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1,021 results on '"Antoniou, Grigoris"'

1. Towards the Use of Machine Learning Classifiers for Human Activity Recognition Using Accelerometer and Heart Rate Data from ActiGraph

Author

Oyeleye, Matthew, Chen, Tianhua, Su, Pan, Antoniou, Grigoris, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Naik, Nitin, editor, Jenkins, Paul, editor, Grace, Paul, editor, Yang, Longzhi, editor, and Prajapat, Shaligram, editor

Published
2024
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4. A Generalised Approach for Encoding and Reasoning with Qualitative Theories in Answer Set Programming

Author

Baryannis, George, Tachmazidis, Ilias, Batsakis, Sotiris, Antoniou, Grigoris, Alviano, Mario, and Papadakis, Emmanuel

Subjects
Computer Science - Artificial Intelligence
Abstract

Qualitative reasoning involves expressing and deriving knowledge based on qualitative terms such as natural language expressions, rather than strict mathematical quantities. Well over 40 qualitative calculi have been proposed so far, mostly in the spatial and temporal domains, with several practical applications such as naval traffic monitoring, warehouse process optimisation and robot manipulation. Even if a number of specialised qualitative reasoning tools have been developed so far, an important barrier to the wider adoption of these tools is that only qualitative reasoning is supported natively, when real-world problems most often require a combination of qualitative and other forms of reasoning. In this work, we propose to overcome this barrier by using ASP as a unifying formalism to tackle problems that require qualitative reasoning in addition to non-qualitative reasoning. A family of ASP encodings is proposed which can handle any qualitative calculus with binary relations. These encodings are experimentally evaluated using a real-world dataset based on a case study of determining optimal coverage of telecommunication antennas, and compared with the performance of two well-known dedicated reasoners. Experimental results show that the proposed encodings outperform one of the two reasoners, but fall behind the other, an acceptable trade-off given the added benefits of handling any type of reasoning as well as the interpretability of logic programs. This paper is under consideration for acceptance in TPLP., Comment: Paper presented at the 36th International Conference on Logic Programming (ICLP 2020), University Of Calabria, Rende (CS), Italy, September 2020, 18 pages, 3 figures

Published
2020

5. Rethinking Defeasible Reasoning: A Scalable Approach

Author

Maher, Michael J., Tachmazidis, Ilias, Antoniou, Grigoris, Wade, Stephen, and Cheng, Long

Subjects
Computer Science - Logic in Computer Science
Abstract

Recent technological advances have led to unprecedented amounts of generated data that originate from the Web, sensor networks and social media. Analytics in terms of defeasible reasoning - for example for decision making - could provide richer knowledge of the underlying domain. Traditionally, defeasible reasoning has focused on complex knowledge structures over small to medium amounts of data, but recent research efforts have attempted to parallelize the reasoning process over theories with large numbers of facts. Such work has shown that traditional defeasible logics come with overheads that limit scalability. In this work, we design a new logic for defeasible reasoning, thus ensuring scalability by design. We establish several properties of the logic, including its relation to existing defeasible logics. Our experimental results indicate that our approach is indeed scalable and defeasible reasoning can be applied to billions of facts., Comment: Under consideration in Theory and Practice of Logic Programming (TPLP)

Published
2020
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6. Investigating Mental Wellbeing in the Technology Workplace Using Machine Learning Techniques

Author

Alam, Tahmid, Chen, Tianhua, Bucholc, Magda, Antoniou, Grigoris, Zhong, Ning, Editor-in-Chief, Kikinis, Ron, Editor-in-Chief, Cai, Weidong, Series Editor, Müller, Henning, Series Editor, Onoe, Hirotaka, Series Editor, Pujol, Sonia, Series Editor, Yu, Philip S., Series Editor, Chen, Tianhua, editor, Carter, Jenny, editor, Mahmud, Mufti, editor, and Khuman, Arjab Singh, editor

Published
2022
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7. Assessing Significance of Cognitive Assessments for Diagnosing Alzheimer’s Disease with Fuzzy-Rough Feature Selection

Author

For the Alzheimer’s Disease Neuroimaging Initiative, Chen, Tianhua, Shang, Changjing, Su, Pan, Shen, Yinghua, Mahmud, Mufti, Moodley, Raymond, Antoniou, Grigoris, Shen, Qiang, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Jansen, Thomas, editor, Jensen, Richard, editor, Mac Parthaláin, Neil, editor, and Lin, Chih-Min, editor

Published
2022
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9. A Trajectory Calculus for Qualitative Spatial Reasoning Using Answer Set Programming

Author

Baryannis, George, Tachmazidis, Ilias, Batsakis, Sotiris, Antoniou, Grigoris, Alviano, Mario, Sellis, Timos, and Tsai, Pei-Wei

Subjects
Computer Science - Artificial Intelligence
Abstract

Spatial information is often expressed using qualitative terms such as natural language expressions instead of coordinates; reasoning over such terms has several practical applications, such as bus routes planning. Representing and reasoning on trajectories is a specific case of qualitative spatial reasoning that focuses on moving objects and their paths. In this work, we propose two versions of a trajectory calculus based on the allowed properties over trajectories, where trajectories are defined as a sequence of non-overlapping regions of a partitioned map. More specifically, if a given trajectory is allowed to start and finish at the same region, 6 base relations are defined (TC-6). If a given trajectory should have different start and finish regions but cycles are allowed within, 10 base relations are defined (TC-10). Both versions of the calculus are implemented as ASP programs; we propose several different encodings, including a generalised program capable of encoding any qualitative calculus in ASP. All proposed encodings are experimentally evaluated using a real-world dataset. Experiment results show that the best performing implementation can scale up to an input of 250 trajectories for TC-6 and 150 trajectories for TC-10 for the problem of discovering a consistent configuration, a significant improvement compared to previous ASP implementations for similar qualitative spatial and temporal calculi. This manuscript is under consideration for acceptance in TPLP., Comment: Paper presented at the 34th International Conference on Logic Programming (ICLP 2018), Oxford, UK, July 14 to July 17, 2018, 20 pages, LaTeX, 16 figures

Published
2018
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10. A Parallel Machine Learning Framework for Detecting Alzheimer’s Disease

Author

Knox, Sean A., Chen, Tianhua, Su, Pan, Antoniou, Grigoris, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Mahmud, Mufti, editor, Kaiser, M Shamim, editor, Vassanelli, Stefano, editor, Dai, Qionghai, editor, and Zhong, Ning, editor

Published
2021
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11. A Hypercat-enabled Semantic Internet of Things Data Hub: Technical Report

Author

Tachmazidis, Ilias, Batsakis, Sotiris, Davies, John, Duke, Alistair, Vallati, Mauro, Antoniou, Grigoris, and Clarke, Sandra Stincic

Subjects
Computer Science - Artificial Intelligence, Computer Science - Databases
Abstract

An increasing amount of information is generated from the rapidly increasing number of sensor networks and smart devices. A wide variety of sources generate and publish information in different formats, thus highlighting interoperability as one of the key prerequisites for the success of Internet of Things (IoT). The BT Hypercat Data Hub provides a focal point for the sharing and consumption of available datasets from a wide range of sources. In this work, we propose a semantic enrichment of the BT Hypercat Data Hub, using well-accepted Semantic Web standards and tools. We propose an ontology that captures the semantics of the imported data and present the BT SPARQL Endpoint by means of a mapping between SPARQL and SQL queries. Furthermore, federated SPARQL queries allow queries over multiple hub-based and external data sources. Finally, we provide two use cases in order to illustrate the advantages afforded by our semantic approach., Comment: Technical report of an accepted ESWC-2017 paper

Published
2017

13. Semantic Artificial Neural Networks

Author

Batsakis, Sotirios, Tachmazidis, Ilias, Baryannis, George, Antoniou, Grigoris, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Harth, Andreas, editor, Presutti, Valentina, editor, Troncy, Raphaël, editor, Acosta, Maribel, editor, Polleres, Axel, editor, Fernández, Javier D., editor, Xavier Parreira, Josiane, editor, Hartig, Olaf, editor, Hose, Katja, editor, and Cochez, Michael, editor

Published
2020
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15. Exploiting Parallelism for Hard Problems in Abstract Argumentation: Technical Report

Author

Cerutti, Federico, Tachmazidis, Ilias, Vallati, Mauro, Batsakis, Sotirios, Giacomin, Massimiliano, and Antoniou, Grigoris

Subjects
Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Abstract argumentation framework (\AFname) is a unifying framework able to encompass a variety of nonmonotonic reasoning approaches, logic programming and computational argumentation. Yet, efficient approaches for most of the decision and enumeration problems associated to \AFname s are missing, thus potentially limiting the efficacy of argumentation-based approaches in real domains. In this paper, we present an algorithm for enumerating the preferred extensions of abstract argumentation frameworks which exploits parallel computation. To this purpose, the SCC-recursive semantics definition schema is adopted, where extensions are defined at the level of specific sub-frameworks. The algorithm shows significant performance improvements in large frameworks, in terms of number of solutions found and speedup., Comment: Technical report of an accepted AAAI-2015 Paper

Published
2014

17. Effective Diagnosis of Diabetes with a Decision Tree-Initialised Neuro-fuzzy Approach

Author

Chen, Tianhua, Shang, Changjing, Su, Pan, Antoniou, Grigoris, Shen, Qiang, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Lotfi, Ahmad, editor, Bouchachia, Hamid, editor, Gegov, Alexander, editor, Langensiepen, Caroline, editor, and McGinnity, Martin, editor

Published
2019
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20. Efficient Computation of the Well-Founded Semantics over Big Data

Author

Tachmazidis, Ilias, Antoniou, Grigoris, and Faber, Wolfgang

Subjects
Computer Science - Artificial Intelligence
Abstract

Data originating from the Web, sensor readings and social media result in increasingly huge datasets. The so called Big Data comes with new scientific and technological challenges while creating new opportunities, hence the increasing interest in academia and industry. Traditionally, logic programming has focused on complex knowledge structures/programs, so the question arises whether and how it can work in the face of Big Data. In this paper, we examine how the well-founded semantics can process huge amounts of data through mass parallelization. More specifically, we propose and evaluate a parallel approach using the MapReduce framework. Our experimental results indicate that our approach is scalable and that well-founded semantics can be applied to billions of facts. To the best of our knowledge, this is the first work that addresses large scale nonmonotonic reasoning without the restriction of stratification for predicates of arbitrary arity. To appear in Theory and Practice of Logic Programming (TPLP)., Comment: 16 pages, 4 figures, ICLP 2014, 30th International Conference on Logic Programming July 19-22, Vienna, Austria

Published
2014
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22. Semantic Web

Author

Antoniou, Grigoris, Plexousakis, Dimitris, Gal, Avigdor, Section Editor, Liu, Ling, editor, and Özsu, M. Tamer, editor

Published
2018
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24. Extended RDF as a Semantic Foundation of Rule Markup Languages

Author

Analyti, Anastasia, Antoniou, Grigoris, Damásio, Carlos Viegas, and Wagner, Gerd

Subjects
Computer Science - Artificial Intelligence
Abstract

Ontologies and automated reasoning are the building blocks of the Semantic Web initiative. Derivation rules can be included in an ontology to define derived concepts, based on base concepts. For example, rules allow to define the extension of a class or property, based on a complex relation between the extensions of the same or other classes and properties. On the other hand, the inclusion of negative information both in the form of negation-as-failure and explicit negative information is also needed to enable various forms of reasoning. In this paper, we extend RDF graphs with weak and strong negation, as well as derivation rules. The ERDF stable model semantics of the extended framework (Extended RDF) is defined, extending RDF(S) semantics. A distinctive feature of our theory, which is based on Partial Logic, is that both truth and falsity extensions of properties and classes are considered, allowing for truth value gaps. Our framework supports both closed-world and open-world reasoning through the explicit representation of the particular closed-world assumptions and the ERDF ontological categories of total properties and total classes.

Published
2011
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25. Hypercat RDF: Semantic Enrichment for IoT

Author

Tachmazidis, Ilias, Davies, John, Batsakis, Sotiris, Antoniou, Grigoris, Duke, Alistair, Stincic Clarke, Sandra, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Li, Yuan-Fang, editor, Hu, Wei, editor, Dong, Jin Song, editor, Antoniou, Grigoris, editor, Wang, Zhe, editor, Sun, Jun, editor, and Liu, Yang, editor

Published
2016
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28. Large Scale Reasoning Using Allen’s Interval Algebra

Author

Mantle, Matthew, Batsakis, Sotirios, Antoniou, Grigoris, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Pichardo-Lagunas, Obdulia, editor, and Miranda-Jiménez, Sabino, editor

Published
2017
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29. A Hypercat-Enabled Semantic Internet of Things Data Hub

Author

Tachmazidis, Ilias, Batsakis, Sotiris, Davies, John, Duke, Alistair, Vallati, Mauro, Antoniou, Grigoris, Stincic Clarke, Sandra, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Blomqvist, Eva, editor, Maynard, Diana, editor, Gangemi, Aldo, editor, Hoekstra, Rinke, editor, Hitzler, Pascal, editor, and Hartig, Olaf, editor

Published
2017
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30. Applying SPARQL-Based Inference and Ontologies for Modelling and Execution of Clinical Practice Guidelines: A Case Study on Hypertension Management

Author

Doulaverakis, Charalampos, Koutkias, Vassilis, Antoniou, Grigoris, Kompatsiaris, Ioannis, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Riaño, David, editor, Lenz, Richard, editor, and Reichert, Manfred, editor

Published
2017
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31. Embedding Defeasible Logic into Logic Programming

Author

Antoniou, Grigoris, Billington, David, Governatori, Guido, and Maher, Michael J.

Subjects
Computer Science - Logic in Computer Science, F.4.1, I.2.3
Abstract

Defeasible reasoning is a simple but efficient approach to nonmonotonic reasoning that has recently attracted considerable interest and that has found various applications. Defeasible logic and its variants are an important family of defeasible reasoning methods. So far no relationship has been established between defeasible logic and mainstream nonmonotonic reasoning approaches. In this paper we establish close links to known semantics of logic programs. In particular, we give a translation of a defeasible theory D into a meta-program P(D). We show that under a condition of decisiveness, the defeasible consequences of D correspond exactly to the sceptical conclusions of P(D) under the stable model semantics. Without decisiveness, the result holds only in one direction (all defeasible consequences of D are included in all stable models of P(D)). If we wish a complete embedding for the general case, we need to use the Kunen semantics of P(D), instead., Comment: To appear in Theory and Practice of Logic Programming

Published
2005
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33. Rule-Based Real-Time ADL Recognition in a Smart Home Environment

Author

Baryannis, George, Woznowski, Przemyslaw, Antoniou, Grigoris, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Alferes, Jose Julio, editor, Bertossi, Leopoldo, editor, Governatori, Guido, editor, Fodor, Paul, editor, and Roman, Dumitru, editor

Published
2016
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34. Toward Automatic Risk Assessment to Support Suicide Prevention

Author

Adamou, Marios, Antoniou, Grigoris, Greasidou, Elissavet, Lagani, Vincenzo, Charonyktakis, Paulos, Tsamardinos, Ioannis, and Doyle, Michael

Abstract

Abstract.Background:Suicide has been considered an important public health issue for years and is one of the main causes of death worldwide. Despite prevention strategies being applied, the rate of suicide has not changed substantially over the past decades. Suicide risk has proven extremely difficult to assess for medical specialists, and traditional methodologies deployed have been ineffective. Advances in machine learning make it possible to attempt to predict suicide with the analysis of relevant data aiming to inform clinical practice. Aims:We aimed to (a) test our artificial intelligence based, referral-centric methodology in the context of the National Health Service (NHS), (b) determine whether statistically relevant results can be derived from data related to previous suicides, and (c) develop ideas for various exploitation strategies. Method:The analysis used data of patients who died by suicide in the period 2013–2016 including both structured data and free-text medical notes, necessitating the deployment of state-of-the-art machine learning and text mining methods. Limitations:Sample size is a limiting factor for this study, along with the absence of non-suicide cases. Specific analytical solutions were adopted for addressing both issues. Results and Conclusion:The results of this pilot study indicate that machine learning shows promise for predicting within a specified period which people are most at risk of taking their own life at the time of referral to a mental health service.

Published
2024
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37. Reasoning over Spatial Orientation Relations Using Rules

Author

Batsakis, Sotiris, Antoniou, Grigoris, Tachmazidis, Ilias, Kacprzyk, Janusz, Series editor, Bassiliades, Nick, editor, Ivanovic, Mirjana, editor, Kon-Popovska, Margita, editor, Manolopoulos, Yannis, editor, Palpanas, Themis, editor, Trajcevski, Goce, editor, and Vakali, Athena, editor

Published
2015
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38. Representing Time for the Semantic Web

Author

Batsakis, Sotiris, Tachmazidis, Ilias, Antoniou, Grigoris, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bikakis, Antonis, editor, and Zheng, Xianghan, editor

Published
2015
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39. iCurate: A Research Data Management System

Author

Liang, Shuo, Holmes, Violeta, Antoniou, Grigoris, Higgins, Joshua, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bikakis, Antonis, editor, and Zheng, Xianghan, editor

Published
2015
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40. Large-Scale Complex Reasoning with Semantics: Approaches and Challenges

Author

Antoniou, Grigoris, Pan, Jeff Z., Tachmazidis, Ilias, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Huang, Zhisheng, editor, Liu, Chengfei, editor, He, Jing, editor, and Huang, Guangyan, editor

Published
2014
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41. Integrated Representation of Spatial Topological and Size Relations for the Semantic Web

Author

Batsakis, Sotiris, Antoniou, Grigoris, Tachmazidis, Ilias, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Siekmann, Jörg, Series editor, Murty, M. Narasimha, editor, He, Xiangjian, editor, Chillarige, Raghavendra Rao, editor, and Weng, Paul, editor

Published
2014
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43. Diagnosing attention-deficit hyperactivity disorder (ADHD) using artificial intelligence: a clinical study in the UK.

Author

Tianhua Chen, Tachmazidis, Ilias, Batsakis, Sotiris, Adamou, Marios, Papadakis, Emmanuel, and Antoniou, Grigoris

Subjects
ATTENTION-deficit hyperactivity disorder, MACHINE learning, ARTIFICIAL intelligence, DIAGNOSIS
Abstract

Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting a large percentage of the adult population. A series of ongoing efforts has led to the development of a hybrid AI algorithm (a combination of a machine learningmodel and a knowledge-basedmodel) for assisting adult ADHDdiagnosis, and its clinical trial currently operating in the largest National Health Service (NHS) for adults with ADHD in the UK. Most recently, more data was made available that has lead to a total collection of 501 anonymized records as of 2022 July. This prompted the ongoing research to carefully examine the model by retraining and optimizing the machine learning algorithm in order to update the model with better generalization capability. Based on the large data collection so far, this paper also pilots a study to examine the effectiveness of variables other than the Diagnostic Interview for ADHD in adults (DIVA) assessment, which adds considerable cost in the screenining process as it relies on specially trained senior clinicians. Results reported in this paper demonstrate that the newly trained machine learning model reaches an accuracy of 75.03% when all features are used; the hybrid model obtains an accuracy of 93.61%. Exceeding what clinical experts expected in the absence of DIVA, achieving an accuracy of 65.27% using a rule-based machine learning model alone encourages the development of a cost effective model in the future. [ABSTRACT FROM AUTHOR]

Published
2023
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45. A System for Automated Agent Negotiation with Defeasible Logic-Based Strategies – Preliminary Report

Author

Skylogiannis, Thomas, Antoniou, Grigoris, Bassiliades, Nick, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Antoniou, Grigoris, editor, and Boley, Harold, editor

Published
2004
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46. A Defeasible Logic Reasoner for the Semantic Web

Author

Bassiliades, Nick, Antoniou, Grigoris, Vlahavas, Ioannis, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Antoniou, Grigoris, editor, and Boley, Harold, editor

Published
2004
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47. Combining Description Logic and Defeasible Logic for the Semantic Web

Author

Wang, Kewen, Billington, David, Blee, Jeff, Antoniou, Grigoris, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Antoniou, Grigoris, editor, and Boley, Harold, editor

Published
2004
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48. Justifications for Logic Programming

Author

Viegas Damásio, Carlos, Analyti, Anastasia, Antoniou, Grigoris, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Cabalar, Pedro, editor, and Son, Tran Cao, editor

Published
2013
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49. Computing the Stratified Semantics of Logic Programs over Big Data through Mass Parallelization

Author

Tachmazidis, Ilias, Antoniou, Grigoris, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Morgenstern, Leora, editor, Stefaneas, Petros, editor, Lévy, François, editor, Wyner, Adam, editor, and Paschke, Adrian, editor

Published
2013
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50. Provenance for SPARQL Queries

Author

Damásio, Carlos Viegas, Analyti, Anastasia, Antoniou, Grigoris, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Cudré-Mauroux, Philippe, editor, Heflin, Jeff, editor, Sirin, Evren, editor, Tudorache, Tania, editor, Euzenat, Jérôme, editor, Hauswirth, Manfred, editor, Parreira, Josiane Xavier, editor, Hendler, Jim, editor, Schreiber, Guus, editor, Bernstein, Abraham, editor, and Blomqvist, Eva, editor

Published
2012
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