Towards a Framework for Acquisition and Analysis of Speeches to Identify Suspicious Contents through Machine Learning
المؤلفون المشاركون
Rahman, Md. Rashadur
Arefin, Mohammad Shamsul
Husayn, Muhammad Bilal
Habib, Mohammad Ashfak
Kayes, A. S. M.
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-11-16
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
The most prominent form of human communication and interaction is speech.
It plays an indispensable role for expressing emotions, motivating, guiding, and cheering.
An ill-intentioned speech can mislead people, societies, and even a nation.
A misguided speech can trigger social controversy and can result in violent activities.
Every day, there are a lot of speeches being delivered around the world, which are quite impractical to inspect manually.
In order to prevent any vicious action resulting from any misguided speech, the development of an automatic system that can efficiently detect suspicious speech has become imperative.
In this study, we have presented a framework for acquisition of speech along with the location of the speaker, converting the speeches into texts and, finally, we have proposed a system based on long short-term memory (LSTM) which is a variant of recurrent neural network (RNN) to classify speeches into suspicious and nonsuspicious.
We have considered speeches of Bangla language and developed our own dataset that contains about 5000 suspicious and nonsuspicious samples for training and validating our model.
A comparative analysis of accuracy among other machine learning algorithms such as logistic regression, SVM, KNN, Naive Bayes, and decision tree is performed in order to evaluate the effectiveness of the system.
The experimental results show that our proposed deep learning-based model provides the highest accuracy compared to other algorithms.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Rahman, Md. Rashadur& Arefin, Mohammad Shamsul& Husayn, Muhammad Bilal& Habib, Mohammad Ashfak& Kayes, A. S. M.. 2020. Towards a Framework for Acquisition and Analysis of Speeches to Identify Suspicious Contents through Machine Learning. Complexity،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1142495
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Rahman, Md. Rashadur…[et al.]. Towards a Framework for Acquisition and Analysis of Speeches to Identify Suspicious Contents through Machine Learning. Complexity No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1142495
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Rahman, Md. Rashadur& Arefin, Mohammad Shamsul& Husayn, Muhammad Bilal& Habib, Mohammad Ashfak& Kayes, A. S. M.. Towards a Framework for Acquisition and Analysis of Speeches to Identify Suspicious Contents through Machine Learning. Complexity. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1142495
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1142495
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر