Mean Square Error Research Articles

PREDICTION OF DRIVING TIME OF ELECTRIC SCOOTER (E-SCOOTER) DRIVERS BY MACHINE LEARNING

This study aims to estimate the driving times of drivers who prefer electric scooter vehicles. In general, e-scooters reduce the loss of time caused by traffic jams because, thanks to their smaller size and maneuverability, these vehicles provide rapid progress in urban journeys. E-scooters also offer an advantage in finding a parking space and easy parking thanks to their more compact structure. In this study, ML algorithms were used to predict the driving times of drivers who prefer e-scooter vehicles. The AB model has performed well with a low Mean Square Error (MSE) value (0.005). The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values are also relatively low (0.069 and 0.039, respectively), indicating that the model's predictions are close to the actual values. Also, the high R-squared-Coefficient of Determination (R2) value (0.947) suggests that this model explains the data quite well, and its predictions approach the actual values with high accuracy. On the other hand, the GB algorithm performed poorly compared to different algorithms, with its high margin of error and low accuracy rate. These results provide an advantage in time management by estimating the travel time a driver will make with the e-scooter. As a result, e-scooters offer drivers the opportunity to save time and manage their daily mobility more effectively, driving these vehicles attractive for transportation.

Translation, cross-cultural adaptation, and psychometric validation of the Malay version of the Assessment of Quality of Life—6 Dimensions (Malay-AQoL-6D) instrument among Malaysians living with chronic heart failure

BackgroundThis study aimed to translate and culturally adapt the Assessment of Quality of Life (AQoL)-6D into Malay (Malay-AQoL-6D), and assesses the instrument’s acceptability, reliability, and validity among Malaysians living with chronic heart failure (HF).MethodsThe translation and cross-cultural adaptation process adhered to international guidelines. The Malay-AQoL-6D underwent content and face validity assessments via expert review, and pretesting among healthy individuals and patients with chronic conditions. Subsequent psychometric validation utilised clinico-sociodemographic data and paired AQoL-6D and EQ-5D-5L data from a health-related quality-of-life (HRQoL) survey involving Malay-speaking patients with HF, which encompassed assessments of Malay-AQoL-6D acceptability, internal consistency and test-retest reliability, as well as its construct, concurrent, convergent and divergent, and known-group validity.ResultsThe Malay-AQoL-6D was deemed acceptable among clinicians and local patients, achieving a 90.8% completion rate among 314 patients surveyed. The instrument demonstrated strong content validity (item-level content validity index [CVI]: 0.83–1.00, average CVI: 0.98), internal consistency (Cronbach’s alpha: 0.72–0.89; MacDonald’s omega: 0.82–0.90, excluding the Senses dimension), and test-retest reliability (average intraclass correlation coefficients: 0.79–0.95). Confirmatory factor analysis confirmed the instrument’s two-level, six-factor structure (Satorra-Bentler [SB]-scaled χ2(df: 164): 283.67, p-value < 0.001; root mean square error of approximation [RMSEA]: 0.051; comparative fix index [CFI]: 0.945, Tucker-Lewis index [TLI]: 0.937; standardised root mean-squared error [SRMR]: 0.058). The Malay-AQoL-6D’s concurrent validity was evident through its good agreement with EQ-5D-5L. Multiple hypothesis tests further affirmed its construct and known-group validity. The Malay-AQoL-6D’s psychometric properties remained consistent across different missing data techniques.ConclusionThe findings suggest that Malay-AQoL-6D could be a culturally acceptable, reliable, and valid HRQoL measure for quantifying HRQoL among the local HF population. Future studies are necessary to further validate the instrument against other measures and confirm the instrument’s test-retest reliability and responsiveness, which are possible with the availability of the Malay-AQoL-6D.

Forecasting Copper Prices Using Deep Learning: Implications for Energy Sector Economies

Energy is a foundational element of the modern industrial economy. Prices of metals play a crucial role in energy sectors’ revenue evaluations, making them the cornerstone of effective payment management employed by resource policymakers. Copper is one of the most important industrial metals, and plays a vital role in various aspects of today’s economies. Copper is strongly associated with many industries, such as electrical wiring, construction, and equipment manufacturing; therefore, the price of copper has become a significant impact factor on the performance of related energy companies and economies. The accurate prediction of copper prices holds particular significance for market participants and policymakers. This study carried out research to address the gap in copper price forecasting using a one-dimensional convolutional neural network (1D-CNN). The proposed method was implemented and tested using extensive data spanning from November 1991 to May 2023. To assess the performance of the CNN model, standard evaluation metrics, such as the R-value, mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE), were employed. For the prediction of global copper prices, the proposed artificial intelligence algorithm demonstrated high accuracy. Lastly, future global copper prices were predicted up to 2027 by the CNN and compared with forecasts published by the International Monetary Fund and the International Society of Automation. The results show the exceptional performance of the CNN, establishing it as a reliable tool for monitoring copper prices and predicting global copper price volatilities near reality, and as carrying significant implications for policymakers and governments in shaping energy policies and ensuring equitable implementation of energy strategies.

Prediction of Slope Safety Factor Based on Attention Mechanism-Enhanced CNN-GRU

This paper proposes a new method for predicting slope safety factors that combines convolutional neural networks (CNNs), gated recurrent units (GRUs), and attention mechanisms. This method can better capture long-term dependencies, enhance the ability to model sequential data, and reduce the dependence on noisy data, thereby reducing the risk of overfitting. The goal is to improve the accuracy of slope safety factor prediction, detect potential slope stability issues in a timely manner, and take corresponding preventive and control measures to ensure the long-term stability and safety of infrastructure and promote sustainable development. The Pearson correlation coefficient is used to analyze the relationship between the target safety factor and the collected parameters. A one-dimensional CNN layer is used to extract high-dimensional features from the input data, and then a GRU layer is used to capture the correlation between parameters in the sequence. Finally, an attention mechanism is introduced to optimize the weights of the GRU output, enhance the influence of key information, and optimize the overall prediction model. The performance of the proposed model is evaluated using metrics such as the mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), root-mean-square error (RMSE), and R2. The results show that the CNN-GRU-SE model outperforms the GRU, CNN, and CNN-GRU models in terms of prediction accuracy for slope safety factors, with improvements of 4%, 2%, and 1%, respectively. Overall, the research in this paper makes valuable contributions to the field of slope safety factor prediction, and the proposed method also has the potential to be extended to other time-series prediction fields, providing support for a wide range of engineering applications and further promoting the realization of sustainable development.

Importance of serum albumin in machine learning-based prediction of cognitive function in the elderly using a basic blood test

IntroductionIn this study, we investigated the correlation between serum albumin levels and cognitive function, and examined the impact of including serum albumin values in the input layer on the prediction accuracy when forecasting cognitive function using deep learning and other machine learning models.MethodsWe analyzed the electronic health record data from Osaka Medical and Pharmaceutical University Hospital between 2014 and 2021. The study included patients who underwent cognitive function tests during this period; however, patients from whom blood test data was not obtained up to 30 days before the cognitive function tests and those with values due to measurement error in blood test results were excluded. The Mini-Mental State Examination (MMSE) was used as the cognitive function test, and albumin levels were examined as the explanatory variable. Furthermore, we estimated MMSE scores from blood test data using deep learning models (DLM), linear regression models, support vector machines (SVM), decision trees, random forests, extreme gradient boosting (XGBoost), and light gradient boosting machines (LightGBM).ResultsOut of 5,017 patients who underwent cognitive function tests, 3,663 patients from whom blood test data had not been obtained recently and two patients with values due to measurement error were excluded. The final study population included 1,352 patients, with 114 patients (8.4%) aged below 65 and 1,238 patients (91.6%) aged 65 and above. In patients aged 65 and above, the age and male sex showed significant associations with MMSE scores of less than 24, while albumin and potassium levels showed negative associations with MMSE scores of less than 24. Comparing MMSE estimation performance, in those aged below 65, the mean squared error (MSE) of DLM was improved with the inclusion of albumin. Similarly, the MSE improved when using SVM, random forest and XGBoost. In those aged 65 and above, the MSE improved in all models.DiscussionOur study results indicated a positive correlation between serum albumin levels and cognitive function, suggesting a positive correlation between nutritional status and cognitive function in the elderly. Serum albumin levels were shown to be an important explanatory variable in the estimation of cognitive function for individuals aged 65 and above.

Predictive modeling of specific fuel consumption in compression ignition engines using neural networks: a comparative analysis across diesel and polymer-based fuels

The study utilized neural network modeling to forecast the fuel consumption in compression ignition engines fueled by diesel, HDPE PO, PP PO, and LDPE PO. Using empirical data, a neural network model was constructed and used to estimate specific fuel consumption (SFC). Employing orthogonal arrays and parameter adjustments ensured accurate prediction of SFC, which was validated through experimentation. The multilayer perception network, coupled with traditional backpropagation, facilitates the nonlinear mapping of inputs to outcomes. In the LM10TP architecture, the key metrics from the training set included an impressive R-squared value of 1, indicating a perfect fit with a root mean square error (RMSE) of 0.0012 and a mean square error (MSE) of 1.5143E-06. Similarly, the validation set exhibited robust performance metrics with an R-squared value of 0.9999, an RMSE of 0.0011, and an MSE of 1.2185E-06. These metrics underscore the efficacy of neural networks in both the training and validation phases, affirming their utility as reliable predictive tools for SFC. Overall, this study highlights the effectiveness of neural network modeling for accurately predicting fuel consumption in compression ignition engines across diesel and polymer-based fuels. By leveraging empirical data and sophisticated modeling techniques, this study contributes to advancing predictive capabilities in the field, offering valuable insights for optimizing engine performance and fuel efficiency.

FringeNet: A cyclic U-Net model with continuity imposed hybrid cyclic loss for demodulation of isochromatics in digital photoelasticity

This study introduces FringeNet, an innovative deep learning-based cyclic model to enhance the fringe order demodulation from single isochromatic images. A Continuity-Imposed Hybrid Cyclic Loss (CHCL) function, which combines Mean Squared Error (MSE), Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and a continuity loss is proposed for optimizing multiple objectives, while enforcing the fringe order continuity in the isochromatic data. The proposed FringeNet is trained using available open-source ischromatic image dataset and the effectiveness of the model is validated using realistic experimental isochromatics. The FringeNet demonstrates a 92.4% improvement in MSE over existing methods, indicating substantial gains in predictive accuracy and model robustness. Additionally, a 16.19% enhancement in PSNR is observed, highlighting superior fidelity compared to existing approaches. The cyclic model employed in FringeNet represents a significant advancement in enhancing the deep learning-based predictive modeling used in the field of digital photoelasticity.

Noninvasive and fast method of calculation for instantaneous wave-free ratio based on haemodynamics and deep learning

Background and ObjectivesInstantaneous wave-free ratio (iFR) is a new invasive indicator of myocardial ischaemia, and its diagnostic performance is as good as the “gold standard” of myocardial ischaemia diagnosis: fractional flow reserve (FFR). iFR can be approximated by iFRCT, which is calculated based on noninvasive coronary CT angiography (CTA) images and computational fluid dynamics (CFD). However, the existing methods for calculating iFRCT fail to accurately simulate the resting state of the coronary artery, resulting in low computational accuracy. Furthermore, the use of CFD technology limits its computational efficiency, making it difficult to meet clinical application needs. The role of coronary microcirculatory resistance compensation suggests that microcirculatory resistance can be adaptively reduced to compensate for increases in coronary stenotic resistance, thereby maintaining stable myocardial perfusion in the resting state. It is therefore necessary to consider this compensation mechanism to establish a high-fidelity microcirculation resistance model in the resting state in line with human physiology, and so to achieve accurate calculation of iFRCT. MethodsIn this study we successfully collected clinical data, such as FFR, in 205 stenotic vessels from 186 patients with coronary heart disease. A neural network model was established to predict coronary artery stenosis resistance. Based on the compensation mechanism of coronary microcirculation resistance, an iterative solution algorithm for microcirculation resistance in the resting state was developed. Combining the two methods, a simplified single-branch model combining coronary stenosis and microcirculation resistance was established, and the noninvasive and rapid numerical calculation of iFRCT was performed. ResultsThe results showed that the mean squared error (MSE) between the pressure drop predicted by the neural network value for the coronary artery stenosis model and the ground truth in the test set was 0.053 %, and correlation analysis proved that there was a good correlation between them (r = 0.99, p < 0.001). With reference to clinical diagnosis of myocardial ischaemia (using FFR as the gold standard), the diagnostic accuracy of the iFRCT calculation model for the 205 cases was 88.29 % (r = 0.71, p < 0.001), and the total calculation time was < 8 s. ConclusionsThe results of this study demonstrate the utility of a simplified single-branch model in an iFRCT calculation method based on haemodynamics and deep learning, which is important for noninvasive and rapid diagnosis of myocardial ischaemia.

Forecasting air quality Index in yan’an using temporal encoded Informer

Predictions of the Air Quality Index (AQI) can provide information on air quality, aiding individuals in personal protection and environmental conservation, and facilitating policymakers in implementing measures to control air pollution. In the prediction of AQI time series, single-step forecasting tasks are less challenging. However, as the length of the forecast sequence increases, the prediction error tends to grow remarkably. Furthermore, tacking only univariate features as model inputs can lead to biases in forecasting outputs, as the models fail to capture the overall trends of the series based on a single feature. To address this issue, we developed a novel time series prediction model named Temporal feature Encoded Informer (TE-Informer) using the historical air pollution dataset from Yan’an City. This model incorporates attention mechanisms and periodic encoding, utilizing multiple pollutant series as feature inputs. It enriches the Informer architecture with more comprehensive temporal and global features to predict AQI series. The experimental results demonstrated that the Informer model, enhanced with periodic time encoding, has a significant advantage in extracting features from the input information. In the context of multi-step AQI time series forecasting, the model achieved an MSE (Mean Squared Error) of 24.8692 and an R2-score of 0.9793, outperforming other comparative models in all comprehensive metrics. Consequently, this model can effectively improve prediction accuracy and better capture important characteristic information within the AQI time series. The source code is available at https://github.com/msskx/TE-Informer.

Application of FEM and Artificial Intelligence Techniques (LRM, RFM &amp; ANN) in Predicting the Ultimate Bearing Capacity of Reinforced Soil Foundation

In this research paper, the behavior of shallow footing with square and rectangular shapes over geosynthetic reinforced soil was studied. A novel geogrid called “3D tube-geogrid” was utilized for this work. The impact of various reinforcement parameters, including the depth of the final layer (z), length (l), inclination (α), filler material used inside the geogrid tube, relative soil density, and the tensile stiffness of the geogrid (EA), were analyzed by running numerical simulations using PLAXIS 3D V20 software. The simulated data were used to quantify the relationship between the ultimate bearing capacity of the soil and the reinforcement parameters. Several artificial intelligence (AI) techniques, such as linear regression analysis, a random forest model, and an artificial neural network (ANN), were employed on the generated dataset. To evaluate the preciseness of these techniques, various statistical indicators, such as the squared correlation coefficient (R2), mean absolute percentage error (MAPE), mean squared error (MSE), and root-mean-square error (RMSE), were calculated, and error percentages of 20.98%, 12.5%, and 6.4% were obtained for the linear regression, random forest, and ANN, respectively. The numerical study determined the optimal values of the reinforcement parameters length, z/B, inclination, and filling material to be 4B, 3, 0°, and aggregate, respectively.

Time Series Prediction of Reservoir Bank Slope Deformation Based on Informer and InSAR: A Case Study of Dawanzi Landslide in the Baihetan Reservoir Area, China

Reservoir impoundment significantly impacts the hydrogeological conditions of reservoir bank slopes, and bank slope deformation or destruction occurs frequently under cyclic impoundment conditions. Ground deformation prediction is crucial to the early warning system for slow-moving landslides. Deep learning methods have developed rapidly in recent years, but only a few studies are on combining deep learning and landslide warning. This paper proposes a slow-moving landslide displacement prediction method based on the Informer deep learning model. Firstly, the Sentinel-1 (S1) data are processed to obtain the cumulative displacement time-series image of the bank slope by the Small-BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) method. Then, combining data on rainfall, humidity, and horizontal and vertical distances of pixel points from the water table line, this study created a dataset with landslide displacement as the target feature. After that, this paper improves the Informer model to make it applicable to our dataset. This study chose the Dawanzi landslide in the Baihetan reservoir area, China, for validation. After training with 50-time series deformation data points, the model can predict the displacement results of 12-time series deformation data points using 12-time series multi-feature data, and compared with the monitoring values, its Mean Square Error (MSE) was 11.614. The results show that the multivariate dataset is better than the deformation univariate data in predicting the displacement in the large deformation zone of bank slopes, and our model has better complexity and prediction performance than other deep learning models. The prediction results show that among zones I–IV, where the Dawanzi Tunnel is located, significant deformation with the maximum deformation rate detected exceeding –100mm/year occurs in Zones I and III. In these two zones, the initiation of deformation relates to the drop in water level after water storage, with the deformation rate of Zone III exhibiting a stronger correlation with the change in water level. It is expected that deformation in Zone III will either remain slow or stop, while deformation in Zone I will continue at the same or a decreased rate. Our proposed method for slow-moving landslide displacement forecasting offers fast, intuitive, and economically feasible advantages. It can provide a feasible research idea for future deep learning and landslide warning research.

A Prediction Model of Ionospheric Total Electron Content Based on Grid-Optimized Support Vector Regression

Evaluating and mitigating the adverse effects of the ionosphere on communication, navigation, and other services, as well as fully utilizing the ionosphere, have become increasingly prominent topics in the academic community. To quantify the dynamical changes and improve the prediction accuracy of the ionospheric Total Electron Content (TEC), we propose a prediction model based on grid-optimized Support Vector Regression (SVR). This modeling processes include three steps: (1) dividing the dataset for training, validation, and testing; (2) determining the hyperparameters C and g by the grid search method through cross-validation using training and validation data; and (3) testing the trained model using the test data. Taking the Gakona station as an example, we compared the proposed model with the International Reference Ionosphere (IRI) model and a TEC prediction model based on Statistical Machine Learning (SML). The performance of the models was evaluated using the metrics of mean absolute error (MAE) and root mean square error (RMSE). The specific results are as follows: the MAE of the CCIR, URSI, SML, and SVR models compared to the observations are 1.06 TECU, 1.41 TECU, 0.7 TECU, and 0.54 TECU, respectively; the RMSE are 1.36 TECU, 1.62 TECU, 0.92 TECU, and 0.68 TECU, respectively. These results indicate that the SVR model has the most minor prediction error and the highest accuracy for predicting TEC. This method also provides a new approach for predicting other ionospheric parameters.

Soft sensor for the dry solid content in thickened primary sludge

ABSTRACT Software sensors, or soft sensors, can be a feasible option to monitor parameters that are difficult (or impossible) to measure with hardware sensors. At Henriksdal water resource recovery facility (WRRF), the operators have long experienced issues with a clogging sensor for the dry solid (DS) content in thickened primary sludge. A soft sensor was developed, and in the process, two methods were compared: long short-term memory (LSTM) network and linear regression. The first is a recurrent neural network that can capture non-linear dynamics, whereas the latter is a linear static model. The LSTM network was the best at predicting the DS content, with a mean squared error (MSE) of 0.341 with respect to laboratory data. The linear regression model performed worse than estimating a long-time average of daily manual samples but outperformed the online sensor. Replacing the existing sensor with the developed soft sensor can open up possibilities for more efficient control and operation of the thickener unit.

Graph-Based Traffic Forecasting with the Dynamics of Road Symmetry and Capacity Performance

Symmetry in traffic patterns is a fundamental aspect of intelligent transportation systems, aiming to precisely predict traffic flow in real time despite the complex interplay of spatial and temporal factors. This paper presents a novel method of traffic forecasting that incorporates parameters related to road symmetry into a Graph Convolution Network model. Our model is crafted to dynamically adjust to real-time changes in road conditions, including the presence of symmetric and asymmetric road layouts, which substantially influence traffic flow. We have developed a GCN model that not only accounts for standard traffic flow metrics but also integrates a matrix representing road symmetry. The model undergoes training and validation on the METR-LA dataset, showcasing a significant enhancement in prediction accuracy. In the comparative analysis of state-of-the-art methods, our model demonstrated a significant enhancement in performance, achieving 30.68% improvement in Mean Squared Error (MSE) and a 24.28% improvement in Mean Absolute Error (MAE) over the best-performing method. The implications of our research are profound for urban planners, traffic management systems, and navigation service providers, as it offers a more dependable tool for forecasting traffic conditions, aiding in road design, and refining route planning strategies based on the symmetry of road networks.

Identification of end-milling chatter based on comprehensive feature fusion

The main barrier impeding the advancement of high-speed milling is chatter, which has a detrimental effect on the dimensional accuracy and quality of the finished workpiece. A reliable and precise chatter identification method is essential to improving the quality of machining. This paper presents a novel method for chatter identification using a comprehensive feature fusion of the Short-Time Fourier Transform (STFT) and the Fourier Synchrosqueezing Transform (FSST). The Wavelet Packet Transform (WPT) was used to pre-process the collected vibration and force signals. Wavelet packets with rich chatter information were then selected and reconstructed for further analysis. To reduce the effects of the rotating frequency and generate a hybrid spectrum with high resolution, a Gabor time-frequency filter is employed. As chatter indicators, standard deviation, skewness, and root mean square are computed. With a higher Time-Frequency Representation (TFR) resolution and a shorter computation time of 0.46 and 0.97 s across vibration and force signals, the proposed method outperforms conventional STFT and FSST. As a result, it can be used to reliably identify chatter from the onset, which is beneficial for machining monitoring.

Stability analysis of patient-specific 4DCT- and 4DCBCT-based correspondence models.

Surrogate-based motion compensation in stereotactic body radiation therapy (SBRT) strongly relies on a constant relationship between an external breathing signal and the internal tumor motion over the course of treatment, that is, a stable patient-specific correspondencemodel. This study aims to develop methods for analyzing the stability of correspondence models by integrating planning 4DCT and pretreatment 4D cone-beam computed tomography (4DCBCT) data and assessing the relation to patient-specific clinicalparameters. For correspondence modeling, a regression-based approach is applied, correlating patient-specific internal motion (vector fields computed by deformable image registration) and external breathing signals (recorded by Varian's RPM and RGSC system). To analyze correspondence model stability, two complementary methods are proposed. (1) Target volume-based analysis: 4DCBCT-based correspondence models predict clinical target volumes (GTV and internal target volume [ITV]) within the planning 4DCT, which are evaluated by overlap and distance measures (Dice similarity coefficient [DSC]/average symmetric surface distance [ASSD]). (2) System matrix-based analysis: 4DCBCT-based regression models are compared to 4DCT-based models using mean squared difference (MSD) and principal component analysis of the system matrices. Stability analysis results are correlated with clinical parameters. Both methods are applied to a dataset of 214 pretreatment 4DCBCT scans (Varian TrueBeam) from a cohort of 46 lung tumor patients treated with ITV-based SBRT (planning 4DCTs acquired with Siemens AS Open and SOMATOM go.OPEN Pro CT scanners). Consistent results across the two complementary analysis approaches (Spearman correlation coefficient of between system matrix-based MSD and GTV-based DSC/ASSD) were observed. Analysis showed that stability was not predominant, with 114/214 fraction-wise models not surpassing a threshold of for the GTV, and only 14/46 patients demonstrating a in all fractions. Model stability did not degrade over the course of treatment. The mean GTV-based DSC is (mean ASSD of ) and the respective ITV-based DSC is (mean ASSD of ). The clinical parameters showed a strong correlation between smaller tumor motion ranges and increasedstability. The proposed methods identify patients with unstable correspondence models prior to each treatment fraction, serving as direct indicators for the necessity of replanning and adaptive treatment approaches to account for internal-external motion variations throughout the course oftreatment.

Simultaneous compensation of geometric and compliance errors for robotics with consideration of variable payload effects

In order to satisfy the high accuracy requirements of robotic applications, it is necessary to consider not only the geometric errors but also the compliance errors which are caused by the self-gravity of the link and the external payloads. A general error model is developed based on the modified Denavit-Hartenberg (DH) model. For the parameters in the error model, there is a coupling between the compliance coefficients and the link parameters, making it difficult to use only the compliance coefficients to compute the compliance errors due to external payloads. As the external payload for the robot manipulator varies, the parameter identification of the model should be conducted again. In this paper, a novel algorithm using a variable payload method is proposed to first identify the compliance coefficients using different payloads and end effector position information. Second, the kinematic parameter errors and link parameters are identified with the given compliance coefficients. Then, the algorithm generates a modified trajectory using the calibrated DH tables for the precision compensation. Simulation and experimental results demonstrate that the positioning accuracy can be improved by 80 % to 90 % even under different payloads. The root mean square, mean, maximum, and standard deviation of the residual errors by using the proposed algorithm could outperform the conventional kinematic algorithm.

Non-orthogonal Multiple Access (NOMA) Channel Estimation for Mobile &amp; PLC-VLC Based Broadband Communication System

Background: The paper focuses on enhancing the performance of 5G wireless mobile communication systems. Furthermore, it addresses the increasing demand for high data rates, improved channel capacity, and spectrum efficiency outlined by the 3rd Generation Partnership Project (3GPP) protocol. Objective: To develop an innovative Non-Orthogonal Multiple Access (NOMA)-based channel estimation (CE) model aimed at improving the performance of 5G wireless mobile communication systems Methods: A proportionate recursive least squares (PRLS) algorithm is utilized for estimating the characteristics of practical Rayleigh fading channels. The applicability of the PRLS algorithm is investigated in Lambertian channels for indoor broadband communication systems such as power line communication (PLC) and visual light communication (VLC) systems. Results: The assessment of evaluation metrics, including mean square error (MSE), bit error rate (BER), spectral efficiency (SE), energy efficiency (EE), capacity, and data rate, have been analysed. Faster convergence and higher accuracy compared to existing state-of-the-art approaches have been demonstrated. Conclusion: The NOMA-based channel estimation model presents significant promise in enhancing the performance of 5G wireless communication systems. The demands for higher data rates and improved spectral efficiency as per 3GPP standards have been addressed.

Prediction of Radiation Therapy Dose Distribution Using Random Forest Algorithm in Volumetric Modulated Arc Therapy Technique for Brain Cancer

Optimization of radiation dose distribution in clinical planning using a Treatment Planning System (TPS) for radiotherapy patients is crucial to achieving a balance between therapeutic effectiveness and patient safety. However, this process is time-consuming and relies heavily on the expertise of medical physicists. In this study, dose prediction using machine learning was performed on the Planning Target Volume (PTV) and Organ at Risk (OAR) for brain cancer cases using the Volumetric Modulated Arc Therapy (VMAT) planning technique. The planning DICOM data were extracted for radiomic and dosiomic values, which were then used as input and output in this study using a random forest algorithm model. Model evaluation results indicated that the random forest model's performance in predicting dose had a Mean Square Error (MSE) value of 0.018. The Homogeneity Index (HI) and Conformity Index (CI) values for the clinical data were 0.136±0.134 and 0.939±0.131 respectively, while the predicted results were 0.136±0.039 and 0.949±0.006, with p-values for PTV and OAR features &gt; 0.05. Thus, it can be concluded that the random forest model is effective in predicting dose for PTV brain cancer and OAR and can function as a reference in the planning process.

Explainable ResNet50 learning model based on copula entropy for cotton plant disease prediction

This paper presents a novel Deep Learning (DL) framework for cotton plant disease prediction based on Explainable Artificial Intelligence (XAI) and Copula entropy based-Grey Wolf Optimization (GWO) algorithm. The suggested framework uses a cotton plant image as input and pre-processes it to produce an image with enhanced contrast. Features are extracted from the leaf images with the ResNet50 CNN model. A crucial pre-processing model known as feature selection helps to improve the effectiveness of image classification by deleting extraneous or irrelevant features. Therefore, the Gray Wolf optimization (GWO) algorithm which is a global search method with potential use in feature selection is employed in this paper. The proposed framework introduces Copula entropy (CE) as an indicator of association to create the GWO’s initial population and enhance the GWO feature engineering process. For the GWO initialization procedure, CE has been used to choose the most significant features which substantially improved the quality of the GWO starting population and as a result improved the performance of the proposed CE-based GWO algorithm by 78.57 % faster than the traditional GWO as stated by the time complexity analysis. In addition, Feature importance explanation is determined using XAI layer. The final classification is achieved using Random Forests (RF) classifier which is an ensemble learning approach. According to the experimental results, the suggested model has a classification accuracy of 99 % and a mean squared error of 0.0383. Furthermore, the proposed model has been compared to state-of-the-art algorithms and the results showed that the proposed model has the superiority performance. The proposed model can therefore be used to track a variety of cotton areas to enable faster analysis and response, resulting in higher productivity.