Utilization of artificial intelligence in disease prevention: Diagnosis, treatment, and implications for the healthcare workforce

SUD Wani, NA Khan, G Thakur, SP Gautam, M Ali… - Healthcare, 2022 - mdpi.com
Artificial intelligence (AI) has been described as one of the extremely effective and promising
scientific tools available to mankind. AI and its associated innovations are becoming more …

A state-of-art-review on machine-learning based methods for PV

GM Tina, C Ventura, S Ferlito, S De Vito - Applied Sciences, 2021 - mdpi.com
In the current era, Artificial Intelligence (AI) is becoming increasingly pervasive with
applications in several applicative fields effectively changing our daily life. In this scenario …

Exploring the dominant features and data-driven detection of polycystic ovary syndrome through modified stacking ensemble machine learning technique

SA Suha, MN Islam - Heliyon, 2023 - cell.com
Polycystic ovary syndrome (PCOS) is the most frequent endocrinological anomaly in
reproductive women that causes persistent hormonal secretion disruption, leading to the …

[HTML][HTML] Social media sentiment analysis and opinion mining in public security: Taxonomy, trend analysis, issues and future directions

MSM Suhaimin, MHA Hijazi, EG Moung… - Journal of King Saud …, 2023 - Elsevier
The interest in social media sentiment analysis and opinion mining for public security events
has increased over the years. The availability of social media platforms for communication …

Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches

R Keshavamurthy, S Dixon, KT Pazdernik, LE Charles - One Health, 2022 - Elsevier
The complex, unpredictable nature of pathogen occurrence has required substantial efforts
to accurately predict infectious diseases (IDs). With rising popularity of Machine Learning …

Enhancing digital health services with big data analytics

N Berros, F El Mendili, Y Filaly… - Big data and cognitive …, 2023 - mdpi.com
Medicine is constantly generating new imaging data, including data from basic research,
clinical research, and epidemiology, from health administration and insurance …

Modeling climate change impacts on vector-borne disease using machine learning models: Case study of Visceral leishmaniasis (Kala-azar) from Indian state of Bihar

S Kumar, A Srivastava, R Maity - Expert Systems with Applications, 2024 - Elsevier
Visceral leishmaniasis or Kala-azar (KA) is a Vector-Borne Disease (VBD) that remains the
second-largest parasitic killer across the globe (mortality rate: 75–95%). More than 60% of …

Powder-bed-fusion additive manufacturing of molybdenum: Process simulation, optimization, and property prediction

Y Wu, M Li, J Wang, Y Wang, X An, H Fu, H Zhang… - Additive …, 2022 - Elsevier
In this paper, the whole process of laser powder-bed-fusion (LPBF) additive manufacturing
(AM) in the fabrication of molybdenum (Mo) material was numerically reproduced. Firstly …

A deep learning approach for dengue fever prediction in Malaysia using LSTM with spatial attention

MA Majeed, HZM Shafri, Z Zulkafli… - International journal of …, 2023 - mdpi.com
This research aims to predict dengue fever cases in Malaysia using machine learning
techniques. A dataset consisting of weekly dengue cases at the state level in Malaysia from …

Analyzing and minimizing the effects of Vector-borne diseases using machine and deep learning techniques: A systematic review

I Kaur, AK Sandhu, Y Kumar - 2021 sixth international …, 2021 - ieeexplore.ieee.org
Among the numerous threats facing our world, Vector-borne illnesses pose the greatest
threat. Although arboviruses have a long history of infecting humans, they have recently …