Machine Learning
Within the Natural Language Processing (NLP) framework, Named Entity Recognition (NER) is regarded as the basis for extracting key information to understand texts in any language. As Bangla is a highly inflectional, morphologically rich, and resource-scarce language, building a balanced NER corpus with large and diverse entities is a demanding task. However, previously developed Bangla NER systems are limited to recognizing only three familiar entities: person, location, and organization.
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Pelagic fish such as mackerel are a source of protein in Indonesia. However, there is no decapterus macarellus as an open dataset for image processing using various classification algorithms. Where its use includes the sensor-assisted sorting process in checking fresh fish and rotten fish. For this reason, this study aims to provide a classification model for pelagic fish and their primary datasets which is available for free on the IEEE data port. Artificial intelligence is used in the process of guided classification with the help of ground truth for the preparation of fish classes.
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Pelagic fish such as mackerel are a source of protein in Indonesia. However, there is no decapterus macarellus as an open dataset for image processing using various classification algorithms. Where its use includes the sensor-assisted sorting process in checking fresh fish and rotten fish. For this reason, this study aims to provide a classification model for pelagic fish and their primary datasets which is available for free on the IEEE data port. Artificial intelligence is used in the process of guided classification with the help of ground truth for the preparation of fish classes.
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Citation contexts of a reference have many extensive applications in the area of computational linguistics and information retrieval. To use the citation contexts, it is common practice to employ the complete sentences in which the reference appears or the text in fixed window around the reference. Most of the time, the whole citation context does not particulary speaks about the target reference but only there is a sentence fragment which is the cause of making a reference.
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This is a fictional data set, provided by the IBM. These data set contains atmost 30 features of categorical and discreet data. These data are kind of both numerical and text values which help in analysing the employee data from hiring to firing and on boarding to attrition.
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The main objective of this project is to design and develop a collaborative framework which facilitates real-time tracking of a target person even when GPS signal is not available, while collecting motion data to infer his or her lifestyle and health status. The framework orchestrates a wide range of technologies such as localization technologies, machine learning and AI, sensor data analytics and cloud computing. The overall framework design also takes into consideration the culture, lifestyles, behaviours and infrastructures of ASEAN countries.
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The dataset analyzed in this study is the result of a systematic literature review and a crowdsourced mini-project that aimed to identify and validate metrics relevant to maternal and neonatal healthcare examinations. The study involved a diverse group of participants, including 193 registered medical personnel from reputable institutions and 161 non-medical individuals who were active on various social media platforms related to maternal and neonatal healthcare.
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Supplementary material for article "A Group Decision-Making Method Based on the Experts’ Behaviour During the Debate". Two files containing the comments provided by four expert during a debate to select the best product.
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This dataset is made of the Channel Impulse Response (CIR) data collected in 9 different environments in Ghent city, Belgium. These environments include:
1. Fourth floor at iGent Tower in the premises of Gent University
2. Zwijnaarde Open Area
3. Stadhuis Street and Nearby
4. Zuid Mall
5. Portus Ganda
6. Sint-Pieters Railway Station
7. Krook library
8. Citadel Park
9. Graffiti Straat
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We build a large-scale dataset for term name generation, which contains the GO terms about Homo sapiens (humankind and yeast). We collect the term ID, term name and the corresponding genes’ ID from \href{http://geneontology.org/}{Gene Ontology Consortium}.
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