The Experts below are selected from a list of 103908 Experts worldwide ranked by ideXlab platform
Jaak Vilo - One of the best experts on this subject based on the ideXlab platform.
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gene expression Data analysis
Microbes and Infection, 2001Co-Authors: Alvis Brazma, Jaak ViloAbstract:Microarrays are one of the latest breakthroughs in experimental molecular biology, which allow monitoring of gene expression for tens of thousands of genes in parallel and are already producing huge amounts of valuable Data. Analysis and handling of such Data is becoming one of the major bottlenecks in the utilization of the technology. The raw microarray Data are images, which have to be transformed into gene expression matrices, tables where rows represent genes, columns represent various samples such as tissues or experimental conditions, and numbers in each cell characterize the expression level of the particular gene in the particular sample. These matrices have to be analyzed further if any knowledge about the underlying biological processes is to be extracted. In this paper we concentrate on discussing bioinformatics methods used for such analysis. We briefly discuss supervised and Unsupervised Data analysis and its applications, such as predicting gene function classes and cancer classification as well as some possible future directions.
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gene expression Data analysis
FEBS Letters, 2000Co-Authors: Alvis Brazma, Jaak ViloAbstract:Microarrays are one of the latest breakthroughs in experimental molecular biology, which allow monitoring of gene expression for tens of thousands of genes in parallel and are already producing huge amounts of valuable Data. Analysis and handling of such Data is becoming one of the major bottlenecks in the utilization of the technology. The raw microarray Data are images, which have to be transformed into gene expression matrices – tables where rows represent genes, columns represent various samples such as tissues or experimental conditions, and numbers in each cell characterize the expression level of the particular gene in the particular sample. These matrices have to be analyzed further, if any knowledge about the underlying biological processes is to be extracted. In this paper we concentrate on discussing bioinformatics methods used for such analysis. We briefly discuss supervised and Unsupervised Data analysis and its applications, such as predicting gene function classes and cancer classification. Then we discuss how the gene expression matrix can be used to predict putative regulatory signals in the genome sequences. In conclusion we discuss some possible future directions.
Victor Callaghan - One of the best experts on this subject based on the ideXlab platform.
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A Fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments
IEEE Transactions on Systems Man and Cybernetics Part A:Systems and Humans., 2005Co-Authors: Faiyaz Doctor, Hani Hagras, Victor CallaghanAbstract:We describe a novel life-long learning approach for intelligent agents that are embedded in intelligent environments. The agents aim to realize the vision of ambient intelligence in intelligent inhabited environments (IIE) by providing ubiquitous computing intelligence in the environment supporting the activities of the user. An Unsupervised, Data-driven, fuzzy technique is proposed for extracting fuzzy membership functions and rules that represent the user's particularized behaviors in the environment. The user's learned behaviors can then be adapted online in a life-long mode to satisfy the different user and system objectives. We have performed unique experiments in which the intelligent agent has learned and adapted to the user's behavior, during a stay of five consecutive days in the intelligent dormitory (iDorm), which is a real ubiquitous computing environment test bed. Both offline and online experimental results are presented comparing the performance of our technique with other approaches. The results show that our proposed system has outperformed the other approaches, while operating online in a life-long mode to realize the ambient intelligence vision.
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an intelligent fuzzy agent approach for realising ambient intelligence in intelligent inhabited environments
2005Co-Authors: Faiyaz Doctor, Hani Hagras, Victor CallaghanAbstract:In this paper we describe a novel life long learning approach for intelligent agents that are embedded in intelligent environments. The agents aim to realise the vision of Ambient Intelligence in Intelligent Inhabited Environments (IIE) by providing 'ubiquitous computing intelligence in the environment supporting the activities of the user. An Unsupervised, Data-driven, fuzzy, technique is proposed for extracting fuzzy membership functions and rules that represent the user's particularised behaviours in the environment. The user's learnt behaviours can then be adapted online in a life long mode to satisfy the different user and system objectives. We have performed unique experiments in which the intelligent agent has learnt and adapted to the user's behaviour, during a stay of five consecutive days in the intelligent Dormitory (iDorm) which is a real ubiquitous computing environment test bed. Both offline and online experimental results are presented comparing the performance of our technique with other approaches. The results show that our proposed system has outperformed the other systems while operating online in a life long mode to realise the ambient intelligence vision.
Alvis Brazma - One of the best experts on this subject based on the ideXlab platform.
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gene expression Data analysis
Microbes and Infection, 2001Co-Authors: Alvis Brazma, Jaak ViloAbstract:Microarrays are one of the latest breakthroughs in experimental molecular biology, which allow monitoring of gene expression for tens of thousands of genes in parallel and are already producing huge amounts of valuable Data. Analysis and handling of such Data is becoming one of the major bottlenecks in the utilization of the technology. The raw microarray Data are images, which have to be transformed into gene expression matrices, tables where rows represent genes, columns represent various samples such as tissues or experimental conditions, and numbers in each cell characterize the expression level of the particular gene in the particular sample. These matrices have to be analyzed further if any knowledge about the underlying biological processes is to be extracted. In this paper we concentrate on discussing bioinformatics methods used for such analysis. We briefly discuss supervised and Unsupervised Data analysis and its applications, such as predicting gene function classes and cancer classification as well as some possible future directions.
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gene expression Data analysis
FEBS Letters, 2000Co-Authors: Alvis Brazma, Jaak ViloAbstract:Microarrays are one of the latest breakthroughs in experimental molecular biology, which allow monitoring of gene expression for tens of thousands of genes in parallel and are already producing huge amounts of valuable Data. Analysis and handling of such Data is becoming one of the major bottlenecks in the utilization of the technology. The raw microarray Data are images, which have to be transformed into gene expression matrices – tables where rows represent genes, columns represent various samples such as tissues or experimental conditions, and numbers in each cell characterize the expression level of the particular gene in the particular sample. These matrices have to be analyzed further, if any knowledge about the underlying biological processes is to be extracted. In this paper we concentrate on discussing bioinformatics methods used for such analysis. We briefly discuss supervised and Unsupervised Data analysis and its applications, such as predicting gene function classes and cancer classification. Then we discuss how the gene expression matrix can be used to predict putative regulatory signals in the genome sequences. In conclusion we discuss some possible future directions.
Raphael Canals - One of the best experts on this subject based on the ideXlab platform.
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deep learning with Unsupervised Data labeling for weed detection in line crops in uav images
Remote Sensing, 2018Co-Authors: Mamadou Dian Bah, Adel Hafiane, Raphael CanalsAbstract:In recent years, weeds have been responsible for most agricultural yield losses. To deal with this threat, farmers resort to spraying the fields uniformly with herbicides. This method not only requires huge quantities of herbicides but impacts the environment and human health. One way to reduce the cost and environmental impact is to allocate the right doses of herbicide to the right place and at the right time (precision agriculture). Nowadays, unmanned aerial vehicles (UAVs) are becoming an interesting acquisition system for weed localization and management due to their ability to obtain images of the entire agricultural field with a very high spatial resolution and at a low cost. However, despite significant advances in UAV acquisition systems, the automatic detection of weeds remains a challenging problem because of their strong similarity to the crops. Recently, a deep learning approach has shown impressive results in different complex classification problems. However, this approach needs a certain amount of training Data, and creating large agricultural Datasets with pixel-level annotations by an expert is an extremely time-consuming task. In this paper, we propose a novel fully automatic learning method using convolutional neuronal networks (CNNs) with an Unsupervised training Dataset collection for weed detection from UAV images. The proposed method comprises three main phases. First, we automatically detect the crop rows and use them to identify the inter-row weeds. In the second phase, inter-row weeds are used to constitute the training Dataset. Finally, we perform CNNs on this Dataset to build a model able to detect the crop and the weeds in the images. The results obtained are comparable to those of traditional supervised training Data labeling, with differences in accuracy of 1.5% in the spinach field and 6% in the bean field.
Faiyaz Doctor - One of the best experts on this subject based on the ideXlab platform.
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A Fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments
IEEE Transactions on Systems Man and Cybernetics Part A:Systems and Humans., 2005Co-Authors: Faiyaz Doctor, Hani Hagras, Victor CallaghanAbstract:We describe a novel life-long learning approach for intelligent agents that are embedded in intelligent environments. The agents aim to realize the vision of ambient intelligence in intelligent inhabited environments (IIE) by providing ubiquitous computing intelligence in the environment supporting the activities of the user. An Unsupervised, Data-driven, fuzzy technique is proposed for extracting fuzzy membership functions and rules that represent the user's particularized behaviors in the environment. The user's learned behaviors can then be adapted online in a life-long mode to satisfy the different user and system objectives. We have performed unique experiments in which the intelligent agent has learned and adapted to the user's behavior, during a stay of five consecutive days in the intelligent dormitory (iDorm), which is a real ubiquitous computing environment test bed. Both offline and online experimental results are presented comparing the performance of our technique with other approaches. The results show that our proposed system has outperformed the other approaches, while operating online in a life-long mode to realize the ambient intelligence vision.
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an intelligent fuzzy agent approach for realising ambient intelligence in intelligent inhabited environments
2005Co-Authors: Faiyaz Doctor, Hani Hagras, Victor CallaghanAbstract:In this paper we describe a novel life long learning approach for intelligent agents that are embedded in intelligent environments. The agents aim to realise the vision of Ambient Intelligence in Intelligent Inhabited Environments (IIE) by providing 'ubiquitous computing intelligence in the environment supporting the activities of the user. An Unsupervised, Data-driven, fuzzy, technique is proposed for extracting fuzzy membership functions and rules that represent the user's particularised behaviours in the environment. The user's learnt behaviours can then be adapted online in a life long mode to satisfy the different user and system objectives. We have performed unique experiments in which the intelligent agent has learnt and adapted to the user's behaviour, during a stay of five consecutive days in the intelligent Dormitory (iDorm) which is a real ubiquitous computing environment test bed. Both offline and online experimental results are presented comparing the performance of our technique with other approaches. The results show that our proposed system has outperformed the other systems while operating online in a life long mode to realise the ambient intelligence vision.
