Road Accident Analysis and Prediction of Accident Severity by Using Machine Learning in Bangladesh

At present, depression is the main reason for suicidal death. Depression also causes different kinds of diseases. Nowadays, people are deeply involved in social media and like to share their feelings on social media. So, it becomes easy to analyze depression through social media. In this paper, a combination of two CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) models has been proposed to make a hybrid CNN-LSTM model, CNN has performed for the image to create a matrix, and LSTM has given the result from the given matrix. In this paper, datasets are prepared based on depression and non-depression-related status. The proposed method has been applied to that dataset. The best result has been obtained using a hybrid neural network with the word embedding technique using the Bengali Facebook status dataset. We have used the SVM (Support Vector Machine) model to predict a small dataset of Bengali Facebook status and count vectorizer to count the word in the document. Finally, this paper has built up a model that makes strength and support for deep learning architecture.

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“…Our model is based on the hybrid model of CNN and LSTM [18]- [28]. The workflow of our proposed model is given in Fig.…”

Section: Lstm and Cnnmentioning

confidence: 99%

Depressed People Detection from Bangla Social Media Status using LSTM and CNN Approach

2021

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“…Normally, integer feature counts are needed for the multinomial distribution. Fractional counts, such as TF-IDF, can also function in practice [25].…”

Section: Naïve Bayesmentioning

confidence: 99%

Sentiment Analysis on Bengali Facebook Comments To Predict Fan's Emotions Towards a Celebrity

et al. 2021

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In this present era, sentiment analysis is considered as one of the most rapidly growing fields of computer science study. It is a text mining technique which is automated and determines the emotion of a text. A text can be divided into many emotions using sentiment analysis. Since there are some studies on emotion analysis in the Bangla language, it is regarded as a key research area in the field of analyzing Bangla language. This paper works with five different emotions and those are Happy, Sad, Angry, Surprise and Excited. Apart from these emotions our paper also deals with two categories, such as Abusive and Religious. We proposed a method of machine learning technique which is the SVM algorithm to extract these five individual emotions from Bangla text.

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“…On the other hand, we identified the following methods when analyzing the ML approaches that show the best performance in the studies consulted. RF [56][57][58], SVM [59][60][61], Light-GBM [62], Gradient Boosting [63], AdaBoost [58], Multi-layer perceptron [64], Nearest Neighbor Classification [25], and SimpleCart model [65]. These methods were commonly used for case studies located in the USA (Connecticut, Michigan, California, Florida, Nebraska), United Kingdom, China (Kunshan), Korea (Seoul), India, and Ghana.…”

Section: Papers Year Citationsmentioning

confidence: 99%

A Bibliometric Analysis and Benchmark of Machine Learning and AutoML in Crash Severity Prediction: The Case Study of Three Colombian Cities

Angarita-Zapata

Maestre-Góngora

Calderín

2021

Sensors

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Traffic accidents are of worldwide concern, as they are one of the leading causes of death globally. One policy designed to cope with them is the design and deployment of road safety systems. These aim to predict crashes based on historical records, provided by new Internet of Things (IoT) technologies, to enhance traffic flow management and promote safer roads. Increasing data availability has helped machine learning (ML) to address the prediction of crashes and their severity. The literature reports numerous contributions regarding survey papers, experimental comparisons of various techniques, and the design of new methods at the point where crash severity prediction (CSP) and ML converge. Despite such progress, and as far as we know, there are no comprehensive research articles that theoretically and practically approach the model selection problem (MSP) in CSP. Thus, this paper introduces a bibliometric analysis and experimental benchmark of ML and automated machine learning (AutoML) as a suitable approach to automatically address the MSP in CSP. Firstly, 2318 bibliographic references were consulted to identify relevant authors, trending topics, keywords evolution, and the most common ML methods used in related-case studies, which revealed an opportunity for the use AutoML in the transportation field. Then, we compared AutoML (AutoGluon, Auto-sklearn, TPOT) and ML (CatBoost, Decision Tree, Extra Trees, Gradient Boosting, Gaussian Naive Bayes, Light Gradient Boosting Machine, Random Forest) methods in three case studies using open data portals belonging to the cities of Medellín, Bogotá, and Bucaramanga in Colombia. Our experimentation reveals that AutoGluon and CatBoost are competitive and robust ML approaches to deal with various CSP problems. In addition, we concluded that general-purpose AutoML effectively supports the MSP in CSP without developing domain-focused AutoML methods for this supervised learning problem. Finally, based on the results obtained, we introduce challenges and research opportunities that the community should explore to enhance the contributions that ML and AutoML can bring to CSP and other transportation areas.

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Road Accident Analysis and Prediction of Accident Severity by Using Machine Learning in Bangladesh

Cited by 70 publications

References 12 publications

Depressed People Detection from Bangla Social Media Status using LSTM and CNN Approach

Depressed People Detection from Bangla Social Media Status using LSTM and CNN Approach

Sentiment Analysis on Bengali Facebook Comments To Predict Fan's Emotions Towards a Celebrity

A Bibliometric Analysis and Benchmark of Machine Learning and AutoML in Crash Severity Prediction: The Case Study of Three Colombian Cities

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