Machine Learning in Cyber-Physical Systems and Manufacturing Singularity – it Does Not Mean Total Automation, Human Is Still in the Centre: Part I – Manufacturing Singularity and a... by Goran D. PUTNIK, Vaibhav SHAH, Zlata PUTNIK, Luis FERREIRA

“…Speaking in terms of manufacturing systems, it would mean that there will be achieved intelligent and total automation (once the humans will be excluded). …”
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A deep multiple self-supervised clustering model based on autoencoder networks by Ling Zhu, Zijin Liu, Guangyu Liu

“…Furthermore, to boost the efficiency of the multi-layer clustering module within our model and minimize algorithmic overhead, we integrate a distance-based Two-stage fuzzy C-Means clustering method. …”
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Forecasting of energy consumption rate and battery stress under real-world traffic conditions using ANN model with different learning algorithms by Anbazhagan Geetha, S. Usha, J. Santhakumar, Surender Reddy Salkuti

“…Post-simulation results were summarized and validated using the root mean square error (RMSE), which indicated that the values collected experimentally were close to those predicted by the models. …”
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Multi-Strategy Improved Red-Tailed Hawk Algorithm for Real-Environment Unmanned Aerial Vehicle Path Planning by Mingen Wang, Panliang Yuan, Pengfei Hu, Zhengrong Yang, Shuai Ke, Longliang Huang, Pai Zhang

“…Then, the quality of the initial population is further improved through a dynamic position update optimization strategy based on stochastic mean fusion, which enhances the exploration capabilities of the algorithm and helps it explore promising solution spaces more effectively. …”
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Remote Photoplethysmography Technology for Blood Pressure and Hemoglobin Level Assessment in the Preoperative Assessment Setting: Algorithm Development Study by Selene Y L Tan, Jia Xin Chai, Minwoo Choi, Umair Javaid, Brenda Pei Yi Tan, Belinda Si Ying Chow, Hairil Rizal Abdullah

“…Systolic BP predictions yielded a mean absolute percentage error of 9.52% and a mean difference of 2.69 mm Hg (SD 7.86 mm Hg). …”
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Prediction of Atmospheric Bioaerosol Number Concentration Based on PKO–AGA–SVM Fusion Algorithm and Fluorescence Lidar Telemetry by Zhimin Rao, Yicheng Li, Jiandong Mao, Hu Zhao, Xin Gong

“…The experimental results show that the model prediction using the PKO–AGA–SVM fusion algorithm is better than the SVM, AGA–SVM, and PKO–SVM algorithms, with mean relative errors of 25.79, 20.75, 16.93, and 11.57%, respectively. …”
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Seasonal forecasting of the hourly electricity demand applying machine and deep learning algorithms impact analysis of different factors by Heba-Allah Ibrahim El-Azab, R. A. Swief, Noha H. El-Amary, H. K. Temraz

“…To clarify the four techniques’ performance and effectiveness, the results were compared using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE), and Mean Absolute Percentage Error (MAPE) values. …”
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Pipeline corrosion rate prediction model using BP neural network based on improved sparrow search algorithm by Shuhui XIAO, Chuanjia DU, Chengjun WANG

“…The MIS-SSA-BP neural network prediction model exhibited very low mean absolute error, mean square error, root mean square error, and mean absolute percentage error. …”
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An Efficient Multisensor Hybrid Data Fusion Approach Based on Artificial Neural Networks and Particle Swarm Optimization Algorithms by Luc Eyembe Ihonock, Jean-François Dikoundou Essiben, Benjamin Salomon Diboma, Joe Suk Yong

“…The results obtained show that the suggested deployment geometry has mean absolute percentage errors of 3.97, mean square errors of 0.182, and mean absolute errors of 0.045 which are lower than those associated with the deployment configurations found in the literature. …”
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Integration of Multiple Models with Hybrid Artificial Neural Network-Genetic Algorithm for Soil Cation-Exchange Capacity Prediction by Mahmood Shahabi, Mohammad Ali Ghorbani, Sujay Raghavendra Naganna, Sungwon Kim, Sinan Jasim Hadi, Samed Inyurt, Aitazaz Ahsan Farooque, Zaher Mundher Yaseen

“…With the use of several evaluation criteria, the results showed that the MM-GANN model involving the predictions of ELM and ANN models calibrated by considering all the soil parameters (e.g., Clay, OM, pH, silt, and CCE) as inputs provided superior soil CEC estimates with a Nash Sutcliffe Efficiency (NSE) = 0.87, Root Mean Square Error (RMSE) = 2.885, Mean Absolute Error (MAE) = 2.249, Mean Absolute Percentage Error (MAPE) = 12.072, and coefficient of determination (R2) = 0.884. …”
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Tropical cyclone track prediction harnessing deep learning algorithms: A comparative study on the Northern Indian Ocean by Sabbir Rahman, M. Fahim Faisal, Prianka Mondal, Md. Mahfuzur Rahman

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Estimating Nurse Workload Using a Predictive Model From Routine Hospital Data: Algorithm Development and Validation by Paul Meredith, Christina Saville, Chiara Dall’Ora, Tom Weeks, Sue Wierzbicki, Peter Griffiths

“…ResultsIn a test set of 11,592 ward assessments from 42 wards with a mean WTE per patient of 1.64, the model’s mean absolute error was 0.078, with a mean percentage error of 4.9%. …”
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Development of an interpretable QSPR model to predict the octanol-water partition coefficient based on three artificial intelligence algorithms by Ao Yang, Shirui Sun, Lu Qi, Zong Yang Kong, Jaka Sunarso, Weifeng Shen

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Research on coal mine robot positioning algorithm based on integration of ORB-SLAM3 vision and inertial navigation by Wei CHEN, Shuaida WU, Zijian TIAN, Fan ZHANG, Yi LIU

“…The results show that: (1) Compared with the ORB-SLAM3 algorithm and the VMS-MONO algorithm, the motion trajectory of the proposed positioning system is the closest to the true value trajectory; (2) All indexes of APE of the positioning system are better than ORB-SLAM3 algorithm and VMS-MONO algorithm; The root-mean-square error of the positioning system is 0.049m (the mean value of four experiments), which is 31.1% lower than that of ORB-SLAM3 (the mean value of four experiments).…”
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Predicting Ocean Current Temperature Off the East Coast of America with XGBoost and Random Forest Algorithms Using Rstudio by Lulut Alfaris, Anas Noor Firdaus, Ukta Indra Nyuswantoro, Ruben Cornelius Siagian, Aldi Cahya Muhammad, Rohana Hassan, Rodulfo T. Aunzo, Jr., Reza Ariefka

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Estimating hip impact velocity and acceleration from video-captured falls using a pose estimation algorithm by Reese Michaels, Tiago V. Barreira, Stephen N. Robinovitch, Jacob J. Sosnoff, Yaejin Moon

“…We examined the agreement between the ground truth and OpenPose measurements in terms of mean of absolute error (MAE), mean of absolute percentage error (MAPE), and bias (mean of error). …”
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Accuracy Prediction of Compressive Strength of Concrete Incorporating Recycled Aggregate Using Ensemble Learning Algorithms: Multinational Dataset by Menghay Phoeuk, Minho Kwon

“…The CatBoost model performed the best, exhibiting an R2 of 0.938 and low mean absolute error and root mean squared error values of 2.639 and 3.885, respectively, in the blind evaluation process. …”
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Development and Validation of a Machine Learning Model for Early Prediction of Sepsis Onset in Hospital Inpatients from All Departments by Pierre-Elliott Thiboud, Quentin François, Cécile Faure, Gilles Chaufferin, Barthélémy Arribe, Nicolas Ettahar

“…We developed a machine learning algorithm, capable of detecting the early onset of sepsis in all hospital departments. …”
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Predicting the Temperature of a Permanent Magnet Synchronous Motor: A Comparative Study of Artificial Neural Network Algorithms by Nabil El Bazi, Nasr Guennouni, Mohcin Mekhfioui, Adil Goudzi, Ahmed Chebak, Mustapha Mabrouki

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Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits. by Mohsen Yoosefzadeh-Najafabadi, Dan Tulpan, Milad Eskandari

“…The RBF algorithm with highest Coefficient of Determination (R2) value of 0.81 and the lowest Mean Absolute Errors (MAE) and Root Mean Square Error (RMSE) values of 148.61 kg.ha-1, and 185.31 kg.ha-1, respectively, was the most accurate algorithm and, therefore, selected as the metaClassifier for the E-B algorithm. …”
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