An Optimised CNN Hardware Accelerator Applicable to IoT End Nodes for Disruptive Healthcare by Arfan Ghani, Akinyemi Aina, Chan Hwang See

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Hyperparameter optimisation in deep learning from ensemble methods: applications to proton structure by Juan Cruz-Martinez, Aron Jansen, Gijs van Oord, Tanjona R Rabemananjara, Carlos M R Rocha, Juan Rojo, Roy Stegeman

“…While focusing on proton structure, our method is fully general and is applicable to any deep learning problem relying on hyperparameter optimisation for an ensemble of models.…”
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Optimisation tools for meeting nutrient requirements of Indian children and adults at optimal cost by Fathima Ayoob, Jawahar R. Manivannan, Ashikh Ahmed, Afsal K. Murikkanchery, Abhishek Kumar, Santu Ghosh, Patrick Webb, Anura V. Kurpad, Tinku Thomas

“…Design: Linear-programming algorithms were adapted into two web-based tools: a Food Optimisation for Population (FOP) tool and a Diet Optimisation Tool (DOT). …”
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The soft computing approaches in optimising multi-objective mechanical design of a weeding robot by Afsaneh Soleimani

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Advanced evaluation of performance of machine learning models for soapstock splitting optimisation under uncertainty by Bartosz Szeląg, Krzysztof Barbusiński, Michał Stachura, Przemysław Kowal, Adam Kiczko, Eldon R. Rene

“…GSA showed that the key influence on Porg (organic phosphorus), COD (chemical oxygen demand) and AN (acid number) was the phosphorus content of the oil, and less important were the operational parameters of the soapstock splitting system. Multi-criteria optimisation under uncertainty using a genetic algorithm (NSGA II) showed a significant influence of phosphorus content uncertainty on the choice of soapstock splitting operating conditions. …”
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A two‐stage, four‐layer robust optimisation model for distributed cooperation in multi‐microgrids by Haobo Rong, Jianhui Wang, Honghai Kuang

“…In the first stage, the initial layer ‘min’ addresses the distributed cooperation problem between MG and CES, while the second stage employs ‘min‐max‐min’ to optimise the scheduling of MG. …”
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Objective Functions for the Blade Shape Optimisation of a Cross-Flow Tidal Turbine under Constraints by Karla Ruiz-Hussmann, Pierre-Luc Delafin, Cyrille Bonamy, Yves Delannoy, Dominique Thévenin, Stefan Hoerner

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Energy Optimisation in Aquaponics—Integrating Renewable Source and Water as Energy Buffer for Sustainable Food Production by Abdul Aziz Channa, Kamran Munir, Mark Hansen, Muhammad Fahim Tariq

“…This study presents a novel energy optimisation approach for aquaponic systems. We employed a dynamic control algorithm to intelligently adjust water temperature based on solar forecasts. …”
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Optimisation of the adaptive neuro-fuzzy inference system for adjusting low-cost sensors PM concentrations by Martina Casari, Piotr A. Kowalski, Laura Po

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Optimisation and Comparison of Markerless and Marker-Based Motion Capture Methods for Hand and Finger Movement Analysis by Valentin Maggioni, Christine Azevedo-Coste, Sam Durand, François Bailly

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Optimising management against dynamic threats: A spatially explicit approach based on integer programming by José Salgado‐Rojas, Virgilio Hermoso, Eduardo Álvarez‐Miranda

“…Abstract Defining strategies to control dynamic threats is a highly complex problem that is defined by the biological characteristics of the threatening agents (e.g. invasive species), the landscape context and the management objective within the constraints of limited financial resources. While different optimisation models have focussed on finding frameworks capable of incorporating complex dynamics between species and threats, they often overlook the spatial aspect of management plans or address it at a very coarse resolution. …”
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Combined use of long short‐term memory neural network and quantum computation for hierarchical forecasting of locational marginal prices by Xin Huang, Guozhong Liu, Jiajia Huan, Shuxin Luo, Jing Qiu, Feiyan Qin, Yunxia Xu

“…The results also validate that the QGWO algorithm significantly reduces time consumption while ensuring optimisation effectiveness when optimising SVM and LSTM.…”
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Optimisation of Integrated Heat Pump and Thermal Energy Storage Systems in Active Buildings for Community Heat Decarbonisation by Zaid Al-Atari, Rob Shipman, Mark Gillott

“…The study employs an algorithm that adjusts thermal storage charging and discharging cycles to align with off-peak periods. …”
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Optimising 3D-printed carbon fibre composites using machine learning: Balancing strength and efficiency by José Humberto S. Almeida, Jr., Guilherme Ferreira Gomes

“…This study introduces a data-driven framework that integrates machine learning (ML) and genetic algorithms (GA) to optimise interlaminar strength and minimise printing time simultaneously. …”
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A Study of Deep Learning Neural Network Algorithms and Genetic Algorithms for FJSP by Xiaofeng Shang

“…The new improved algorithm is based on the combination of deep learning neural networks LSTM and CNN with genetic algorithm (GA), namely, CNN-LSTM-GA algorithm. …”
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Hydrodynamic optimisation of a horizontal-axis hydrokinetic turbine for the River Ganga using a semi-analytical approach by Kripal, Anoop Kanjirakat

“…The turbine configuration was determined using a rotor optimisation tool integrating a genetic algorithm with a semi-analytical blade-element momentum theory. …”
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Fibre-orientation-driven defect probability mapping for machining-induced delamination and burr minimisation in carbon fibre-reinforced polymer (CFRP) composites by Norbert Geier

“…Machining-induced burrs and delamination compromise the integrity of polymer composite components reinforced by chopped carbon tows. An image-based optimisation algorithm was therefore developed that locates the ideal hole centre within the preform allowance to minimise defect risk. …”
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Pilot observational cohort study to determine whether waveform and flow traces from mechanical insufflation–exsufflation (MI-E) can be used to identify laryngeal responses to MI-E... by Stephanie K Mansell, Swapna Mandal, Amar Shah, Francesca Gowing, Rhiannon Parry, Charlotte Greenfield, Yogesh Bhatt

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Design of an efficient dynamic context‐based privacy policy deployment model via dual bioinspired Q learning optimisations by Namrata Jiten Patel, Ashish Jadhav

“…The approach employs Firefly Optimiser (FFO) and Ant Lion Optimiser (ALO) algorithms to dynamically adjust privacy parameters and handle large datasets efficiently. …”
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Secure and efficient trust enabled routing in mobile ad hoc network using game theory and grey wolf optimisation techniques by Ujwala Ravale, Gautam M. Borkar

“…Additionally, the proposed protocol leverages the Grey Wolf Optimisation Routing Algorithm (GWO), which establishes efficient routes from the source to the sink node. …”
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