages of the expert system and decision support system technology in order to enhance its effectiveness. Presents a a brief review on expert systems and decision support systems in marketing and then discusses the selection of a strategic marketing planning model and software for HYMS. Describes the architecture of the prototype system and the knowledge elicitation issues. Finally, proposes the further extension and enhancement of the HYMS system and offers a brief conclusion from current research.

Ignorance of user factors can be seen as one of the nontechnical issues contributing to expert system failure. An expert advisory system is built for nonexpert users; the users' acceptance is a very important factor for its successful implementation. If an expert advisory system satisfactorily represents the expertise in the domain, there still remains the question: "Will the end-users use the system?" This paper aims to address users' issues by analysing their reactions towards an expert advisory system called ADGAME, developed to help its users make better decisions in playing a competitive business game. Two experiments with ADGAME have been carried out. The research results show that, when the use of the expert advisory system is optional, there is considerable reluctance to use it, particularly amongst the "worst" potential users. Users also doubt the potential benefits in terms of improved learning and confidence in decisions made. Strangely, the one positive expectation that users had, that the system would save them time, proved not to be the case in practice; ADGAME appears to improve the users' effectiveness rather than their efficiency. © 1995.

The complexity of strategic market planning is well known to those working in this area. It requires many expert and intuitive judgements based on a number of interrelated, yet diverse, market factors. It would seem that expert systems and decision support systems might have a large impact in this area but the development of such systems has proven difficult with results, so far, less than originally conceived. This paper describes a hybrid expert system HYMS for strategic marketing planning. HYMS aims to provide a structured marketing planning process, guide a user through this process, offer expert advice at key stages and finally make recommendations for users in setting objectives and strategies. It combines the advantages of the expert system and decision support system technology in order to enhance its effectiveness. The paper first presents a brief review on expert systems and decision support systems in marketing. It also discusses the selection of a strategic marketing planning model fo...

Convergence of technologies in the Internet and the field of expert systems (ES) offer new ways of sharing and distributing knowledge (Sedbrook, 1998). Power (2000) argues that rapid advances in Internet technologies have opened new opportunities for enhancing traditional decision support systems and expert systems. Internet technology can change the way that an expert system is developed and distributed. For the first time, knowledge on any subject can directly be delivered to users anywhere and anytime through a Web-based ES. Because the main function of an ES is to mimic expertise and distribute expert knowledge to nonexperts, these benefits can be greatly enhanced with the emergence of the Internet. The current focus on networked and Internet-based applications demands new architectures for “intelligent” systems as well as creating new possibilities for research and development in this field (Caldwell, Breton, & Holburn, 2003). This article provides an overview of Web-based expert systems with examples. Benefits and challenges are discussed by comparing Web-based ES with traditional standalone ES from both the development and the application perspectives using Turban and Aronson’s knowledge engineering framework.

Due to the rapid growth of e-commerce, the physical boundaries between parties in business transaction have been eliminated by the fast and convenient network connection. Nevertheless, most payment still has to be carried out offline by conventional methods. Over the last decade, a large number of online payment solutions have been developed, but many are still remained at the trial stage; while others are competing with each other; some even failed to reach a customer acceptance stage before developers quit the business. Reasons of slow acceptance are technological, but more importantly, societal. Developers are struggling in pushing increasingly secure and convenient technological solutions to the public. On the other side, users are seeking the balance between benefits and the risks of using online e-payment, which prolongs the process of wider acceptance. This article offers a brief introduction to typical online e-payment instruments and classifications of existing payment systems. It intends to provide researchers and developers with a clearer view on e-payment by comparing various existing systems. The article also attempts to shed light on the issue of social acceptance and adoption of online e-payment.

In the context of increasing usage of intelligent agent and ontology technologies in business, this study explores the ways of adopting these technologies to revitalize current executive information systems (EIS) with a focus on semantic information scanning, filtering, and reporting/alerting. Executives’ perceptions on an agent-based EIS are investigated through a focus group study in the UK, and the results are used to inform the design of such a system. A visualization prototype has been developed to demonstrate the main features of the system. This study presents a specific business domain for which ontology and intelligent agent technology could be applied to advance information processing for executives.

Fish disease diagnosis is a complicated process and requires high level of expertise, an expert system for fish disease diagnosis is considered as an effective tool to help fish farmers. However, many farmers have no computers and are not able to access the Internet. Telephone and mobile uses increase rapidly, so, the provision of call centre service appears as a sound alternative support channel for farmer to acquire counseling and support. This paper presents a research attempt to develop and evaluate a call center oriented Hybrid disease diagnosis & consulting system (H-Vet) in aquaculture in China. This paper looks at why H-Vet is needed and what are the advantages and difficulties in the developing and using such a system. A machine learning approach is adopted, which helps to acquire knowledge when enhancing expert systems with the user information collected through call center. This paper also proposes a fuzzy Group Support Systems (GSS) framework for acquiring knowledge from individual expert and aggregating knowledge into workgroup knowledge by H-Vet in the situation of difficult disease diagnosis. The system’s architecture and components are described.