Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review

RK Oruganti, AP Biji, T Lanuyanger, PL Show… - Science of The Total …, 2023 - Elsevier
The increased water scarcity, depletion of freshwater resources, and rising environmental
awareness are stressing for the development of sustainable wastewater treatment …

Microalgae conversion to alternative energy, operating environment and economic footprint: an influential approach towards energy conversion, and management

RK Goswami, K Agrawal, HM Upadhyaya… - Energy Conversion and …, 2022 - Elsevier
Microalgae (MA) biorefinery is a vital platform for the conversion of biomass into biofuels,
biomaterials, and bioactive substances and it can remediate the pollutant from the …

[HTML][HTML] Sensors, features, and machine learning for oil spill detection and monitoring: A review

R Al-Ruzouq, MBA Gibril, A Shanableh, A Kais… - Remote Sensing, 2020 - mdpi.com
Remote sensing technologies and machine learning (ML) algorithms play an increasingly
important role in accurate detection and monitoring of oil spill slicks, assisting scientists in …

Machine learning and deep learning based computational approaches in automatic microorganisms image recognition: methodologies, challenges, and …

P Rani, S Kotwal, J Manhas, V Sharma… - … Methods in Engineering, 2022 - Springer
Microorganisms or microbes comprise majority of the diversity on earth and are extremely
important to human life. They are also integral to processes in the ecosystem. The process of …

A novel deep learning instance segmentation model for automated marine oil spill detection

ST Yekeen, AL Balogun, KBW Yusof - ISPRS Journal of Photogrammetry …, 2020 - Elsevier
The visual similarity of oil slick and other elements, known as look-alike, affects the reliability
of synthetic aperture radar (SAR) images for marine oil spill detection. So far, detection and …

Image analysis for individual identification and feeding behaviour monitoring of dairy cows based on Convolutional Neural Networks (CNN)

B Achour, M Belkadi, I Filali, M Laghrouche… - Biosystems …, 2020 - Elsevier
Highlights•Top head image is used as Region of Interest for dairy cow individual
identification.•Dairy cows are classified in the feeder zone as standing or feeding.•Cow is …

DRNet: Dual-stage refinement network with boundary inference for RGB-D semantic segmentation of indoor scenes

E Yang, W Zhou, X Qian, J Lei, L Yu - Engineering Applications of Artificial …, 2023 - Elsevier
Semantic segmentation is a dense pixel prediction task, and its accuracy depends on the
extraction of long-range contextual knowledge and refinement of segmentation boundaries …

A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches

P Ma, C Li, MM Rahaman, Y Yao, J Zhang… - Artificial Intelligence …, 2023 - Springer
Microorganisms play a vital role in human life. Therefore, microorganism detection is of great
significance to human beings. However, the traditional manual microscopic detection …

[HTML][HTML] Delineation of agricultural fields using multi-task BsiNet from high-resolution satellite images

J Long, M Li, X Wang, A Stein - … Journal of Applied Earth Observation and …, 2022 - Elsevier
This paper presents a new multi-task neural network, called BsiNet, to delineate agricultural
fields from high-resolution satellite images. BsiNet is modified from a Psi-Net by structuring …

Survey of supervised learning for medical image processing

A Aljuaid, M Anwar - SN Computer Science, 2022 - Springer
Medical image interpretation is an essential task for the correct diagnosis of many diseases.
Pathologists, radiologists, physicians, and researchers rely heavily on medical images to …