Opportunities and challenges of quantum computing for climate modeling by Mierk Schwabe, Lorenzo Pastori, Inés de Vega, Pierre Gentine, Luigi Iapichino, Valtteri Lahtinen, Martin Leib, Jeanette Miriam Lorenz, Veronika Eyring

“…We discuss how quantum computers could accelerate climate models by solving the underlying differential equations faster, how quantum machine learning could better represent subgrid-scale phenomena in ESMs even with currently available noisy intermediate-scale quantum devices, how quantum algorithms aimed at solving optimization problems could assist in tuning the many parameters in ESMs, a currently time-consuming and challenging process, and how quantum computers could aid in the analysis of climate models. …”
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Burning rate research of NEPE propellant containing CL-20 on the base of improved Canny edge detection and PSO-BP by Cai Wenxiang, Yu Xintong, Wang Zhixiang

“…Furthermore, the Particle Swarm Optimization algorithm -Back Propagation neural network model (PSO-BP) is developed for predicting the burning rate, which is compared with the traditional BP algorithm. …”
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Research and application of a novel grey multivariable model in port scale prediction under the impact of Free Trade Zone by Yuyu Sun, Yuchen Zhang, Zhiguo Zhao

“…Purpose – Considering the impact of the Free Trade Zone (FTZ) policy on forecasting the port cargo throughput, this paper constructs a fractional grey multivariate forecasting model to improve the prediction accuracy of port cargo throughput and realize the coordinated development of FTZ policymaking and port construction. …”
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Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination by Ying Liu, Lihua Huang

“…The main characteristics of this approach include that (1) a novel noise filtering scheme that avoids the noisy examples based on fuzzy clustering and principal component analysis algorithm is proposed to remove both attribute noise and class noise to achieve an optimal clean set, and (2) support vector machine classifiers, based on the improved particle swarm optimization algorithm, are seen as component classifiers. …”
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Predicting the chemical equilibrium point of reacting components in gaseous mixtures through a novel Hierarchical Manta-Ray Foraging Optimization Algorithm by Oguz Emrah Turgut, Hadi Genceli, Mustafa Asker, Mustafa Turhan Çoban, Mohammad Akrami

“…To improve the solution diversity in the trial Manta-Ray population and enhance the general optimization effectivity of the algorithm, an ordered hierarchy is integrated into the original algorithm, taking into account the efficient search strategies of Elite-Opposition learning, Dynamic Opposition Learning, and Quantum search operator. …”
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Research on Optimal Dispatching of Multi-Microgrid Considering Economy by Li HE, Hongfang LÜ

“…To reduce the cost of power generation, an optimal scheduling model of multi micro grid energy and an optimal scheduling model for single micro grid were established on the premise of satisfying the constraints of the related micro power supply. …”
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Comparative Performance of Autoencoders and Traditional Machine Learning Algorithms in Clinical Data Analysis for Predicting Post-Staged GKRS Tumor Dynamics by Simona Ruxandra Volovăț, Tudor Ovidiu Popa, Dragoș Rusu, Lăcrămioara Ochiuz, Decebal Vasincu, Maricel Agop, Călin Gheorghe Buzea, Cristian Constantin Volovăț

“…These results demonstrate that hybrid models combining deep learning and traditional ML techniques can improve predictive accuracy. …”
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Clinical-radiomics hybrid modeling outperforms conventional models: machine learning enhances stratification of adverse prognostic features in prostate cancer by Minghan Jiang, Minghan Jiang, Zeyang Miao, Run Xu, Mengyao Guo, Xuefeng Li, Guanwu Li, Peng Luo, Su Hu, Su Hu

“…LASSO regression selected optimal features, followed by model construction via five algorithms (logistic regression, decision tree, random forest, SVM, AdaBoost). …”
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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

“…In these diversified test scenarios, the improved TD3 algorithm exhibits remarkable adaptability and stability. …”
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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 study of high-resolution remote sensing image landslide detection with optimized anchor boxes and edge enhancement by Kun Wang, Ling Han, Juan Liao

“…Using the You Only Look Once v5 network (YOLOv5) in combination with the Gabor filter, its edge detection, detection anchor frame and small object detection scale are improved and optimized. The YOLOv5(ISODATA) model was finally established for landslide image detection by incorporating the edge control factor and four clustering algorithms (K-means, K-means + +, k-medoid, and Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) to evaluate the accuracy of the detection anchor frame and add small target large-scale sampling. …”
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Priority Load Management for Improving Supply Reliability of Critical Loads in Healthcare Facilities Under Highly Unreliable Grids by Ndukwe Henry Ibiam, Fadi Kahwash, Jubaer Ahmed

“…It was found that the priority-based algorithm improved the reliability served to the most critical loads at the expense of the least critical. …”
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HSoMLSDP: A Hybrid Swarm-Optimized Machine Learning Framework for Software Defect Prediction by Madhusmita Das, Biju R. Mohan, Ram Mohana Reddy Guddeti

“…In pursuit of enhancing the defect prediction accuracy of the SoMLDP model, this paper designed two novel hybrid swarm-optimization algorithms (SOAs) referred to as gravitational force grasshopper optimization algorithm-artificial bee colony (GFGOA-ABC), and levy flight grasshopper optimization algorithm-artificial bee colony (LFGOA-ABC) algorithms. …”
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Coupling Artificial Intelligence with Proper Mathematical Algorithms to Gain Deeper Insights into the Biology of Birds’ Eggs by Valeriy G. Narushin, Natalia A. Volkova, Alan Yu. Dzhagaev, Darren K. Griffin, Michael N. Romanov, Natalia A. Zinovieva

“…Avian eggs are products of consumer demand, with modern methodologies for their morphometric analysis used for improving quality, productivity and marketability. …”
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MULTI-OBJECTIVE ROBUST OPTIMIZATION DESIGN OF COMPLIANT HINGE BASED ON BP NEURAL NETWORK (MT) by WU JianJun, LI JiaHui

“…In order to improve the robustness of the compliant hinge, genetic algorithm and BP neural network methods are introduced to optimize the parameters of the compliant mechanism. …”
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Research on Vibration Fatigue Damage Locations of Offshore Oil and Gas Pipelines Based on the GA-Improved BP Neural Network by Yaoguo Xie, Chengang Gao, Puzhe Wang, Lei Zhou, Chuanjie Zhang, Xianqiang Qu

“…To study vibration fatigue damage localization of offshore oil and gas pipelines, aiming at the location error caused by uncertainty of the initial parameters in backpropagation (BP) neural network training, an improved BP neural network based on the genetic algorithm (GA) is proposed to locate pipeline damage. …”
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Design and Research on a Reed Field Obstacle Detection and Safety Warning System Based on Improved YOLOv8n by Yuanyuan Zhang, Zhongqiu Mu, Kunpeng Tian, Bing Zhang, Jicheng Huang

“…This study proposes a lightweight model for farmland obstacle detection by improving the YOLOv8n object detection algorithm. …”
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A Prediction Method for Floor Water Inrush Based on Chaotic Fruit Fly Optimization Algorithm–Generalized Regression Neural Network by Zhijie Zhu, Chen Sun, Xicai Gao, Zhuang Liang

“…However, because the prediction effect of the GRNN is influenced by the smoothing factor, the CFOA is adopted to optimize this factor. In this way, influences of human factors during parameter determination of the GRNN prediction model are decreased, and the prediction accuracy and applicability of the model are improved. …”
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Optimization of Construction Projects Time-Cost-Quality-Environment Trade-off Problem Using Adaptive Selection Slime Mold Algorithm by Pham Vu Hong Son, Luu Ngoc Quynh Khoi

“…To improve the suggested model's superiority and effectiveness, it is compared to the multiple-target swarm algorithm (MOPSO), multi-objective artificial bee colony (MOABC) and non-dominant sort genetic algorithm (NSGA-II). …”
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Leveraging self attention driven gated recurrent unit with crocodile optimization algorithm for cyberattack detection using federated learning framework by Manal Abdullah Alohali, Hatim Dafaalla, Mohammed Baihan, Sultan Alahmari, Achraf Ben Miled, Othman Alrusaini, Ali Alqazzaz, Hanadi Alkhudhayr

“…Initially, the Z-score normalization is utilized to scale and standardize data to improve analysis consistency and accuracy. Furthermore, the feature selection (FS) process uses the crocodile optimization algorithm (COA) model. …”
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