Debris Flow Susceptibility Prediction Using Transfer Learning: A Case Study in Western Sichuan, China by Tiezhu Li, Qidi Huang, Qigang Chen

“…In this study, debris flow susceptibility models were developed using three machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). …”
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Method of calculating the pressure on the soil of wheeled tractors by V. Yu. Revenko, A. N. Nazarov, V. I. Skorlyakov

“…Insignificant differences in the assessment of the maximum pressure on the soil, obtained as a result of measurements and calculations (the difference is 1.9-3.3 %) indicate a high degree of reliability of the developed methodology, as well as the possibility of its application in engineering practice to evaluate the functional indicators of tractors, including taking into account changes in their size and mass parameters when equipped with ballast weights, agricultural machines, implements, dual tires, etc. …”
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Waiting Experience: Optimization of Feedback Mechanism of Voice User Interfaces Based on Time Perception by Junfeng Wang, Yue Li, Shuyu Yang, Shiyu Dong, Jialin Li

“…In this paper, the speech rate of user-machine voice interaction is collected through prototype experimentation. …”
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Frailty identification using a sensor-based upper-extremity function test: a deep learning approach by Mehran Asghari, Hossein Ehsani, Nima Toosizadeh

“…Results showed that incorporating muscle model parameters significantly improved frailty prediction. …”
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Data-driven modeling of the Yld2000 yield criterion and its efficient application in numerical simulation by Xiaomin Zhang, Jianzhong Mao, Zhi Cheng

“…Regression models for the yield stress and its first-order derivatives based on the Yld2000–2d yield criterion are developed using several machine learning algorithms, including Random Forest (RF), Multilayer Perceptron (MLP), Histogram-Based Gradient Boosting (HGB), and Support Vector Machine (SVM). …”
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A Deep Learning Algorithm for Multi-Source Data Fusion to Predict Effluent Quality of Wastewater Treatment Plant by Shitao Zhang, Jiafei Cao, Yang Gao, Fangfang Sun, Yong Yang

“…The results show that the R² of LSTM and GRU is 1.36%~31.82% higher than that of MLP and 9.10%~47.75% higher than that of traditional machine learning algorithms. Finally, the RReliefF approach was used to identify the key parameters affecting the water quality behaviour of IETP effluent, and it was found that, by optimising the multi-source feature structure, not only the monitoring and management strategies can be optimised, but also the modelling efficiency of the model can be further improved.…”
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Rancang Bangun Mesin Pemanas Akrilik Tipe Turbular Skala Industri Kecil by Noviyanti Nugraha, M. Khenbakti, Rakha Rakha, Teguh Siswanto, Muraz Muraz, Rizkia Munajat

“…The purpose of this research is to design and manufacture an acrylic heating machine that can be used up to a dimension of 600 mm wide and unlimited in length, and can heat up to a thickness of 10 mm with a continuous heating process and is relatively short. …”
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Color and Grey-Level Co-Occurrence Matrix Analysis for Predicting Sensory and Biochemical Traits in Sweet Potato and Potato by Judith Ssali Nantongo, Edwin Serunkuma, Gabriela Burgos, Mariam Nakitto, Joseph Kitalikyawe, Thiago Mendes, Fabrice Davrieux, Reuben Ssali

“…With instrumental color and texture parameters as predictors, low to moderate accuracy was detected in the machine learning models developed to predict sensory panel traits. …”
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Semi-in-situ cutting force measurement of a jigsaw by Sándor Apáti, György Hegedűs, Sándor Hajdu

“…This research helps advance cutting technology for woodworking and precision machining.…”
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Engine Mass Flow Estimation through Neural Network Modeling in Semi-Transient Conditions: A New Calibration Approach by T. Savioli, M. Pampanini, G. Visani, L. Esposito, C. A. Rinaldini

“…The present work aims to investigate a novel approach for engine control system calibration, by adopting machine learning techniques to model physical parameters of the engine starting from experimental data measured at the test bench. …”
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A comprehensive analysis of deep learning and transfer learning techniques for skin cancer classification by Manishi Shakya, Ravindra Patel, Sunil Joshi

“…All approaches are trained using different parameters like epoch, batch size, and learning rate. …”
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Supervised Sentiment Analysis of Indirect Qualitative Student Feedback for Unbiased Opinion Mining by Smitha Bidadi Anjan Prasad, Raja Praveen Kumar Nakka

“…Performance parameters such as the F1-score, recall, accuracy, and precision are compared. …”
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Optimization of Intelligent Maintenance System in Smart Factory Using State Space Search Algorithm by Nuttawan Thongtam, Sukree Sinthupinyo, Achara Chandrachai

“…The design began with the development of a new IMS concept that incorporates three key elements: the automation pyramid standard, Industrial Internet of Things (IIoT) sensors, and a computerized maintenance management system (CMMS). The CMMS collects machine data from the maintenance database, while real-time parameters are gathered via IIoT sensors from the supervisory control and data acquisition (SCADA) system. …”
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Container Flow-Transport Technology of Selection Grain Production by Mikhail L. Kryukov, Viktor K. Pyshkin, Andrey S. Chulkov, Svetlana V. Vlasova, Maksim V. Ivanov, Kirill A. Stepanov

“…(Conclusions) The authors suggest using the developed methodology to improve the technological process of harvesting, transportation and postharvest processing of seed grain, organize this process, as well as select machine parameters and technical equipment on the farms of the Central region of Russia. …”
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METHODICAL ASPECTS OF DETERMINATION OF THE BENTONITIC MOLDING CLAYS CHARACTERISTICS by N. N. Fedorov

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The Impact of Various Filling Patterns and Building Orientations on the Mechanical Characteristics and Building Time of PLA Using FDM by M. Hamoud, Sachin Salunkhe, Lenka Cepova, H. M. A. Hussien

“…In addition, the part can be built with high strength, hardness, and minimum building time, which is useful information for the best utilization of the 3DP machine. Also, the chosen parameters optimize the building process with little human intervention.…”
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Artificial intelligence models for determining the strength of centrally compressed pipe-concrete columns with square cross-section by Chepurnenko Anton, Yazyev Batyr, Tyurina Vasilina, Akopyan Vladimir

“…The article is devoted to the development of machine learning models for predicting the ultimate load during central compression of concrete-filled steel tubular (CFST) columns with square cross-section. …”
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Enhanced detection of accounting fraud using a CNN-LSTM-Attention model optimized by Sparrow search by Peifeng Wu, Yaqiang Chen

“…To further improve the model’s performance, the sparrow search algorithm (SSA) is employed for parameter optimization, ensuring the best configuration of the CNN-LSTM-Attention framework. …”
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Short-Term Power Load Forecasting Based on DPSO-LSSVM Model by Shujun Ji, Linhao Zhang, Jinteng Wang, Tao Wei, Jiadong Li, Bu Ling, Jinglong Xu, Zuoping Wu

“…A short-term load forecasting model based on least squares support vector machine is constructed, and the optimal parameters of the model are established. …”
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Research on deep reinforcement learning based intelligent shop scheduling method by Zihui LUO, Chengling JIANG, Liang LIU, Xiaolong ZHENG, Huadong MA

“…The unprecedented prosperity of the industrial internet of things (IIoT) has opened up a new path for the traditional industrial manufacturing model.Intelligent shop scheduling is one of the key technologies to achieve the overall control and flexible production of the whole production process.It requires an effective plan with a minimum makespan to allocate multiple processes and multiple machines for production scheduling.Firstly, the shop scheduling problem was defined as a Markov decision process (MDP), and a shop scheduling model based on the pointer network was established.Secondly, the job scheduling process was regarded as a mapping from one sequence to another, and a new shop scheduling algorithm based on deep reinforcement learning (DRL) was proposed.By analyzing the convergence of the model under different parameter settings, the optimal parameters were determined.Experimental results on different scales of public data sets and actual production data sets show that the proposed DRL algorithm can obtain better performances.…”
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