Promoting maternal health is significant as GDM is a severe health concern for both infants and mothers. As opposed to the conventional systems based primarily on clinical diagnosis, this study attempts to develop a s...
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In the field of Human Activity Recognition (HAR), the precise identification of human activities from time-series sensor data is a complex yet vital task, given its extensive applications across various industries. Th...
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The classical Rician Weichselberger channel and the emerging holographic multiple-input multiple-output (MIMO) channel share a common characteristic of non-separable correlation, which captures the interdependence bet...
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Urbanization and rapid environmental change impact the natural habitats of various living species, including birds, putting avian biodiversity at risk throughout the world. This study addresses the critical challenge ...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
Urbanization and rapid environmental change impact the natural habitats of various living species, including birds, putting avian biodiversity at risk throughout the world. This study addresses the critical challenge of reliably identifying and classifying bird species in urban and rural areas, with a particular emphasis on Bangladeshi bird species. To solve this challenge, we present the development of an audio solution, Birds Sound Classification, to accurately identify and classify bird species in Bangladesh, a biodiversity-rich region with limited resources collected in an organized manner for bird calls. To overcome this, we propose the BD Birds Song dataset, a carefully selected collection of more than 25 hours of annotated audio recordings covering 23 unique Bangladeshi bird species. This dataset captures a variety of acoustic habitats, including rural, urban, and forest environments, ensuring robustness against background noise and overlapping calls. We apply BirdsSoundClassification model, a pretrained WAV2VEC2 model to evaluate the quality of our dataset, achieving a classification accuracy of 97% across the 23 species, with strong performance in precision, recall, and F1-score. This high accuracy demonstrates the potential of the model in identifying Bangladeshi bird species and supports its use in avian biodiversity monitoring and conservation efforts.
Can we localize a robot on a map only using monocular vision? This study presents NuRF, an adaptive and nudged particle filter framework in radiance fields for 6-DoF robot visual localization. NuRF leverages recent ad...
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Federated Learning (FL) has gained significant attention in recent years due to its distributed nature and privacy-preserving benefits. However, a key limitation of conventional FL is that it learns and distributes a ...
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Alzheimer’s disease is a neurological sickness that damages the brain and memory functions and progresses irreversibly. The brain begins to shrink with Alzheimer’s disease, and over time, dementia develops as a resu...
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Viruses have consistently presented a substantial threat to living creatures since ancient times. Despite progress in developing therapeutics, ongoing efforts are necessary to address emerging viruses. Recently, sever...
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The rapid growth of social media platforms has resulted in a vast and diverse collection of user-generated content in multiple languages, such as user comments. Analyzing the sentiment expressed in these comments in m...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
The rapid growth of social media platforms has resulted in a vast and diverse collection of user-generated content in multiple languages, such as user comments. Analyzing the sentiment expressed in these comments in multiple languages can provide valuable insights into public opinion. Again, conducting sentiment analysis on multilingual and multi-regional data in real-time presents unique challenges, particularly due to language and cultural variations. While sentiment analysis has been extensively explored using various state-of-the-art methods, hybrid deep learning models have proven effective in capturing complex language structures. Therefore, the objective of this research is to propose a hybrid deep learning model for analyzing sentiments based on multilingual comments from trending YouTube videos across different regions. To achieve this objective, this study proposes a hybrid deep learning model that combines Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) algorithms with GloVe embeddings for sentiment analysis. The research focuses on Bangla and English languages; and evaluating the model’s performance using trending YouTube videos from four countries: Bangladesh, the USA, the UK, and Canada. The proposed hybrid model achieved an accuracy of 90.95% for user comments in Bangla and 97.42% for comments in English that demonstrate its effectiveness in analyzing multi-lingual comments from multi-regional YouTube trending videos.
Social media platforms, such as YouTube, generate an extensive amount of unstructured data, offering valuable insights into user behavior, engagement patterns, and preferences. This project focuses on predictive analy...
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ISBN:
(数字)9798331505745
ISBN:
(纸本)9798331505752
Social media platforms, such as YouTube, generate an extensive amount of unstructured data, offering valuable insights into user behavior, engagement patterns, and preferences. This project focuses on predictive analysis of YouTube data using a hybrid machine learning framework. Various video metadata, including descriptions, titles, tags, views, likes, and comments, are analyzed to forecast audience engagement and trends. The pipeline begins with data preprocessing, including handling missing values, removing duplicates, and preparing the data for analysis. Exploratory Data Analysis (EDA) is performed to uncover hidden patterns and relationships, followed by feature selection techniques to identify the most influential variables. The dataset is then split into training and testing subsets, and advanced machine learning models are employed to predict video performance and user engagement. The results are evaluated using key performance metrics, delivering actionable insights to improve content strategies and audience targeting. This study highlights the effectiveness of machine learning techniques in optimizing YouTube content and enhancing decision-making in digital marketing.
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