A Hierarchical Machine Learning-Based Strategy for Mapping Grassland in Manitoba’s Diverse Ecoregions by Mirmajid Mousavi, James Kobina Mensah Biney, Barbara Kishchuk, Ali Youssef, Marcos R. C. Cordeiro, Glenn Friesen, Douglas Cattani, Mustapha Namous, Nasem Badreldin

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Multi strategy Horned Lizard Optimization Algorithm for complex optimization and advanced feature selection problems by Marwa M. Emam, Mosa E. Hosney, Reham R. Mostafa, Essam H. Houssein

“…The Horned Lizard Optimization Algorithm (HLOA) is a nature-inspired metaheuristic that mathematically mimics the adaptive defense mechanisms of horned lizards, including crypsis, skin color modulation, blood-squirting, and escape movements. …”
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Enhanced Network Traffic Classification Using Bayesian-Optimized Logistic Regression and Random Forest Algorithm by Manisankar Sannigrahi, R. Thandeeswaran

“…The research evaluates four key machine learning algorithms: Random forest, logistic regression, support vector machine (SVM), and K-nearest neighbors (K-NN). …”
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Value-difference learning based mMTC devices access algorithm in multi-cell network by Xin LI, Jun SUN

“…In the massive machine type communication scenario of 5G, the access congestion problem of massive machine type communication devices (mMTCD) in multi-cell network is very important.A double deep Q network with value-difference based exploration (VDBE-DDQN) algorithm was proposed.The algorithm focused on the solution that could reduce the collision when a number of mMTCDs accessed to eNB in multi-cell network.The state transition process of the deep reinforcement learning algorithm was modeled as Markov decision process.Furthermore, the algorithm used a double deep Q network to fit the target state-action value function, and it employed an exploration strategy based on value-difference to adapt the change of the environment, which could take advantage of both current conditions and expected future needs.Moreover, each mMTCD updated the probability of exploration according to the difference between the current value function and the next value function estimated by the network, rather than using the same standard to select the best base eNB for the mMTCD.Simulation results show that the proposed algorithm can effectively improve the access success rate of the system.…”
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5G network slicing function migration mechanism based on particle swarm optimization algorithm by Qiang CHEN, Caixia LIU, Lingshu LI

“…In multi-application scenarios of 5G,data traffic often increases dramatically.Virtual machine resources in network slicing may not meet the normal needs of users.In view of this,a network slicing function migration mechanism aiming at load balancing was proposed.The mechanism simulates the virtual machine into particles based on particle swarm optimization algorithm.In the process of migration,all particles were divided into several subgroups,and particle swarm optimization algorithm was applied within and among groups.According to the historical optimal solution and the current global optimal solution,the particle location was updated,and the best target particles were determined by selecting the smaller particle size of the particle in real time.The mechanism not only improves the convergence speed,but also improves the accuracy of the algorithm.Compared with other migration methods,the results show that the proposed migration mechanism has the advantages of high accuracy and fast convergence.And it can also improve the efficiency of resource utilization,reduce the energy consumption of data center,and has better adaptability.…”
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Intelligent irrigation strategy model for farmland using dung beetle optimization-random forest algorithms by Wenwen Hu, Yong Liu, Jun An, Shipu Xu, Zhiwen Zhou, Mingming An, Xiaokun Guo, Xiang Ma, Wenfei Jiang, Yunsheng Wang

“…This study proposes an optimized machine learning prediction model using the Dung Beetle Optimization-Random Forest (DBO-RF) algorithm, thus improving irrigation predictability. …”
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A Learning Probabilistic Boolean Network Model of a Smart Grid with Applications in System Maintenance by Pedro Juan Rivera Torres, Chen Chen, Jaime Macías-Aguayo, Sara Rodríguez González, Javier Prieto Tejedor, Orestes Llanes Santiago, Carlos Gershenson García, Samir Kanaan Izquierdo

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Detection method of slight bruises of apples based on hyperspectral imaging and RELIEF-extreme learning machine by ZHANG Meng, LI Guanghui

“…Then, based on full wavebands and characteristic wavebands, an extreme learning machine (ELM) model was built, as comparison with support vector machine (SVM) and K- mean algorithm. …”
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A Study on the Establishment of a Variable Stiffness Physical Model of Abdominal Soft Tissue and an Interactive Massage Force Prediction Algorithm by Xinyi Tang, Ping Shi, Zhenjie Luo, Sujiao Li, Hongliu Yu

“…Furthermore, a transformer-based machine learning algorithm was developed. This algorithm predicts interaction forces using anthropometric and physiological characteristics. …”
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Hybrid particle swarm optimization and semi-supervised extreme learning machine for cellular network localization by Fagui Liu, Hengrui Qin, Xin Yang, Yi Yu

“…The experiments demonstrate that applying particle swarm optimization in our optimization framework makes the hyper parameters of semi-supervised extreme learning machine algorithm self-adaptive in different conditions. …”
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Design of an evolutionary model for international trade settlement based on genetic algorithm and fuzzy neural network. by Jiaqing Huang, Yang Liu, Miaomiao Tu, Osama Sohaib

“…The objective is to improve the classification of bank-related risks by integrating the adaptability of fuzzy logic with the global optimization capability of genetic algorithms. …”
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Improved classification algorithm for hyperspectral remote sensing images based on the hybrid spectral network model by Lv Deguo

“…Comparative analyses demonstrate a-HybridSN’s superior performance against established methods such as HybridSN, random forest, SoftMax, decision tree, and support vector machine. These results underscore the robustness and adaptability of a-HybridSN.…”
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Intelligent Fault Severity Detection of Rotating Machines Based on VMD-WVD and Parameter-Optimized DBN by Ning Jia, Yao Cheng, Youyuan Tian, Feiyu Yang

“…Compared with BPNN, the traditional DBN, VMD-DBN, VMD-PSO-DBN, and other methods, the proposed algorithm has strong adaptive feature extraction ability and generalization of application.…”
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SENTIMENT ANALYSIS WITH LONG-SHORT TERM MEMORY (LSTM) AND GATED RECURRENT UNIT (GRU) ALGORITHMS by Muhammad Nazhif Abda Putera Khano, Dewi Retno Sari Saputro, Sutanto Sutanto, Antoni Wibowo

“…Therefore, this article examines the combination of Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. GRU is an algorithm that is used to make each recurrent unit able to record adaptively at different time scales. …”
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Securing IoT Communications via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method by Behnam Seyedi, Octavian Postolache

“…The second phase focuses on optimal feature selection using a Genetic Algorithm enhanced with eagle-inspired search strategies. …”
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Multi-objective optimization for smart cities: a systematic review of algorithms, challenges, and future directions by YiFan Chen, Weng Howe Chan, Eileen Lee Ming Su, Qi Diao

“…While established methods such as Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multiobjective Evolutionary Algorithm based on Decomposition (MOED/D) remain prevalent, hybrid frameworks that couple deep learning with evolutionary search display superior adaptability in high-dimensional, dynamic environments. …”
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Continuous genetic algorithm for grasping an object of a priori unknown shape by a robotic manipulator by A. D. Voronkov, S. A.K. Diane

“…A heuristic search algorithm (continuous genetic algorithm) is used. The complex scene analysis algorithm uses classical image processing methods. …”
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A Decomposition-Integration Framework of Carbon Price Forecasting Based on Econometrics and Machine Learning Methods by Zhehao Huang, Benhuan Nie, Yuqiao Lan, Changhong Zhang

“…By optimizing the GRU model with the grey wolf optimizer (GWO) algorithm, the framework enhances both prediction stability and adaptability, while the nonlinear integration approach effectively mitigates error accumulation. …”
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A Location Selection Policy of Live Virtual Machine Migration for Power Saving and Load Balancing by Jia Zhao, Yan Ding, Gaochao Xu, Liang Hu, Yushuang Dong, Xiaodong Fu

“…We propose a novel approach MOGA-LS, which is a heuristic and self-adaptive multiobjective optimization algorithm based on the improved genetic algorithm (GA). …”
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Estimating shear strength of dredged soils for marine engineering: experimental investigation and machine learning modeling by Zheng Yao, Kaiwei Xu, Zejin Wang, Haodong Sun, Peng Cui, Peng Cui

“…Drawing from the test data, a structured database was assembled, and a new learning framework was developed by combining the Logical Development Algorithm (LDA), Adaptive Boosting (BA), and Artificial Neural Networks (ANN). …”
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