The Potential of Artificial Intelligence in IT Project Portfolio Selection

The rapid growth of innovative technologies and the complexity of IT projects lead to the change in the tools and competency required for organization management and project management. Also, the scope of an IT product is no longer within a single project and team but requires the collaboration among multiple projects, teams and the alignment with the organization’s strategies. Therefore, project portfolio selection becomes a challenging process due to the complexity and uncertainty of various factors and risks. In the IT industry, the emergence of artificial intelligence (AI) could bring opportunities to organizations to address different challenges including challenges in project portfolio selection. In this paper, we have discussed the current challenges in IT project portfolio selection, the available methods and tools and their limitations. Then an overview of the potential applications of AI in IT project portfolio selection is explored. Finally, we conclude the paper by providing future research directions

Design Considerations for a Disaster eHealth Appliance

Disaster eHealth is a new area of research and endeavour. In order to make a practical contribution Disaster eHealth approaches should consider the role of a Disaster eHealth appliance. Both disaster management and disaster medicine may find that such approaches allow critical information to be gathered and situational awareness improved. This paper proposes the development of a Disaster eHealth appliance to support self-care of chronic disease and caregiving by others. Injuries and disease caused by the disaster may be also supported by this approach. It also attempts to address some of the potential problems and suggest some solutions for the use of such appliances. Re-using existing devices may offer a relatively low-cost and sustainable approach to providing such devices, and infrastructure to use them. &nbsp

Temporary Access to Medical Records in Emergency Situations

Access to patients Electronic Health Records (EHR) is a daily operation in mainstream healthcare. However, having access to EHR in emergencies while is vitally important to save patients’ life, it could potentially lead to security breaches and violating patients’ privacy. In this regards, getting access to patients’ medical records in emergency situations is one of the issues that emergency responder teams are facing. This access can be temporary until patients reach hospitals or healthcare centers. In this paper, we aim to explore different technology-based solutions to give responders temporary access to patients\u27 medical records in emergency situations. The core of this study is patients and responders authentication methods that can save precious emergency time and protect the privacy and confidentiality of patients data to the utmost. We also have explored control access mechanism and security audits to increase the security of the procedure and patient privacy

Disaster e-Health Scope and the Role of RFID for Healthcare Purposes

Disasters, either natural or manmade, are sudden and mostly unpredictable. They are of such enormous scale that it is necessary for societies to embark upon special planning and preparedness before disasters strike to prevent or diminish their destructive consequences. These plans aim at enhancing and facilitating response and recovery efforts. They are a part of two well-established domains: disaster management and disaster medicine. There is intensifying research in the fields of disaster management and disaster medicine. The intention of these studies has been to find ways to enhance disaster response missions by concentrating on coordination, communication, situation awareness, and the overall decision-making process of top authorities and managers (Abkowitz, 2008). In this regard, some researchers, such as Sieben, Scott, and Palacios (2012) and Norris et al. (2015), have suggested systematic utilisation and integration of e-health technologies for healthcare purposes within the Disaster Management Cycle (DMC). This specific focus had not been addressed previously. This research has identified an emerging field of Disaster e-Health (DEH) in the intersection of three areas: disaster management, disaster medicine, and e-health. The ultimate aim of DEH is enhancing the delivery of healthcare services and their quality in all phases of disasters through e-health technologies. Although currently various e-health technologies have been used within DMC for different purposes, their usage is ad hoc. To exploit the full potential of e-health technologies, a model is required that has not been addressed before; this model is DEH. To better understand the potential of DEH, this research was conducted in three phases. First, a scoping review across the three above-mentioned fields that constitute DEH was undertaken. The outcome of this phase was defining the scope of DEH in which the included e-health technologies, their applications, services and futures as well as the related stakeholders were specified. Then, by finding inspiration from use-case methodology, the researcher defined a method for a systematic generation of functional scenarios within DMC. The aim of this phase was to identify the e-health technologies that could be used in these scenarios. Finally, in the third phase, the researcher tried to identify the role of RFID during the DMC. Potential uses of this technology were proposed for a number of scenarios that were generated for different disaster phases. Finally, a Delphi method was conducted in which experts were asked to evaluate both the scope of DEH as well as the potential usefulness of RFID applications in the generated scenarios within DMC and DEH scope. This research study contributes to the body of knowledge through defining the scope of DEH that clearly demonstrates its functionalities and potentials within DMC and for the involved stakeholders. Moreover, this research proposes a method for systematic scenario generation within DMC. A further key contribution of this study is the provision of a comprehensive investigation of RFID roles and applications within DMC

Identifying the Potential of RFID in Disaster Healthcare: An International Delphi Study

Mainstream healthcare has been facing numerous challenges, and it is expected to see that these challenges become more severe and frequent when healthcare is dealing with disasters. This points to the necessity of utilising technologies to support healthcare and disaster managers in making quality decisions during chaotic and rapidly changing conditions in disaster situations. Therefore, in this research, the objective is to identify the role of RFID technology in healthcare-related activities before, during, and after disasters in terms of application areas and phases of the disaster management cycle (DMC). A Delphi approach was used in this research. Two rounds of questionnaires were administered to a panel of experts to evaluate the actual and potential use of RFID applications for healthcare within DMC. The Delphi participants were the field experts in the areas of disaster management, disaster medicine, and information systems. Based on the Delphi results, RFID applications were seen to be most useful in the response and recovery phases of disasters. RFID was seen as being most helpful for health-related supply management and casualty information. There were concerns that privacy and security may be barriers to adoption and use. Other applications identified by this study include identifying and tracking medical resources (including clinicians and first responders) and their accurate coordination in the response missions, determining idle resources, and maximising their utilisation during response activities. In this research, 35 potential scenarios of RFID applications for healthcare purposes within DMC and Disaster e-Health (DEH) were evaluated with the Delphi participants. RFID technologies could play an important role in DMC and DEH to provide more reliable and timely information to support healthcare during disasters. Based on the research results, managing the supply chain emerged as a major RFID application for supporting disaster healthcare

A Hard Voting Policy-Driven Deep Learning Architectural Ensemble Strategy for Industrial Products Defect Recognition and Classification

Manual or traditional industrial product inspection and defect-recognition models have some limitations, including process complexity, time-consuming, error-prone, and expensiveness. These issues negatively impact the quality control processes. Therefore, an efficient, rapid, and intelligent model is required to improve industrial products’ production fault recognition and classification for optimal visual inspections and quality control. However, intelligent models obtained with a tradeoff of high accuracy for high latency are tedious for real-time implementation and inferencing. This work proposes an ensemble deep-leaning architectural framework based on a deep learning model architectural voting policy to compute and learn the hierarchical and high-level features in industrial artefacts. The voting policy is formulated with respect to three crucial viable model characteristics: model optimality, efficiency, and performance accuracy. In the study, three publicly available industrial produce datasets were used for the proposed model’s various experiments and validation process, with remarkable results recorded, demonstrating a significant increase in fault recognition and classification performance in industrial products. In the study, three publicly available industrial produce datasets were used for the proposed model’s various experiments and validation process, with remarkable results recorded, demonstrating a significant increase in fault recognition and classification performance in industrial products

A Robust Deep Learning Ensemble-Driven Model for Defect and Non-Defect Recognition and Classification Using a Weighted Averaging Sequence-Based Meta-Learning Ensembler

The need to overcome the challenges of visual inspections conducted by domain experts drives the recent surge in visual inspection research. Typical manual industrial data analysis and inspection for defects conducted by trained personnel are expensive, time-consuming, and characterized by mistakes. Thus, an efficient intelligent-driven model is needed to eliminate or minimize the challenges of defect identification and elimination in processes to the barest minimum. This paper presents a robust method for recognizing and classifying defects in industrial products using a deep-learning architectural ensemble approach integrated with a weighted sequence meta-learning unification framework. In the proposed method, a unique base model is constructed and fused together with other co-learning pretrained models using a sequence-driven meta-learning ensembler that aggregates the best features learned from the various contributing models for better and superior performance. During experimentation in the study, different publicly available industrial product datasets consisting of the defect and non-defect samples were used to train, validate, and test the introduced model, with remarkable results obtained that demonstrate the viability of the proposed method in tackling the challenges of the manual visual inspection approach

Critical Success Factors of Agile ERP Development and Implementation Projects: A Systematic Literature Review

Failure in Enterprise Resource Planning (ERP) systems implementation can result in substantial costs, waste of resources, and the inability to meet the company’s requirements. Agile methodology is the modern project management methodology that can benefit ERP project development and implementation. This paper investigates the critical success factors that enable agile methods to achieve successful ERP development and implementations. This research aims to create the basis for further research into ERP system development and implementation and lays the groundwork for an agile ERP industry standard development and implementation model. The research will consolidate in one place existing research towards improving ERP development and implementation success rates using agile techniques. Based on the results of this research, agile assists ERP projects to be flexible in requirements, effectively monitor progress, aid team communication and collaboration, and ensure stakeholder involvement leading to faster deliveries. This, in turn, reduces costs and enables meeting user requirements, thereby making the ERP implementation a success

Prediction of Natural Gas Consumption in Bahçeşehir Using Machine Learning Models

Accurate prediction of natural gas consumption is of great importance for supply-demand balances and investments. This paper aims to utilize and compare the performance of multiple powerful machine learning algorithms to accurately predict the consumption of natural gas in Bahçeşehir, Istanbul. The utilized algorithms include Linear Regression, Random Forest Regression, Multilayer perceptron with back propagation (MLP) and gradient boosting (XGBoost). The algorithms were evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and R Squared to ensure consistency. The final results indicated that XGBoost outperformed MLP by 0.02, Forest Regression and Linear Regression by 0.04 Mean Absolute Error. XGBoost is highly scalable, efficiently reduces compute time and makes optimal use of memory which makes it a more suitable model for prediction. Accurate predictions reduce loss to the economy and ensure a balance between supply and demand

Patients' perspectives on digital health tools

Objective: Digital technology has changed the way healthcare is delivered and accessed. However, the focus is mostly on technology and clinical aspects. This review aimed to integrate and critically analyse the available knowledge regarding patients' perspectives on digital health tools and identify facilitators and barriers to their uptake. Methods: A narrative review was conducted using the Scopus and Google Scholar databases. Information related to facilitators and barriers to uptake was synthesised and interpreted using thematic and content analyses, respectively. Results: Seventy-one out of 1722 articles identified were eligible for inclusion. Patient empowerment, self-management, and personalisation were identified as the main factors that contributed to patient uptake in using digital health tools. Digital literacy, health literacy, and privacy concerns were identified as barriers to the uptake of digital health technology. Conclusion: Digital health technologies have changed the way healthcare is experienced by patients. Research highlights the disconnect between the development and implementation of digital health tools and the patients they are created for. This review may serve as the foundation for future research incorporating patients' perspectives to help increase patients' engagement with emerging technologies. Innovation: Participatory design approaches have the potential to support the creation of patient-centred digital health tools