PREDICTIVE MODELS FOR EARLY DETECTION OF PARKINSON’S DISEASE: A MACHINE LEARNING APPROACH by S. Jeyantha Jafna Juliet, D. Jasmine David, J. S. Raj Kumar, Angelin Jeba P., R. Golden Nancy, M. Selvarathi, T. Jemima Jebaseeli

“…To diagnose PD, the proposed method uses two different data sets. Algorithms for machine learning are also capable of helping in producing specific details from such data. …”
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Motion estimation algorithm using 2 bit-depth pixel and fuzzy quantization by Chuan-ming SONG, Yan-wen GUO, Xiang-hai WANG, Dan LIU

“…To further predict the precision of the proposed algorithm, a bit resolution reduction error-motion vector precision model was built by exploiting the auto-correlation function. …”
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The prognostic predictive value of indirect bilirubin-inflammation score in patients with nasopharyngeal carcinoma by JI Huojin, LI Jun, LUO Yonglin, QIN Weiling, YE Yinxin, CAI Yonglin

“…It can provide a basis for personalized prognostic predictions and the formulation of clinical treatment strategies for NPC.…”
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Research and application of the algorithm editing method for improving active noise control performance by Haisheng Song, Yahui Dong, Na Yang, Zhiyong Chen

“…Additionally, the frequent switching of algorithms poses challenges in fully leveraging the performance of individual algorithms. …”
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Link quality prediction based on random forest by Linlan LIU, Shengrong GAO, Jian SHU

“…Link quality prediction is vital to the upper layer protocol design of wireless sensor networks.Selecting high quality links with the help of link quality prediction mechanisms can improve data transmission reliability and network communication efficiency.The Gaussian mixture model algorithm based on unsupervised clustering was employed to divide the link quality level.Zero-phase component analysis (ZCA) whitening was applied to remove the correlation between samples.The mean and variance of signal to noise ratio,link quality indicator,and received signal strength indicator were taken as the estimation parameters of link quality,and a link quality estimation model was constructed by using a random forest classification algorithm.The random forest regression algorithm was used to build a link quality prediction model,which predicted the link quality level at the next moment.In different scenarios,comparing with exponentially weighted moving average,triangle metric,support vector regression and linear regression prediction models,the proposed prediction model has higher prediction accuracy.…”
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Proton Range Measurement Precision in Ionoacoustic Experiments with Wavelet-Based Denoising Algorithm by Elia Arturo Vallicelli, Andrea Baschirotto, Lorenzo Stevenazzi, Mattia Tambaro, Marcello De Matteis

“…Then, the WTDA was applied to the simulated signals from a 200 MeV clinical beam where, compared to state-of-the-art algorithms, it achieved a −80% dose reduction when achieving the same 30 μm precision and a six-fold precision improvement for the same 17 Gy dose deposition.…”
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Molecular predictive biomarker testing in advanced thyroid cancer – a European consensus by Aleš Ryška, Jaume Capdevila, Matthias S Dettmer, Rossella Elisei, Dagmar Führer, Julien Hadoux, Barbara Jarząb, Laura D Locati, Kate Newbold, Giovanni Tallini, Silvia Uccella, Lori Wirth, Ravinder Singh, Iris M Simon, Pilar Camacho, Laura Fugazzola

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Comprehensive characterization of T cell subtypes in lung adenocarcinoma: Prognostic, predictive, and therapeutic implications by Shiquan Liu, Hao Sun, Tianye Song, Ce Liang, Lele Deng, Haiyong Zhu, Fangchao Zhao, Shujun Li

“…A Lasso + PLSRcox-based signature was a significant risk factor for predicting LUAD patient outcomes, outperforming traditional clinicopathological factors. …”
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Deep learning for predicting the occurrence of tipping points by Chengzuo Zhuge, Jiawei Li, Wei Chen

“…Here, we address this challenge by developing a deep learning algorithm for predicting the occurrence of tipping points in untrained systems, by exploiting information about normal forms. …”
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Slipping Trend Prediction Based on Improved Informer by Jingchun Huang, Sheng He, Haoxiang Feng, Yongjiang Yu

“…The transformer-based Informer algorithm performs well in time series prediction and analysis. …”
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Modify possibilities of the secondary structures prediction method by Alvydas Špokas, Albertas Timinskas

“… It was analyzed dependence of the average accuracy of secondary protein structure prediction on various GOR algorithm modifications. In essence new modification has expanded informational parameter set by taking into account secondary structure of neighboring amino acid. …”
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Content-Aware Fast Motion Estimation Algorithm for IoT Based Multimedia Service by Ryong-Baek, Kyung-Soon Jang, Jae-Hyun Nam, Byung-Gyu Kim

“…Experimental results show that the proposed algorithm achieves speed-up factors of up to 48.57% and 16.03%, on average, with good bitrate performance, compared with fast integer-pel and fractional-pel motion estimation for H.264/AVC (UMHexagonS), and an enhanced predictive zonal search for single and multiple frame motion estimation (EPZS) methods using JM 18.5, respectively. …”
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Knee Point-Guided Multiobjective Optimization Algorithm for Microgrid Dynamic Energy Management by Wenhua Li, Guo Zhang, Tao Zhang, Shengjun Huang

“…Model predictive control (MPC) technology can effectively reduce the bad effect caused by inaccurate data prediction in microgrid energy management problem. …”
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Image Reconstruction Algorithm Based on Extreme Learning Machine for Electrical Capacitance Tomography by SU Ziheng, CHEN Deyun, WANG Lili

“…Aiming at the problem that the traditional ECT is not accurate in complex situations, this paper proposes a depth learning based inversion method Through the improvement and optimization of the traditional extreme learning machine, the image feature information obtained by the reconstructed image method is used as the training data, and the result obtained by inputting the data into the predictive model is used as the prior information The cost function is used to encapsulate the prior knowledge and domain expertise, and spatial regularizers and time regularizers are introduced to enhance sparsity The separated Bregman (SB) algorithm and the iterative shrinkage threshold (FIST) method are used to solve the specified cost function The final imaging result is obtained The simulation results show that the image reconstructed by this method has less than 10% error compared with the original flow pattern, and reduces artifacts and distortion, which improves the reconstructed image quality…”
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Outcome prediction of the measles vaccination in healthcare employees by A. A. Ereshchenko, O. A. Gusyakova, N. B. Migacheva, F. N. Gilmiyarova, A. V. Lyamin

“…These models allowed to develop algorithm for predicting failures of the measles vaccination in healthcare workers that can be used for detection of persons at risk for non-forming specific humoral immunity. …”
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Predictive modelling and identification of critical variables of mortality risk in COVID-19 patients by Olawande Daramola, Tatenda Duncan Kavu, Maritha J. Kotze, Jeanine L. Marnewick, Oluwafemi A. Sarumi, Boniface Kabaso, Thomas Moser, Karl Stroetmann, Isaac Fwemba, Fisayo Daramola, Martha Nyirenda, Susan J. van Rensburg, Peter S. Nyasulu

“…This study aimed to investigate the performance and interpretability of several ML algorithms, including deep multilayer perceptron (Deep MLP), support vector machine (SVM) and Extreme gradient boosting trees (XGBoost) for predicting COVID-19 mortality risk with an emphasis on the effect of cross-validation (CV) and principal component analysis (PCA) on the results. …”
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Investigation of predictive factors for fatty liver in children and adolescents using artificial intelligence by Aliakbar Sayyari, Amin Magsudy, Yasamin Moeinipour, Amirhossein Hosseini, Hamidreza Amiri, Mohammadreza Arzaghi, Fereshteh Sohrabivafa, Seyedeh Fatemeh Hamzavi, Ashkan Azizi, Tahereh Hatamii, AmirAli Okhovat, Naghi Dara, Negar Imanzadeh, Farid Imanzadeh, Mahmoud Hajipour

“…Liver biopsy is the gold standard for NAFLD diagnosis. Machine learning algorithms could assist in an early diagnostic approach and leading to a favorable prognosis.ObjectiveThis study aimed to identify predictive factors for NAFLD in children and adolescents using machine learning models, focusing on liver biopsy outcomes such as fibrosis, infiltration, ballooning, and steatosis.MethodsData from 659 children suspected of NAFLD, who underwent liver biopsy at Mofid Children's Hospital between 2011 and 2023, were analyzed. …”
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An Improved Whale Optimization Algorithm for the Clean Production Transformation of Automotive Body Painting by Qin Yang, Xinning Li, Teng Yang, Hu Wu, Liwen Zhang

“…Experimental validation using the painting processes of TJ Corporation’s New Energy Vehicles (NEVs) demonstrates the superiority of the proposed algorithm over the MHWOA, WOA-RBF, and WOA-VMD. Results show that the method achieves a 42.1% increase in coating production efficiency, over 98% exhaust gas purification rate, 18.2% average energy-saving improvement, and 17.9% reduction in manufacturing costs. …”
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Explainable machine learning to predict the cost of capital by Niklas Bussmann, Paolo Giudici, Paolo Giudici, Alessandra Tanda, Alessandra Tanda, Ellen Pei-Yi Yu

“…Our findings pave the way for future investigations on the impact of ESG and country factors in predicting the cost of capital.…”
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Prediction of amphipathic helix-membrane interactions with Rosetta. by Alican Gulsevin, Jens Meiler

“…The AmphiScan protocol predicted the coordinates of amphipathic helices within less than 3Å of the reference structures and identified membrane-embedded residues with a Matthews Correlation Constant (MCC) of up to 0.57. …”
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