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Workload is crucial in managing and maintaining good performance of human resources and allocations. In an advanced manufacturing industry, human job functions had shifted to cognitive tasks. Thus, cognitive workload evaluation should be used to monitor worker’s workload in optimal condition. Most common tool of cognitive workload tools are perceiv...
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... the remaining 24 subsets were used as training data. The 6 subsets for regression testing performance could be seen in Table 3 in sub section 4.1, along with its predicted value. ...Context 2
... doing regressor analysis using random forest regressor model, we obtained a moderate r 2 value 0.576 and mean absolute error (MAE) 4.557. The graph and actual against predicted value of NASA -TLX score using all four features were presented in Table 3 and Figure 3. The r 2 = 0.576 shows a moderate relationship between the four features to the NASA -TLX score, meaning combination of EF LF, EF TP, EF Ratio and EF HR explained 57.6% variability of the NASA -TLX score. ...Citations
... RFR is a type of ensemble learning that improves accuracy by combining multiple decision trees as shown in Figure 2. It is used for classification and regression problems. It uses the ensemble's bagging, boosting, and stacking methods for random feature selection [16], [17]. Different parameters were used to tune this algorithm. ...
Rice, a staple food source globally, is in high demand and production across the world. Its consumption varies in different countries, with each nation having its unique way of incorporating rice into its diet. Recognizing the global nature of rice, its production is a crucial aspect of ensuring its availability, agriculture forecasting, economic stability, and food security. By predicting its production, we can develop a global plan for its production and stock, thereby preventing issues like famine. This paper proposes machine learning (ML) and deep learning (DL) models like linear regression, ridge regression, random forest (RF), adaptive boosting (AdaBoost), categorical boosting (CatBoost), extreme gradient boosting (XGBoost), gradient boosting, decision tree, and long short-term memory (LSTM) to predict international rice production. A total of nine ML and one DL models are trained and tested on the international dataset, which contains the rice production details of 192 countries over the last 62 years. Notably, linear regression and the LSTM algorithm predict rice production with the highest percentage of R-squared (R 2), 98.40% and 98.19%, respectively. These predictions and the developed models can play a vital role in resolving crop-related international problems, uniting the global agricultural community in a common cause.
... It allows for scheduled repairs to be done efficiently without causing maximum downtime [37]. Finally, Harmayanti et al. (2024) used Random Forest regressors applied to some cognitive manufacturing problems, proved that ML could impose an opening for human computer interaction optimization [38]. From this perspective, these examples definitely exhibit the kind of change facilitated by ML and hence it establishes a compulsion supporting the incorporation of ML into AM processes creating improvement and productivity. ...
... It allows for scheduled repairs to be done efficiently without causing maximum downtime [37]. Finally, Harmayanti et al. (2024) used Random Forest regressors applied to some cognitive manufacturing problems, proved that ML could impose an opening for human computer interaction optimization [38]. From this perspective, these examples definitely exhibit the kind of change facilitated by ML and hence it establishes a compulsion supporting the incorporation of ML into AM processes creating improvement and productivity. ...
The necessity to produce intricate components results in considerable progress in manufacturing methods. Additive manufacturing (AM) is a disruptive technology that allows intricate and custom-tailored components to be fabricated with great precision and efficiency. It is applied in advanced sectors like aerospace, healthcare, automotive industries, and it starts having their interest in many other areas. Machine learning (ML) has become a powerful tool for overcoming problems in AM, offering process efficiency, defect detection, quality assurance, and predictive modelling of mechanical properties. This review discusses how ML transforms AM by providing design evaluation, process optimization, and production control innovation. The approach taken in the study is systematic, examining the current literature and case studies of ML application to AM. Hybrid data collection techniques that combine machine settings with physics aware features and yield robust predictive models are the focus. Additionally, the review evaluates various ML algorithms used to predict mechanical properties, optimize process parameters, and characterize AM processes. The measurements indicate groundbreaking improvements in ML powered solutions, like process monitoring in real time, automatic parameter adaptation, and defect mitigation that offer greater accuracy, ease, and reliability in AM. Yet, data scarcity, computational challenges and a gap between research and industrial applications of ML exist. To realize the full potential of ML in AM it is critical to address these challenges. It closes with the identification of promising research directions including standardization of data improvement, developing new advanced ML algorithms, and building an interdisciplinary research effort to spur additional progress in this field.
... This paper is to study the optimum concentration of camellia sinensis extract as corrosion inhibitor in a 1M HCl environment, on Aluminium 6061, by utilizing supervised machine learning model. Machine learning models have been used widely in various applications such as predicting human fatigue [20] and perceived workload [21], architectural material selections [22] and also in predicting corrosion and analysing inhibitor effectivity on metals [23], [24]. In corrosion studies, the artificial neural networks (ANN), was proven effective to analyze corrosion inhibition of 304SS in sulfuric acid solution using timoho leaf extract [23], which is considerable to be used in this study. ...
Aluminum alloys, well known for their corrosion resistance, could encounter corrosion issues in acidic environments. These environments induced pitting and exfoliation corrosion due to the absence of oxide layers from such alloys. Camellia sinensis extract, one of the organic extract, had inhibition compounds, including tannins and cathecins, that could inhibit corrosion and reduce the corrosion rate. Hence, this study investigated the corrosion inhibition efficacy of Camellia sinensis extract on Aluminum 6061 alloys when exposed to an acidic environment (1M HCl). Different concentrations of the extract were prepared to obtain the optimum concentration and achieve the highest inhibitor efficiency. In addition, an artificial neural network (ANN) model, was employed to predict polarization current in both inhibited and unhibited solutions. The model was designed using a configuration of one input, six hidden and one output ;ayers. The study discovered that the efficiency reached a remarkable level of 82.68% when using a concentration of 3000 ppm of Camellia sinensis extract. Furthermore, the ANN model demonstrated excellent performance in predicting polarization current across all variations, with determinant coefficient (R2) values of 0.995 for 0 ppm, 0.990 for 1000 ppm, 0.997 for 2000 ppm, 0.9996 for 3000 ppm, and 0.996 for 4000 ppm. These results indicated that the model was reliable in simulating the electrochemical analysis of corrosion behavior which could be used to develop a corrosion rate predictor in the future.
