A Novel Approach to Categorize News Articles From Headlines and Short Text

Abyaad, Rafid; Kabir, Rayhan; Hasan, S. M. Shajedul

doi:10.1109/tensymp50017.2020.9230675

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…In recent years, deep learning-based models such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) have become more commonly used than classical machine learningbased approaches [30]. In the research presented in [9], the authors proposed a novel model to categorize news articles from news headlines and short text descriptions of the news.…”

Section: Text Classification Using Machine Learningmentioning

confidence: 99%

“…The authors used LSTM and Gated Recurrent Unit (GRU) as their proposed model's backbone. The authors stated that the accuracy of their proposed model showed an average performance in categorizing news articles, but this could be improved by applying other data optimization algorithms [9]. In the study presented in [10] [10].…”

Section: Text Classification Using Machine Learningmentioning

confidence: 99%

“…The performance metrics used to evaluate the performance of the Search are discussed next.=… … … … … … … … … . …[9]…”

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A Parallel Processing Technique for Filtering and Storing User Specified Data

Chanda¹

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Users are often interested in a specific type of data (user-preferred data) from a largevolume dataset. An efficient system that only stores user-preferred data from the large dataset can reduce the search latency, which allows the users to search for relevant information in a timely manner. The motivation behind this thesis is to devise a technique that filters a large dataset and stores only the filtered data, thereby saving storage space for the user. Running the filtering operation can be CPU-intensive, which can lead to high latency in extracting preferred data from the dataset. To solve this problem, the technique employs parallel processing and machine learning. A proof-of-concept prototype for this technique has been built on Apache Spark. The performance of the prototype subjected to synthetic datasets is analyzed. The analysis of experimental results shows the viability of this technique and provides insights into the system behavior and performance.

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Section: Text Classification Using Machine Learningmentioning

confidence: 99%

Section: Text Classification Using Machine Learningmentioning

confidence: 99%