[HTML][HTML] Reviewing the application of machine learning methods to model urban form indicators in planning decision support systems: Potential, issues and …

SCK Tekouabou, EB Diop, R Azmi, R Jaligot… - Journal of King Saud …, 2022 - Elsevier
Modern cities dynamically face several challenges including digitalization, sustainability,
resilience and economic development. Urban planners and designers must develop urban …

Artificial intelligence for antimicrobial resistance prediction: challenges and opportunities towards practical implementation

T Ali, S Ahmed, M Aslam - Antibiotics, 2023 - mdpi.com
Antimicrobial resistance (AMR) is emerging as a potential threat to many lives worldwide. It
is very important to understand and apply effective strategies to counter the impact of AMR …

Detection of cardiovascular diseases in ECG images using machine learning and deep learning methods

MB Abubaker, B Babayiğit - IEEE transactions on artificial …, 2022 - ieeexplore.ieee.org
Cardiovascular diseases (heart diseases) are the leading cause of death worldwide. The
earlier they can be predicted and classified; the more lives can be saved. Electrocardiogram …

[PDF][PDF] Recommendations to advance the cloud data analytics and chatbots by using machine learning technology

AR Kunduru - International Journal of Engineering and Scientific …, 2023 - academia.edu
The selection of machine learning tools for data analytics might be challenging due to the
ever-growing number of alternatives. The various tools each have benefits and limitations …

RBF-SVM kernel-based model for detecting DDoS attacks in SDN integrated vehicular network

GO Anyanwu, CI Nwakanma, JM Lee, DS Kim - Ad Hoc Networks, 2023 - Elsevier
The development of the intelligent transport space (ITS) comes with the challenge of
securing transportation data. As the vehicular network is highly dynamic, the network …

An IoT based smart water quality assessment framework for aqua-ponds management using Dilated Spatial-temporal Convolution Neural Network (DSTCNN)

PG Arepalli, KJ Naik - Aquacultural Engineering, 2024 - Elsevier
Assuring the quality of water is crucial for the growth and survival of fish in aquaculture
ponds. Traditional methods of water quality monitoring can be inefficient which makes real …

Unsupervised machine anomaly detection using autoencoder and temporal convolutional network

Z Li, Y Sun, L Yang, Z Zhao… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Anomaly detection is the cornerstone of the health management of much large industrial
mechanical equipment. Most machinery anomaly detection methods try to find a variable …

A deep learning-enabled IoT framework for early hypoxia detection in aqua water using light weight spatially shared attention-LSTM network

PG Arepalli, KJ Naik - The journal of Supercomputing, 2024 - Springer
Dissolved oxygen (DO) is a critical factor in maintaining healthy aquatic ecosystems,
including aquaculture ponds. Low DO levels can lead to hypoxia conditions, which are …

[HTML][HTML] Contextual embeddings based on fine-tuned Urdu-BERT for Urdu threatening content and target identification

MSI Malik, U Cheema, DI Ignatov - … of King Saud University-Computer and …, 2023 - Elsevier
Identification of threatening text on social media platforms is a challenging task. Contrary to
the high-resource languages, the Urdu language has very limited such approaches and the …

[HTML][HTML] Novel hyper-tuned ensemble random forest algorithm for the detection of false basic safety messages in internet of vehicles

GO Anyanwu, CI Nwakanma, JM Lee, DS Kim - ICT Express, 2023 - Elsevier
Detection of nodes disseminating false data is a prerequisite for effective deployment of
Internet of Vehicles (IoV) services. This work proposed a novel hyper-tuned ensemble …