KBS-CLASS: a neural network tool for automatic content recognition of building texts

In this study, the artificial neural network was deployed to develop a classification model for predicting the class of a drug-related suspect into either the drug peddler or non-drug peddler class. A dataset consisting of 262 observations on drug suspects and offenders in central Nigeria was used to train the model which uses parameters such as exhibit type, suspect’s age, exhibit weight, and suspect’s gender to predict the class of a suspect, with a predictive accuracy of 83%. The model sets the pace for the implementation of a full system for use at airports, seaports, police stations, and by security agents concerned with drug-related matters. The accurate classification of suspects and offenders will ensure a faster and correct reference to the sections of the drug law that correspond to a particular offence for appropriate actions such as prosecution or rehabilitation.

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Research on advertising content recognition based on convolutional neural network and recurrent neural network

International Journal of Computational Science and Engineering ◽

10.1504/ijcse.2021.117022 ◽

2021 ◽

Vol 24 (4) ◽

pp. 398

Author(s):

Xiaomei Liu ◽

Fazhi Qi

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Recurrent Neural Network ◽

Content Recognition ◽

Advertising Content

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Sequence to Sequence Model Performance for Education Chatbot

International Journal of Emerging Technologies in Learning (iJET) ◽

10.3991/ijet.v14i24.12187 ◽

2019 ◽

Vol 14 (24) ◽

pp. 56 ◽

Cited By ~ 4

Author(s):

Kulothunkan Palasundram ◽

Nurfadhlina Mohd Sharef ◽

Nurul Amelina Nasharuddin ◽

Khairul Azhar Kasmiran ◽

Azreen Azman

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Question Answering ◽

Model Performance ◽

Neural Machine Translation ◽

Class A ◽

Great Progress ◽

Artificial Intelli ◽

Education Administrators ◽

Generation Problem

Chatbot for education has great potential to complement human educators and education administrators. For example, it can be around the clock tutor to answer and clarify any questions from students who may have missed class. A chatbot can be implemented either by ruled based or artificial intel-ligence based. However, unlike the ruled-based chatbots, artificial intelli-gence based chatbots can learn and become smarter overtime and is more scalable and has become the popular choice for chatbot researchers recently. Recurrent Neural Network based Sequence-to-sequence (Seq2Seq) model is one of the most commonly researched model to implement artificial intelli-gence chatbot and has shown great progress since its introduction in 2014. However, it is still in infancy and has not been applied widely in educational chatbot development. Introduced originally for neural machine translation, the Seq2Seq model has been adapted for conversation modelling including question-answering chatbots. However, in-depth research and analysis of op-timal settings of the various components of Seq2Seq model for natural an-swer generation problem is very limited. Additionally, there has been no ex-periments and analysis conducted to understand how Seq2Seq model handles variations is questions posed to it to generate correct answers. Our experi-ments add to the empirical evaluations on Seq2Seq literature and provides insights to these questions. Additionally, we provide insights on how a cu-rated dataset can be developed and questions designed to train and test the performance of a Seq2Seq based question-answer model.

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