A case-based framework for leveraging nutrigenomics knowledge and personal nutrition counseling

Paper presented at the 7th European Conference in Case-Based Reasoning, Madrid, Spain.NutriGenomics is the bioscience that links the way nutrients and other dietary components shape genetic activity. It builds on the success of Human Genome Project by applying systems biology methods to explain how the molecular components of food, supplements and pharmaceuticals dynamically influence and shape the activity of genomic subsystems, which in turn define how a person can stay healthy or become ill. Applying NutriGenomics knowledge is done through Directive Genomics, which develops purposeful dietary strategies that influence gene expression at the individual level with the goal of better genetic function and health. This paper proposes a case-based framework for leveraging nutrigenomics knowledge and Directive Genomics applications. The unique features of the proposed system include a selfmaintained distributed case base structure and a CBR-based nutrition counseling module that can learn, adapt, and maintain its case base via the integrated distributed case bases as well as external resources

A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms

Abstract The adaptive educational systems within e-learning platforms are built in response to the fact that the learning process is different for each and every learner. In order to provide adaptive e-learning services and study materials that are tailor-made for adaptive learning, this type of educational approach seeks to combine the ability to comprehend and detect a person’s specific needs in the context of learning with the expertise required to use appropriate learning pedagogy and enhance the learning process. Thus, it is critical to create accurate student profiles and models based upon analysis of their affective states, knowledge level, and their individual personality traits and skills. The acquired data can then be efficiently used and exploited to develop an adaptive learning environment. Once acquired, these learner models can be used in two ways. The first is to inform the pedagogy proposed by the experts and designers of the adaptive educational system. The second is to give the system dynamic self-learning capabilities from the behaviors exhibited by the teachers and students to create the appropriate pedagogy and automatically adjust the e-learning environments to suit the pedagogies. In this respect, artificial intelligence techniques may be useful for several reasons, including their ability to develop and imitate human reasoning and decision-making processes (learning-teaching model) and minimize the sources of uncertainty to achieve an effective learning-teaching context. These learning capabilities ensure both learner and system improvement over the lifelong learning mechanism. In this paper, we present a survey of raised and related topics to the field of artificial intelligence techniques employed for adaptive educational systems within e-learning, their advantages and disadvantages, and a discussion of the importance of using those techniques to achieve more intelligent and adaptive e-learning environments.</jats:p

5th International Symposium on Ambient Intelligence

Ambient Intelligence (AmI) is a recent paradigm emerging from Artificial Intelligence (AI), where computers are used as proactive tools assisting people with their day-to-day activities, making everyone’s life more comfortable. Another main concern of AmI originates from the human computer interaction domain and focuses on offering ways to interact with systems in a more natural way by means user friendly interfaces. This field is evolving quickly as can be witnessed by the emerging natural language and gesture based types of interaction. The inclusion of computational power and communication technologies in everyday objects is growing and their embedding into our environments should be as invisible as possible. In order for AmI to be successful, human interaction with computing power and embedded systems in the surroundings should be smooth and happen without people actually noticing it. The only awareness people should have arises from AmI: more safety, comfort and wellbeing, emerging in a natural and inherent way. ISAmI is the International Symposium on Ambient Intelligence and aiming to bring together researchers from various disciplines that constitute the scientific field of Ambient Intelligence to present and discuss the latest results, new ideas, projects and lessons learned, namely in terms of software and applications, and aims to bring together researchers from various disciplines that are interested in all aspects of this area

Lepidoptera Collection Curation and Data Management

The collections of Lepidoptera often serve as foundational basis for a wide range of biological, ecological, and climate science disciplines. Species identification and higher taxa delimitation based on collection specimens and especially, on types test scientific hypotheses, provide multiple types of evidence for a broad range of users. Curation and data management approaches applied in Lepidoptera collections benefit greatly from many newly developed information techniques, which link and integrate data. Mostly attention is focused on clean verified collection and taxonomic literature mining data to obtain correct species-group and higher taxa names, as well as reliable data on the distribution of Lepidoptera and their trophic interactions. Collection creation and management became a subject of natural sciences itself. The chapter provides a historic overview on collection creation and curation together with a short discussion on collection goals and purposes. The creation of a virtual collection based on interlinked data is emphasized. Information science and data management tools became very important in Lepidoptera collection curation. The complexity of techniques and computing tools used in taxonomy and the increase in the amount of data that can be obtained by collection-based disciplines make it necessary to automate data gathering, manipulation, analysis, and visualization processes

Criteria and Indicators for Sustainable Community Based Rural Tourism (CBRT) Development: the case of East Coast Economic Region (ECER), Malaysia

The launch of sustainable community based rural tourism (CBRT) programs in 1996 by the Ministry of Tourism of Malaysia (MOTOUR) indicated the government's commitment to incorporate sustainable development principles into the national tourism planning and development framework. Since then, the programs have been widely promoted by the government through various agencies and strongly embraced by the rural communities. Although the programs promise much potential such as job creations, provide an alternative of income for the rural household while promoting culture preservation and environment protection, recent studies showed that there was an issue of lack of monitoring of performance and progress of the programs due to the absence of criteria and indicators. From this research point of view, the absence of monitoring tools such as indicators could create obstacles and challenges, especially for the government and other donor agencies, in assessing the return on their investment in the programs and other impacts on the communities involved. Through extensive review of literature, a sufficient number of a preliminary list of criteria and indicators were identified. Each criteria and indicators were assigned into different category of sustainable CBRT namely economic, socio-cultural, environment and institutional. 64 preliminary indicators covered by eight criteria were identified by brought forward for the next stage: formulation of survey questionnaire. The identification and selection of a set of indicators using questionnaire survey was carried out using a Delphi exercise with experts and survey of local stakeholders. For the Delphi exercise, 20 experts were identified (academics, government officials, NGOs and tourism consultants) and consulted during the Stage One of Delphi consultation (selection of importa!lt indicators). However, due to the unavoidable issue of experts' dropout, a smaller number of 11 experts were maintained for Stage Two (ranking of indicators). The surveys of local stakeholders were carried out during the Stage Two involving 85 respondents from three selected villages as case studies (Le. Kuala Medang, Teluk Ketapang and Seterpa) located in the East Coast Economic Region (BCER). As a result, out of 64 indicators initially listed in the survey questionnaire, 47 indicators were selected both by the experts and by local stakeholders and included in the final list of indicators. The fieldwork results indicate that both the experts and local stakeholders are interested to support the idea of indicators formulation for monitoring the CBRT progress. At the final stage of the research, the proposed list of 47 indicators was put to test to assess the applicability and measurability of indicators for monitoring CBRT performances in the three villages i.e. Kuala Medang, Teluk Ketapang and Seterpa where 50 respondents participated in the survey. The field test intended to measure the uptake of sustainable economic, socio-cultural, environment and institution practices of CBRT program in all three villages. The outcomes for the analysis on uptake of CBRT economic and institution practices has shown a moderate success level with both 54% and 76% of an overall achievement while the analysis on uptake of CBRT socio-cultural and environment practices has shown a high success level with both 72% and 52% of an overall achievement. The field test revealed that the proposed indicators have been shown to be useful for measuring CBRT performance in the three case study villages. Furthermore, the achievement of CBR T practices could be determined as either low, or moderate or highly sustainable using index score approach. The results from quantitative and qualitative data collection processes could provide vital information to researchers, local hosts and other stakeholders about the current performance in the CBR T program from all major categories of indicators: economic, social-cultural, and environment and institution. In conclusion, the results from field test of indicators could inform decision makers and the CBRT participants in general about "where they are", i.e. based on the current level of sustainability practices, and "where they want to go", i.e. the local hosts' go~l or target setting for development of CBRT program. More importantly, indicators could also reveal to local hosts and other stakeholders "how far they are from achieving their goal/target"

ADAPTABLES 2006 : proceedings of the joint CIB, Tensinet, IASS international conference on adaptability in design and construction, Volume 3

No abstract

Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust Perspective

In recent years, XAI researchers have been formalizing proposals and developing new methods to explain black box models, with no general consensus in the community on which method to use to explain these models, with this choice being almost directly linked to the popularity of a specific method. Methods such as Ciu, Dalex, Eli5, Lofo, Shap and Skater emerged with the proposal to explain black box models through global rankings of feature relevance, which based on different methodologies, generate global explanations that indicate how the model's inputs explain its predictions. In this context, 41 datasets, 4 tree-ensemble algorithms (Light Gradient Boosting, CatBoost, Random Forest, and Gradient Boosting), and 6 XAI methods were used to support the launch of a new XAI method, called eXirt, based on Item Response Theory - IRT and aimed at tree-ensemble black box models that use tabular data referring to binary classification problems. In the first set of analyses, the 164 global feature relevance ranks of the eXirt were compared with 984 ranks of the other XAI methods present in the literature, seeking to highlight their similarities and differences. In a second analysis, exclusive explanations of the eXirt based on Explanation-by-example were presented that help in understanding the model trust. Thus, it was verified that eXirt is able to generate global explanations of tree-ensemble models and also local explanations of instances of models through IRT, showing how this consolidated theory can be used in machine learning in order to obtain explainable and reliable models.Comment: 54 pages, 15 Figures, 3 Equations, 7 tabl