Robotic Arm Trajectory Planning Based on Improved Slime Mould Algorithm by Changyong Li, Hao Xing, Pengbo Qin

“…The simulation and real-machine experimental results show that the improved algorithm shortens the movement time of the robotic arm, reduces the joint impacts, minimizes the vibration and wear, and prolongs the service life of the robotic arm.…”
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An Optimization Model for Production Scheduling in Parallel Machine Systems by Leting Zu, Wenzhu Liao, Xiaoxia Yang

“…Furthermore, the optimization model developed in this paper can be adapted to tackle the production scheduling problem with modifications tailored for parallel machines.…”
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ML-RASPF: A Machine Learning-Based Rate-Adaptive Framework for Dynamic Resource Allocation in Smart Healthcare IoT by Wajid Rafique

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Enhanced securities investment strategy using ISSA–SVM: a hybrid model combining adaptive moving average, support vector machine, and multi-strategy sparrow search algorithm for improved trend tracking and risk adjustment by Wei Ni, Qingqing Chen, Xiaochen Guo, Yanan Liu

Subjects: “…Adaptive moving average (AMA)…”
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An energy-saving virtual machine scheduling algorithm for resource management based on cloud computing technology by Liangyu Zhang

“…This paper innovatively designs a universal chromosome structure with regions to adapt to different data center server compositions and introduces adaptive mutation operators based on genetic algorithms to improve global search capabilities and optimize virtual machine scheduling schemes. …”
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The machine learning algorithm based on decision tree optimization for pattern recognition in track and field sports. by Guomei Cui, Chuanjun Wang

“…Secondly, an optimized machine learning algorithm based on decision tree is adopted. …”
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A novel machine-learning algorithm to screen for trisomy 21 in first-trimester singleton pregnancies by James Osborne, Chris Cockcroft, Carolyn Williams

“…This study investigates the use of machine-learning algorithms in the prediction of T21 in first-trimester singleton pregnancies and compares their performance to existing screening models.Methods A total of 86,354 anonymised, first trimester, singleton pregnancy screening cases, including 211 with T21, were used to train and test machine-learning models using adaptive boosting technology. …”
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Travel Time Prediction in a Multimodal Freight Transport Relation Using Machine Learning Algorithms by Nikolaos Servos, Xiaodi Liu, Michael Teucke, Michael Freitag

“…., by mobile sensors, and adequate prediction methods. Machine Learning (ML) algorithms are well suited to solve non-linear and complex relationships in the collected tracking data. …”
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Hybridization of Machine Learning Algorithms and an Empirical Regression Model for Predicting Debris-Flow-Endangered Areas by Xiang Wang, Mi Tian, Qiang Qin, Jingwei Liang

“…This paper proposes a hybrid method for predicting debris-flow hazard zone by integrating machine-learning algorithms and an empirical regression model. …”
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Ensemble machine learning algorithm for anti-VEGF treatment efficacy prediction in diabetic macular edema by Yu Fang, Jianwei Lin, Peiwen Xie, Huishan Zhu, Tsz Kin Ng, Guihua Zhang

“…A total of 20 image and clinical features were selected for the model construction and test in DME patients responded to anti-VEGF therapy. Adaptive boosting (AdaBoost), GradientBoosting, and light gradient boosting machine (LightGBM) exhibited better performances than the remaining 8 models. …”
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Ensemble Learning-Based Metamodel for Enhanced Surface Roughness Prediction in Polymeric Machining by Elango Natarajan, Manickam Ramasamy, Sangeetha Elango, Karthikeyan Mohanraj, Chun Kit Ang, Ali Khalfallah

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Medical Image Segmentation Algorithm Based on Optimized Convolutional Neural Network-Adaptive Dropout Depth Calculation by Feng-Ping An, Jun-e Liu

“…Based on the above ideas, this paper proposes a medical image segmentation algorithm based on an optimized convolutional neural network with adaptive dropout depth calculation. …”
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Single-Source and Multi-Source Cross-Subject Transfer Based on Domain Adaptation Algorithms for EEG Classification by Rito Clifford Maswanganyi, Chunling Tu, Pius Adewale Owolawi, Shengzhi Du

“…In this paper, domain adaptation algorithms, including two machine learning (ML) algorithms, are contrasted based on the single-source-to-single-target (STS) and multi-source-to-single-target (MTS) transfer paradigms, mainly to mitigate the challenge of immense inter-subject variations in EEG neural dynamics that lead to poor classification performance. …”
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On the development of diagnostic support algorithms based on CPET biosignals data via machine learning and wavelets by Rafael F. Pinheiro, Rui Fonseca-Pinto

“…Leveraging support vector machine (SVM) technology, a well-known machine learning classification method, in combination with wavelet transforms for feature extraction, the algorithm takes an innovative approach. …”
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Optimizing protein-ligand docking through machine learning: algorithm selection with AutoDock Vina by Ala’ Omar Hasan Zayed

“…Methods We developed a comprehensive algorithm set comprising eighty-one distinct configurations of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm within AutoDock Vina. …”
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Optimizing energy forecasts at Boma for 2023 to 2053 Using machine learning techniques of the PSO algorithm by André Mampuya Nzita, Bernard Ndaye Nkanka, Guyh Dituba Ngoma, Clément N’zau Umba-di-Mbudi

“…The lessons learned highlight the need for a thorough understanding of consumption behaviors and regular data updates to adapt forecasts to future developments. Keywords: Optimization, energy forecasting, PSO algorithm, machine learning techniques, energy management…”
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Versatile machine learning algorithms for FTIR spectroscopy: differentiating crosslinked and non-crosslinked gelatin samples by Juliana Rincón-López, Eliana Álvarez-Valdés, Daniela Velez-Arango, Estefanía Rojas Zuleta, Leidy Yuliana Vargas Soto, Liliana Lellesch, Victor Alonso García Londoño, Milton Rosero-Moreano, Gonzalo Taborda-Ocampo

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Evaluating the generalisability of region-naïve machine learning algorithms for the identification of epilepsy in low-resource settings. by Ioana Duta, Symon M Kariuki, Anthony K Ngugi, Angelina Kakooza Mwesige, Honorati Masanja, Daniel M Mwanga, Seth Owusu-Agyei, Ryan Wagner, J Helen Cross, Josemir W Sander, Charles R Newton, Arjune Sen, Gabriel Davis Jones

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Investigating the Machining Behavior of the Additively Manufactured Polymer-Based Composite Using Adaptive Neuro-Fuzzy Learning by Anastasios Tzotzis, Dumitru Nedelcu, Simona-Nicoleta Mazurchevici, Panagiotis Kyratsis

“…Multi-objective optimization using the Non-Dominated Sorting Genetic Algorithm 2 (NSGA-II) algorithm was employed to minimize both <i>Ra</i> and <i>T</i> simultaneously. …”
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In-depth Analysis on Machine Learning Approaches by Abdulhady A. Abdullah, Nergz S. Mohammed, Maryam Khanzadi, Safar M. Asaad, Zrar Kh. Abdul, Halgurd S. Maghdid

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