Determination of Sequential Well Placements Using a Multi-Modal Convolutional Neural Network Integrated with Evolutionary Optimization by Seoyoon Kwon, Minsoo Ji, Min Kim, Juliana Y. Leung, Baehyun Min

“…This complex multi-million-dollar problem involves optimizing multiple parameters using computationally intensive reservoir simulations, often employing advanced algorithms such as optimization algorithms and machine/deep learning techniques to find near-optimal solutions efficiently while accounting for uncertainties and risks. …”
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Three-dimensional localization algorithm of mobile nodes based on received signal strength indicator-angle of arrival and least-squares support-vector regression by Lieping Zhang, Huihao Peng, Jiajie He, Shenglan Zhang, Zuqiong Zhang

“…Finally, the unknown mobile nodes were localized based on least-squares support-vector regression modeling. The experimental results showed that compared with the localization algorithms without optimized ranging information or least-squares support-vector regression modeling, the algorithm proposed in this study exhibited significantly improved stability, a reduced mean localization error by more than 50%, and increased localization accuracy.…”
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A novel deep learning algorithm for real-time prediction of clinical deterioration in the emergency department for a multimodal clinical decision support system by Arom Choi, Kwanhyung Lee, Heejung Hyun, Kwang Joon Kim, Byungeun Ahn, Kyung Hyun Lee, Sangchul Hahn, So Yeon Choi, Ji Hoon Kim

“…The algorithm’s predictions, based solely on triage information, significantly outperformed traditional logistic regression models, with notable improvements in the area under the precision-recall curve (AUPRC). …”
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Optimal energy management for multi-energy microgrids using hybrid solutions to address renewable energy source uncertainty by M. Siva Ramkumar, Jaganathan Subramani, M. Sivaramkrishnan, Arunkumar Munimathan, Goh Kah Ong Michael, Mohammad Mukhtar Alam

“…In this study, a new hybrid algorithm is used for system modelling and low-cost, optimal management of Micro Grid (MG) networked systems. …”
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Piecewise Model and Parameter Obtainment of Governor Actuator in Turbine by Jie Zhao, Li Wang, Dichen Liu, Jun Wang

“…Several testing functions are selected to compare genetic algorithm and particle swarm optimization algorithm (GA-PSO) with other basic intelligence algorithms. …”
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CNN-LSTM optimized with SWATS for accurate state-of-charge estimation in lithium-ion batteries considering internal resistance by Zhaowei Zhang, Chen Liu, Tianyu Li, Tian Wang, Yaoyao Cui, Pengcheng Zhao

“…In addition, commonly used network optimization algorithms do not always maximize the convergence speed and performance simultaneously. …”
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Tackling the Optimal Phasor Measurement Unit Placement and Attack Detection Problems in Smart Grids by Incorporating Machine Learning by Ramzi Al-Sharawi, Abdelfatah Ali, Mostafa Shaaban, Nasser Qaddoumi, Mohamed S. Abdalzaher

“…Specifically, the k-Nearest Neighbors (KNN) model’s Brier loss is used as an objective function within the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) optimization framework to represent the FDIA detection performance. …”
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Intelligent Energy-Efficient Train Trajectory Optimization Approach Based on Supervised Reinforcement Learning for Urban Rail Transits by Guannan Li, Siu Wing Or, Ka Wing Chan

“…Second, an IETTO model is established to handle uncertain disturbances in real-time train operation and generate optimal energy-efficient train trajectories online by optimizing energy efficiency and receiving supervisory information from trajectories of pre-trained TTO models. …”
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Performance Optimization of Synchronous Reluctance Motor using Adaptive MTPA Curve Fitting with Anti-Windup Current Control by Alex V, Rammohan A

“…Conventional scalar control methods typically rely on precomputed lookup tables or iterative optimization algorithms, which introduce computational overhead and require significant memory resources. …”
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A dual-layer planning method based on improved MOPSO for distribution networks considering source–load temporal uncertainty by Guilian Wu, Guilian Wu, Jia Lin, Jinlin Liao

“…Third, an improved multi-objective particle swarm optimization (MOPSO) algorithm with adaptive inertia weights accelerates the convergence by 25%. …”
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Research on Ship Heave Motion Compensation Control Under Complex Sea State Environment Based on Improved Reinforcement Learning by ZHANG Qin, ZHOU Jingyi, WANG Xingyue, HU Xiong

“…This algorithm surpasses step control methods optimized through particle swarm optimization and outperforms traditional TD3 reinforcement learning methodologies. …”
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Modified invasive weed optimization MPPT approach for PV system interfaced with BLDC motor for water pumping system by S. Nagaraja Rao, B. M. Kiran Kumar, B. M. Manjunatha, A. Suresh Kumar

“…Therefore, the modified invasive weed optimization (MIWO) approach integrated with P&O approach to improve performance under PSC. …”
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Neural network inspired efficient scalable task scheduling for cloud infrastructure by Punit Gupta, Arnaav Anand, Pratyush Agarwal, Gavin McArdle

“…In this work, we aim to use the Harmony Search Algorithm and neural networks to create a fault aware system for optimal usage of cloud resources. …”
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Optimizing High-Speed Railroad Timetable with Passenger and Station Service Demands: A Case Study in the Wuhan-Guangzhou Corridor by Jin Wang, Leishan Zhou, Yixiang Yue, Jinjin Tang, Zixi Bai

“…The results show that the proposed model and algorithm can quickly reduce the defined cost function by 38.2% and improve the average travel speed by 10.7 km/h, which indicates that our proposed model and algorithm can effectively improve the quality of a constructed train timetable and the travel efficiency for passengers.…”
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Enhancing microgrid forecasting accuracy with a TCNN-TLS framework: A novel approach to mitigating uncertainty in renewable energy and load predictions by Md. Omer Faruque, Md. Majharul Islam, Md Jakaria Talukder, Arif Mia, Shahriar Tasnim, Md. Alamgir Hossain, S.M. Muyeen

“…The employed settings contain a temporal convolutional neural network (TCNN) optimized with a pelican optimization algorithm (POA) to enhance its hyper-parameter selection. …”
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Proximity-based solutions for optimizing autism spectrum disorder treatment: integrating clinical and process data for personalized care by Annarita Vignapiano, Annarita Vignapiano, Francesco Monaco, Francesco Monaco, Stefania Landi, Luca Steardo, Carlo Mancuso, Claudio Pagano, Gianvito Petrillo, Alessandra Marenna, Martina Piacente, Stefano Leo, Carminia Marina Ingenito, Rossella Bonifacio, Benedetta Di Gruttola, Marco Solmi, Marco Solmi, Marco Solmi, Marco Solmi, Maria Pontillo, Giorgio Di Lorenzo, Giorgio Di Lorenzo, Alessio Fasano, Alessio Fasano, Alessio Fasano, Giulio Corrivetti, Giulio Corrivetti

“…This project utilizes the power of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), to improve Autism Spectrum Disorder diagnosis and treatment. …”
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Enhanced PID for pedal vehicle force control using hybrid spiral sine-cosine optimization and experimental validation by Azrul Azim Abdullah Hashim, Nor Maniha Abdul Ghani, Mohammad Osman Tokhi

“…This study develops and validates a force feedback control system for automotive pedals utilizing an optimized PID controller using the hybrid Spiral Sine-Cosine algorithm (SSCA). …”
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Algorithmic Generation of Realistic 3D Graphics for Liquid Surfaces Within Arbitrary-Form Vessels in a Virtual Laboratory and Application in Flow Simulation by Dimitrios S. Karpouzas, Vasilis Zafeiropoulos, Dimitris Kalles

“…Moreover, the process of surface discretization with finite triangular elements is analyzed with the aim of optimizing their representation during the algorithmic implementation. …”
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YOLOv7-DWS: tea bud recognition and detection network in multi-density environment via improved YOLOv7 by Xiaoming Wang, Xiaoming Wang, Zhenlong Wu, Guannan Xiao, Guannan Xiao, Chongyang Han, Cheng Fang

“…First, we make a series of improvements to the YOLOv7 algorithm, including decouple head to replace the head of YOLOv7, to enhance the feature extraction ability of the model and optimize the class decision logic. …”
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A novel approach for breast cancer detection using a Nesterov accelerated adam optimizer with an attention mechanism by Abeer Saber, Tamer Emara, Samar Elbedwehy, Esraa Hassan

“…The attention module improves the MobileNet-V2 model’s performance by highlighting disease-affected areas within the images. …”
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