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Automated Learning

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Elyes Lamine – One of the best experts on this subject based on the ideXlab platform.

  • A Semantic Reasoning Method Towards Ontological Model for Automated Learning Analysis
    Advances in intelligent systems and computing, 2015
    Co-Authors: Kingsley Okoye, Abdel-rahman H. Tawil, Usman Naeem, Elyes Lamine

    Abstract:

    Semantic reasoning can help solve the problem of regulating the evolving and static measures of knowledge at theoretical and technological levels. The technique has been proven to enhance the capability of process models by making inferences, retaining and applying what they have learned as well as discovery of new processes. The work in this paper propose a semantic rule-based approach directed towards discovering learners interaction patterns within a Learning knowledge base, and then respond by making decision based on adaptive rules centred on captured user profiles. The method applies semantic rules and description logic queries to build ontology model capable of automatically computing the various Learning activities within a Learning Knowledge-Base, and to check the consistency of Learning object/data types. The approach is grounded on inductive and deductive logic descriptions that allows the use of a Reasoner to check that all definitions within the Learning model are consistent and can also recognise which concepts that fit within each defined class. Inductive reasoning is practically applied in order to discover sets of inferred learner categories, while deductive approach is used to prove and enhance the discovered rules and logic expressions. Thus, this work applies effective reasoning methods to make inferences over a Learning Process Knowledge-Base that leads to Automated discovery of Learning patterns/behaviour.

  • A Semantic Rule-Based Approach Supported by Process Mining for Personalised Adaptive Learning
    , 2014
    Co-Authors: Kingsley Okoye, Abdel-rahman H. Tawil, Usman Naeem, Rabih Bashroush, Elyes Lamine

    Abstract:

    Currently, Automated Learning systems are widely used for educational and training purposes within various organisations including, schools, universities and further education centres. There has been a big gap between the extraction of useful patterns from data sources to knowledge, as it is crucial that data is made valid, novel, potentially useful and understandable. To meet the needs of intended users, there is requirement for Learning systems to embody technologies that support learners in achieving their Learning goals and this process don’t happen automatically. This paper propose a novel approach for Automated Learning that is capable of detecting changing trends in Learning behaviours and abilities through the use of process mining techniques. The goal is to discover user interaction patterns within Learning processes, and respond by making decisions based on adaptive rules centred on captured user profiles. The approach applies semantic annotation of activity logs within the Learning process in order to discover patterns automatically by means of semantic reasoning. Therefore, our proposed approach is grounded on Semantic Modelling and Process Mining techniques. To this end, it is possible to apply effective reasoning methods to make inferences over a Learning Process Knowledge-Base that leads to Automated discovery of Learning patterns or behaviour.

  • HPCC/CSS/ICESS – A Semantic Rule-Based Approach Towards Process Mining for Personalised Adaptive Learning
    2014 IEEE Intl Conf on High Performance Computing and Communications 2014 IEEE 6th Intl Symp on Cyberspace Safety and Security 2014 IEEE 11th Intl Con, 2014
    Co-Authors: Kingsley Okoye, Abdel-rahman H. Tawil, Usman Naeem, Rabih Bashroush, Elyes Lamine

    Abstract:

    In recent years, Automated Learning systems are widely used for educational and training purposes within various organisations including, schools, universities and further education centres. A common challenge for Automated Learning approaches is the demand for an effectively well-designed and fit for purpose system that meets the requirements and needs of intended learners to achieve their Learning goals. This paper proposes a novel approach for Automated Learning that is capable of detecting changing trends in Learning behaviours and abilities through the use of process mining techniques. The goal is to discover user interaction patterns, and respond by making decisions based on adaptive rules centred on captured user profiles. The approach applies semantic annotation of activity logs within the Learning process in order to discover patterns automatically by means of semantic reasoning. Therefore, our proposed approach is grounded on Semantic modelling and process mining techniques. To this end, it is possible to apply effective reasoning methods to make inferences over a Learning Process Knowledge-Base that leads to Automated discovery of Learning patterns or behaviour.

Paul Szyszka – One of the best experts on this subject based on the ideXlab platform.

  • Social foraging extends associative odor-food memory expression in an Automated Learning assay for Drosophila melanogaster.
    The Journal of experimental biology, 2019
    Co-Authors: Aarti Sehdev, Yunusa Garba Mohammed, Cansu Tafrali, Paul Szyszka

    Abstract:

    Animals socially interact during foraging and share information about the quality and location of food sources. The mechanisms of social information transfer during foraging have been mostly studied at the behavioral level, and its underlying neural mechanisms are largely unknown. Fruit flies have become a model for studying the neural bases of social information transfer, because they provide a large genetic toolbox to monitor and manipulate neuronal activity, and they show a rich repertoire of social behaviors. Fruit flies aggregate, they use social information for choosing a suitable mating partner and oviposition site, and they show better aversive Learning when in groups. However, the effects of social interactions on associative odor-food Learning have not yet been investigated. Here, we present an Automated Learning and memory assay for walking flies that allows the study of the effect of group size on social interactions and on the formation and expression of associative odor-food memories. We found that both inter-fly attraction and the duration of odor-food memory expression increase with group size. This study opens up opportunities to investigate how social interactions during foraging are relayed in the neural circuitry of Learning and memory expression.

  • Social foraging extends associative odor–food memory expression in an Automated Learning assay for Drosophila melanogaster
    The Journal of Experimental Biology, 2019
    Co-Authors: Aarti Sehdev, Yunusa Garba Mohammed, Cansu Tafrali, Paul Szyszka

    Abstract:

    ABSTRACT Animals socially interact during foraging and share information about the quality and location of food sources. The mechanisms of social information transfer during foraging have been mostly studied at the behavioral level, and its underlying neural mechanisms are largely unknown. Fruit flies have become a model for studying the neural bases of social information transfer, because they provide a large genetic toolbox to monitor and manipulate neuronal activity, and they show a rich repertoire of social behaviors. Fruit flies aggregate, they use social information for choosing a suitable mating partner and oviposition site, and they show better aversive Learning when in groups. However, the effects of social interactions on associative odor–food Learning have not yet been investigated. Here, we present an Automated Learning and memory assay for walking flies that allows the study of the effect of group size on social interactions and on the formation and expression of associative odor–food memories. We found that both inter-fly attraction and the duration of odor–food memory expression increase with group size. This study opens up opportunities to investigate how social interactions during foraging are relayed in the neural circuitry of Learning and memory expression.

  • Social foraging extends associative odor-food memory expression in an Automated Learning assay for Drosophila melanogaster
    bioRxiv, 2019
    Co-Authors: Aarti Sehdev, Yunusa Garba Mohammed, Cansu Tafrali, Paul Szyszka

    Abstract:

    Animals socially interact during foraging and share information about the quality and location of food sources. The mechanisms of social information transfer during foraging have been mostly studied at the behavioral level, and its underlying neural mechanisms are largely unknown. The fruit fly Drosophila melanogaster has become a model for studying the neural bases of social information transfer, as fruit flies show a rich repertoire of social behaviors and provide a well-developed genetic toolbox to monitor and manipulate neuronal activity. Social information transfer has already been characterized for fruit flies9 egg laying, mate choice, foraging and aversive associative Learning, however the role of social information transfer on associative odor-food Learning during foraging are unknown. Here we present an Automated Learning and memory assay for walking flies that allows studying the effect of group size on social interactions and on the formation and expression of associative odor-food memories. We found that both inter-fly attraction and the duration of odor-food memory expression increase with group size. We discuss possible behavioral and neural mechanisms of this social effect on odor-food memory expression. This study opens up opportunities to investigate how social interactions are relayed in the neural circuitry of Learning and memory expression.

Kingsley Okoye – One of the best experts on this subject based on the ideXlab platform.

  • A Semantic Reasoning Method Towards Ontological Model for Automated Learning Analysis
    Advances in intelligent systems and computing, 2015
    Co-Authors: Kingsley Okoye, Abdel-rahman H. Tawil, Usman Naeem, Elyes Lamine

    Abstract:

    Semantic reasoning can help solve the problem of regulating the evolving and static measures of knowledge at theoretical and technological levels. The technique has been proven to enhance the capability of process models by making inferences, retaining and applying what they have learned as well as discovery of new processes. The work in this paper propose a semantic rule-based approach directed towards discovering learners interaction patterns within a Learning knowledge base, and then respond by making decision based on adaptive rules centred on captured user profiles. The method applies semantic rules and description logic queries to build ontology model capable of automatically computing the various Learning activities within a Learning Knowledge-Base, and to check the consistency of Learning object/data types. The approach is grounded on inductive and deductive logic descriptions that allows the use of a Reasoner to check that all definitions within the Learning model are consistent and can also recognise which concepts that fit within each defined class. Inductive reasoning is practically applied in order to discover sets of inferred learner categories, while deductive approach is used to prove and enhance the discovered rules and logic expressions. Thus, this work applies effective reasoning methods to make inferences over a Learning Process Knowledge-Base that leads to Automated discovery of Learning patterns/behaviour.

  • A Semantic Rule-Based Approach Supported by Process Mining for Personalised Adaptive Learning
    , 2014
    Co-Authors: Kingsley Okoye, Abdel-rahman H. Tawil, Usman Naeem, Rabih Bashroush, Elyes Lamine

    Abstract:

    Currently, Automated Learning systems are widely used for educational and training purposes within various organisations including, schools, universities and further education centres. There has been a big gap between the extraction of useful patterns from data sources to knowledge, as it is crucial that data is made valid, novel, potentially useful and understandable. To meet the needs of intended users, there is requirement for Learning systems to embody technologies that support learners in achieving their Learning goals and this process don’t happen automatically. This paper propose a novel approach for Automated Learning that is capable of detecting changing trends in Learning behaviours and abilities through the use of process mining techniques. The goal is to discover user interaction patterns within Learning processes, and respond by making decisions based on adaptive rules centred on captured user profiles. The approach applies semantic annotation of activity logs within the Learning process in order to discover patterns automatically by means of semantic reasoning. Therefore, our proposed approach is grounded on Semantic Modelling and Process Mining techniques. To this end, it is possible to apply effective reasoning methods to make inferences over a Learning Process Knowledge-Base that leads to Automated discovery of Learning patterns or behaviour.

  • HPCC/CSS/ICESS – A Semantic Rule-Based Approach Towards Process Mining for Personalised Adaptive Learning
    2014 IEEE Intl Conf on High Performance Computing and Communications 2014 IEEE 6th Intl Symp on Cyberspace Safety and Security 2014 IEEE 11th Intl Con, 2014
    Co-Authors: Kingsley Okoye, Abdel-rahman H. Tawil, Usman Naeem, Rabih Bashroush, Elyes Lamine

    Abstract:

    In recent years, Automated Learning systems are widely used for educational and training purposes within various organisations including, schools, universities and further education centres. A common challenge for Automated Learning approaches is the demand for an effectively well-designed and fit for purpose system that meets the requirements and needs of intended learners to achieve their Learning goals. This paper proposes a novel approach for Automated Learning that is capable of detecting changing trends in Learning behaviours and abilities through the use of process mining techniques. The goal is to discover user interaction patterns, and respond by making decisions based on adaptive rules centred on captured user profiles. The approach applies semantic annotation of activity logs within the Learning process in order to discover patterns automatically by means of semantic reasoning. Therefore, our proposed approach is grounded on Semantic modelling and process mining techniques. To this end, it is possible to apply effective reasoning methods to make inferences over a Learning Process Knowledge-Base that leads to Automated discovery of Learning patterns or behaviour.