Exploring novel features and decision rules to identify cardiovascular autonomic neuropathy using a hybrid of wrapper-filter based feature selection
- Huda, Shamsul, Jelinek, Herbert, Ray, Biplob, Stranieri, Andrew, Yearwood, John
- Authors: Huda, Shamsul , Jelinek, Herbert , Ray, Biplob , Stranieri, Andrew , Yearwood, John
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at the 2010 6th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2010 p. 297-302
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- Description: Cardiovascular autonomic neuropathy (CAN) is one of the important causes of mortality among diabetes patients. Statistics shows that more than 22% of people with type 2 diabetes mellitus suffer from CAN and which in turn leads to cardiovascular disease (heart attack, stroke). Therefore early detection of CAN could reduce the mortality. Traditional method for detection of CAN uses Ewing's algorithm where five noninvasive cardiovascular tests are used. Often for clinician, it is difficult to collect data from for the Ewing Battery patients due to onerous test conditions. In this paper, we propose a hybrid of wrapper-filter approach to find novel features from patients' ECG records and then generate decision rules for the new features for easier detection of CAN. In the proposed feature selection, a hybrid of filter (Maximum Relevance, MR) and wrapper (Artificial Neural Net Input Gain Measurement Approximation ANNIGMA) approaches (MR-ANNIGMA) would be used. The combined heuristics in the hybrid MRANNIGMA takes the advantages of the complementary properties of the both filter and wrapper heuristics and can find significant features. The selected features set are used to generate a new set of rules for detection of CAN. Experiments on real patient records shows that proposed method finds a smaller set of features for detection of CAN than traditional method which are clinically significant and could lead to an easier way to diagnose CAN. © 2010 IEEE.
- Authors: Huda, Shamsul , Jelinek, Herbert , Ray, Biplob , Stranieri, Andrew , Yearwood, John
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at the 2010 6th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2010 p. 297-302
- Full Text:
- Reviewed:
- Description: Cardiovascular autonomic neuropathy (CAN) is one of the important causes of mortality among diabetes patients. Statistics shows that more than 22% of people with type 2 diabetes mellitus suffer from CAN and which in turn leads to cardiovascular disease (heart attack, stroke). Therefore early detection of CAN could reduce the mortality. Traditional method for detection of CAN uses Ewing's algorithm where five noninvasive cardiovascular tests are used. Often for clinician, it is difficult to collect data from for the Ewing Battery patients due to onerous test conditions. In this paper, we propose a hybrid of wrapper-filter approach to find novel features from patients' ECG records and then generate decision rules for the new features for easier detection of CAN. In the proposed feature selection, a hybrid of filter (Maximum Relevance, MR) and wrapper (Artificial Neural Net Input Gain Measurement Approximation ANNIGMA) approaches (MR-ANNIGMA) would be used. The combined heuristics in the hybrid MRANNIGMA takes the advantages of the complementary properties of the both filter and wrapper heuristics and can find significant features. The selected features set are used to generate a new set of rules for detection of CAN. Experiments on real patient records shows that proposed method finds a smaller set of features for detection of CAN than traditional method which are clinically significant and could lead to an easier way to diagnose CAN. © 2010 IEEE.
Managing uncertainty in movement knowledge for environmental decisions
- Smith, Annabel, Kujala, Heini, Lahoz-Monfort, José, Guja, Lydia, Barton, Philip
- Authors: Smith, Annabel , Kujala, Heini , Lahoz-Monfort, José , Guja, Lydia , Barton, Philip
- Date: 2019
- Type: Text , Journal article
- Relation: Conservation Letters Vol. 12, no. 3 (2019), p.
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- Description: Species’ movements affect their response to environmental change but movement knowledge is often highly uncertain. We now have well-established methods to inte-grate movement knowledge into conservation practice but still lack a framework to deal with uncertainty in movement knowledge for environmental decisions. We provide a framework that distinguishes two dimensions of species’ movement that are heavily influenced by uncertainty: knowledge about movement and relevance of movement to environmental decisions. Management decisions can be informed by their position in this knowledge-relevance space. We then outline a framework to support decisions around (1) increasing understanding of the relevance of movement knowledge, (2) increasing robustness of decisions to uncertainties and (3) improving knowledge on species’ movement. Our decision-support framework provides guid-ance for managing movement-related uncertainty in systematic conservation plan-ning, agri-environment schemes, habitat restoration and international biodiversity policy. It caters to different resource levels (time and funding) so that species’ movement knowledge can be more effectively integrated into environmental decisions. © 2018 The Authors. Conservation Letters published by Wiley Periodicals, Inc.
- Authors: Smith, Annabel , Kujala, Heini , Lahoz-Monfort, José , Guja, Lydia , Barton, Philip
- Date: 2019
- Type: Text , Journal article
- Relation: Conservation Letters Vol. 12, no. 3 (2019), p.
- Full Text:
- Reviewed:
- Description: Species’ movements affect their response to environmental change but movement knowledge is often highly uncertain. We now have well-established methods to inte-grate movement knowledge into conservation practice but still lack a framework to deal with uncertainty in movement knowledge for environmental decisions. We provide a framework that distinguishes two dimensions of species’ movement that are heavily influenced by uncertainty: knowledge about movement and relevance of movement to environmental decisions. Management decisions can be informed by their position in this knowledge-relevance space. We then outline a framework to support decisions around (1) increasing understanding of the relevance of movement knowledge, (2) increasing robustness of decisions to uncertainties and (3) improving knowledge on species’ movement. Our decision-support framework provides guid-ance for managing movement-related uncertainty in systematic conservation plan-ning, agri-environment schemes, habitat restoration and international biodiversity policy. It caters to different resource levels (time and funding) so that species’ movement knowledge can be more effectively integrated into environmental decisions. © 2018 The Authors. Conservation Letters published by Wiley Periodicals, Inc.
An evidence theoretic approach for traffic signal intrusion detection
- Chowdhury, Abdullahi, Karmakar, Gour, Kamruzzaman, Joarder, Das, Rajkumar, Newaz, Shah
- Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Das, Rajkumar , Newaz, Shah
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 10 (2023), p. 4646
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- Description: The increasing attacks on traffic signals worldwide indicate the importance of intrusion detection. The existing traffic signal Intrusion Detection Systems (IDSs) that rely on inputs from connected vehicles and image analysis techniques can only detect intrusions created by spoofed vehicles. However, these approaches fail to detect intrusion from attacks on in-road sensors, traffic controllers, and signals. In this paper, we proposed an IDS based on detecting anomalies associated with flow rate, phase time, and vehicle speed, which is a significant extension of our previous work using additional traffic parameters and statistical tools. We theoretically modelled our system using the Dempster-Shafer decision theory, considering the instantaneous observations of traffic parameters and their relevant historical normal traffic data. We also used Shannon's entropy to determine the uncertainty associated with the observations. To validate our work, we developed a simulation model based on the traffic simulator called SUMO using many real scenarios and the data recorded by the Victorian Transportation Authority, Australia. The scenarios for abnormal traffic conditions were generated considering attacks such as jamming, Sybil, and false data injection attacks. The results show that the overall detection accuracy of our proposed system is 79.3% with fewer false alarms.
- Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Das, Rajkumar , Newaz, Shah
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 10 (2023), p. 4646
- Full Text:
- Reviewed:
- Description: The increasing attacks on traffic signals worldwide indicate the importance of intrusion detection. The existing traffic signal Intrusion Detection Systems (IDSs) that rely on inputs from connected vehicles and image analysis techniques can only detect intrusions created by spoofed vehicles. However, these approaches fail to detect intrusion from attacks on in-road sensors, traffic controllers, and signals. In this paper, we proposed an IDS based on detecting anomalies associated with flow rate, phase time, and vehicle speed, which is a significant extension of our previous work using additional traffic parameters and statistical tools. We theoretically modelled our system using the Dempster-Shafer decision theory, considering the instantaneous observations of traffic parameters and their relevant historical normal traffic data. We also used Shannon's entropy to determine the uncertainty associated with the observations. To validate our work, we developed a simulation model based on the traffic simulator called SUMO using many real scenarios and the data recorded by the Victorian Transportation Authority, Australia. The scenarios for abnormal traffic conditions were generated considering attacks such as jamming, Sybil, and false data injection attacks. The results show that the overall detection accuracy of our proposed system is 79.3% with fewer false alarms.
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