Driven by significant a dvancements i n computer vision, image classification h as e ntered a r evolutionary phase with improved accuracy and prospective applications. Nowadays, classifying plant species and leaves is...
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Sarcasm in social media postings significantly impacts automated sentiment extraction due to its potential to invert the overall polarity of phrases. It poses a formidable challenge in extracting genuine sentiments fr...
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Menstrual cycle prediction is a critical issue for many women, as it can help them plan their daily activities, and prepare for potential physical and emotional changes. However, current methods for predicting menstru...
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ISBN:
(纸本)9798350370096
Menstrual cycle prediction is a critical issue for many women, as it can help them plan their daily activities, and prepare for potential physical and emotional changes. However, current methods for predicting menstruation often rely on physiological data such as age, cycle length, and ovulation history, which may not be convenient or accurate for some *** paper presents a comparative study of menstruation prediction models using daily social media sentiment data. The underlying hypothesis is that social media posts can reflect the user's emotions and feelings, which may be related to the menstrual cycle. By tracking emotions from social media posts, it may be possible to monitor potential symptoms associated with the menstrual cycle, such as premenstrual syndrome (PMS) and premenstrual dysphoric disorder (PMDD).The proposed models are evaluated using a dataset of social media posts and self-reported menstruation data. The results demonstrate that the proposed models can effectively predict the onset of menstruation and identify potential symptoms of PMS and PMDD. This work has the potential to contribute to the development of new tools and interventions for women's health and *** experimental research utilized a dataset of posts on X from one woman over a 3-year period (2020-2023), covering both normal and abnormal menstrual cycles. The challenge of this research was to transform ordinary text that could be posted on social media but could not be used for prediction into text that could be used for in-depth prediction and analysis. This was achieved through two processes: 1) Sentiment Analysis using the WangchanBERTa model to determine the sentiment of the posts, which achieved an accuracy of 61.7%;2) the Time Series Forecasting process for predicting the date of menstruation using the Random Forest model was the most appropriate if compared to Linear Regression and SVR, which is considered to be the most accurate. Challenge new faces This is bec
This paper aims to deal with the difficulties faced by blind while interacting with their surroundings. The proposed system introduces VEye - An AI Vision Assistant, a wearable device with an integrated camera and an ...
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Neurodegenerative disease Parkinson's, which have both motor and non-motor type of symptoms, is one of the biggest problems, which gets consideration in the public health department. To assurance of patients and b...
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In recent years, blockchain technology has gained immense popularity due to its decentralized and secure nature. However, traditional blockchain systems still face security challenges in terms of confidentiality, inte...
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Artificial intelligence is a field of computerscience dedicated to solving reasonable problems mostly associated with human intelligence such as pattern recognition and problem solving. This chapter proposes a projec...
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For the performance evaluation of the clustering algorithm, evaluation metrics are used. For this purpose, the obtained set of clusters are compared with the actual set of clusters (or gold standard). Various evaluati...
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Bangladesh has recently adopted the worldwide trend of digitalization, namely in the area of financial activities. In Bangladesh, the centralized nature of digital payment systems poses notable obstacles, such as leng...
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In this research, the performance of different deep learning models, including CNN + RNN, VGG16 + RNN, VGG19 + RNN, ResNet50 + RNN, and InceptionV3 + RNN in predicting brain tumour grades by employing Electronic Healt...
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