International journal of biomedical soft computing and human sciences, 2009
Minko?sia **2ueen AdZirly vasion Laborator:B Department ofCZ)mputer Science, eueen imr:)t tiniven... more Minko?sia **2ueen AdZirly vasion Laborator:B Department ofCZ)mputer Science, eueen imr:)t tinivensity ofLondbn, England "*"Centre foF Multimodal Signal Processing, nmOS Berhad, Malaysia (The paper was received on Dec. 30, 2007.
Pap smear screening is the most successful attempt of medical science and practice for the early ... more Pap smear screening is the most successful attempt of medical science and practice for the early detection of cervical cancer. Manual analysis of the cervical cells is time consuming, laborious and error prone. This paper presents a Neural Network (NN) based system for classifying cervical cells as normal, low-grade squamous intra-epithelial lesion (LSIL) and high-grade squamous intra-epithelial lesion (HSIL). The system consists of three stages. In the first stage, cervical cells are segmented using the Adaptive Fuzzy Moving K-means (AFMKM) clustering algorithm. In the second stage, the feature extraction process is performed. In the third stage, the extracted data is classified using Fuzzy Min-Max (FMM) NN. The empirical results show that the proposed method can achieve acceptable results.
In this paper, the Fuzzy Min-Max (FMM) neural network along with two modified FMM models are used... more In this paper, the Fuzzy Min-Max (FMM) neural network along with two modified FMM models are used for tackling medical diagnostic problems. The original FMM network establishes hyperboxes with fuzzy sets in its structure for classifying input patterns into different output categories. While the first modified FMM model uses the membership function and the Euclidian distance to classify the input patterns, the second modified FMM model employs only the Euclidian distance for the same process. Unlike the original FMM network, the two modified FMM models undergo a pruning process, after network training, to remove hyperboxes with low confidence factors. To assess the effectiveness of the three FMM networks in medical diagnosis, a set of real medical records from suspected Acute Coronary Syndrome (ACS) patients is collected and used for experimentation. The bootstrap method is used to analyze the results statistically. Implications of the experimental outcomes are discussed, and the potential of using the FMM networks a decision support tool for medical prognostic and diagnostic problems is demonstrated.
The Fuzzy Min-Max (FMM) network is a supervised neural network classifier that forms hyperbox fuz... more The Fuzzy Min-Max (FMM) network is a supervised neural network classifier that forms hyperbox fuzzy sets for learning and classification. In this paper, we propose modifications to FMM in an attempt to improve its classification performance in situations when large hyperboxes are formed by the network. To achieve the goal, the Euclidean distance is computed after network training. We also propose to employ both the membership value of the hyperbox fuzzy sets and the Euclidean distance for classification. To assess the effectiveness of the modified FMM network, benchmark pattern classification problems are first used, and the results from different methods are compared. In addition, a fault classification problem with real sensor measurements collected from a power generation plant is used to evaluate the applicability of the modified FMM network. The results obtained are analyzed and explained, and implications of the modified FMM network in real environments are discussed.
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2013
In this paper, we propose a Multi-Agent Classifier (MAC) system based on the Trust, Negotiation, ... more In this paper, we propose a Multi-Agent Classifier (MAC) system based on the Trust, Negotiation, and Communication (TNC) model. A novel trust measurement method, based on the recognition and rejection rates, is proposed. Two agent teams, each consists of three neural network (NN) agents, are formed. The first is the Fuzzy Min-Max (FMM) NN agent team and the second is the Fuzzy ARTMAP (FAM) NN agent team. An auctioning method is also used for the negotiation phase. The effectiveness of the proposed model and the bond (based on trust) is measured using two benchmark classification problems. The bootstrap method is applied to quantify the classification accuracy rates statistically. The results demonstrate that the MAC system is able to improve the performances of individual agents as well as the team agents. The results also compare favorably with those from other methods published in the literature.
Background: Tracking objects in a sequence of images is one of the challenging problems in comput... more Background: Tracking objects in a sequence of images is one of the challenging problems in computer vision today. The applications of tracking objects can be seen in different fields such as surveillance, traffic control and security sectors. Objective: In this paper, two different techniques are used to track colored objects. The first technique is based on moving object region, this method identifies and tracks a blob token or a bounding box, which is calculated for connected components of moving objects in 2D space. It's relies on properties of these blobs such as size, color, and centroid. The other technique is Kalman filters, introduced in the early 1960's by Rudulf Emil Kalman. In this paper a system in Matlab that is able to track and estimate the position of colored moving ball from the background in a video sequence based on background subtraction analysis in a sequence of frames with different resolution is proposed. Results: The presented system was tested using different background noises and different colored balls. The system was successful in the tracking and estimating process. Conclusion: The obtained results show that the proposed system was able to successfully track colored objects using the Kalman filter technique
2011 IEEE International Electric Machines & Drives Conference (IEMDC), 2011
... [4] Sri R. Kolla and Shawn D. Altman, Artificial neural network based fault identification s... more ... [4] Sri R. Kolla and Shawn D. Altman, Artificial neural network based fault identification schemeimplementation for a ... Felho, Wesley F. Usida, Adriano AFM Carneiro and Leandro RSPires, Power quality analysis applying a hybrid methodology with wavelet transforms and ...
In this paper, we propose a neural network (NN)-based multi-agent classifier system (MACS) using ... more In this paper, we propose a neural network (NN)-based multi-agent classifier system (MACS) using the trust, negotiation, and communication (TNC) reasoning model. The main contribution of this work is that a novel trust measurement method, based on the recognition and rejection rates, is proposed. Besides, an auctioning procedure, based on the sealed bid, first price method, is adapted for the negotiation phase. Two agent teams are formed; each consists of three NN learning agents. The first is a fuzzy min-max (FMM) NN agent team and the second is a fuzzy ARTMAP (FAM) NN agent team. Modifications to the FMM and FAM models are also proposed so that they can be used for trust measurement in the TNC model. To assess the effectiveness of the proposed model and the bond (based on trust), five benchmark data sets are tested. The results compare favorably with those from a number of classification methods published in the literature.
In this paper, a neural network (NN)-based multi-agent classifier system (MACS) utilising the tru... more In this paper, a neural network (NN)-based multi-agent classifier system (MACS) utilising the trust-negotiation-communication (TNC) reasoning model is proposed. A novel trust measurement method, based on the combination of Bayesian belief functions, is incorporated into the TNC model. The Fuzzy Min-Max (FMM) NN is used as learning agents in the MACS, and useful modifications of FMM are proposed so that it can be adopted for trust measurement. Besides, an auctioning procedure, based on the sealed bid method, is applied for the negotiation phase of the TNC model. Two benchmark data sets are used to evaluate the effectiveness of the proposed MACS. The results obtained compare favourably with those from a number of machine learning methods. The applicability of the proposed MACS to two industrial sensor data fusion and classification tasks is also demonstrated , with the implications analysed and discussed. Keywords Multi-agent classifier systems Á Neural networks Á Trust measurement Á Bayesian belief function Á Fuzzy Min-Max neural network
International journal of biomedical soft computing and human sciences, 2009
Minko?sia **2ueen AdZirly vasion Laborator:B Department ofCZ)mputer Science, eueen imr:)t tiniven... more Minko?sia **2ueen AdZirly vasion Laborator:B Department ofCZ)mputer Science, eueen imr:)t tinivensity ofLondbn, England "*"Centre foF Multimodal Signal Processing, nmOS Berhad, Malaysia (The paper was received on Dec. 30, 2007.
Pap smear screening is the most successful attempt of medical science and practice for the early ... more Pap smear screening is the most successful attempt of medical science and practice for the early detection of cervical cancer. Manual analysis of the cervical cells is time consuming, laborious and error prone. This paper presents a Neural Network (NN) based system for classifying cervical cells as normal, low-grade squamous intra-epithelial lesion (LSIL) and high-grade squamous intra-epithelial lesion (HSIL). The system consists of three stages. In the first stage, cervical cells are segmented using the Adaptive Fuzzy Moving K-means (AFMKM) clustering algorithm. In the second stage, the feature extraction process is performed. In the third stage, the extracted data is classified using Fuzzy Min-Max (FMM) NN. The empirical results show that the proposed method can achieve acceptable results.
In this paper, the Fuzzy Min-Max (FMM) neural network along with two modified FMM models are used... more In this paper, the Fuzzy Min-Max (FMM) neural network along with two modified FMM models are used for tackling medical diagnostic problems. The original FMM network establishes hyperboxes with fuzzy sets in its structure for classifying input patterns into different output categories. While the first modified FMM model uses the membership function and the Euclidian distance to classify the input patterns, the second modified FMM model employs only the Euclidian distance for the same process. Unlike the original FMM network, the two modified FMM models undergo a pruning process, after network training, to remove hyperboxes with low confidence factors. To assess the effectiveness of the three FMM networks in medical diagnosis, a set of real medical records from suspected Acute Coronary Syndrome (ACS) patients is collected and used for experimentation. The bootstrap method is used to analyze the results statistically. Implications of the experimental outcomes are discussed, and the potential of using the FMM networks a decision support tool for medical prognostic and diagnostic problems is demonstrated.
The Fuzzy Min-Max (FMM) network is a supervised neural network classifier that forms hyperbox fuz... more The Fuzzy Min-Max (FMM) network is a supervised neural network classifier that forms hyperbox fuzzy sets for learning and classification. In this paper, we propose modifications to FMM in an attempt to improve its classification performance in situations when large hyperboxes are formed by the network. To achieve the goal, the Euclidean distance is computed after network training. We also propose to employ both the membership value of the hyperbox fuzzy sets and the Euclidean distance for classification. To assess the effectiveness of the modified FMM network, benchmark pattern classification problems are first used, and the results from different methods are compared. In addition, a fault classification problem with real sensor measurements collected from a power generation plant is used to evaluate the applicability of the modified FMM network. The results obtained are analyzed and explained, and implications of the modified FMM network in real environments are discussed.
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2013
In this paper, we propose a Multi-Agent Classifier (MAC) system based on the Trust, Negotiation, ... more In this paper, we propose a Multi-Agent Classifier (MAC) system based on the Trust, Negotiation, and Communication (TNC) model. A novel trust measurement method, based on the recognition and rejection rates, is proposed. Two agent teams, each consists of three neural network (NN) agents, are formed. The first is the Fuzzy Min-Max (FMM) NN agent team and the second is the Fuzzy ARTMAP (FAM) NN agent team. An auctioning method is also used for the negotiation phase. The effectiveness of the proposed model and the bond (based on trust) is measured using two benchmark classification problems. The bootstrap method is applied to quantify the classification accuracy rates statistically. The results demonstrate that the MAC system is able to improve the performances of individual agents as well as the team agents. The results also compare favorably with those from other methods published in the literature.
Background: Tracking objects in a sequence of images is one of the challenging problems in comput... more Background: Tracking objects in a sequence of images is one of the challenging problems in computer vision today. The applications of tracking objects can be seen in different fields such as surveillance, traffic control and security sectors. Objective: In this paper, two different techniques are used to track colored objects. The first technique is based on moving object region, this method identifies and tracks a blob token or a bounding box, which is calculated for connected components of moving objects in 2D space. It's relies on properties of these blobs such as size, color, and centroid. The other technique is Kalman filters, introduced in the early 1960's by Rudulf Emil Kalman. In this paper a system in Matlab that is able to track and estimate the position of colored moving ball from the background in a video sequence based on background subtraction analysis in a sequence of frames with different resolution is proposed. Results: The presented system was tested using different background noises and different colored balls. The system was successful in the tracking and estimating process. Conclusion: The obtained results show that the proposed system was able to successfully track colored objects using the Kalman filter technique
2011 IEEE International Electric Machines & Drives Conference (IEMDC), 2011
... [4] Sri R. Kolla and Shawn D. Altman, Artificial neural network based fault identification s... more ... [4] Sri R. Kolla and Shawn D. Altman, Artificial neural network based fault identification schemeimplementation for a ... Felho, Wesley F. Usida, Adriano AFM Carneiro and Leandro RSPires, Power quality analysis applying a hybrid methodology with wavelet transforms and ...
In this paper, we propose a neural network (NN)-based multi-agent classifier system (MACS) using ... more In this paper, we propose a neural network (NN)-based multi-agent classifier system (MACS) using the trust, negotiation, and communication (TNC) reasoning model. The main contribution of this work is that a novel trust measurement method, based on the recognition and rejection rates, is proposed. Besides, an auctioning procedure, based on the sealed bid, first price method, is adapted for the negotiation phase. Two agent teams are formed; each consists of three NN learning agents. The first is a fuzzy min-max (FMM) NN agent team and the second is a fuzzy ARTMAP (FAM) NN agent team. Modifications to the FMM and FAM models are also proposed so that they can be used for trust measurement in the TNC model. To assess the effectiveness of the proposed model and the bond (based on trust), five benchmark data sets are tested. The results compare favorably with those from a number of classification methods published in the literature.
In this paper, a neural network (NN)-based multi-agent classifier system (MACS) utilising the tru... more In this paper, a neural network (NN)-based multi-agent classifier system (MACS) utilising the trust-negotiation-communication (TNC) reasoning model is proposed. A novel trust measurement method, based on the combination of Bayesian belief functions, is incorporated into the TNC model. The Fuzzy Min-Max (FMM) NN is used as learning agents in the MACS, and useful modifications of FMM are proposed so that it can be adopted for trust measurement. Besides, an auctioning procedure, based on the sealed bid method, is applied for the negotiation phase of the TNC model. Two benchmark data sets are used to evaluate the effectiveness of the proposed MACS. The results obtained compare favourably with those from a number of machine learning methods. The applicability of the proposed MACS to two industrial sensor data fusion and classification tasks is also demonstrated , with the implications analysed and discussed. Keywords Multi-agent classifier systems Á Neural networks Á Trust measurement Á Bayesian belief function Á Fuzzy Min-Max neural network
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