[HTML][HTML] A systematic review of social media-based sentiment analysis: Emerging trends and challenges
In the present information age, a wide and significant variety of social media platforms have
been developed and become an important part of modern life. Massive amounts of user …
been developed and become an important part of modern life. Massive amounts of user …
Applications of convolutional neural networks for intelligent waste identification and recycling: A review
With the implementations of “Zero Waste” and Industry 4.0, the rapidly increasing
applications of artificial intelligence in waste management have generated a large amount of …
applications of artificial intelligence in waste management have generated a large amount of …
[PDF][PDF] Diabetes Risk Assessment Using Machine Learning: A Comparative Study of Classification Algorithms
CK Suryadevara - IEJRD-International Multidisciplinary Journal, 2023 - researchgate.net
Diabetes is a serious health condition with high blood glucose/sugar levels. Diabetes is a
chronicle disease that can cause worldwide health care crisis but we can take some steps to …
chronicle disease that can cause worldwide health care crisis but we can take some steps to …
A patient network-based machine learning model for disease prediction: The case of type 2 diabetes mellitus
In recent years, the prevalence of chronic diseases such as type 2 diabetes mellitus (T2DM)
has increased, bringing a heavy burden to healthcare systems. While regular monitoring of …
has increased, bringing a heavy burden to healthcare systems. While regular monitoring of …
Role of artificial intelligence (AI) in fish growth and health status monitoring: A review on sustainable aquaculture
Aquaculture plays a crucial role in meeting the growing global demand for seafood, but it
faces challenges in terms of fish growth and health monitoring. The advancement of artificial …
faces challenges in terms of fish growth and health monitoring. The advancement of artificial …
Artificial intelligence, machine learning, and deep learning for clinical outcome prediction
AI is a broad concept, grou** initiatives that use a computer to perform tasks that would
usually require a human to complete. AI methods are well suited to predict clinical outcomes …
usually require a human to complete. AI methods are well suited to predict clinical outcomes …
Development of digitalization road map for healthcare facility management
Effective Healthcare Facility Management (HFM) remain a crucial concern for high quality
built healthcare sectors, both in the public and private areas. The anticipated resource …
built healthcare sectors, both in the public and private areas. The anticipated resource …
A reliable and robust deep learning model for effective recyclable waste classification
In response to the growing waste problem caused by industrialization and modernization,
the need for an automated waste sorting and recycling system for sustainable waste …
the need for an automated waste sorting and recycling system for sustainable waste …
Extraction and interpretation of deep autoencoder-based temporal features from wearables for forecasting personalized mood, health, and stress
Continuous wearable sensor data in high resolution contain physiological and behavioral
information that can be utilized to predict human health and wellbeing, establishing the …
information that can be utilized to predict human health and wellbeing, establishing the …
[HTML][HTML] Medical disease analysis using neuro-fuzzy with feature extraction model for classification
Medical disease classification using machine learning algorithms is a challenging task due
to the nature of data, which can contain incomplete, uncertain, and imprecise information …
to the nature of data, which can contain incomplete, uncertain, and imprecise information …