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The purpose of this paper is to discuss an online sensor-based support system which the authors believe can be useful in such scenarios. Persons with a cognitive impairment, such as those with Alzheimer’s disease, suffer from deficiencies in cognitive skills which reduce their independence; such patients can benefit from the provision of further assistance such as reminders for carrying out instrumental activities of daily living (IADLs).

The system proposed processes data from a network of sensors that have the capability of sensing user interactions and on-going IADLs in the living environment itself. A probabilistic learning model is built that computes joint probability distributions over different activities representing users’ behavioural patterns in performing activities. This probability model can underpin an intervention framework that prompts the user with the next step in the IADL when inactivity is being observed. This prompt for the next step is inferred from the conditional probability taken into consideration the IADL steps that have already been completed, in addition to contextual information relating to the time of day and the amount of time already spent on the activity. The originality of the work lies in combining partially observed sensor sequences and duration data associated with the IADLs. The prediction of the next step is then adjusted as further steps are completed and more time is spent towards the completion of the activity, thus updating the confidence that the prediction is correct. A reminder is only issued when there has been sufficient inactivity on the part of the patient and the confidence is high that the prediction is correct.

The results of this study verify that by including duration information the prediction accuracy of the model is increased and the confidence level for the next step in the IADL is also increased. As such, there is approximately a 10 per cent rise in the prediction performance in the case of single sensor activation in comparison to an alternative approach which did not consider activity durations.

Duration information to a certain extent has been widely ignored by activity recognition researchers and has received a very limited application within smart environments.

This study concludes that incorporating progressive duration information into partially observed sensor sequences of IADLs has the potential to increase performance of a reminder system for patients with a cognitive impairment, such as Alzheimer’s disease.

This paper aims to discuss an online sensor-based support system which is believed to be useful for persons with a cognitive impairment, such as those with Alzheimer’s disease, suffering from deficiencies in cognitive skills which reduce their independence. Such patients can benefit from the provision of further assistance such as reminders for carrying out instrumental activities of daily living (iADLs).

The system proposed processes data from a network of sensors that have the capability of sensing user interactions and ongoing iADLs in the living environment itself. A probabilistic learning model is built that computes joint probability distributions over different activities representing users’ behavioural patterns in performing activities. This probability model can underpin an intervention framework that prompts the user with the next step in the iADL when inactivity is being observed. This prompt for the next step is inferred from the conditional probability, taking into consideration the iADL steps that have already been completed, in addition to contextual information relating to the time of day and the amount of time already spent on the activity. The originality of the work lies in combining partially observed sensor sequences and duration data associated with the iADLs. The prediction of the next step is then adjusted as further steps are completed and more time is spent towards the completion of the activity; thus, updating the confidence that the prediction is correct. A reminder is only issued when there has been sufficient inactivity on the part of the patient and the confidence is high that the prediction is correct.

The results verify that by including duration information, the prediction accuracy of the model is increased, and the confidence level for the next step in the iADL is also increased. As such, there is approximately a 10 per cent rise in the prediction performance in the case of single-sensor activation in comparison to an alternative approach which did not consider activity durations. Thus, it is concluded that incorporating progressive duration information into partially observed sensor sequences of iADLs has the potential to increase performance of a reminder system for patients with a cognitive impairment, such as Alzheimer’s disease.

Activity duration information can be a potential feature in measuring the performance of a user and distinguishing different activities. The results verify that by including duration information, the prediction accuracy of the model is increased, and the confidence level for the next step in the activity is also increased. The use of duration information in online prediction of activities can also be associated to monitoring the deterioration in cognitive abilities and in making a decision about the level of assistance required. Such improvements have significance in building more accurate reminder systems that precisely predict activities and assist its users, thus, improving the overall support provided for living independently.

Process mining provides a new means to improve processes in a variety of application domains. The purpose of this paper is to abstract a process model and then use the discovered models from process mining to make useful optimization via predictions.

The paper divides the process model into a combination of “pair-adjacent activities” and “pair-adjacent persons” in the event logs. First, two new handover process models based on adjacency matrix are proposed. Second, by adding the stage, frequency, and time for every activity or person into the matrix, another two new handover prediction process models based on stage adjacency matrix are further proposed. Third, compute the conditional probability from every stage to next stage through the frequency. Finally, use real data to analyze and demonstrate the practicality and effectiveness of the proposed handover optimization process.

The process model can be extended with information to predict what will actually happen, how possible to reach the next activity, who will do this activity, and the corresponding probability if there are several people executing the same activity, etc.

The contribution of this paper is to predict what will actually happen, how possible it is to reach the following activities or persons in the next stage, how soon to reach the following activities or persons by calculating all the possible interval time via different traces, who will do this activity, and the corresponding probability if there are several people executing the same activity, etc.

Location‐prediction enables the next generation of location‐based applications. The purpose of this paper is to provide a historical summary of research in personal location‐prediction. Location‐prediction began as a tool for network management, predicting the load on particular cellular towers or WiFi access points. With the increasing popularity of mobile devices, location‐prediction turned personal, predicting individuals' next locations given their current locations.

This paper includes an overview of prediction techniques and reviews several location‐prediction projects comparing the raw location data, feature extraction, choice of prediction algorithms and their results.

A new trend has emerged, that of employing additional context to improve or expand predictions. Incorporating temporal information enables location‐predictions farther out into the future. Appending place types or place names can improve predictions or develop prediction applications that could be used in any locale. Finally, the authors explore research into diverse types of context, such as people's personal contacts or health activities.

This overview provides a broad background for future research in prediction.

The study is aimed to compare the prediction success of basic machine learning and ensemble machine learning models and accordingly create novel prediction models by combining machine learning models to increase the prediction success in construction labor productivity prediction models.

Categorical and numerical data used in prediction models in many studies in the literature for the prediction of construction labor productivity were made ready for analysis by preprocessing. The Python programming language was used to develop machine learning models. As a result of many variation trials, the models were combined and the proposed novel voting and stacking meta-ensemble machine learning models were constituted. Finally, the models were compared to Target and Taylor diagram.

Meta-ensemble models have been developed for labor productivity prediction by combining machine learning models. Voting ensemble by combining et, gbm, xgboost, lightgbm, catboost and mlp models and stacking ensemble by combining et, gbm, xgboost, catboost and mlp models were created and finally the Et model as meta-learner was selected. Considering the prediction success, it has been determined that the voting and stacking meta-ensemble algorithms have higher prediction success than other machine learning algorithms. Model evaluation metrics, namely MAE, MSE, RMSE and R², were selected to measure the prediction success. For the voting meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0499, 0.0045, 0.0671 and 0.7886, respectively. For the stacking meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0469, 0.0043, 0.0658 and 0.7967, respectively.

The study shows the comparison between machine learning algorithms and created novel meta-ensemble machine learning algorithms to predict the labor productivity of construction formwork activity. The practitioners and project planners can use this model as reliable and accurate tool for predicting the labor productivity of construction formwork activity prior to construction planning.

The study provides insight into the application of ensemble machine learning algorithms in predicting construction labor productivity. Additionally, novel meta-ensemble algorithms have been used and proposed. Therefore, it is hoped that predicting the labor productivity of construction formwork activity with high accuracy will make a great contribution to construction project management.

In this chapter, we examine two theorized approaches to entrepreneurial activity: experiential versus prediction based strategies. We empirically assess the comparative performance of several commonly recommended approaches – researching customer needs, researching the competitive landscape, writing a business plan, conceptually adapting the business plan or experimentally adapting the primary business activity. We found that the majority of nascent entrepreneurs began with a business plan, but only about a third adapted their plan in later stages. We also found that talking with customers and examining the competitive landscape were normative activities. Those who started a plan were more likely to create a venture, although the effects much stronger for those who changed their plan later on, as well as for those who researched customer needs.

Our results show that the selection of these activities is both ubiquitous and driven by pre-start-up experience and new venture characteristics. The activities themselves do not robustly link with successful new venture foundation. Hence, pre-start-up experiences, venture characteristics, and the institutional environment are more important in explaining successful performance than recommended activities. Implications for research, practice, and pedagogy are discussed.

This is the third in a series of papers aimed at providing models effective in predicting the degree of pain and discomfort in canines. The first two papers provided benchmarking and examination of dogs suffering from osteoarthritis (OA). In this chapter, we extend the study to include dogs suffering from OA, sarcoma, and oral mucositis (a side effect of chemotherapy and radiation treatments). The R programming language and SAS JMP are used to clean data, generate ANOVA, LSR regression, decision tree, and nominal logistic regression models to predict changes in activity levels associated with the progression of arthritis. The predictive models provide a diagnostic basis for determining the degree of disease in a dog (based on demographics and activity levels) and provide forecasts that assist in establishing appropriate medication dosages for suffering dogs.

With an aim to develop an integrated approach for effectively managing natural disasters, this paper has three research objectives. First, it provides a framework for effective natural disaster management from a public project management perspective. Second, it proposes an integrated approach for successfully and effectively managing disaster crisis. Third, it specifies a set of critical success factors for managing disaster related public projects.

A detailed case study of the tsunami was carried out to identify specific problems associated with managing natural disaster in Thailand.

The investigations reveal that the country lacked a master plan for natural disaster management including prediction, warning, mitigation and preparedness, unspecified responsible governmental authority, unclear line of authority, ineffective collaboration among institutions in different levels, lack of encouragement for participation of local and international NGOs, lack of education and knowledge for tsunami in potential disaster effected communities, and lack of information management or database system.

This study identifies the specific problems associated with natural disasters management based on a detailed case study of managing tsunami disaster in Thailand in 2004.

The proposed integrated approach which includes both proactive and reactive strategies can be applied to managing natural disasters successfully in Thailand.

This paper highlights the importance of having proactive and reactive strategies for natural disaster management.

Predicting the final status of an ongoing process or a subsequent activity in a process is an important aspect of process management. Semi-structured business processes cannot be predicted by precise and mathematical methods. Therefore, artificial intelligence is one of the successful methods. This study aims to propose a method that is a combination of deep learning methods, in particular, the recurrent neural network and Markov chain.

The proposed method applies the BestFirst algorithm for the search section and the Cfssubseteval algorithm for the feature comparison section. This study focuses on the prediction systems of social insurance and tries to present a method that is less costly in providing real-world results based on the past history of an event.

The proposed method is simulated with real data obtained from Iranian Social Security Organization, and the results demonstrate that using the proposed method increases the memory utilization slightly more than the Markov method; however, the CPU usage time has dramatically decreased in comparison with the Markov method and the recurrent neural network and has, therefore, significantly increased the accuracy and efficiency.

This research tries to provide an approach capable of producing the findings closer to the real world with fewer time and processing overheads, given the previous records of an event and the prediction systems of social insurance.

The purpose of this paper is to investigate elements of socio-academic-related sustainability in educational institutes and propose a structure of an advising system that can aid the most critical stakeholders in such educational institutes, i.e the student. Hence, after reviewing the contemporary University Social Responsibility (USR) Venn diagram, the paper focuses on the need to develop a social and academic responsibility advisor (SARA) system as a catalyst toward fulfilling social responsibility to the most important stakeholder and alternatively leads to enhanced sustainability of such educational institutes.

A combination of research methods used in this paper, defined as by identifying the need for SARA from a literature survey. By distributing a questionnaire to students investigating their desire of an academic advisor system and by establishing a focus group to study the academic and social aspects and its implications to students’ “quality of life” as an essential aspect toward the educational institutes' sustainability. Various issues related to the features of the SARA discussed.

Literature review shows only a few articles combine both aspects of advising activities (social and academic), most of which are not interrelated to the sustainability of educational institutes. This paper highlights the need and connectivity of SARA to contemporary USR sustainability. The descriptive statistics of the questionnaire showed about 86 per cent of student participants interested in applying the proposed features. The outcome of the focus group resulted in more detailed features of academic and social aspects of the expected SARA system.

The proposed features of SARA described where the inter-related social and academic activities could be managed, logged and used by students. The proposed “heterogeneous study group” was investigated by observation implemented in four different courses in two different semesters. The result was not conclusive, and further study recommended. Even though this experimentation was not conclusive, the lesson learned from this study highlighted different issues associated with “study groups” within a course.

Applicability of SARA would enhance the quality of life of students in general and provide a mechanism to motivate low aptitude students to get engaged with study and projects with high-aptitude students.

Avoiding the “feel” of isolation by some students due to the inability to find study partner who can also act as mentor to a study group. The proposed “Heterogeneous study group” would provide a mechanism for “Practiced student-to-student Social responsibility”.

The paper highlights and proves the need for SARA in contemporary USR where SARA can act as a catalyst for enhancing the socio-academic zone of the reviewed USR Venn diagram. Features of SARA identified as an outcome of the study in this paper. Proposal of “Heterogeneous Study Group” was proposed as mechanism of “social learning”. “Group health” testing was proposed as a criteria resulted from a balance between collaboration, social affinity and project effort.