The expeditiously growing World Wide Web (WWW) consists of a large number of data sources from di... more The expeditiously growing World Wide Web (WWW) consists of a large number of data sources from diverse organizations all over the world. The heterogeneous nature of these data poses challenges to researchers to extract specific information. In this regard, the area of finding answers of a specific question from available web contents is an emerging area of research. Questions are normally expressed in natural language and for finding answers to natural language questions from web contents; Question Answering (QA) is the most promising framework, which can be implemented on either closed domain or open domain. In this paper, we propose an automated QA system which can answer binary and wh-interrogated questions about closed domain using wikipedia articles as its knowledge source. The system allows us to generate questions from wikipedia pages and then to extract answers to questions from wikipedia pages in real time.
2019 International Conference on Sustainable Technologies for Industry 4.0 (STI), 2019
The expeditiously growing World Wide Web (WWW) consists of a large number of data sources from di... more The expeditiously growing World Wide Web (WWW) consists of a large number of data sources from diverse organizations all over the world. The heterogeneous nature of these data poses challenges to researchers to extract specific information. In this regard, the area of finding answers of a specific question from available web contents is an emerging area of research. Questions are normally expressed in natural language and for finding answers to natural language questions from web contents; Question Answering (QA) is the most promising framework, which can be implemented on either closed domain or open domain. In this paper, we propose an automated QA system which can answer binary and wh-interrogated questions about closed domain using wikipedia articles as its knowledge source. The system allows us to generate questions from wikipedia pages and then to extract answers to questions from wikipedia pages in real time.
Scene classification is an important and elementary problem in image understanding. It deals with... more Scene classification is an important and elementary problem in image understanding. It deals with large number of scenes in order to discover the common structure shared by all the scenes in a class. It is used in medical science (X-Ray, ECG and Endoscopy etc), criminal detection, gender classification, skin classification, facial image classification, generating weather information from satellite image; identify vegetation types, anthropogenic structures, mineral resources, or transient changes in any of these properties. In this paper, at first we propose a feature extraction method named LHOG or Localized HOG. We consider that an image contains some important region which helps to find similarity with same class of images. We generate local information from an image via our proposed LHOG method. Then by combing all the local information we generate the global descriptor using Bag of Feature (BoF) method which is finally used to represent and classify an image accurately and efficiently. In classification purpose, we use Support Vector Machine (SVM) that analyze data and recognize patterns. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output. In our paper, we use six different classes of images.
The expeditiously growing World Wide Web (WWW) consists of a large number of data sources from di... more The expeditiously growing World Wide Web (WWW) consists of a large number of data sources from diverse organizations all over the world. The heterogeneous nature of these data poses challenges to researchers to extract specific information. In this regard, the area of finding answers of a specific question from available web contents is an emerging area of research. Questions are normally expressed in natural language and for finding answers to natural language questions from web contents; Question Answering (QA) is the most promising framework, which can be implemented on either closed domain or open domain. In this paper, we propose an automated QA system which can answer binary and wh-interrogated questions about closed domain using wikipedia articles as its knowledge source. The system allows us to generate questions from wikipedia pages and then to extract answers to questions from wikipedia pages in real time.
2019 International Conference on Sustainable Technologies for Industry 4.0 (STI), 2019
The expeditiously growing World Wide Web (WWW) consists of a large number of data sources from di... more The expeditiously growing World Wide Web (WWW) consists of a large number of data sources from diverse organizations all over the world. The heterogeneous nature of these data poses challenges to researchers to extract specific information. In this regard, the area of finding answers of a specific question from available web contents is an emerging area of research. Questions are normally expressed in natural language and for finding answers to natural language questions from web contents; Question Answering (QA) is the most promising framework, which can be implemented on either closed domain or open domain. In this paper, we propose an automated QA system which can answer binary and wh-interrogated questions about closed domain using wikipedia articles as its knowledge source. The system allows us to generate questions from wikipedia pages and then to extract answers to questions from wikipedia pages in real time.
Scene classification is an important and elementary problem in image understanding. It deals with... more Scene classification is an important and elementary problem in image understanding. It deals with large number of scenes in order to discover the common structure shared by all the scenes in a class. It is used in medical science (X-Ray, ECG and Endoscopy etc), criminal detection, gender classification, skin classification, facial image classification, generating weather information from satellite image; identify vegetation types, anthropogenic structures, mineral resources, or transient changes in any of these properties. In this paper, at first we propose a feature extraction method named LHOG or Localized HOG. We consider that an image contains some important region which helps to find similarity with same class of images. We generate local information from an image via our proposed LHOG method. Then by combing all the local information we generate the global descriptor using Bag of Feature (BoF) method which is finally used to represent and classify an image accurately and efficiently. In classification purpose, we use Support Vector Machine (SVM) that analyze data and recognize patterns. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output. In our paper, we use six different classes of images.
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Papers by Md. Faisal Bin Abdul Aziz