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It is often argued that anything observable may be simulated on a computer. Using this as a basis, workers in artificial intelligence (AI) often go forward to maintain that machines can be made intelligent by machine simulation of human intelligence processes. There are two difficulties with this concept. The first difficulty lies in the knowledge of human intelligence processes that we have presently obtained and may possibly obtain in the near future. A more basic question is of the sufficiency of the concept itself. Simulation in itself is not sufficient to produce intelligent action where perhaps modelling might be. There are fundamental difficulties in the problem of establishing an adequate mapping function. It is held that there is insufficient correspondence between human and machine intelligence processes to allow human intelligence modelling on existing digital computers.

Newspaper cartoons can graphically display the results of ambiguity in human speech; the result can be unexpected and funny. Likewise, computer analysis of natural language statements also needs to successfully resolve ambiguous situations. Computer techniques already developed use restricted world knowledge in resolving ambiguous language use. This paper illustrates how these techniques can be used in resolving ambiguous situations arising in cartoons.

Knowledge‐based systems have been successfully utilised in the develop‐ment of complex systems. In many cases, these systems have emphasised the need for techniques to integrate knowledge‐based processing with methods for managing both large amounts of data and knowledge. However, many potential applications for expert systems are precluded by limitations in the ability of conventional expert system technology to function in conjunction with data systems without manual intervention. The author focuses on the integration of knowledge‐bases and databases with the capability to: store and context select between parallel, competing expert system rule structures; cascade variable rule structures; allow an expert system to be interrupted and to be subsequently restarted by storing the state of the inference engine; handle simple data storage and retrieval.

The last decade has witnessed a growing concern among computer scientists to understand the complex interactions between humans and computer hardware. The work described in this paper is an experimental study of a user‐computer interaction on a time‐sharing computer terminal network over a period of 1 year. The user‐system interaction described in this paper refers to a university environment. The user‐system performance variables considered are arrival patterns of jobs, inter‐arrival time, connect time, cpu time and think time. The users of the systems are grouped into on‐ and off‐campus users; a two‐way analysis of variance without replications established that arrival volume depended upon the weekday but not upon the user group. The pattern of arrivals throughout one day required an empirical distribution. Coefficient of variation indicated hyper‐exponential distributions for inter‐arrival time, connect time and cpu time, but an exponential distribution for think time. Furthermore, the experimental research described in this paper supports the fact that a hypothesis to characterize the interaction between the user and the computing system can be developed for an efficient use of the system.

The difficulty of attributing subject to editorial cartoons for indexing purposes exists both for traditional paper-based cartoon formats and for digitized or born-digital cartoons. This paper presents a selective review of literature on indexing editorial cartoons and the associated challenges.

A gap exists in published research on indexing collections of editorial cartoons for online search and retrieval. This paper presents a review of selected works that specifically address the topic of editorial cartoon indexing within a wider context of research that addresses image indexing, subject analysis and indexing challenges more generally. Works that address the interpretation of cartoons by readers and how readers respond to information communicated by editorial cartoons are also considered.

Cartoon controversies in transnational and multicultural contexts, experienced through the international news media since 2000, have dramatically increased research attention and publications in this area. Profound changes in media publication since the advent of the Internet have had an impact on editorial cartoonists and cartoon publishing. Subject indexing of editorial cartoons remains a challenge.

The potential for large indexed cartoon collections to be data-mined for topic modeling for research in the social sciences points to the need for indexers of cartoon collections to improve metadata standards and structures to allow improved access to cartoon metadata for computational analysis.

This paper places discussion of the technical challenges facing indexers of editorial cartoons within a broader context of discussions about the nature and future of editorial cartooning in rapidly changing media and publishing environments.

During the past few decades, Lean Six Sigma (LSS) in the health-care sector has received increasing attention from both researchers and practitioners because it plays an imperative role in quality improvement and cost reduction initiatives. Although researchers have often focussed on evidence of model effectiveness through the study of performance indicators, too little attention has been given to the factors that lead to implementation failure and the causal relationships among them. This study aims to investigate the factors that may inhibit the successful implementation of the method by focussing on Italian public hospitals.

Through the use of the Delphi technique and fuzzy cognitive maps, this paper derives new and relevant results for researchers, hospital managers and policymakers.

The results show the factors with the greatest impact on LSS implementation and provide insight into the causal links and degrees of influence between critical failure factors and performance variables.

The findings could be considered useful, in particular, to hospital managers and policymakers, who could leverage the suggestions derived from the study to address LSS implementation.

This work overcomes a gap in the literature related to the absence of studies on the causal relationships between factors that determine the success or failure of LSS implementation.

Financial distress is the most notable distress for companies. During the past four decades, predicting corporate bankruptcy and financial distress has become a significant concern for the various stakeholders in firms. This paper aims to predict financial distress of Iranian firms, with four techniques: support vector machines, artificial neural networks (ANN), k-nearest neighbor and na

ve bayesian classifier by using accounting information of the firms for two years prior to financial distress.

The distressed companies in this study are chosen based on Article 141 of Iranian Commercial Codes, i.e. accumulated losses exceeds half of equity, based on which 117 companies qualified for the current study. The research population includes all the companies listed on Tehran Stock Exchange during the financial period from 2011-2012 to 2013-2014, that is, three consecutive periods.

By making a comparison between performances of models, it is concluded that ANN outperforms other techniques.

The current study is almost the first study in Iran which used such methods to analyzing the data. So, the results may be helpful in the Iranian condition as well for other developing nations.

This paper aims to propose a new genetically evolved fuzzy cognitive mapping approach as a decision-making framework for analyzing the relationships between the drivers and strategies for green computing adoption.

A focus group discussion among stakeholders in the Philippines is used to establish the relationships between the drivers and strategies of green computing adoption.

The proposed approach significantly reduces the time complexity for developing the fuzzy cognitive maps and provides a basis for comprehensively clustering drivers and strategies that share similar characteristics.

This paper’s results provide insights into how the drivers and strategies of green computing adoption facilitate the intention of adopting stakeholders. Moreover, it provides a framework for analyzing structural relationships that exist between factors in a compliant manner.

To the best of the author’s knowledge, the paper is the first to analyze the drivers and strategies of green computing under a complex systems’ perspective. Moreover, this is the first study to offer lenses in a Philippine scenario.

IT support for marketing planning can aid in the use of marketing tools, facilitate group planning, and support moves towards continuous planning based on a live marketing model of the business. But, amongst other factors, achieving these benefits depends on the style of support provided by the system. After a review of relevant decision support system (DSS) literature, describes here the findings relating to support style from a qualitative evaluation of a system named EXMAR. The findings support Little’s classic rules of “decision calculus”, such as the importance of ensuring that managers understand and can control the system, rather than the objective influenced by management science of prescribing an optimal recommendation. Also emphasises the role of systems in enhancing mutual understanding in a cross‐functional planning team, and hence in building commitment to the resulting plan.