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To consider the complex soil-structure interaction in a pile-slope system, it is necessary to analyze the performance of pile-slope systems based on a three-dimensional (3D) numerical model. Reliability analysis of a pile-slope system based on 3D numerical modeling is very challenging because it is computationally expensive and the performance function of the pile failure mode is only defined in the safe domain of soil stability. In this paper, an efficient hybrid response surface method is suggested to study the system reliability of pile-reinforced slopes, where the support vector machine and the Kriging model are used to approximate performance functions of soil failure and pile failure, respectively. The versatility of the suggested method is illustrated in detail with an example. For the example examined in this paper, it is found that the pile failure can significantly contribute to system failure, and the reinforcement ratio can effectively reduce the probability of pile failure. There exists a critical reinforcement ratio beyond which the system failure probability is not sensitive to the reinforcement ratio. The pile spacing affects both the probabilities of soil failure and pile failure of the pile-reinforced slope. There exists an optimal location and an optimal length for the stabilizing piles.

Energy consumption optimization is crucial for improving the quality of application services in the industrial Internet of Things (IIoT) environment. Traditional optimization methods often adopt blind search strategies, which leads to a significant waste of computing resources and low efficiency. This paper proposes a Hybrid Cat Swarm Optimization and Monarch Butterfly Optimization (HCSMBO), which combines the advantages of Cat Swarm Optimization (CSO) and Monarch Butterfly Optimization (MBO) to solve this problem. By utilizing the characteristics of swarm intelligence, including strong spatial search ability, high stability, and fast convergence, HCSMBO focuses on detailed optimization of the coverage, efficiency, and energy balance coefficient of Support Vector Machine (SVM) networks. By adjusting the weight factors of the fitness function, this algorithm can effectively find the global optimal solution. The experimental results confirm that HCSMBO significantly improves the intelligence, accuracy, and stability of task scheduling in resource management configuration, while achieving lower energy consumption. The research results of this paper are of great significance for improving the service quality of IIoT applications, meeting personalized business needs, and improving resource utilization and execution efficiency. In addition, they also provide new ideas and methods for energy consumption optimization research in the future IIoT.

The processing and recognition of electroencephalogram (EEG) signal is the most important part of brain-computer interface (BCI) system, and the quality of signal processing and recognition is directly related to the effectiveness of BCI system. Aiming at the problems of incomplete removal of artifacts and inadequate retention of active components in EEG signal, a fusion method of wavelet transform (WT) and Fast Independent Component Analysis (FastICA) is utilized to preprocess the raw EEG signals. The fusion method can remove noise artifacts while preserving effective information. Aiming at the problems of poor time-frequency resolution and low classification accuracy of the traditional feature extraction method, a feature extraction algorithm on the basis of hybrid Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) is proposed. Firstly, DWT is used to analyze the pre-processed EEG signal to obtain a series of sub-band signals. Then, EMD decomposition is applied to subband signal and eigenmode function is extracted to complete feature integration. Finally, the feature extraction results are input into the Support Vector Machine (SVM) for classification. Comparative experiments show that the classification accuracy of the proposed method reaches 91.32 %, which is significantly higher than other algorithms.

In the intensive care unit (ICU), bedside surveillance data can appropriately predict the onset of sepsis, probably saving lives and lowering costs by permitting early intervention. Sepsis triggers a complicated immune reaction to pathogenic microbes, which frequently leads to septic shock and organ failure. Early detection is essential, but the excessive-pressure environment of emergency rooms can stress clinical personnel. Suggest a machine learning-based support vector machine (ML-SVM) technique to address this. The goal is to offer a reliable prediction of sepsis onset by studying ICU monitoring records to uncover subtle developments and early warning signs. This technology-driven approach complements their clinical judgment by aiding healthcare experts in making timely, knowledgeable selections. The ML-SVM machine automates the prediction of sepsis onset with a sensitivity of 91 % and a specificity of 93 %, supplying an accuracy of 95.2 %. This excessive- Overall Performance version offers improvements over present-day techniques, assisting scientific employees in making informed choices faster and decreasing the chance of sepsis-related problems. By improving early detection and optimizing resource allocation, the ML-SVM technique can significantly reduce affected person effects, keep lives, lessen healthcare prices, and alleviate the workload on healthcare experts in crucial care settings.

Neuropsychological disorders (e.g., dementia, epilepsy, brain cancer, autism, stroke, and multiple sclerosis) adversely affect the quality of life of patients and their families; moreover, in some instances, they may lead to loss of life. The primary aim was to evaluate and compare the use of machine learning in neuropsychological research in contrast to traditional approaches such as through case studies. This was achieved by referring to earlier studies on this subject. This article presented the use of support vector machines (SVMs) and convolutional neural networks (CNN) for detecting and predicting neuropsychological diseases, such as dementia and Alzheimer's disease. Challenges in using these models include data availability, quality, variability, model interpretability, and validation. Experimental findings have demonstrated the potential of these models in this field. It has been shown that SVM models are robust and efficient in processing and classifying data, particularly in neuroimaging applications, such as magnetic resonance imaging (MRI). CNNs have excelled in handling visual input; thus, they have been used in neuroimaging segregation, recognition, and classification, with applications in brain tumor segmentation, radiation therapy, robotic neurosurgery, and disease prediction. Future research will explore asymmetric differences among left- and right-handed patients, incorporate longitudinal studies, and utilize larger sample sizes. The use of machine learning models has the potential to revolutionize the diagnosis and treatment of neuropsychological diseases, allowing for early detection and intervention. This approach could offer significant advantages to healthcare, such as cost-effective diagnosis and treatment, to help save lives and preserve the quality of life of patients.

Feature extraction and classification is a difficult area in motor imagery electroencephalogram (EEG) signal processing. In order to improve the classification accuracy of EEG signals, both a feature extraction method based on the combination of LMD-CSP and a classification algorithm based on the fusion of PSO-SVM are proposed. Firstly, the extended informax ICA algorithm is used to denoise the signal and reduce the influence of noise on the signal. Then, the pre-processed EEG signals are decomposed into multiple Product Function (PF) components by Local Mean Decomposition (LMD), and the most discriminative PF component is selected. Next, feature extraction is carried out from the selected PF components using the Common Space Pattern (CSP). Finally, the obtained features are input into a Support Vector Machine (SVM) classifier improved by Particle Swarm Optimization (PSO) for classification recognition. The experimental results show that compared with the traditional CSP-SVM method, CapsNet method, WPT-CSP+CNN method, FDCSP-SVM method, and EMD-CNN method, the classification accuracy of the proposed method is increased by 26.94 %, 14.9 %, 13.34 %, 8.34 % and 4.04 %, respectively. This further proves the superiority of the proposed method for EEG signal processing technology.

Eucalyptus plantations are widespread in the highlands of northern Ethiopia. The species has been used for centuries for various purposes. However, there are controversies surrounding the species with excessive soil nutrient and water consumption. Modelling the spatial distribution of the species is fundamental to understand its ecological and hydrological effects in the region for policy inputs. Therefore, the purpose of this study is to develop a model for mapping the spatial distribution of Eucalyptus globulus. We used the spectral bands of Sentinel-2 data, vegetation indices, and environmental data as predictor variables and three machine learning algorithms (Random Forest, Support Vector Machine, and Boosted Regression Trees) to model the current distribution of Eucalyptus globulus. Eleven of the twenty-five predictor variables were filtered using a variance inflation factor (VIF). 419 in situ georeferenced data points were used for training, and validating the models. The area under the curve (AUC), kappa statistic (K), true skill statistic (TSS), Root Mean Squared Error and coefficient of determination (R2) were used to validate the models’ performance. The model validation metrics confirmed the highest performance of Random Forest. The prediction map of Random Forest revealed that Eucalyptus globulus was fairly detected in non-Eucalyptus globulus woody vegetation (R2 = 0.86, P < 0.001; RMSE = 0.31). We found that the Green Normalized Difference Vegetation Index and environmental variables, such as elevation and distance from the road, were the most important predictor variables in explaining the distribution of Eucalyptus globulus. Our findings demonstrate that machine learning algorithms with Sentinel-2 spectral bands and vegetation indices compounded with environmental data can effectively model the spatial distribution of Eucalyptus globulus.

Acute hyperglycemia is a common endocrine and metabolic disorder that seriously threatens the health and life of patients. Exploring effective diagnostic methods and treatment strategies for acute hyperglycemia to improve treatment quality and patient satisfaction is currently one of the hotspots and difficulties in medical research. This article introduced a method for diagnosing acute hyperglycemia based on data-driven prediction models. In the experiment, clinical data from 1000 patients with acute hyperglycemia were collected. Through data cleaning and feature engineering, 10 features related to acute hyperglycemia were selected, including BMI (Body Mass Index), TG (triacylglycerol), HDL-C (High-density lipoprotein cholesterol), etc. The support vector machine (SVM) model was used for training and testing. The experimental results showed that the SVM model can effectively predict the occurrence of acute hyperglycemia, with an average accuracy of 96 %, a recall rate of 84 %, and an F1 value of 89 %. The diagnostic method for acute hyperglycemia based on data-driven prediction models has a certain reference value, which can be used as a clinical auxiliary diagnostic tool to improve the early diagnosis and treatment success rate of acute hyperglycemia patients.

Background: While patients affected by mild cognitive impairment (MCI) exhibit characteristic voxel-mirrored homotopic connectivity (VMHC) alterations, the ability of such VMHC abnormalities to predict the diagnosis of MCI in these patients remains uncertain. As such, this study was performed to evaluate the potential role of VMHC abnormalities in the diagnosis of MCI. Methods: MCI patients and healthy controls (HCs) were enrolled and subjected to resting-state functional magnetic resonance imaging (rs-fMRI) and neuropsychological testing. VMHC and support vector machine (SVM) techniques were then used to examine the collected imaging data. Results: Totally, 53 MCI patients and 68 healthy controls were recruited. Compared to HCs, MCI patients presented with an increase in postcentral gyrus VMHC. SVM classification demonstrated the ability of postcentral gyrus VMHC values to classify HCs and MCI patients with accuracy, sensitivity, and specificity values of 63.64 %, 71.69 %, and 89.71 %, respectively. Conclusion: VMHC abnormalities in the postcentral gyrus may be mechanistically involved in the pathophysiological progression of MCI patients, and these abnormal VMHC patterns may also offer utility as a neuroimaging biomarker for MCI patient diagnosis.

This paper demonstrates the potential of Raman spectroscopy for differentiating neoplastic from non-neoplastic colon tumors, obtained with the CAM (chicken chorioallantoic membrane) model. For the CAM model two human cell lines were used to generate two types of tumors, the RKO cell line for neoplastic colon tumors and the NCM460 cell line for non-neoplastic colon tumors. The Raman spectra were acquired with a 785 nm excitation laser. The measured Raman spectra from the CAM samples (n = 14) were processed with several methods for baseline correction and to remove artifacts. The corrected spectra were analyzed with PCA (principal component analysis). Additionally, machine learning based algorithms were used to create a model capable of classifying neoplastic and non-neoplastic tumors. The principal component scores showed a clear differentiation between neoplastic and non-neoplastic colon tumors. The classification model had an accuracy of 93 %. Thus, a complete methodology to process and analyze Raman spectra was validated, using a rapid, accessible, and well-established tumor model that mimics the human tumor pathology with minor ethical concerns.

The PROSPECT model, widely employed for leaf radiation transfer analysis, relies heavily on input biochemical parameters to calculate spectral reflectance. This dependence often results in similar simulated spectra for different parameter combinations, which complicates the inversion of leaf chlorophyll content (Cab). To address this ill-posed problem, we enhanced the model's application by integrating a support vector machine (SVM)-based parameter combination discriminator with the Look-Up Table (LUT) constructed from the PROSPECT model. We marked samples in the LUT to reflect their closeness to measured parameters, facilitating the identification of reasonable versus unreasonable parameter combinations. The discriminator could effectively discriminate between reasonable and unreasonable parameter combinations, achieving accuracies of 0.894 and 0.888 in the training and test sets, respectively. The discriminator was then employed to refine the LUT, and an improved third-generation non-dominated ranking genetic algorithm (NSGA-III) was used to optimize the extreme learning machine. The inversion of rice Cab using the refined LUT and the NSGA-III demonstrated substantial improvements. The LUT was significantly improved after integration with the discriminator, yielding R2 and RMSE of 0.665 and 7.220 ​μg ​cm−2, respectively. The NSGA-III inversion, which utilized the “constraint method” with discriminator results as optimization objectives, achieved the best inversion accuracy, with R2 and RMSE values of 0.809 and 4.788, respectively. This study demonstrates that the effective use of a parameter discriminator can significantly enhance the accuracy of Cab inversion based on the PROSPECT model, offering a substantial advancement in addressing its inherent ill-posed challenges.

The distribution information of irrigated cultivated land is important to many studies, including grain yield estimation, water resources management, drought risk assessment, and so on. This study developed a feature variable, called the irrigation probability index (IPI), based on the principle that irrigation can slow down or inhibit the evolution from meteorological drought to agricultural drought. Nebraska, which has a good irrigation information database, was selected as the study area. Combining IPI with the irrigation statistical data and other selected feature variables, irrigated cultivated land in Nebraska was mapped in 2017 using both the spatialization method and remote sensing classification methods (random forest and support vector machine). We verified the effectiveness of IPI in identifying irrigated cultivated land, analyzed the importance of different feature variables for irrigated cultivated land identification, and compared the accuracy of different irrigation mapping methods. The results show that the IPI is significantly correlated with the actual irrigation area, the area of irrigation facilities, and the number of active irrigation wells. It has a better indicative ability of irrigation in areas with a suitable climate or arid areas, than in areas with a humid climate. The crop water stress index (CWSI), IPI, vegetation index (EVI and NDVI), and land surface temperature difference between day and night are the most important feature variables that are used to distinguish irrigated cultivated land from rain-fed cultivated land. There are differences between the recognition accuracies of the three mapping methods in different climatic regions. Overall, the spatialization method had the highest accuracy (overall accuracy = 86.92 %, kappa = 0.74) and the highest similarity with the existing high-resolution irrigation maps. This study provides a direct and useful reference for other researchers to select feature variables and mapping methods in irrigated cultivated land mapping research.

Background: Brain stroke is a serious health issue that requires timely and accurate prediction for effective treatment and prevention. This study described a hybrid system that used the best feature selection method and classifier to predict brain stroke. Methods: The Stroke Prediction Dataset from Kaggle was used for this study. Synthetic minority over-sampling technique (SMOTE) analysis was used to accomplish class balancing. Accuracy, sensitivity, specificity, precision, and the F-Measure were the main performance parameters considered for investigation. To determine the best combination for predicting brain stroke, the performance of five classifiers, Naïve Bayes (NB), support vector machine (SVM), random forest (RF), adaptive boosting (Adaboost), and extreme gradient boosting (XGBoost), was compared along with three feature selection techniques, mutual information (MI), Pearson correlation (PC), and feature importance (FI). The performance parameters were assessed using k-fold cross-validation. Results: The hybrid system proposed in this study identified a reduced set of features that were able to effectively predict brain stroke. FI provided a feature reduction ratio of 36.3%. The most successful hybrid system for predicting brain stroke used FI as the feature selection technique and RF as the classifier, achieving an accuracy of 97.17%. Conclusion: The proposed system predicted brain stroke with high accuracy. These findings could be used to inform the early detection and prevention of brain stroke, allowing healthcare professionals to provide timely and targeted care to at-risk patients.

The discrimination of the source for xenon gases' release can provide an important clue for detecting the nuclear activities in the neighboring countries. In this paper, three machine learning techniques, which are logistic regression, support vector machine (SVM), and k-nearest neighbors (KNN), were applied to develop the predictive models for discriminating the source for xenon gases’ release based on the xenon isotopic activity ratio data which were generated using the depletion codes, i.e., ORIGEN in SCALE 6.2 and Serpent, for the probable sources. The considered sources for the neighboring countries of South Korea include PWRs, CANDUs, IRT-2000, Yongbyun 5 MWe reactor, and nuclear tests with plutonium and uranium. The results of the analysis showed that the overall prediction accuracies of models with SVM and KNN using six inputs, all exceeded 90%. Particularly, the models based on SVM and KNN that used six or three xenon isotope activity ratios with three classification categories, namely reactor, plutonium bomb, and uranium bomb, had accuracy levels greater than 88%. The prediction performances demonstrate the applicability of machine learning algorithms to predict nuclear threat using ratios of xenon isotopic activity.

Large- and small-scale subsidence coexist in the world's coastal cities due to extensive land reclamation and fast urbanization. Synthetic aperture radar (SAR) images are typically limited by either low resolution or small coverage, making them ineffective for fully monitoring displacement in coastal areas. In this research, a machine learning-based method is developed to investigate the reclaimed land subsidence based on multi-satellite SAR data integration. The proposed method requires at least a pair of SAR images from complementary tracks. First, the line-of-sight (LOS) displacements are recovered in connection to a series of extremely coherent points based on the differential interferometry synthetic aperture radar (DInSAR). These LOS displacements are then converted into their vertical component, geocoded to a common grid, and simultaneously integrated (i.e., pixel-by-pixel) based on Support Vector Regression (SVR). The proposed methodology does not necessitate the simultaneous processing of huge DInSAR interferogram sequences. The experiments include high-resolution COSMO-SkyMed (CSK) and TerraSAR-X (TSX) images, as well as a small monitoring cycle Sentinel-1 (S1) images of reclaimed territories near Hong Kong Kowloon City. The overall average annual displacement (AAD) ranges from -12.86 to 11.63 mm/year derived from 2008 to 2019. The evaluation metrics including RMSE, MAE, correlation coefficient, and R-squared are used to investigate the impact of SVR in the integration of SAR datasets. Based on these evaluation metrics, SVR is superior in terms of integration performance, accuracy, and generalization ability. Thus, the proposed method has potentially performed multi-satellite SAR data integration.

Accurate detection of defects on steel surfaces is crucial for maintaining quality standards in steel production. This paper addresses the challenge of classifying steel sheets into distinct defect categories by presenting a robust method that leverages deep learning and advanced optimization techniques. We propose a novel approach that utilizes the ResNet101 model to extract deep features, which are then classified using a support vector machine (SVM). To enhance the SVM's performance, Bayesian optimization is employed for hyperparameter tuning. Our method is validated using the ''Severstal: Steel Defect Detection'' dataset from Kaggle, achieving a validation accuracy of 89.1 % and a test accuracy of 90.6 %, with a classification error of 0.10934. Additionally, the area under the curve (AUC) for each class exceeds 0.95 in both the validation and test sets, demonstrating excellent discriminatory power. Further evaluation on the DAGM dataset achieved flawless results, with an accuracy of 100 %, AUC of 1, sensitivity of 100 %, specificity of 100 %, precision of 100 %, MCC of 100 %, F1 score of 1, and kappa of 100 %. On the NEU dataset, our method achieved an accuracy of 97.92 %, sensitivity of 97.92 %, specificity of 99.58 %, precision of 98.06 %, F1 score of 0.9791, MCC of 97.55 %, and kappa of 92.50 %. These results demonstrate the robustness and adaptability of the proposed method, offering an efficient and reliable solution for automating steel defect detection and surface defect classification in industrial applications.

Electromyography (EMG) signals are biomedical signals that measure electrical currents generated by the activity of muscles when they contract. EMG is essential for optimizing the control of various prosthetic devices, particularly for transfemoral amputees, where the complexity of muscle signal integration presents significant challenges. The proposed study aims to develop a prosthetic knee that actuates in real-time using the EMG signals from the amputee’s residual limb. Pre-processing techniques are employed to obtain EMG signals from the femoris and vastus muscle targets in the transfemoral region. Moving average filters and Butterworth bandpass filters are implemented to process the raw signals. Sliding windows of various widths were applied for feature extraction. The window size of 200 ms is determined for our study based on the outcomes of the t-SNE plots and the corresponding silhouette scores. After the extraction of the pertinent features, several supervised classifier algorithms are put into practice to classify the knee flexion and extension motion. The k-nearest Neighbor (KNN) algorithm, with an accuracy rating of 80 %, proved to be suitable for motor control. Real-time control is implemented using the Raspberry Pi board to power the prosthesis allowing above-the-knee amputees to voluntarily move the leg back and forth. The EMG signals are then extracted and used to drive the DC motor. The prosthesis would therefore be able to move more precisely since the EMG readings are being gathered in real-time. Thus, this work can enhance the patient’s comfort with the ease of carrying out knee movements.

With the advent of the information age, to provide better services and ensure the security management of libraries, intelligent facial recognition technology has gradually become a hot research direction in library management. Meanwhile, to further improve the comprehensive performance of facial recognition, this study attempts to integrate principal component analysis and linear discriminant analysis on the basis of analyzing the framework of recognition technology. Afterwards, it introduced support vector machines for recognition and classification, and proposed a new recognition model. The experimental results show that the recognition accuracy of the proposed model is up to 97 % in the ORL dataset and 94 % in the Yale dataset. The recognition error rate is as low as 0.1 % when the number of training samples is 215 and the number of iterations is 200. The model has the best recognition performance when the image size is 25 × 25 mm and the number of noises is 10. In addition, the model is particularly effective in recognition on single person color or gray images, with the highest P-value of 98.7 %, the highest R-value of 98.8 %, and the highest F1-value of 97.5 %. These results show that the proposed model significantly improves the accuracy and robustness of face recognition, and provides strong technical support for intelligent service innovation in smart libraries.

Painting color matching technology is widely used in the production and printing process of products. Traditional painting and color matching have been unable to meet market demands. Based on this, a large-scale corpus under the existing semantic intelligent understanding system is used as the knowledge source. The computer automated painting color matching model is constructed. It is applied in case studies to address issues such as unclear query intentions, mismatched system retrieval terms, and return errors caused by uncertain factors such as synonyms and polysemy. This provides new ideas for the application of semantic intelligence understanding and automated painting color matching technology. The experimental results showed that the precision, recall, and F1 of the method used in the research were 0.8639, 0.8026, and 0.8309, respectively, significantly superior to commonly used methods. This indicates that the proposed automated painting color matching technology based on semantic intelligent understanding has high performance, which can effectively meet the painting color matching requirements.

In response to the problems of insufficient model accuracy and poor real-time performance in real-time monitoring and optimization of data models in traditional power systems, this paper explored them based on machine learning. It used methods such as box plots and Kalman filters to preprocess power data and used Fourier transform and long short-term memory (LSTM) network to extract data features. Based on long-term and short-term memory networks, this paper constructed a deep neural network model to process data. Recursive feature elimination method can be used for feature selection in the model, and multi-model integration method can be used for feature fusion. The experiment trained and tested the model on two datasets, IEEE 39-Bus and Pecan Street. The experimental results show that the accuracy of the paper's model in both the training and testing sets is higher than that of the convolutional neural network, decision tree, and support vector machine models in the comparative experiments, reaching 93 % and 94 %, respectively. The average response times in three different testing methods were 139.8 ms, 151 ms, and 140.6 ms, respectively. The lowest daily failure rate for 30 consecutive days of work was 0 %, which reflects the stability of the model; in the satisfaction survey of practitioners, the proportion of high recognition answers to each question was higher than 80 %. The experimental results show that the machine learning intelligent control system has achieved good application in power data modeling technology, providing a reference for similar research in the future.

To improve the efficiency of intelligent procurement of library literature and intelligence resources, the study conducts the design of literature and intelligence resources procurement model. The procurement model is constructed by using the support vector machine, and the optimal parameters of the support vector machine are obtained by using the genetic algorithm. The experimental results demonstrated that the mean square error of the proposed model was only 0.03, which was 40 % lower compared with the procurement models based on other optimization algorithms. The average accuracy of the proposed model was as high as 95.18 % and the prediction accuracy was 95.78 % compared to other methods. The accuracy was improved by 15.11 %, 24.57 % and 19.67 % respectively compared to other models. The results show that using genetic algorithm to optimize support vector machine can effectively improve the prediction speed and prediction efficiency of the model. The proposed hybrid procurement model based on genetic algorithm and support vector machine can effectively meet the needs of library literature and intelligence resources procurement construction. The model has positive application significance in library literature and intelligence resources procurement.

Predicting the strength of asphalt mixtures with different specifications under various conditions was a highly challenging task. The standard strength test lacked consideration of multiple factors, resulting in an inability to accurately characterize the properties of the pavement. This paper proposed a strength prediction approach based on influence factor analysis using the back-propagation neural network (BPNN) and support vector machine (SVM). The strength dataset was processed to realize the physical analysis of factors influencing asphalt mixture strength. Stress state (Direct tensile with250mm×50mm×50mm, Uniaxial compression with 100mm×Φ100mm, Indirect tensile with63.5mm×Φ100mm,Four-point bending with 380mm×63.5mm×50mm), temperatures (35 ̊C, 25 ̊C, 15 ̊C, 0 ̊C, −15 ̊C, −25 ̊C), and load rates (0.02 MPa/s, 0.05 MPa/s, 0.1 MPa/s, 0.5 MPa/s) were selected as input features to train the BPNN and SVM. The strength prediction model for asphalt mixture under complex conditions was established by optimizing the parameters of algorithms. The performance of the BPNN and SVM was evaluated and compared by the root mean square error, determination coefficient, and mean absolute percentage deviation. The results show that the asphalt mixture specimen with different specifications under various stress states presents significant discrepancies. The maximum compressive strength is followed by the bending strength, then comes the indirect tensile strength, and the smallest is the direct tensile strength. The difference in the role of asphalt or aggregate is the main reason for the diversity in strength. The increase in temperature leads to asphalt softening, which reduces the strength of the asphalt mixture. The increased loading rate meant the loading time was short cause the strength increased. In addition, the predictive value of the strength under various conditions was consistent with the results of the experiments. The hidden neurons in the BPNN were set to 9, achieving the prediction accuracy is high (R2=0.99). The penalty coefficient of the SVM was set to 500 and the kernel function parameter was set to 300, resulting in the error within 0.02 %. When comparing the performance metrics of BPNN and SVM, it becomes evident that SVM outperforms BPNN in terms of prediction accuracy. Specifically, SVM exhibits a coefficient of determination of 0.9983, a root mean square error of 0.208, and a mean absolute percentage deviation of 0.145, whereas BPNN demonstrates respective values of 0.9979, 0.233, and 0.067. This study lays a theoretical foundation for the digital and intelligent road construction.

With the advancement of semiconductor technology, pedestrian navigation and positioning technology based on smartphones is becoming increasingly important in people's travel. However, precise positioning is challenging due to the use of inertial measurement units in low-cost smartphones and the complex motion states of pedestrians. To navigate and locate pedestrians in complex motion states, a method for converting between smartphone coordinate systems and navigation coordinate systems was studied and designed, and the errors of the built-in sensors of smartphones were analyzed and calibrated. In addition, support vector machines were used to optimize pedestrian trajectory prediction algorithms, and a pedestrian motion state recognition algorithm was designed based on this. To solve the classification problem of multiple human motion states, a multi classification model was constructed and adjacent gait correlation constraints were introduced to correct the classification results. Experiments indicated that the sum of squared errors for traditional algorithms estimating pedestrian trajectories was 0.92, whereas the optimized algorithms produced an improved sum of squared errors of 0.26. Consequently, the average sum of squared errors was reduced by 71.74 %, and the convergence speed increased by 55.56 %. The pedestrian trajectory prediction algorithm optimized by support vector machine could significantly lift the positioning and navigation efficiency, with a correct recognition rate of over 93 % and a position recognition accuracy of 78.8 % - 88.4 %. By optimizing recognition of the motion state of pedestrians, more accurate determination of their position and motion state can be achieved.

As the information age develops, the scale and form of data become more diverse and diverse. Therefore, people need to use effective means to process information. For large-scale data mining problems, a clustering-based kernel matrix inner product filtering method is introduced to decompose the original quadratic programming problem into multiple sub-problems to support parallel training. And a Spark-based multiple submodels parallel support vector machine is proposed. By introducing open-source tools such as OpenCV, image feature extraction can be performed on large-scale video data. Finally, combined with the designed parallel support vector machine algorithm, video facial and expression recognition is carried out. These experiments confirmed that the research method achieved a maximum acceleration ratio of 2090 times when processing Covtype datasets. The research model could achieve an accuracy of over 99 %. Under the maximum data scale experiment, the research model improved prediction accuracy by 21 percentage points with an acceptable additional time cost of about 4 min only. Task parallel processing could more fully utilize cluster performance, increasing by approximately 3.5 m/s2 from 30 to 150 cores. The research model had the highest recognition accuracy for facial expressions, further demonstrating the effectiveness and superiority of this method. The research method has improved the efficiency of big data analysis and mining, and is of great significance in parallel analysis of video data.

Progressive supranuclear palsy (PSP) is an atypical Parkinsonian syndrome characterized initially by falls and eye movement impairment. This multimodal imaging study aimed at eliciting structural and functional disease-specific brain alterations. T1-weighted and resting-state functional MRI were applied in multi-centric cohorts of PSP and matched healthy controls. Midbrain, cerebellum, and cerebellar peduncles showed severely low gray/white matter volume, whereas thinner cortical gray matter was observed in cingulate cortex, medial and temporal gyri, and insula. Eigenvector centrality analyses revealed regionally specific alterations. Multivariate pattern recognition classified patients correctly based on gray and white matter segmentations with up to 98 % accuracy. Highest accuracies were obtained when restricting feature selection to the midbrain. Eigenvector centrality indices yielded an accuracy around 70 % in this comparison; however, this result did not reach significance. In sum, the study reveals multimodal, widespread brain changes in addition to the well-known midbrain atrophy in PSP. Alterations in brain structure seem to be superior to eigenvector centrality parameters, in particular for prediction with machine learning approaches.

Adulteration detection in plant-based medicinal powders is necessary to provide high quality products due to the economic and health importance of them. According to advantages of imaging technology as non-destructive tool with low cost and time, the present research aims to evaluate the ability of the visible imaging combined with machine learning for distinguish original products and the adulterated samples with different levels of chickpea flour. The original products were black pepper, red pepper, and cinnamon, the adulterant was chick pea, and the adulteration levels were 0, 5, 15, 30, and 50 %. The results showed that the accuracies of the classifier based on the artificial neural networks method for classification of black pepper, red pepper, and cinnamon were 97.8, 98.9, and 95.6 %, respectively. The results for support vector machine with one-to-one strategy were 93.33, 97.78 and 92.22 %, respectively. Visible imaging combined with machine learning are reliable technologies to detect adulteration in plant-based medicinal powders so that can be applied to develop industrial systems and improving performance and reducing operation costs.

Advances in radiotherapy, particularly the exploration of alternative radiation types such as carbon ions have updated our understanding of its effects and applicability on chondrosarcoma cells. Here we compare the optical effects produced by carbon ions (CI) and X-rays (XR) radiations on chondrosarcoma cells nuclei and set an automated method for evaluating the radiation-induced alterations without the need of chemical marking. Hyperspectral images (HSI) of SW1353 chondrosarcoma line carry detectable optical changes of the cells irradiated either with CI or XR compared to non-irradiated ones (REF). The differences between the spectral profiles of CI, XR and REF nuclei classes led to partitioning the HSIs into spectral sub-images. The changes are detected by support vector machine (SVM) classifiers whose performances are evaluated by the most used point metrics: sensitivity (SEN), accuracy (ACC), and precision (PREC), applied on spatial feature values. Specific interaction mechanisms by radiation type reveal distinct subintervals where HSIs changes are more prominent, and the classifiers perform at best. For CI the best classifiers are obtained for sub-images in the interval (424–436 nm), while for XR the best classifiers are obtained for sub-images in the interval (436–445 nm). The classifiers work better with texture features than roughness features in both cases. The classifier with the best SEN point metric in the testing phase is the most suitable to measure the irradiation efficiency irrespective of the radiation type. The altered nuclei are easier to discriminate when irradiated with CI than with XR. The study proves that SVM with optical data offers a rapid, automated, and label-free method for evaluating radiation-induced alterations in chondrosarcoma nuclei, thereby enabling effective analysis of extensive data.

Objective: Inflammation plays an important role in the transformation of pulmonary nodules (PNs) from benign to malignant. Prediction of benignancy and malignancy of PNs is still lacking efficacy methods. Although Mayo or Brock model have been widely applied in clinical practices, their application conditions are limited. This study aims to construct a diagnostic model of PNs by machine learning using inflammation-related biological markers (IRBMs). Methods: Inflammatory related genes (IRGs) were first extracted from GSE135304 chip data. Then, differentially expressed genes (DEGs) and infiltrating immune cells were screened between malignant pulmonary nodules (MN) and benign pulmonary nodule (BN). Correlation analysis was performed on DEGs and infiltrating immune cells. Molecular modules of IRGs were identified through Consistency cluster analysis. Subsequently, IRBMs in IRGs modules were filtered through Weighted gene co-expression network analysis (WGCNA). An optimal diagnostic model was established using machine learning methods. Finally, external dataset GSE108375 was used to verify this result. Results: 4 hub IRGs and 3 immune cells showed significantly difference between MN and BN, C1 and C2 module, namely PRTN3, ELANE, NFKB1 and CTLA4, T cells CD4 naïve, NK cells activated and Monocytes. IRBMs were screened from black module and yellowgreen module through WGCNA analysis. The Support vector machines (SVM) was identified as the optimal model with the Area Under Curve (AUC) was 0.753. A nomogram was established based on 5 hub IRBMs, namely HS.137078, KLC3, C13ORF15, STOM and KCTD13. Finally, external dataset GSE108375 verified this result, with the AUC was 0.718. Conclusion: SVM model established by 5 hub IRBMs was able to effectively identify MN or BN. Accumulating inflammation and immune dysfunction were important to the transformation from BN to MN.

This study was conducted to investigate the profound impact of human activities on the environment, based on scientific data, recognizing the potential of environmental problems to turn into devastating crises if appropriate measures are not taken. It emphasizes the important role of education in developing environmental awareness, knowledge and sensitivity to counter adverse environmental consequences. For this purpose, a dataset was created for the emotional tendencies of university students, who represent a demographic that has the potential to influence the sustainable future of the world. A survey data including 34 different variables was collected from 388 university students in Turkey. Environmental Sensory Tendencies Dataset is intended to provide valuable guidance for the development of effective environmental education programs and policies aimed at increasing university students' awareness and participation in environmental issues. Our research underlines the vital importance of developing responsible attitudes and behaviors to effectively address environmental challenges and thereby contribute to a healthier and more sustainable global ecosystem. This study will make a significant contribution to the literature and highlight the interconnection between human actions and environmental well-being.

Ecological services in metropolitan areas are degrading more quickly due to changes in LULC brought about by urban expansion. To make a sustainable choice about the ideal location, however, merging the existing simulation approach with LULC optimization approaches involves several intricate procedures. Therefore, the goal of this study is to develop a unique technique that can forecast urban expansion over an extended period and to link with optimization of LULC techniques so as to make meaningful decisions on the impacts of urbanization on loss of biodiversity. In this study, three primary procedures were used: (1) an SVM-based supervised classification technique for LULC classification; (2) a Markov chain and cross-tabulation method for the examination of LULC trends in space and time, (3) utilizing the CA-Markov approach to forecast urban growth. By using Landsat imagery of 2008, 2015, and 2023, the study determined how urban cover changed over time, and what kind of LULC-to-urban transition occurred. The study revealed that uncontrolled and haphazard urban expansion was observed in the Sululta sub-city, which could have disastrous repercussions on physical, biological and urban ecosystem. The %age of urban area increased from 9.04% in 2008 to 13.07% in 2015. However, because of the internally displaced people from the Ethio-Somali Region, who have been resettled there since 2017, the ratio of urban areas grew from 13.7% in 2015 to 24.65% in 2023. Furthermore, by 2040, the sub-city will have grown by 27.69 %. The kappa coefficient statistics of the three classified images of the years 2008, 2015, and 2023 were 93.3 %, 97.5%, and 97.5 %, respectively. To identify the areas that would be covered by future city growth, it is advised that this innovative technique be integrated with optimizing land use strategies.

Support Vector Machine (SVM) is a robust Machine Learning (ML) algorithm used extensively in classification tasks. This work proposes a reconfigurable hardware implementation of the SVM classification algorithm for the linear and three kernel cases on FPGA. Efficient implementations of two generalization techniques, One-versus-All (OvA) and One-versus-One (OvO), to deal with multi-class problems are also realized on FPGA to overcome the binary nature of the SVM algorithm. The presented model is fully reconfigurable and can easily be adapted to any dataset with any number of classes or features. The results show that the proposed model excels in power efficiency, requires low area utilization, and reaches high performance up to 250.7 MHz. The two realized generalization methods, OvO and OvA, offer a trade-off between accuracy and hardware cost. OvA provides lower accuracy than OvO and is more affected by the data imbalance problem, which becomes more dominant as the number of classes increases; however, it is more resource-efficient than OvO. The proposed hardware design is realized and experimentally tested on FPGA for three different datasets, employing different linear and kernel SVM cases. The classification accuracy achieved by the reconfigurable hardware design matches the software simulation results.

Accurate estimation of state-of-charge (SoC) is significant for monitoring the operation of LiFePO4 batteries. This paper addresses the challenges posed by the nonlinear characteristics of LiFePO4 batteries, which adversely affect the accuracy of SoC estimation. The proposed novel methodology for SoC estimation in LiFePO4 batteries involves a Support Vector Machine (SVM) assisted Artificial Neural Network (ANN) model incorporating principal component analysis (PCA) to efficiently interpret the input data. To improve the prediction accuracy, the non-linear characteristics of LiFePO4 are divided into three parts and each of these parts is used to train a separate ANN model. Further, an optimal number of hidden layers and neurons are selected for each ANN model to minimize the prediction errors. The usage of dedicated smaller datasets for each ANN model simplifies the structure. SVM classifies the battery operating regions and selects the most suitable ANN model for SoC estimation. PCA is applied to process the obtained experimental data resulting in three principal components serving as inputs for the SVM-assisted ANN model. Further, with PCA the input dimensions are reduced from four to three, thereby leading to computational simplicity. The input data comprises of current, voltage, open-circuit voltage and temperature of the battery. An experimental prototype, comprising a customized battery pack and sensing mechanisms, is developed for data collection for training SVM and ANN models. With the proposed SVM-assisted ANN involving PCA, the loss function is minimized and an average Root Mean Square Error (RMSE) of 0.3133 is achieved. This demonstrates the feasibility, accuracy and applicability of SoC estimation with the developed SVM-assisted ANN model for LiFePO4 batteries.

Brownfield redevelopment and land sustainable management have many interconnected aspects, which could be evaluated by regional development. Most contaminated sites in Taiwan often lack comprehensive consideration during remediation for land reuse planning; therefore, they are operated independently from regional development. Regional development is a dynamic process; however, there is a research gap in understanding how regional development can serve as an incentive for brownfield redevelopment. This study established a model for assessing regional sustainability using machine learning (ML-RSD), that model used principal component analysis (PCA) and support vector machine (SVM) to predict the comprehensive evaluation rating of regional development at the township level and to explore the trends of regional governance sustainability. The results of ML-RSD that are compared to commonly used methods (driver-pressure-state-impact-response and entropy weight method) were consistent. Indicators such as the protection of vulnerable groups (SDG1), food security (SDG2), good health and well-being (SDG3), inclusive and equitable quality education (SDG4), environmental protection (SDG6), accessible and reliable energy (SDG7), and sustainable economic growth (SDG8) were identified as the main factors affecting regional development. Additionally, the model achieved an accuracy value exceeding 80%. Additionally, the results showed that there was no significant correlation between the delisted site (completed remediation) and regional development in southern Taiwan. The model underscored the overall lower comprehensive sustainable values for regions affected by inadequate land rehabilitation governance (SDG15). The results present that brownfield redevelopment strategies should enhance their effectiveness in land adaptation and integrating brownfield redevelopment with regional sustainable development is essential for it to become a win–win solution.

Studying the changes in seismicity, and the potential of the occurrences of large earthquakes in a seismic zone is not only extremely important from the aspect of seismological research, but it is additionally significant in the decisions of crisis management. Since, nowadays Machine learning techniques have proven the high ability for analyzing information, and discovering the relations among the parameters, in this research were tested some of these techniques for the earthquake prediction. For analysis, the north Zagros seismic catalogue was selected. A region that is an active seismic zone, and large cities are located there. Moreover, nine seismic parameters were used to study the possibility of large earthquake prediction for 1 month using three different Machine Learning (ML) techniques (Artificial Neural Network (ANN), Random Forest, and Support Vector Machine (SVM)). The accuracy of prediction models was evaluated using four different statistical measures (recall, accuracy, precision, and F1-score). The results showed that the (ANN) method is more accurate than other methods. Based on three investigated methodologies, greater accuracy results have been produced to forecast the earthquakes with bigger scale earthquakes about the completeness of the seismic catalogue in large magnitude. These achievements promise the possibility of successful prediction in a short period, which is hopeful for better crisis management performance.

Study region: Eight governorates in upper Egypt namely Aswan, Asyut, Beni-Suef, Fayoum, Luxor, Minya, Qena and Sohag. Study focus: This study aims to develop novel hybrid machine learning (ML) models for forecasting the drought phenomena based on limited inputs for the eight Egyptian govern-orates, and ii) evaluate the performance and accuracy of the developed ML models for predicting Palmer Drought Severity Index (PDSI) to recommend the optimal model based on performance statistical metrics. The hybrid ML models were Convolution Neural Networks (CNN)-Long Short-Term Memory (LSTM), CNN-Random Forest (RF), CNN-Support Vector Machine (SVR), and CNN-Extreme Gradient Boosting (XGB). New hydrological insights for the region: Results showed that CNN-LSTM model outperformed the others followed by CNN-RF. Values of NSE, MAE, MARE, IA, R2, and RMSE for CNN-LSTM were 0.885, 0.915, − 2.073, 0.967, 0.885, and 0.573, respectively. For the testing stage CNN-SVR model was found to perform the best; average values of NSE, MAE, MARE, IA, R2, and RMSE were 0.828, 0.364, − 2.903, 0.950, 0.828 and 0.688, respectively. This study provided a way forward for convenient estimation of the PDSI Index from the meteorological data in terms of advancing deep learning algorithms. The developed hybrid models, more or less, can satisfactory predict PDSI values. Additionally, the study suggests the CNN-LSTM model as the most suitable model to advance future investigation in the study area.

Landslides are natural hazards that cause significant damage to both property and human lives. This study employs potential machine learning models such as Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM) to assess landslide susceptibility in Van Yen District, Yen Bai Province, Vietnam, that experiences a higher frequency of landslides compared to other localities in the region. The study incorporates thirteen input variables, including elevation, slope angle, aspect, plan curvature, profile curvature, Topographic Wetness Index (TWI), distance to faults, lithology, distance to roads, distance to rivers, land cover, rainfall, and Normalized Difference Vegetation Index (NDVI). To construct the models, landslide statistics reports were utilized, consisting of 302 landslide points collected through field surveys and 52 landslide points determined using Radar Sentinel-1 images. The Google Earth Engine cloud computing platform is utilized for constructing the landslide susceptibility models. The outcome of the research is a landslide susceptibility map with five levels: very low, low, moderate, high, and very high. The Area Under the Curve (AUC) is used as a metric to evaluate the performance of all three models. The findings indicate that, besides similarities observed in landslide susceptibility maps for previously occurred landslides, the Random Forest model demonstrates a favorable performance compared to the other models, with an AUC of 0.883.

This study introduced a metal-oxide-semiconductor (MOS) based electronic nose (E-nose) to perform on-the-spot classification of superior-quality black tea. A piecewise feature method based on a line-fitting model was introduced to extract comprehensive features of E-nose sensor response curves. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used for data dimensionality reduction and structure visualization. Support vector machine (SVM) with a Radial kernel function was used to assess the performance of E-nose. The results indicated that the SVM model coupled with the piecewise feature method performed better and achieved the best classification rates of 99.50 %, 95.30 %, and 96.50 %, for training, validation, and testing datasets respectively, with testing sensitivity and specificity of up to 98.60 % and 99.10 %. The E-nose result was further correlated with compound concentrations in the black tea, measured using gas chromatography-mass spectrometry (GC–MS). Based on its enhanced performance evaluation, the introduced lab-built E-nose system yielded promising results in assessing superior-quality black tea.

The American healthcare system allocates considerable resources compared to peer-developed nations. However, outcomes significantly trail behind, particularly in life expectancy. This study addresses questions about the enduring trends in healthcare spending as a percentage of Gross Domestic Product (GDP), notable factors contributing to this concerning trend, and the timing to apply an emergency brake to curb this accelerating trajectory. Advanced machine learning algorithms, such as Random Forest and Support Vector Regression (SVR), in conjunction with traditional statistical forecasting methods, are used to forecast future patterns. The research underscores the importance of healthcare analytics in unraveling the intricacies of the healthcare system. The findings highlight the pressing need for effective policies to confront this mounting challenge.

Due to the brittle nature of ceramic, the ceramic and construction industry produces a large volume of waste that imposes a severe environmental threat due to its non-biodegradability. In this study, the suitability of ceramic waste as a replacement of natural coarse and fine aggregate in concrete has been investigated by evaluating engineering properties such as bulk density, water absorption, workability, etc. with respect to different concrete samples made with different mix proportions. Furthermore, a prediction model is introduced to predict compressive and splitting tensile strength using the machine learning tool support vector machine (SVM). A data set containing 108 records either for compressive or tensile strength was used for the training and testing purposes of the SVM model. A total of 9 mix proportions was selected and cast cylinders were cured for 7, 28, and 56 days. Four different kernel functions were used to optimize the results and different accuracy parameters like the value of R2, mean absolute error, mean square error, root mean square error, etc. were compared to find the best kernel function for this study. By primarily evaluating the coefficient of determination (R2), SVM showed an acceptable result with an accuracy of over 90%. Moreover, in terms of other accuracy measurement parameters result indicates that the SVM is an effective tool to predict the compressive and splitting tensile strength of concrete comprised of different proportions of ceramic content.

Due to the importance of accurate diagnosis and prompt treatment of this condition, the medical world is searching for a solution for its early detection and efficient treatment. Heart disease is one of the leading causes of death in modern society. With the development of computer science today, this issue can be resolved using computers. Data mining is one of the solutions for diagnosing this illness. One of the cutting-edge disciplines, data mining, can aid in better decision-making in many areas of medicine, including disease diagnosis and treatment. In order to improve diagnosis accuracy, a combination method using the evolutionary algorithms locust and support vector machine has been tested in this study. Use should be made of heart disease. Because of the hybrid nature of this approach, normalization is actually carried out in three steps: first, by using pre-processing operations to remove unknown and outlier data from the data set; second, by using the locust evolutionary algorithm to choose the best features from the available features; and third, by classifying the data set using a support vector machine. The accuracy criterion for the proposed method compared to Niobizin methods, neural networks, and J48 trees improved by 18 %, 30 %, and 24 %, respectively, after implementing it on the data set and comparing it with other algorithms used in the field of heart disease diagnosis.

Spatial and seasonal evaluation and monitoring of groundwater (GW) quality is essential for the sustainable management of this priceless resource and the provision of safe drinking water. Nevertheless, GW quality appraisal was not given due attention in the current study area (flat terrain part of the Tana sub-basin). This study sought to assess the seasonal and regional physicochemical GW quality parameters for drinking appropriateness using the drinking water quality index (DWQI) and support vector machine (SVM). The main cations in both the dry and wet study seasons were, in decreasing order, Na+, Ca2+, Mg2+, K+, and Fe2+, according to the results. Conversely, the main anions were HCO3−, CO32−, Cl− or NO3−, SO42− and PO43−, ordered from higher to lower. During the two research seasons, Ca–HCO3 and Na–HCO3 were the predominant water types based on Piper diagram results. Reverse ion exchange and evaporation were the principal hydrogeochemical processes that control the hydrogeochemistry identified by Durov and Gibbs diagrams, respectively. Excellent GW quality class for drinking was demonstrated by the majority of geographical and seasonal DWQI readings over the two seasons. Nevertheless, during the rainy season, there was a noticeable decline in the GW quality condition around the northern shores of Lake Tana. Therefore, it is advised to implement comprehensive GW quality protection measures and improve system management to mitigate pollution to reduce health hazards in the examined region.

Globally, ovarian cancer affects women disproportionately, causing significant morbidity and mortality rates. The early diagnosis of ovarian cancer is necessary for enhancing patient health and survival rates. This research article explores the utilization of Machine Learning (ML) techniques alongside eXplainable Artificial Intelligence (XAI) methodologies to aid in the early detection of ovarian cancer. ML techniques have recently gained popularity in developing predictive models to detect early-stage ovarian cancer. These predictions are made using XAI in a transparent and understandable way for healthcare professionals and patients. The primary aim of this study is to evaluate the effectiveness of various ovarian cancer prediction methodologies. This includes assessing K Nearest Neighbors, Support Vector Machines, Decision trees, and ensemble learning techniques such as Max Voting, Boosting, Bagging, and Stacking. A dataset of 349 patients with known ovarian cancer status was collected from Kaggle. The dataset included a comprehensive range of clinical features such as age, family history, tumor markers, and imaging characteristics. Preprocessing techniques were applied to enhance input data, including feature scaling and dimensionality reduction. A Minimum Redundancy Maximum Relevance (MRMR) algorithm was used to select the features in the model. Our experimental results demonstrate that in Support Vector Machines, we found 85 % base model accuracy and 89 % accuracy after stacking several ensemble learning techniques. With the help of XAI, complex ML algorithms can be given more profound insights into their decision-making, improving their applicability. This paper aims to introduce the best practices for integrating ML and artificial intelligence in biomarker evaluation. Building and evaluating Shapley values-based classifiers and visualizing results were the focus of our investigation. The study contributes to the field of oncology and women's health by offering a promising approach to the early diagnosis of ovarian cancer.

Background: At present, the diagnosis of post-traumatic stress disorder（PTSD） mainly relies on clinical symptoms and psychological scales, and finding objective indicators that are helpful for diagnosis has always been a challenge in clinical practice and academic research. Neuroimaging is a useful and powerful tool for discovering the biomarkers of PTSD,especially functional MRI (fMRI), structural MRI (sMRI) and Diffusion Weighted Imaging（DTI）are the most commonly used technologies, which can provide multiple perspectives on brain function, structure and its connectivity. Machine learning (ML) is an emerging and potentially powerful method, which has aroused people's interest because it is used together with neuroimaging data to define brain structural and functional abnormalities related to diseases, and identify phenotypes, such as helping physicians make early diagnosis. Objectives: According to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) declaration, a systematic review was conducted to assess its accuracy in distinguishing between PTSD patients, TEHC(Trauma-Exposed Healthy Controls), and HC(healthy controls). Methods: We searched PubMed, Embase, and Web of Science using common words for ML methods and PTSD until June 2023, with no language or time limits. This review includes 13 studies, with sensitivity, specificity, and accuracy taken from each publication or acquired directly from the authors. Results: All ML techniques have an diagnostic accuracy rate above 70%，and support vector machine（SVM） are the most commonly used techniques. This series of studies has revealed significant neurobiological differences in key brain regions among individuals with PTSD, TEHC, and HC. The connectivity patterns of regions such as the Insula and Amygdala hold particular significance in distinguishing these groups. TEHC exhibits more normal connectivity patterns compared to PTSD, providing valuable insights for the application of machine learning in PTSD diagnosis. Conclusion: In contrast to any currently available assessment and clinical diagnosis, ML techniques can be used as an effective and non-invasive support for early identification and detection of patients as well as for early screening of high-risk populations.

Urbanisation is a global trend that significantly impacts sustainable urban development and the quality of urban life. Assessing urban sprawl is critical for sustainable urban planning and aligns with the key objectives of the United Nations sustainable development goals. This study employed geospatial technology and landscape metrics to comprehensively assess, map, and quantify the extent of urban sprawl in Bulawayo from 1984 to 2022. The study leveraged the Support Vector Machine (SVM) supervised machine learning algorithm coupled with landscape metrics to achieve this objective. The combined approach allowed for the classification, detection of land cover changes, analysis of urban dynamics, and quantification of the degree of urban sprawl. The results revealed a 228% increase in built-up areas between 1984 and 2022, while non-built-up areas (agricultural land, vegetation, bare land) decreased by 29.28%. The landscape metrics and change analysis indicated an encroachment of urban-like conditions into urban areas. Land use change assessment revealed that Bulawayo exhibits four district types of urban sprawl: leapfrog, strip/ribbon, low density, and infill. Urban expansion is attributed to urbanisation and evolving land use policy. Urban sprawl has numerous urban planning implications on transport management, habitat loss and deforestation, reduction and contamination of freshwater sources, and many others. This study is strategic to planners, researchers, and decision-makers/policy makers as it provides relevant, up-to-date, and accurate information for sustainable urban planning.

With the development of the modern industry, to continuously use large precision instruments, the circuits protecting method through circuit design has gradually been promoted. And this method can prevent the excessive accumulation of electric heat from causing fires. The voltage at the starting point of power transmission is constant. However, during the transportation process, some lines have low resistance values, which can generate large instantaneous currents. To address this issue, this study conducted simulation experiments on self-collected Circuit dataset based on particle swarm optimization and backpropagation neural networks. This study first introduced new parameter term learning factors into backpropagation neural networks. Then they were imported into the support vector machine, and the nonlinear variables were mapped to high plane, and the optimal hyperplane was established. Then the traditional circuit design method was improved, 40 Resistor were connected in parallel and connected to the experimental circuit in series with the rheostat. Finally, the algorithm was introduced into Circuit dataset collected in this experiment, and its protective effect on the circuit was compared with the other three algorithms. Under the protection of this design, the working times of four algorithms were 0.28, 0.42, 0.38, and 0.43 s, respectively. Their phase displacements were 0.19, 0.26, 0.36, and 0.41, respectively. The circuit design method proposed in this study can effectively address circuit faults. And the fusion algorithm can disconnect the circuit at the fastest speed and significantly reduce excitation current intensity, and it is suitable for circuit design in the field of industrial design.

This study looked at the application of multiple bulk stable isotope ratio analysis to accurately authenticate organic rice and counteract organic fraud within the expanding global organic market. Variations of δ13C, δ15N, δ18O, and δ34S in organic, pesticide-free, and conventional rice were assessed across different milling states (brown, milled, and bran). Individual stable isotope ratio alone such as δ15N demonstrated limited capacity to correctly differentiate organic, pesticide-free, and conventional rice. A support vector machine model—incorporating δ13C, δ15N, δ18O, and δ34S in milled rice—yielded overall predictability (95%) in distinguishing organic, pesticide-free, and conventional rice, where δ18O emerged as the pivotal variable based on the feature weights in the SVM model. These findings suggest the potential of multi-isotope and advanced statistical approaches in combating organic fraud and ensuring authenticity in the food supply chain.

Currently, many individuals are experiencing diabetes, which is attributed to work-related stress and unhealthy lifestyles. Often, people are only aware of their health status once symptoms manifest or they undergo a medical examination to obtain a diagnosis. However, the conventional machine learning methods failed to classify diabetes in the initial stage. Therefore, this work implements the user-cloud-based ensemble framework for type-2 diabetes prediction (DP-UCE). Initially, the Pima Indian Diabetes (PID) dataset is considered. Here, the cloud application trains the PID dataset using an ensemble model. Here, the dataset preprocessing is performed to remove missing values for unknown symbols. Then, three individual models, such as a decision tree classifier (DTC), support vector machine (SVM), and artificial neural network (ANN), were trained with the preprocessed dataset. Then, the bagging ensemble classifier is trained with the DTC, ANN, and SVM features. In addition, the user application is initialized, and type-2 diabetes status is predicted from the uploaded test data of the user using a bagging ensemble classifier. Moreover, based on prediction results, the user is also given a diet plan. The simulations conducted on the PID dataset show that the ensemble classifier resulted in 87.41 % accuracy, which is superior to state-of-the-art methods.

In order to better perform thermal hydraulic calculation and analysis of supercritical water reactor, based on the experimental data of supercritical water, the model training and predictive analysis of the heat transfer coefficient of supercritical water were carried out by using the support vector machine (SVM) algorithm. The changes in the prediction accuracy of the supercritical water heat transfer coefficient are analyzed by the changes of the regularization penalty parameter C, the slack variable epsilon and the Gaussian kernel function parameter gamma. The predicted value of the SVM model obtained after parameter optimization and the actual experimental test data are analyzed for data verification. The research results show that: the normalization of the data has a great influence on the prediction results. The slack variable has a relatively small influence on the accuracy change range of the predicted heat transfer coefficient. The change of gamma has the greatest impact on the accuracy of the heat transfer coefficient. Compared with the calculation results of traditional empirical formula methods, the trained algorithm model using SVM has smaller average error and standard deviations. Using the SVM trained algorithm model, the heat transfer coefficient of supercritical water can be effectively predicted and analyzed.

Nanofluids are employed in variety of thermal energy systems owing to their modified properties and ability of enhancement in heat transfer. Among the properties of nanofluids, thermal conductivity and dynamic viscosity have significant role in the heat transfer characteristics and fluid flow specifications. Among the intelligent techniques, Support Vector Machines (SVMs) are attractive choices regarding their significant exactness in prediction and modeling. In this article, research works on the forecasting and modeling of the mentioned properties of nanofluids with SVMs are reviewed and their outcomes are presented. In accordance with the results of the reviewed works it can be stated that the developed approaches based on the SVM technique have shown significant performance in accurate modeling of both thermal conductivity and dynamic viscosity and can have superior performance in comparison with other intelligent methods in some cases. Furthermore, it can be pointed out that the performance of these systems is dependent on some elements like the employed optimization algorithms. Some recommendations are represented for future studies to reach models with more favorable performance.

Covid-19 pandemic and the restrictions imposed to control its spreading had a profound impact on several aspects of society. Studies on energy consumption show a pattern shift during the first stages of the pandemic, mainly due to the abrupt and forced change in human behaviour. For some residential consumers, such changes translated into higher energy expenses, which could be avoided through optimal energy management. This, however, depends on accurate load forecasting models whose performance tends to decrease under drastic pattern changes. This paper aims to improve forecasting techniques based on machine learning methods to assess its impact on the accuracy of day-ahead electrical load forecasting from individual residential households from the city of Barcelona, Spain, during a lockdown situation. Support Vector Machine (SVM) is the proposed algorithm for the short-term hourly forecasting of the households’ electricity consumption of the following 24 h. The algorithm’s accuracy over different pandemic periods – Lockdown, Reopening and New Normal – is evaluated using different retraining strategies based on literature findings.