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With the rising concern of safety, health and environmental performance, eco-labeled product and service are becoming more and more popular. However, the long and complex process of eco-labeling sometimes demotivates manufacturers and service providers to be certificated. The purpose of this paper is to propose a decision support platform aiming at further improvement and acceleration of the eco-labeling process in order to democratize a broader application and certification of eco-labels, also to consolidate the credibility and validity of eco-labels.

This decision support platform is based on a comprehensive knowledge base composed of various domain ontologies that are constructed according to an official eco-label criteria documentation.

Through standard Resource Description Framework and Web Ontology Language ontology query interface, the assets of the decision support platform will stimulate domain knowledge sharing and can be applied into other applications. A case study of laundry detergent eco-labeling process is also presented in this paper.

The authors present a reasoning methodology based on inference with Semantic Web Rule Language (SWRL) rules which allows decision making with explanation.

The purpose of this paper is to develop e-health and patient monitoring systems remotely to overcome the difficulty of patients going to hospitals especially in times of epidemics such as virus disease (COVID-19). Artificial intelligence (AI) technology will be combined Internet of Things (IoT) in this research to overcome these challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the neural network (NN). Then, define the patient data sent through protocols of the IoT. NN checks the patient’s medical sensors data to make the appropriate decision. Then it sends this diagnosis to the doctor. Using the proposed solution, the patients can diagnose and expect the disease automatically and help physicians to discover and analyze the disease remotely without the need for patients to go to the hospital.

AI technology will be combined with the IoT in this research. The research aims to select the most appropriate’ best-hidden layers numbers’ and the activation function types for the NN.

Decision support health-care system based on IoT and deep learning techniques was proposed. The authors checked out the ability to integrate the deep learning technique in the automatic diagnosis and IoT abilities for speeding message communication over the internet has been investigated in the proposed system. The authors have chosen the appropriate structure of the NN (best-hidden layers numbers and the activation function types) to build the e-health system is performed in this work. Also, depended on the data from expert physicians to learn the NN in the e-health system. In the verification mode, the overall evaluation of the proposed diagnosis health-care system gives reliability under different patient’s conditions. From evaluation and simulation results, it is clear that the double hidden layer of feed-forward NN and its neurons contain Tanh function preferable than other NN.

AI technology will be combined IoT in this research to overcome challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the NN.

This paper aims to propose a theoretical decision support framework, which integrates artificial intelligence (AI), discrete-event simulation (DES) and database management technologies so as to determine the steady state flow of items (e.g. fixtures, jigs, tools, etc.) in manufacturing.

The existing literature was carefully reviewed to address the state of the arts in decision support systems (DSS), the shortcomings of pure simulation-based and pure AI-based DSS. A conceptual example is illustrated to show the integrated application of AI, simulation and database components of the proposed DSS framework.

Recent DSS studies have revealed the limitations of pure simulation-based and pure AI-based DSS. A new DSS framework is required in manufacturing to address these limitations, taking into account the problems of flowing items.

The theoretical DSS framework is proposed using simple rules and equations. This implies that it is not complex for software development and implementation. Practical data are not presented in this paper. A real DSS will be developed using the proposed theoretical framework and realistic results will be presented in the near future.

The proposed theoretical framework reveals how the integrated components of DSS can work together in manufacturing in order to determine the stable flow of items in a specific production period. Especially, the integrated performance of case-based reasoning (CBR) and DES is conceptually illustrated.

This paper presents an internet‐based decision support system (DSS) conceptual proposal for the appraisal stage of healthcare construction projects. The underlying philosophy of the approach is to integrate architectural space programming and costestimating processes with a risk assessment approach in order to obtain a more accurate inception cost estimate of the healthcare project along with its preliminary space program outline. The system’s main objectives focus on assisting decision makers in the United Arab Emirates in examining different space program alternatives with their associated capital budgets. In addition, the system will assist in reflecting associated risk factors and affecting budget approval decisions in their inception stage. The Internet is utilized as a mechanism for communicating and updating project data and cost information. This paper describes a conceptual design proposal for the system and the proposed strategy for its design and construction.

The purpose of this paper is to show how a clinical decision support system can help in prescription and knowledge acquisition processes.

An integrated electronic medical records system (iEMRS) is designed to enhance the decision support quality in prescription.

By evaluating the system performance through 135 prescription records collected from a Hong Kong medical organization, iEMRS shows a satisfactory result in suggesting medicines that is properly the same as the decisions made by the physicians.

Compared with the static clinical guidelines built (manually) in the traditional clinical decision support system, knowledge in iEMRS is generated by the knowledge discovery result from professional experiences of various physicians and patient histories, which are more dynamic in nature. A treatment algorithm, designed in data mining technique, is introduced to improve information management in medical organizations by integration of decision support capability and EMRS, and supplement the deficiencies of traditional clinical decision support system.

Discusses the findings of research into the development of marketing decision support systems. States that MDSS represents a break with traditional, more rigid management information systems development, offering a developmental approach. Offers a framework for MDSS development together with possible applications for such support systems, based on case‐study analysis.

Discusses the findings of research into the development of marketing decision support systems. States that MDSS represents a break with traditional, more rigid management information systems development, offering a developmental approach. Offers a framework for MDSS development together with possible applications for such support systems, based on case study analysis.

The tightening competition and performance pressure in companies often leave no time or space for the assessment of business impacts of different investments and projects. In addition, in many cases the assessment may be challenging and there is no experience available to undertake it. Despite that companies often commit to different projects and investments without careful planning and vision of the costs it may cause. The purpose of this paper is to create a decision support system in order to facilitate and increase the assessment of business impacts of different investments concerning to machine-to-machine (M2M) systems.

The created decision support system is composed of cost-benefit analysis including several investment decision methods. In order to deepen the understanding on it, the system was applied to two cases from the M2M business.

During the study it was found that different financial metrics might give contradictory results when deciding whether to undertake an investment. In addition, a significant finding was how much some variables may have significance to the eligibility of an investment than others. The study also gave understanding how long payback time can be and how risky the investments might be in different M2M applications.

The study describes the created decision support system and it is applied to two different M2M applications. The system provides a comprehensive combination of different financial metrics, which will help any manager make decisions whether an investment is eligible or not.

Data quality metadata (DQM) is a set of quality measurements associated with the data. Prior research in data quality has shown that DQM improves decision performance. The same research has also shown that DQM overloads the cognitive capacity of decision-makers. Visualization is a proven technique to reduce cognitive overload in decision-making. This paper aims to describe a prototype decision support system with a visual interface and examine its efficacy in reducing cognitive overload in the context of decision-making with DQM.

The authors describe the salient features of the prototype and following the design science paradigm, this paper evaluates its usefulness using an experimental setting.

The authors find that the interface not only reduced perceived mental demand but also improved decision performance despite added task complexity due to the presence of DQM.

A drawback of this study is the sample size. With a sample size of 51, the power of the model to draw conclusions is weakened.

In today’s decision environments, decision-makers deal with extraordinary volumes of data the quality of which is unknown or not determinable with any certainty. The interface and its evaluation offer insights into the design of decision support systems that reduce the complexity of the data and facilitate the integration of DQM into the decision tasks.

To the best of my knowledge, this is the only research to build and evaluate a decision-support prototype for structured decision-making with DQM.

The study aims to explore the utilisation of Clinical Decision Support (CDS) tools in Tanzanian healthcare facilities by identifying the tools used, the challenges encountered and the adaptive strategies employed by healthcare practitioners. It utilises an Activity Theory (AT) approach to understand the dynamic interactions between healthcare providers, CDS tools and the broader healthcare system.

The research adopts a qualitative approach in two prominent regions of Tanzania, Dar es Salaam and Dodoma. It involves semi-structured interviews with 26 healthcare professionals and key stakeholders across ten healthcare facilities, supplemented by document reviews. The study employs AT to analyse the interactions between healthcare professionals, CDS tools and the broader healthcare system, identifying best practices and providing recommendations for optimising the use of CDS tools.

The study reveals that Tanzanian healthcare practitioners predominantly rely on non-computerised CDS tools, such as clinical guidelines prepared by the Ministry of Health. Despite the availability of Health Information Systems (HIS), these systems often lack comprehensive decision-support functionalities, leading practitioners to depend on traditional methods and their professional judgement. Significant challenges include limited accessibility to updated clinical guidelines, unreliable infrastructure and inadequate training. Adaptive strategies identified include using non-standardised tools like Medscape, professional judgement and reliance on past experiences and colleagues’ opinions.

The investigation was constrained by access limitations because it was challenging to get some respondents to share information. However, a sufficient number of individuals participated in the interviews, and their knowledge was very beneficial in understanding the procedures and tools for clinical decision support.

This study contributes to AT by extending its application to a low-resource healthcare setting, uncovering new dimensions of the theory related to socio-cultural and technological constraints in healthcare facilities in Tanzania. It provides valuable insights into the practical barriers and facilitators of HIS and CDS tool implementation in developing countries, emphasising the need for context-specific adaptations, robust training programs and user-centred designs. The findings highlight the resilience and imagination of healthcare practitioners in adapting to systemic limitations, offering recommendations to enhance clinical decision-making and improve patient care outcomes in Tanzania.