Performance Measurement of Gesture-Based Human–Machine Interfaces Within eXtended Reality Head-Mounted Displays by Leopoldo Angrisani, Mauro D’Arco, Egidio De Benedetto, Luigi Duraccio, Fabrizio Lo Regio, Michele Sansone, Annarita Tedesco

“…Without compromising generality, the obtained results show that the proposed method can provide valuable insights into performance trends across individuals and gesture parameters. Moreover, the statistical analyses employed can determine whether increased individual familiarity with the Human–Machine Interface results in faster task completion without a corresponding decrease in accuracy. …”
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DESIGN AND FEM STATIC ANALYSIS OF AN INSTRUMENT FOR SURFACE PLASTIC DEFORMATION OF NON-PLANAR FUNCTIONAL SURFACES OF MACHINE PARTS by STOYAN SLAVOV, ILIYAN ILIEV

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DESIGN AND FEM STATIC ANALYSIS OF AN INSTRUMENT FOR SURFACE PLASTIC DEFORMATION OF NON-PLANAR FUNCTIONAL SURFACES OF MACHINE PARTS by STOYAN SLAVOV, ILIYAN ILIEV

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Machine learning prediction and explainability analysis of high strength glass powder concrete using SHAP PDP and ICE by Muhammad Sarmad Mahmood, Tariq Ali, Inamullah Inam, Muhammad Zeeshan Qureshi, Syed Salman Ahmad Zaidi, Muwaffaq Alqurashi, Hawreen Ahmed, Muhammad Adnan, Abdul Hakim Hotak

“…This study aims to evaluate the compressive strength (CS) of high strength glass-powder concrete (HSGPC) using machine learning (ML) models and enhance predictive accuracy through hybrid optimization techniques. …”
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Development and validation of a machine learning model to predict hemostatic intervention in patients with acute upper gastrointestinal bleeding by Kajornvit Raghareutai, Watcharaporn Tanchotsrinon, Onuma Sattayalertyanyong, Uayporn Kaosombatwattana

“…Eighteen features, including demographic characteristics, clinical presentation, and laboratory parameters, were selected as input for 15 machine learning models. …”
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Harnessing machine learning approach for hardness optimization of Al-Si alloy composites reinforced with coconut shell ash by M Poornesh, Shreeranga Bhat, Mithun Kanchan

“…The purpose of this study was to utilize Machine Learning (ML) to optimize the hardness of aluminum-silicon (Al-Si) alloy composites reinforced with Coconut Shell Ash (CSA). …”
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Mathematical model of operation of stacker belt conveyors of mobile road-building machines with tracing control of belt tension by Goncharov K.A., Grishin A.V.

“…The paper proposes a mathematical model of operation of stacker belt conveyors of mobile road-building machines with tracing control of belt tension. This model allows one to evaluate the operation processes of the conveyor taking into account the variation of the rolling resistance coefficient of the belt on the dumping track, as well as to determine parameters of the tracing tensioning devices with a view to further selecting the appropriate driven equipment. …”
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Data-Driven Approach for Intelligent Classification of Tunnel Surrounding Rock Using Integrated Fractal and Machine Learning Methods by Junjie Ma, Tianbin Li, Roohollah Shirani Faradonbeh, Mostafa Sharifzadeh, Jianfeng Wang, Yuyang Huang, Chunchi Ma, Feng Peng, Hang Zhang

“…This study compiled a database containing 246 railway and highway tunnel cases based on these parameters. Then, four SRC models were constructed, integrating Bayesian optimization (BO) with support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) algorithms. …”
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Machine learning analysis of survival outcomes in breast cancer patients treated with chemotherapy, hormone therapy, surgery, and radiotherapy by Eyachew Misganew Tegaw, Betelhem Bizuneh Asfaw

“…Performance of the models was assessed using parameters such as Accuracy, Precision, Recall, F1-Score and Area under the Receiver Operating Characteristic Curve (AUC-ROC). …”
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Machine Learning Model Integrating CT Radiomics of the Lung to Predict Checkpoint Inhibitor Pneumonitis in Patients with Advanced Cancer by François Cousin MD, PhD, Thomas Louis MS, PhD, Pierre Frères MD, PhD, Julien Guiot MD, PhD, Mariaelena Occhipinti MD, Fabio Bottari MS, Wim Vos MS, Roland Hustinx MD, PhD

“…We demonstrated the potential role of machine learning models associating clinical parameters and lung CT radiomic features to better identify patients treated with ICIs at risk of developing CIP. …”
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Machine learning for cardio-oncology: predicting global longitudinal strain from conventional echocardiographic measurements in cancer patients by Tagayasu Anzai, Kenji Hirata, Ken Kato, Kohsuke Kudo

“…If reduced GLS could be predicted from conventional echocardiographic parameters, it could help identify patients who would most benefit from direct GLS assessment. …”
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Machine learning for predicting the outcomes and risks of cardiovascular diseases in patients with hypertension: results of ESSE-RF in the Primorsky Krai by V. A. Nevzorova, N. G. Plekhova, L. G. Priseko, I. N. Chernenko, D. Yu. Bogdanov, M. V. Mokshina, N. V. Kulakova

“…We analyzed the main and additional parameters (35) of CVD risk factors in 2131 people as a part of ESSE-RF study (2014-2019). …”
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A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm by Yerui Fan, Chao Zhang, Yu Xue, Jianguo Wang, Fengshou Gu

“…It is based on a high-performance support vector machine (SVM) that is developed with a multifeature fusion and self-regulating particle swarm optimization (SRPSO). …”
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Improvement on Electromagnetic Performance of Axial–Radial Flux Type Permanent Magnet Machines by Optimal Stator Slot Number by Ran Yi, Chunwei Yuan, Hongbo Qiu, Wenhao Gao, Junyi Ren

“…A full comparison of the main parameters and electromagnetic performances of the ARFTPM machine with different stator slot numbers is presented, including winding coefficient, back electromotive force (EMF), cogging torque, average torque, and torque-angle characteristics. …”
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RELIABILITY ANALYSIS OF COMPLEX MAN-MACHINE SYSTEM TECHNIQUE UNDER KNOWN DISTRIBUTION LAWS OF TIME TO COMPONENT FAILURE by Anastas Leonidovich Boran-Keshishyan

“…Complete information on the component reliability of the complex man-machine systems is obtained in the case of availability of the component uptime distribution laws and of their parameters, as well as of the information on the components independence. …”
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Simulation and Generalized Dynamic Modeling of Metro Pantograph-Catenary System Based on Least Squares Support Vector Machine by Huang Yuping, JIANG Wei, TONG Jingquan, SHI Luxing

“…Secondly the model parameters were identified based on the method of LSSVM. …”
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Machine learning for predicting 5-year mortality risks: data from the ESSE-RF study in Primorsky Krai by V. A. Nevzorova, T. A. Brodskaya, K. I. Shakhgeldyan, B. I. Geltser, V. V. Kosterin, L. G. Priseko

“…To build predictive models, we used following machine learning (ML) methods: multivariate LR, Weibull regression, and stochastic gradient boosting.Results. …”
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Prediction of anisotropic property of activated metal inert gas welding by employing different supervised machine learning models by Ruturaj U. Kakade, Nitin Khedkar, Amol Dalavi

“…Material characterization was performed on samples with the highest and lowest TS to evaluate the correlation between microstructure and strength. Machine learning models Linear Regression, Random Forest Regression, and Support Vector Regression (SVR) were applied to predict TS based on welding parameters.• The SVR model achieved the best predictive performance, with an R² of 0.8750 and a model accuracy of 96.73 %.• The results confirm the potential of SVR for accurately forecasting TS in A-MIG welded EN10028, facilitating process optimization in pressure applications…”
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Influence of Flax Fibre Hybridization on Mechanical Behaviour of Sisal Fibre-Polypropylene Composites Prepared with an Injection Moulding Machine by J. Allen Jeffrey, A. Sivakumar, R. Naveen Kumar, A. Anbazhagan, G. Manojkumar, Sami Al Obaid, Saleh Alfarraj, S. Sivakumar, S. Rajkumar

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Non-Invasive Fatigue Detection and Human–Machine Interaction Using LSTM and Multimodal AI: A Case Study by Muon Ha, Yulia Shichkina, Xuan-Hien Nguyen

“…This study introduces a non-invasive fatigue detection system utilizing facial parameters processed via a Long Short-Term Memory (LSTM) neural network, coupled with a human–machine interaction interface via a Telegram chatbot. …”
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