Recurrent Neural Networks With TF-IDF Embedding Technique for Detection and Classification in Tweets of Dengue Disease

Abstract

With the increased usage of Web 2.0 and data-affluent tools such as social media platforms and web blog services, the challenge of extracting public sentiment and disseminating personal health information has become more common than ever in the last decade. This paper proposes a novel model for Dengue disease detection based on social media posts alone. The model does not access any personal information of people or any medical record. The model extracts the presence of a Dengue disease based on tweets only and decides whether it is a general discussion about the disease, and no one is actually infected, or people are actually infected with that disease. This paper uses efficient machine/deep learning approaches to utilize tweets data for automatic and efficient disease detection. Experimental results demonstrate that the proposed model is able to achieve 92% accuracy compared to the current state-of-theart techniques in this domain.

Early Detection of Seasonal Outbreaks from Twitter Data Using Machine Learning Approaches

“…Public awareness about the seasonal epidemic outbreaks, especially regarding dengue fever, actual problem understanding, and the approaches to monitor dengue outbreaks are significant considerations. e perceptions, behaviour, and approaches regarding dengue in cities are explored in many studies [4,25]. Social networking could be utilized efficiently to 2 Complexity classify people contaminated with diseases and health awareness influences (e.g., influenza, dengue fever, anxiety, malaria, measles, etc.)…”

“…Alessa et al [27] 2019 In future work, the proposed state transition probabilities can be utilized for traditional epidemiological approaches. 4 Complexity…”

“…e barrier of influenza can harm the patient and can create a much severe condition if not treated. As reported in [4], flu is an epidemic outbreak, and in such cases where the proliferation of infectious diseases, particularly influenza, is a real risk to people, the government must implement appropriate health surveillance to control the epidemic. Similarly, dengue infection is a mosquito-borne virus that causes serious ILI and also causes a potentially fatal risk factor called severe infection with dengue fever.…”

Preference Cognitive Diagnosis for Student Performance Prediction

“…Considering the correlation between words in the text, the authors in [20] proposed the latent Dirichlet allocation model (LDA), which added the Dirichlet prior distribution to the polynomial distribution of texts, topics, and words. Sentence-based approaches, such as convolutional neural networks (CNNs) [21], [22] and recurrent neural networks (RNNs) [23], [24], presented the numerical representation of text at the sentence level, which was effectively adopted in natural language processing (NLP) [25], [26]. However, the CNN and RNN input was based on the word vector generated by word embedding methods.…”

Efficient Processing of Image Processing Applications on CPU/GPU

“…In this paper, we present a new technique for the efficient distribution of images in a heterogeneous cluster. e goal is to maximize the utilization of the processing resources [17][18][19] (i.e., both CPUs and GPUs) and throughput. We provide a programming framework that ensures efficient workload distribution amongst the nodes by dividing the data into equal size splits and then distribute the split data between CPU and GPU cores based on their computational capabilities [20].…”