A Comprehensive Comparison of Machine Learning Based Methods Used in Bengali Question Classification

Anika, Afra; Rahman, Md. Moklesur; Islam, Salekul; Jameel, Abu Shafin Mohammad Mahdee; Rahman, Chowdhury Rafeed

doi:10.1109/spicscon48833.2019.9065107

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…A mixed algorithm combining word2vec and tf-idf is suggested for question categorization in a QAS. A study evaluates various ML-based techniques for question classification in Bengali, with potential applications in intelligent Bengali QA systems [48][49][50]. Studies investigate DL methods (RNN, LSTM, CNN) for identifying similar questions on Stack Overflow [51].…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Questions from Text Data Using LSTM-Deep Learning Model

Nadir Hussain,

Dr. Sheikh Muhammad Saqib,

Hamza Arif

et al. 2024

VAWKUM trans. comput. sci.

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This paper discusses the importance of detecting questions in textual data for various applications in natural language processing (NLP), such as question answering and chatbot creation. The proposed approach employs long short-term memory (LSTM) models to accurately identify questions by leveraging the sequential nature of language.The paper highlights that LSTM models address challenges like ambiguous language and varying sentence structures. They allow the model to learn from sequential patterns, crucial for understanding the intent behind the text. The preprocessing steps, including tokenization, embedding, and padding, are detailed to prepare the data for training and testing. The study investigates the impact of hyperparameters like hidden layers, hidden states, and optimizer choice on the LSTM algorithm’s performance. In experiments on benchmark datasets, the proposed LSTM-based approach consistently outperforms conventional machine learning models, achieving a remarkable accuracy of 99.25% on the test dataset. The paper concludes by suggesting future directions, including applyingthe approach to other NLP tasks like named entity recognition, sentiment analysis, and text classification. Further optimization for specific datasets or domains is also encouraged. Overall, this research contributes to robust question detection models in NLP, with potential applications in various fields.

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Section: Literature Reviewmentioning

confidence: 99%

Detection of Questions from Text Data Using LSTM-Deep Learning Model

Nadir Hussain,

Dr. Sheikh Muhammad Saqib,

Hamza Arif

et al. 2024

VAWKUM trans. comput. sci.

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“…Although Deep Learning is a subsection of Machine Learning the prediction accuracy and the selflearning properties of these algorithms are particularly useful to stay up with the current breakthroughs in IoT. The algorithms suggested here are thoroughly analyzed in Tables 22 to 29 to provide a comprehensive understanding of the jobs they accomplish, their time and space complexity, the training time needed, the computational difficulties [89,90], advantages, and disadvantages as well as classification accuracy.…”

Section: Performance Analysis Of ML and Dl Algorithms For Detecting A...mentioning

confidence: 99%

A Novel IoT Architecture, Assessment of Threats and Their Classification with Machine Learning Solutions

Debnath,

Karmakar

et al. 2023

Journal on Internet of Things

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The Internet of Things (IoT) will significantly impact our social and economic lives in the near future. Many Internet of Things (IoT) applications aim to automate multiple tasks so inactive physical objects can behave independently of others. IoT devices, however, are also vulnerable, mostly because they lack the essential built-in security to thwart attackers. It is essential to perform the necessary adjustments in the structure of the IoT systems in order to create an end-to-end secure IoT environment. As a result, the IoT designs that are now in use do not completely support all of the advancements that have been made to include sophisticated features in IoT, such as Cloud computing, machine learning techniques, and lightweight encryption techniques. This paper presents a detailed analysis of the security requirements, attack surfaces, and security solutions available for IoT networks and suggests an innovative IoT architecture. The Seven-Layer Architecture in IoT provides decent attack detection accuracy. According to the level of risk they pose, the security threats in each of these layers have been properly categorized, and the essential evaluation criteria have been developed to evaluate the various threats. Also, Machine Learning algorithms like Random Forest and Support Vector Machines, etc., and Deep Learning algorithms like Artificial Neural Networks, Q Learning models, etc., are implemented to overcome the most damaging threats posing security breaches to the different IoT architecture layers.

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A Hybrid Approach Towards Two Stage Bengali Question Classification Utilizing Smart Data Balancing Technique

Rahman

Amin

et al. 2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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A Comprehensive Comparison of Machine Learning Based Methods Used in Bengali Question Classification

Cited by 5 publications

References 16 publications

Detection of Questions from Text Data Using LSTM-Deep Learning Model

Detection of Questions from Text Data Using LSTM-Deep Learning Model

A Novel IoT Architecture, Assessment of Threats and Their Classification with Machine Learning Solutions

A Hybrid Approach Towards Two Stage Bengali Question Classification Utilizing Smart Data Balancing Technique

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