Evaluating Supervised Learning Classifier Performance for OFDM Communication in AWGN-Impacted Systems by Lavanya Vaishnavi D A, Anil Kumar C

“…With the obtained results we have proposed an hybrid model by implementing various ML algorithm for existing communication system pertaining to the receiver for reconstruction of the received signals by training the system using the data transmitted and received data is subjected to testing proposed ML algorithm. …”
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An AI-Based Deep Learning with K-Mean Approach for Enhancing Altitude Estimation Accuracy in Unmanned Aerial Vehicles by Prot Piyakawanich, Pattarapong Phasukkit

“…By synergistically combining K-Means Clustering with a multiple-input deep learning regression-based model (DL-KMA), we have achieved substantial improvements in altitude estimation accuracy. …”
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Performance Analysis of Distributed Incremental LMS Algorithm with Noisy Links by Azam Khalili, Mohammad Ali Tinati, Amir Rastegarnia

“…In this paper, we study the effect of noisy links on the performance of distributed incremental least-mean-square (DILMS) algorithm for the case of Gaussian regressors. …”
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Chaos Time Series Prediction Based on Membrane Optimization Algorithms by Meng Li, Liangzhong Yi, Zheng Pei, Zhisheng Gao, Hong Peng

“…This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction (τ,m) and least squares support vector machine (LS-SVM) (γ,σ) by using membrane computing optimization algorithm. …”
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A Partition-Based Hybrid Algorithm for Effective Imbalanced Classification by Kittipong Theephoowiang, Anantaporn Hanskunatai

“…To further evaluate its performance, the study compares the proposed algorithm with previous methods using G-Mean. The comparison confirms that the proposed algorithm also exhibits strong performance, further highlighting its potential. …”
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Phase Error Criterion Based Adaptive Algorithm for Frequency Estimation by Prayuth Inban, Rachu Punchalard, Chawalit Benjangkaprasert

“…Theoretical analysis for the mean value of the estimated frequency and the steady-state mean square error (MSE) of the frequency estimate are derived in closed forms. …”
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A novel model for malaria prediction based on ensemble algorithms. by Mengyang Wang, Hui Wang, Jiao Wang, Hongwei Liu, Rui Lu, Tongqing Duan, Xiaowen Gong, Siyuan Feng, Yuanyuan Liu, Zhuang Cui, Changping Li, Jun Ma

“…<h4>Results</h4>The root mean square errors (RMSEs) of the four sub-models were 13.176, 14.543, 9.571 and 7.208; the mean absolute scaled errors (MASEs) were 0.469, 0.472, 0.296 and 0.266 and the mean absolute deviation (MAD) were 6.403, 7.658, 5.871 and 5.691. …”
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Error-Index-Based Algorithm for Low-Velocity Impact Localization by Tao Peng, Lilin Cui, Xiufeng Huang, Wenjing Yu, Rongwu Xu

“…The impact test results on a flat plate and cylindrical shell indicated that, compared to the Morlet wavelet method, the proposed algorithm improved the mean relative error of impact point localization on the flat plate by 0.22%, 15.64%, and 15.26% under three different noise conditions, respectively (i.e., no noise, and SNR = 5 and 0 dB). …”
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First Simulations of Feedback Algorithm‐Regulated Marine Cloud Brightening by Walker Raymond Lee, Chih‐Chieh Chen, Jadwiga Richter, Douglas G. MacMartin, Ben Kravitz

“…Our methodology is able to control global mean temperature in this way, but controlling global mean temperature does not by itself mitigate regional impacts common to tropical MCB; additionally, the algorithm takes longer than intended to converge, indicating room for future improvement.…”
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Variational Autoencoders-Based Algorithm for Multi-Criteria Recommendation Systems by Salam Fraihat, Qusai Shambour, Mohammed Azmi Al-Betar, Sharif Naser Makhadmeh

“…Movies multi-criteria dataset demonstrate that the proposed model surpasses other state-of-the-art recommendation algorithms, achieving a Mean Absolute Error (MAE) of 0.6038 and a Root Mean Squared Error (RMSE) of 0.7085, demonstrating its superior performance in providing more precise recommendations for multi-criteria recommendation tasks.…”
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Optimizing Models and Data Denoising Algorithms for Power Load Forecasting by Yanxia Li, Ilyosbek Numonov Rakhimjon Ugli, Yuldashev Izzatillo Hakimjon Ugli, Taeo Lee, Tae-Kook Kim

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ICBoost: An XGBoost-Based Unbiased Transformed Algorithm for Survival Regression by Jingyi Zhang, Shishun Zhao, Yang Xu, Tao Hu

“…ICBoost outperformed other methods, as evidenced by lower root-mean-square error, mean absolute error, and Brier score values.…”
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The development and validation of an easy to use automatic QT-interval algorithm. by Ben J M Hermans, Arja S Vink, Frank C Bennis, Luc H Filippini, Veronique M F Meijborg, Arthur A M Wilde, Laurent Pison, Pieter G Postema, Tammo Delhaas

“…Intra-class coefficient indicated excellent agreement (>0.9) between the algorithm and all observers individually as well as between the algorithm and the mean QT-interval of the observers.…”
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A Brand-New Algorithm for Mapping Algal Biomass in Lakes by Zhengyang Yu, Ronghua Ma, Minqi Hu, Kun Xue, Zhigang Cao, Junfeng Xiong

“…Validated with match-up satellite data, the algorithm generates acceptable results (RMSE = 5.69 mg/m2, mean absolute percentage error = 30.9%, N = 16). …”
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SL-n iterative localization algorithm in wireless sensor networks by LUO Xu, CHAI Li, YANG Jun

“…To compensate for the lackness of robustness caused by MMSE（minimum mean square estimation） in overall situation in wireless sensor network iterative multilateration algorithm,a new estimation algorithm called SL-n were proposed.Firstly all samples were obtained using trilateration or partial MMSE from every combination of n reference nodes which belonged to some blind node,then the location of the blind node were estimated.Simulation experiments show that the proposed SL-n algorithm outperforms MMSE method in overall situation and can reduce the position error sufficiently when the reference error is large.…”
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Robust FCM Algorithm with Local and Gray Information for Image Segmentation by Hanane Barrah, Abdeljabbar Cherkaoui, Driss Sarsri

“…The FCM (fuzzy c-mean) algorithm has been extended and modified in many ways in order to solve the image segmentation problem. …”
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Bidirectional RNN-based private car trajectory reconstruction algorithm by Zhu XIAO, Xin QIAN, Hongbo JIANG, Chenglin CAI, Fanzi ZENG

“…To address the problem that in the complex urban environment, due to the inevitable interruption of GNSS positioning signal and the accumulation of errors during vehicle driving, the collected vehicle trajectory data was likely to be inaccurate and incomplete.a bidirectional weighted trajectory reconstruction algorithm was proposed based on RNN neural network.The GNSS-OBD trajectory acquisition device was used to collect vehicle trajectory information, and multi-source data fusion was adopted to achieve bidirectional weighted trajectory reconstruction.Furthermore, the neural arithmetic logic unit (NALU) was leveraged with the purpose of enhancing the extrapolation ability of deep network and ensuring the accuracy of trajectory reconstruction.For the evaluation, real-world experiments were conducted to evaluate the performance of the proposed method in comparison with existing methods.The root mean square error (RMSE) indicator shows the algorithm accuracy and the reconstructed trajectory is visually displayed through Google Earth.Experimental results validate the effectiveness and reliability of the proposed algorithm.…”
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Salp Swarm Algorithm for Node Localization in Wireless Sensor Networks by Huthaifa M. Kanoosh, Essam Halim Houssein, Mazen M. Selim

“…The simulation results show that the proposed localization algorithm is better than the other algorithms in terms of mean localization error, computing time, and the number of localized nodes.…”
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A Hyperchaotic Lorenz and DNA-Encoded Image Encryption Algorithm by Shuangyuan Li, Mengfan Li

“…Key space analysis, histogram analysis, correlation analysis of adjacent pixels, and information entropy analysis of the algorithm results are performed. Meanwhile, the mean square error (MSE) of pepper and salt noise density is analyzed by the peak signal-to-noise ratio (PSNR). …”
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Image similarity functions in non-parametric algorithms of voice identification by Cz. BASZTURA, J. ZUK

“…The usefulness of 10 similarity functions (8 distances and 2 nearness'es) in three non-parametric identification algorithms – NN (nearest neighbour), k-NN (k-nearest neighbours) and NM (nearest mean) – was investigated for three sets of parameters (1 natural and 2 normalized). …”
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