Prediction of energy consumption in four sectors using support vector regression optimized with genetic algorithm by Md. Sadikul Hasan, Md. Tarequzzaman, Md. Moznuzzaman, Md Abdul Ahad Juel

“…The proposed model's efficacy is assessed by calculating the R2 value, mean absolute error (MAE), root mean squared error (RMSE), and residual plot. …”
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Short-Term Power Load Prediction Based on Level Processing Method and Improved GWO Algorithm by Yuntong Li

“…The genetic algorithm is applied to optimize the traditional grey wolf algorithm. …”
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Innovative approaches to beam forming antenna array systems with adaptive Partial Update NLMS algorithms by Zahraa A. Shubber, Thamer M. Jamel, Ali.K. Nahar

“…For the most part, we will be discussing PU Normalized Least Mean Square (PU NLMS) algorithms like M-max NLMS, Periodic-NLMS, and Stochastic-NLMS. …”
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Beyond accuracy: a framework for evaluating algorithmic bias and performance, applied to automated sleep scoring by Michal Bechny, Luigi Fiorillo, Julia van der Meer, Markus Schmidt, Claudio Bassetti, Athina Tzovara, Francesca Faraci

“…Additionally, performance metrics are typically reported as the mean of on-subject results, neglecting critical scenarios—such as different quantiles—that could better convey the algorithm’s capabilities and limitations to clinicians as end-users. …”
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Pseudorange estimation algorithm combining code and carrier phase based on Gaussian sum particle filtering by LI Li-min1, GONG Wen-bin1, LIU Hui-jie1, YU Jin-pei1

“…According to the characteristic that code measurements yielded noisy but unambiguous pseudorange estimates and carrier phase measurements were almost noiseless but were affected by integer ambiguity,a novel Gaussian sum particle filter（GSPF） algorithm was proposed to combine the advantages of the two measurements.The theoretical analysis and simulation results show that the proposed algorithm is immune from cycle slips and produces smaller mean square errors.…”
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An algorithm to assess importance of predictors in systematic reviews of prediction models: a case study with simulations by Ruohua Yan, Chen Wang, Chao Zhang, Xiaohang Liu, Dong Zhang, Xiaoxia Peng

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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

“…Finally, the performance metrics (i.e., coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE)) of the proposed hybrid models are comprehensively investigated and compared with the single predictive model (i.e., MARS, RF, SVM, and the empirical model) under fivefold cross-validation. …”
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Advanced Signal Processing Algorithm-Based State Estimation and Denoising of ECG Signals for Biomedical Applications by Saad Arif, Mohammad Maroof Maroof Siddiqui, Nayef Alqahtani, Kamran Shah, Muhammad Attique Attique Khan, Naoufel Kraiem

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Optimizing Vital Signs in Patients With Traumatic Brain Injury: Reinforcement Learning Algorithm Development and Validation by Hongwei Zhang, Mengyuan Diao, Sheng Zhang, Peifeng Ni, Weidong Zhang, Chenxi Wu, Ying Zhu, Wei Hu

“…Compared with clinical doctors, AI algorithms select normal temperatures more frequently (36.56 °C to 36.83 ℃) and recommend mean arterial pressure levels of 87.5‐95.0 mm Hg. …”
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A scale adaptive visual object tracking algorithm based on weighted neutrosophic similarity coefficient by Keli HU, En FAN, Jun YE, Shigen SHEN, Yuzhang GU

“…The weight of the truth,indeterminacy,and falsity membership under the neutrosophic framework may be different when dealing with different problems.Due to this,a component weighted cosine similarity coefficient was proposed,and it was introduced into the mean shift tracking algorithm.Firstly,the corresponding methods for calculating the membership of the truth,indeterminacy,and falsity were proposed based on the theory of 3σ,as well as the similarity between the features of the corresponding area of the object and background.Then the weighted cosine similarity coefficient was used to construct the weight vector.In addition,a weighted cosine similarity coefficient based scale updating method was proposed.The experimental results demonstrate that the modified visual tracking algorithm performs well,even when there exists challenges like similar background,illumination or scale variation.…”
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Exploring the Capacity of Large Language Models to Assess the Chronic Pain Experience: Algorithm Development and Validation by Jacopo Amidei, Rubén Nieto, Andreas Kaltenbrunner, Jose Gregorio Ferreira De Sá, Mayte Serrat, Klara Albajes

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Creation of a Custom 3D Algorithm for Proper Alignment of Straight Nails in Tibiotalocalcaneal Arthrodesis by Omer Subasi PhD, Wei Shao Tung BS, Julian Hollander Bsc, Samir Ghandour MD, Atta Taseh MD, Nour Nassour MD, Adarsh Aratikatla, Alireza Ebrahimi MD, Bernard Burgesson MD, Soheil Ashkani-Esfahani MD, John Y. Kwon MD

“…The root mean (SD) square error of the affine transformation was 1.62 ± 1.02 mm, demonstrating the validity of mapping. …”
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A low complexity detection algorithm for large scale multiuser MIMO based on message passing by Qiong WANG, Wei YE, Mingming JI

“…According to the problem of high complexity of base station detection in large scale multiuser multiple input multiple output (MIMO) system,a low complexity multiuser variable node full information Gaussian message passing iterative detection algorithm based on forced convergence (VFI-GMPID-FC) was proposed.Firstly,the traditional Gaussian message passing iterative detection (GMPID) algorithm was improved to obtain VFI-GMPID algorithm,the detection performance of the VFI-GMPID algorithm approximates the minimum mean square error detection (MMSE) algorithm,but the complexity was considerably less than the MMSE algorithm.Then,the VFI-GMPID-FC algorithm was proposed to reduce the complexity of the algorithm and improve the detection efficiency.Finally,the simulation results show that the proposed algorithm can effectively reduce the algorithm complexity while ensuring the detection performance.…”
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Distributed Algorithm for Traffic Data Collection and Data Quality Analysis Based on Wireless Sensor Networks by Nan Ding, Guozhen Tan, Wei Zhang, Hongwei Ge

“…Considering the certain correlated characteristics, this algorithm firstly processes the data samples with an aggregation model based on the mean filter, and then, the data quality is analyzed, and partial bad data are repaired by the cusp catastrophe theory. …”
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Near-field localization algorithm of multiple sound sources based on approximated kernel density estimator by Yu-zhuo FANG, Zhi-yong XU, Zhao ZHAO

“…For near-field localization of multiple sound sources in reverberant environments,a algorithm model based on approximated kernel density estimator (KDE) was proposed.Multi-stage (MS) of sub-band processing was introduced to effectively solve the spatial aliasing by wide spacing.Spatial likelihood function (SLF) was built for multi-dimensional fusion by using two operators,sum (S) and prod (P).Then four algorithms,S-KDE,P-KDE,S-KDEMS,P-KDEMS,were derived.By the comprehensive comparison of the two statistical indicators root mean square error (RMSE) and percentage of SLF (PSLF) which denoted the recognition,P-KDEMS is confirmed as a near-field localization algorithm of multiple sound sources with high robustness and recognition.…”
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Research on DV-Hop location algorithm based on range correction and improved gray wolf optimizer by Xiaoqiang ZHAO, Shuai WU, Chuanyi GAO, Ning LI, Bodong LI, Xiaoyong YANG

“…Node location is an important problem in wireless sensor network.Although the location algorithm based on distance measurement has small positioning error, it has many limitations when applied to outdoor environments.Therefore, based on the original distance vector-hop (DV-Hop) algorithm, received signal strength indication (RSSI) technology and the minimum mean square error (MMSE) criterion to modify the algorithm’s ranging process were introduced, and the improved gray wolf optimizer was used to optimize the process of determining the coordinates of unknown nodes.Simulation results show that, compared with the original DV-Hop algorithm and IPDV-Hop algorithm, the average location error rate of the IGDV-Hop algorithm under the initial parameters was reduced by 28% and 17% respectively, and the location effect was significantly improved.…”
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Prediction method of soil water content based on SVM optimized by improved salp swarm algorithm by Xiaoqiang ZHAO, Fan YANG, Zhufeng YAN

“…Aiming at the problems of low accuracy and low efficiency of traditional soil water content prediction methods, support vector machine (SVM) was used to establish a prediction model, and the soil water content prediction method based on SVM optimized was proposed by the improved salp swarm algorithm.Firstly, the opposition-based learning and chaotic optimization were introduced to improve the standard salp swarm algorithm to solve the problem that the algorithm was easy to fall into the local optimal solution and its convergence speed was slow.Secondly, the improved salp swarm algorithm was used to optimize the parameters that affect the performance of SVM and the corresponding prediction model was built.Finally, the proposed model was compared with the particle swarm optimization SVM and the whale algorithm optimized SVM prediction model.The experimental results show that the mean square error and decision coefficient of the proposed model are 0.42 and 0.901, which are better than the other two models which verified the effectiveness of the proposed method.…”
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DNN-based Sub-6 GHz assisted millimeter wave network power allocation algorithm by Changyin SUN, Liyan LIU, Fan JIANG, Jing JIANG

“…Aimed at the problems of the signaling cost and power consumption in the power control measurement of the millimeter wave system, as well as the complexity caused by iteration operations, a millimeter wave link power allocation prediction algorithm using the Sub-6 GHz frequency band was proposed.Firstly, the mapping between the Sub-6 GHz band channel information and the optimal power allocation of the millimeter wave band was analyzed.Then, a deep neural network (DNN) model was utilized to realize this mapping function.To predict the power allocation of millimeter wave channel with Sub-6 GHz channel as input, the neural network was trained with the weighted mean square error minimization method (WMMSE) as the supervisor in different scenarios.The simulation results show that compared with the WMMSE algorithm in millimeter wave band, the proposed algorithm can obtain more than 97% of its sum-rate performance while taking less than 0.1% of the time.…”
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Prediction method of soil water content based on SVM optimized by improved salp swarm algorithm by Xiaoqiang ZHAO, Fan YANG, Zhufeng YAN

“…Aiming at the problems of low accuracy and low efficiency of traditional soil water content prediction methods, support vector machine (SVM) was used to establish a prediction model, and the soil water content prediction method based on SVM optimized was proposed by the improved salp swarm algorithm.Firstly, the opposition-based learning and chaotic optimization were introduced to improve the standard salp swarm algorithm to solve the problem that the algorithm was easy to fall into the local optimal solution and its convergence speed was slow.Secondly, the improved salp swarm algorithm was used to optimize the parameters that affect the performance of SVM and the corresponding prediction model was built.Finally, the proposed model was compared with the particle swarm optimization SVM and the whale algorithm optimized SVM prediction model.The experimental results show that the mean square error and decision coefficient of the proposed model are 0.42 and 0.901, which are better than the other two models which verified the effectiveness of the proposed method.…”
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Sistem Monitoring Tekanan Darah Berbasis Maximum Amplitude Algorithm Menggunakan Android Secara Realtime by Barlian Henryranu Prasetio, Brylliano Maza Putra, Dahnial Syauqy

“…Dengan menggunakan pendekatan menggunakan sinyal osilometrik ini akan diproses pada mikrokontroler Arduino UNO R3 untuk mencari nilai Mean Arterial Pressure, systole dan diastole menggunakan metode Maximum Amplitude Algorithm (MAA).  …”
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