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Abstract The fast growth of the film business, along with an ever-increasing number of movie options, has highlighted the need for better recommendation algorithms. This study investigates the application of sentiment analysis in a movie recommendation system with the goal of improving the user experience. The importance of this sector stems from its ability to bridge the gap between user interests and the vast number of cinematic products, addressing individual emotional states and preferences. Researchers choose to generate movie recommendations based on the sentiments conveyed by viewers’ reviews of the movies. Sentiment-based movie recommendation system research employs techniques such as natural language processing and hybrid models with the goal of increasing user satisfaction. To this purpose, the integration of advanced machine learning algorithms such as cosine similarity, support vector machine, and Naive Bayes improves recommendation systems with sentiment analysis. Cosine similarity improves movie recommendations by recognizing minor user preferences, while support vector machines and Naive Bayes enhance sentiment analysis by offering a nuanced interpretation of textual attitudes. Through trials, the proposed system employs two public datasets for sentiment analysis, namely the TMDB5k dataset and the Reviews dataset, and makes predictions (positive, negative, or neutral) based on the content of the review through conducting sentiment analysis on the text using the Viscous Accretion Disk Evolution Resource (VADER) approach. The findings, based on users’ feedback, are more accurate and informative regarding movie quality, where SVM accuracy is 99.28% and Naïve Bayes accuracy is 96.60% when used with VADER sentiment analysis.

This research aims to analyze sentiment on DANA application reviews to find out user perceptions by comparing Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Extra Trees Classifier classification methods. DANA application review data is obtained from the Kaggle site which consists of 50,000 Indonesian-language reviews labeled with positive and negative sentiments. The research stages include data preprocessing to clean and prepare the review text, applying word weighting using Word2Vec to give weight to words based on their context, balancing sentiment classes using SMOTE to address the imbalance of positive and negative review classes. It should be noted that the initial proportion of data before applying SMOTE may affect the results. The data is then divided into training and testing sets, then the models are trained and evaluated using Confusion Matrix and K-Fold Cross-Validation. The results of the three classification methods are measured by the accuracy matrix and F1-Score to assess model performance, the SVM and XGBoost methods obtained an accuracy of 93% and the ETC method achieved an F1-Score value of 96% at K=6, the three models proved to be very accurate in predicting the sentiment of DANA application reviews both positive and negative. The practical implications of this research can identify areas for application improvement, develop popular features, personalize services based on user preferences, and manage application reputation.

Flooding occurs when the water's surface elevation exceeds the average level, overflowing river water and creating inundation in low-lying areas. Early warning for potential floods significantly reduces losses, such as human casualties and property damage. In this context, the flood disaster classification system uses water surface elevation data from the Water Resources Agency to predict the likelihood of floods using the K-Nearest Neighbors (KNN) Algorithm. This research aims to classify flood status based on water surface elevation using the K-Nearest Neighbors and Support Vector Machine(SVM) methods in the Ciliwung River. The study results indicate that the SVM algorithm outperforms the KNN algorithm. The SVM algorithm used parameter C ranging from 1 to 10 in the scenarios, and the RBF kernel achieved 100% accuracy. On the other hand, the KNN algorithm achieved 100% accuracy only for K values of 1, 2, 3, 4, and 5 in scenarios where K ranged from 1 to 10.

Abstract Customer churn is a common problem faced by many industries, including telecommunication industries. This has resulted in the development of advanced techniques for the prediction and prevention of customer churn. The availability of stored customer data in the form of big data, together with the use of advanced and tuned machine learning (ML) algorithms, have paved the way for the realisation and extraction of useful features associated with customer behaviour and consequently the prediction of customer churn. An effective way to further improve churn prediction capability of different ML algorithms is through the employment of topological data analysis (TDA). TDA is a framework that applies topological methods to uncover the underlying hidden structural features in complex, high-dimensional data. Here, a TDA summary of 0- and 1-dimensional holes of the data, called barcode statistics, was extracted and used as an additional feature to the preprocessed customer data. To address issues such as the effective preprocessing and analysis of large customer datasets and the effective tuning of ML hyperparameters, we implement an advanced data preprocessing technique that consists of different stages such as handling of missing data, feature engineering, encoding of categorical features using the hashing encoding method, and feature selection. Without including barcode statistics in the model, the XGBoost algorithm with tuned hyperparameters achieved the best results, with accuracy of 92.71%, precision of 85.95%, recall of 92.71%, and F-measure of 89.20%. Including barcode statistics as an additional feature, the XGBoost algorithm with tuned hyperparameters achieved the best and much improved results, with accuracy of 98.50%, precision of 98.50%, recall of 98.50%, and F-measure of 98.50%. The use of TDA barcode statistics significantly improved the churn prediction capability of the ML algorithms. In addition, hyperparameter tuning is not needed when an effective data preprocessing technique is used, or when barcode statistics is used. The best accuracy of 98.5% from this work was in line with the best accuracy of 98.7% from a related work, but interestingly, the best precision of 98.5% from this work was superior to the 94.3% precision from the same related work with higher accuracy.

Abstract Aiming at the problem of high volatility and intermittency of new energy-containing power systems, which affects the prediction accuracy of carbon emissions, we study the adaptive prediction method of carbon emissions of a new energy-containing power system based on a least-squares support vector machine. The carbon emission coefficients of the nodes of the new energy-containing power system are determined based on the trend analysis method. Historical carbon emissions and carbon emission coefficients of the power system are collected, and the rough set method is used to simplify the carbon emission attributes and obtain a simplified attribute set for carbon emission prediction. The least-squares support vector mechanism is used to construct the carbon emission prediction model, and the simplified attribute set is used as the input of the carbon emission prediction model. The whale optimization algorithm was selected to determine the optimal parameters of the least-squares support vector machine through the process of encircling prey, hunting behavior, searching for predation, and adaptively optimizing the least-squares support vector machine to output carbon emission prediction results. The experimental results show that the method can accurately predict the carbon emissions of the power system, and the mean square error of the carbon emissions prediction is lower than 7.

Abstract Emotions play a vital role in recognizing a person’s thoughts and vary significantly with stress levels. Emotion and stress classification have gained considerable attention in robotics and artificial intelligence applications. While numerous methods based on machine learning techniques provide average classification performance, recent deep learning approaches offer enhanced results. This research presents a hybrid deep learning model that extracts features using AlexNet and DenseNet models, followed by feature fusion and dimensionality reduction via Principal Component Analysis (PCA). The reduced features are then classified using a multi-class Support Vector Machine (SVM) to categorize different types of emotions. The proposed model was evaluated using the DEAP and EEG Brainwave datasets, both well-suited for emotion analysis due to their comprehensive EEG signal recordings and diverse emotional stimuli. The DEAP dataset includes EEG signals from 32 participants who watched 40 one-minute music videos, while the EEG Brainwave dataset categorizes emotions into positive, negative, and neutral based on EEG recordings from participants exposed to six different film clips. The proposed model achieved an accuracy of 95.54% and 97.26% for valence and arousal categories in the DEAP dataset, respectively, and 98.42% for the EEG Brainwave dataset. These results significantly outperform existing methods, demonstrating the model’s superior performance in terms of precision, recall, F1-score, specificity, and Mathew correlation coefficient. The integration of AlexNet and DenseNet, combined with PCA and multi-class SVM, makes this approach particularly effective for capturing the intricate patterns in EEG data, highlighting its potential for applications in human-computer interaction and mental health monitoring, marking a significant advancement over traditional methods.

A fast method for the identification of the characteristic components of Chrysanthemum was proposed using three-dimensional excitation emission matrix (3DEEM) spectroscopy coupled with parallel factor analysis (PARAFAC). The 3DEEM spectra of four varieties of Chrysanthemum were obtained and preprocessed to remove interference data such as Raman and Rayleigh scattering as well as outliers, and the spectral characteristics were analyzed. Feature extraction was then performed using PARAFAC, and amino acids and flavonoids were identified as characteristic fluorescent components of Chrysanthemum based on variance interpretation rate and residual analysis. Finally, support vector machine (SVM) and back propagation neural network (BPNN) were employed to analyze the characteristic variables and a fast and non-destructive identification model was developed. The accuracy of the SVM and BPNN models were 100% and 95.93% for the training set, and 94.50% and 89.61% for the test set, respectively. This study demonstrates that 3DEEM-PARAFAC combined with SVM enables both qualitative and quantitative analysis of Chrysanthemum’s characteristic components and rapid identification of Chrysanthemum.

Abstract The fundamental objective of this paper is to use Machine Learning (ML) methods for building models on temperature (T) prediction using input features r and z for a membrane separation process. A hybrid model was developed based on computational fluid dynamics (CFD) to simulate the separation process and integrate the results into machine learning models. The CFD simulations were performed to estimate temperature distribution in a vacuum membrane distillation (VMD) process for separation of liquid mixtures. The evaluated ML models include Support Vector Machine (SVM), Elastic Net Regression (ENR), Extremely Randomized Trees (ERT), and Bayesian Ridge Regression (BRR). Performance was improved using Differential Evolution (DE) for hyper-parameter tuning, and model validation was performed using Monte Carlo Cross-Validation. The results clearly indicated the models’ effectiveness in temperature prediction, with SVM outperforming other models in terms of accuracy. The SVM model had a mean R2 value of 0.9969 and a standard deviation of 0.0001, indicating a strong and consistent fit to the membrane data. Furthermore, it exhibited the lowest mean squared error, mean absolute error, and mean absolute percentage error, signifying superior predictive accuracy and reliability. These outcomes highlight the importance of selecting a suitable model and optimizing hyperparameters to guarantee accurate predictions in ML tasks. It demonstrates that using SVM, optimized with DE improves accuracy and consistency for this specific predictive task in membrane separation context.

Abstract Determining the geographical origin of kimchi holds significance because of the considerable variation in quality and price among kimchi products from different regions. This study explored the feasibility of employing Fourier transform near-infrared spectroscopy in conjunction with supervised chemometric techniques to differentiate domestic and imported kimchi products. A total of 30 domestic and 30 imported kimchi products were used to build datasets. Three categories of preprocessing methods such as scattering correction (multiplicative signal correction and standard normal variate), spectral derivatives (the first derivative and the second derivative), and data smoothing (Savitzky–Golay filtering and Norris derivative filtering) were used. K-nearest neighbors, support vector machine, random forest, and partial least squares-discriminant analysis were employed. By appropriately preprocessing spectral data, these four methods successfully distinguished between the two sample groups based on their origin. Notably, the k-nearest neighbors method exhibited exceptional performance, accurately classifying the sample groups irrespective of the preprocessing method employed and swiftly achieving this classification. In comparison, classification and regression tree as well as naïve Bayes methods were outperformed by the aforementioned four classification techniques. Particularly, the efficiency and accuracy of the k-nearest neighbors method make it the most recommended chemometric tool for determining the geographical origins of kimchi.

Objective·To develop a machine learning approach for early identification of metabolic syndromes associated with inflammatory metabolic state changes in breast cancer patients after neoadjuvant therapy, using common laboratory and transthoracic echocardiography indices.Methods·Female patients with primary invasive breast cancer diagnosed at the Department of Breast Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, between September 2020 and September 2022, were included. General patient information, laboratory test results, and transthoracic echocardiography data were collected. After feature extraction, five machine learning algorithms, including random forest (RF), gradient boosting (GB), support vector machine (SVM), K-nearest neighbor (KNN), and decision tree (DT), were applied to construct a prediction model for the changes of the patients′ metabolic state after neoadjuvant therapy, and the prediction performances of the five models were compared.Results·A total of 232 cases with valid clinical data were included, comprising 135 cases before neoadjuvant therapy and 97 cases after completing 4 cycles of neoadjuvant therapy. Feature extraction identified five key features: white blood cell count, hemoglobin, high-density lipoprotein (HDL), interleukin-2 receptor, and interleukin-8. In the multi-feature analysis, the area under the receiver operating characferistic curve (AUC) was higher in the combination of white blood cell count, hemoglobin and HDL compared to the combination of interleukin-2 receptor and interleukin-8 (RF: 0.928 vs 0.772, GB: 0.900 vs 0.792, SVM: 0.941 vs 0.764, KNN: 0.907 vs 0.762, DT: 0.799 vs 0.714). The RF, SVM, and GB models showed higher AUC (0.928, 0.941, 0.900) and accuracy (0.914, 0.897, 0.776). The SVM model exhibited superior accuracy in the training data compared to the RF and GB models (P=0.394, 0.122 and 0.097, respectively).Conclusion·The SVM model can be used to establish a prediction model for identifying breast cancer patients at high risk of developing inflammatory metabolic state-related metabolic syndrome after neoadjuvant therapy by incorporating five common clinical indicators, namely, white blood cell count, hemoglobin, high-density lipoprotein, interleukin-2 receptor, and interleukin-8. SVM modeling may be useful for clinicians to establish individualized screening protocols based on a patient′s inflammatory metabolic state.

Abstract Purpose To investigate the application value of support vector machine (SVM) model based on diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) and amide proton transfer- weighted (APTW) imaging in predicting isocitrate dehydrogenase 1(IDH-1) mutation and Ki-67 expression in glioma. Methods The DWI, DCE and APTW images of 309 patients with glioma confirmed by pathology were retrospectively analyzed and divided into the IDH-1 group (IDH-1(+) group and IDH-1(-) group) and Ki-67 group (low expression group (Ki-67 ≤ 10%) and high expression group (Ki-67 > 10%)). All cases were divided into the training set, and validation set according to the ratio of 7:3. The training set was used to select features and establish machine learning models. The SVM model was established with the data after feature selection. Four single sequence models and one combined model were established in IDH-1 group and Ki-67 group. The receiver operator characteristic (ROC) curve was used to evaluate the diagnostic performance of the model. Validation set data was used for further validation. Results Both in the IDH-1 group and Ki-67 group, the combined model had better predictive efficiency than single sequence model, although the single sequence model had a better predictive efficiency. In the Ki-67 group, the combined model was built from six selected radiomics features, and the AUC were 0.965 and 0.931 in the training and validation sets, respectively. In the IDH-1 group, the combined model was built from four selected radiomics features, and the AUC were 0.997 and 0.967 in the training and validation sets, respectively. Conclusion The radiomics model established by DWI, DCE and APTW images could be used to detect IDH-1 mutation and Ki-67 expression in glioma patients before surgery. The prediction performance of the radiomics model based on the combination sequence was better than that of the single sequence model.

Abstract This study aimed to classifying wheat genotypes using support vector machines (SVMs) improved with ensemble algorithms and optimization techniques. Utilizing data from 302 wheat genotypes and 14 morphological attributes to evaluate six SVM kernels: linear, radial basis function (RBF), sigmoid, and polynomial degrees 1–3. Various optimization methods, including grid search, random search, genetic algorithms, differential evolution, and particle swarm optimization, were used. The radial basis function kernel achieves the highest accuracy at 93.2%, and the weighted accuracy ensemble further improves it to 94.9%. This study shows the effectiveness of these methods in agricultural research and crop improvement. Notably, optimization-based SVM classification, particularly with particle swarm optimization, saw a significant 1.7% accuracy gain in the test set, reaching 94.9% accuracy. These findings underscore the efficacy of RBF kernels and optimization techniques in improving wheat genotype classification accuracy and highlight the potential of SVMs in agricultural research and crop improvement endeavors.

Deep packet inspection is a network security solution that identifies and flags anomalous network traffic patterns in a local network environment. Traditional signature-based techniques for intrusion detection are limited in identifying different attacks or completely new kinds, which makes them unsuitable in some situations. In addition, most previous methods for anomaly detection have low detection rate and high false alarm. In this study, a deep packet inspection model based on support vector machine (SVM) for anomaly detection in local area networks was proposed. The proposed method combined the SelectKBest method and SVM for the categorization of anomaly in a local network environment. Results showed that the proposed method outperformed other related machine learning methods with accuracy, precision, recall, and F1-score of 94.81%, 94.03%, 94.13%, and 94.0799%, respectively. The accuracy result shows that most network traffic can be correctly identified by the SVM using the SelectKBest approach, with minimal false positives or negatives.

A This paper proposes a modified real-time transient modelling (MRTTM) framework to address the critical challenge of leak detection and localization in pipeline transmission systems. Pipelines are essential infrastructure for transporting liquids and gases, but they are susceptible to leaks, with severe environmental and economic impacts. MRTTM tackles this challenge with a three-stage operational process. First, “Data Collection” gathers sensor data from designated observation points. Second, the “Detection” stage identifies leaks. Finally, “Decision-Making” utilizes MRTTM to pinpoint the exact leak magnitude and location. This paper introduces an innovative method designed to significantly enhance pipeline leak detection and localization through the application of artificial intelligence and advanced signal processing techniques. The improved MRTTM framework integrates AI for pattern recognition, state space modelling for leak segment identification, and an extended Kalman filter (EKF) for precise leak location estimation, addressing the limitations of traditional methods. This paper showcases the application of MRTTM through a case study using the K-nearest neighbors (KNN) method on a water transmission pipeline for leak detection. KNN aids in classifying leak patterns and identifying the most likely leak location. Additionally, MRTTM incorporates the EKF, enabling real-time updates during transient events for faster leak identification. Preprocessing sensor data before comparison with the leakage pattern bank (LPB) minimizes false alarms and enhances detection reliability. Overall, the AI-powered MRTTM framework offers a powerful solution for swift and precise leak detection and localization in pipeline systems. The functionality of the framework is examined, and the results effectively approve the effectiveness of this methodology. The experimental results validate the practical utility of the MRTTM framework in real-world applications, demonstrating up to 90% detection accuracy and an F1 score of 0.92.

Abstract As the core of ontology integration, the task of ontology mapping is to find the semantic relationship between ontologies. Nevertheless, most existing ontology mapping methods only rely ontext information to calculate entity similarity, thereby disregarding semantic nuances and necessitating substantial manual intervention. Therefore, this paper introduces an ontology mapping method. Based on the traditional ontology mapping method, the process employs a deep learning model to mine the semantic information of entity concepts, entity properties and ontology structure to obtain the embedding vector. We use the similarity mechanism to calculate the similarity between different embedding vectors, and combine the similarity values obtained from multiple strategy entities into a similarity matrix. The similarity matrix serves as input to the support vector machine (SVM), and the ontology mapping problem is finally transformed into a binary classification problem. However, since the number of mapped pairs is much larger than the number of non-mapped pairs, the number of positive samples in the data set is much smaller than the number of negative samples. Therefore, based on the traditional SVM, the paper adopts cost-sensitive strategy to deal with the class imbalance problem. In comparative evaluations against contemporary ontology mapping techniques, our method demonstrates a noteworthy 5.0% enhancement in recall and a 3.0% improvement in F1 score when tested on both public benchmark datasets and domain-specific datasets.

In the rapidly evolving digital era, the Population Identity Application (IKD) plays a crucial role in streamlining civil administration processes in Indonesia, allowing easier and faster access to population services. This study aims to explore the application of machine learning algorithms in analyzing user responses to the IKD application. Three popular algorithms: Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Naïve Bayes were chosen to classify sentiment from 1301 user reviews on the Google Play Store into positive and negative categories. After performing data preprocessing such as tokenization and stemming, hyperparameter optimization was conducted using GridSearchCV to enhance classification accuracy. The research results indicate that the SVM algorithm, optimized with hyperparameters, including the use of the rbf kernel and a parameter value of C = 1, achieved the highest accuracy of 85.60%, making it the most effective method for sentiment classification of the IKD application. These findings provide valuable insights for the government and developers in refining the features and performance of IKD, contributing to the efficiency and security of digital administration in Indonesia. Furthermore, this study opens opportunities for further development that is more responsive to user needs and expectations in the future.

There is a large amount of unstructured data in the current corn cross-border supply chain system and it has the characteristics of multi-source heterogeneous. Traditional risk early warning methods have defects such as over-reliance on manual decision-making and low accuracy of early warning. In order to solve the above problems, this paper proposed a system risk early warning method of corn cross-border supply chain based on deep belief network and multi-class fuzzy support vector machine. Firstly, based on the principle of embedding coding and normalization, a large number of unstructured data in the corn cross-border supply chain system were preprocessed and converted into structured data for subsequent calculation. Then, based on the deep belief network, the high-latitude features of the data were extracted, and the change trend and correlation of risk indicators in the corn cross-border supply chain system were adaptively mined. Finally, the extracted high-dimensional features were input into the multi-class fuzzy support vector machine model for training to realize the risk classification early warning of corn cross-border supply chain. The accuracy of the algorithm proposed in this paper can reach 94.88% under the condition of similar running time. It is 52.17% higher than that of the worst algorithm, and the comprehensive performance was superior to other algorithms which can provide theoretical support for the application of system risk regulation of corn cross-border supply chain.

Mengmeng Wang,1 Huanhuan Wang,1,2 Zhaoyan Feng,1 Shuai Wu,1 Bei Li,1,2 Fang Han,1 Fulong Xiao1 1Division of Sleep Medicine, Peking University People’s Hospital, Beijing, People’s Republic of China; 2School of Nursing, Peking University, Beijing, People’s Republic of ChinaCorrespondence: Fulong Xiao; Fang Han, Division of Sleep Medicine, Peking University People’s Hospital, Beijing, 100044, People’s Republic of China, Email xiaofulong1681@163.com; hanfang1@hotmail.comObjective: Depression is a common psychiatric issue among patients with narcolepsy type 1 (NT1). Effective management requires accurate screening and prediction of depression in NT1 patients. This study aims to identify relevant factors for predicting depression in Chinese NT1 patients using machine learning (ML) approaches.Methods: A total of 203 drug-free NT1 patients (aged 5– 61), diagnosed based on the ICSD-3 criteria, were consecutively recruited from the Sleep Medicine Center at Peking University People’s Hospital between September 2019 and April 2023. Depression, daytime sleepiness, and impulsivity were assessed using the Center for Epidemiologic Studies Depression Scale for Children (CES-DC) or the Self-Rating Depression Scale (SDS), the Epworth Sleepiness Scale for adult or children and adolescents (ESS or ESS-CHAD), and the Barratt Impulse Scale (BIS-11). Demographic characteristics and objective sleep parameters were also analyzed. Three ML models-Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM)-were used to predict depression. Model performance was evaluated using receiver operating curve (AUC), accuracy, precision, recall, F1 score, and decision curve analysis (DCA).Results: The LR model identified hallucinations (OR 2.21, 95% CI 1.01– 4.90, p = 0.048) and motor impulsivity (OR 1.10, 95% CI 1.02– 1.18, p = 0.015) as predictors of depression. Among the ML models, SVM showed the best performance with an AUC of 0.653, accuracy of 0.659, sensitivity of 0.727, and F1 score of 0.696, reflecting its effectiveness in integrating sleep-related and psychosocial factors.Conclusion: This study highlights the potential of ML models for predicting depression in NT1 patients. The SVM model shows promise in identifying patients at high risk of depression, offering a foundation for developing a data-driven, personalized decision-making tool. Further research should validate these findings in diverse populations and include additional psychological variables to enhance model accuracy.Keywords: narcolepsy type 1, depression, machine learning, support vector machine

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.

Background The comprehensive management of heart failure in conjunction with frailty necessitates a multidimensional approach to frailty assessment. However, the social frailty, despite being an incremental predictor of negative health outcomes in heart failure patients, has not been adequately addressed. Objective To understand the current status of social frailty in patients with chronic heart failure and analyze its influencing factors. Methods From September 2022 to July 2023, convenience sampling was used to select patients with chronic heart failure from the First Affiliated Hospital with Nanjing Medical University as the research objects, the general information questionnaire, the HALFT Scale, the Loneliness Scale, the Brief 2-Way Social Support Scale, Personal Mastery Scale, and the Patient Health Questionnaire were used to investigate. Univariate analysis and support vector machine-feature recursive elimination were used to filter the feature, SVM classification model was constructed, and SHAP value was introduced to analyze the influencing factors. Results A total of 228 patients were screened in this study, of which 8 patients refused to fill in. A total of 220 questionnaires were distributed and 213 valid questionnaires were returned, with an effective recovery rate of 96.81%. The proportion of pre-social frailty and social frailty in patients with chronic heart failure was 46.0% (98/213) and 17.8% (38/213), respectively. Statistically significant differences were observed among chronic heart failure patients with different degrees of social frailty in terms of education level, place of residence, working status, economic burden of disease, personal monthly income, course of disease, exercise habits, medical satisfaction, traffic, the UCLA Loneliness Scale score, the Brief 2-Way Social Support Scale score, the PMS score, and the PHQ-9 score. When the SVM-RFE model play the best performance, the optimal feature subset was used to construct the SVM classification prediction model and perform SHAP interpretability analysis. The accuracy of the model was 0.73 in the training set and 0.63 in the test set, respectively. At this time, the ranking of feature importance from high to low was no exercise habit (+), personal mastery (-), heavy economic burden of disease (+), 2-way social support (-), depression (+), loneliness (+), unemployment (+) . Conclusion Patients with chronic heart failure experiencing severe social frailty. Healthcare providers should prioritize identifying and addressing the resource deficits of patients and the underlying factors contributing to social frailty. Targeted interventions should be implemented to mitigate social frailty in patients with heart failure by enhancing external support systems, fostering positive beliefs, addressing negative emotional experiences, developing comprehensive management plans, coordinating medical resources, and implementing strategies to delay or reverse social frailty progression. These interventions aim to enhance the prognosis and quality of life for patients with heart failure.

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.

Currently, the results of lung sound auscultation with either physical or electronic stethoscopes still rely mainly on the doctor's professional auscultation identification ability, which has not yet been able to realise intelligent diagnosis and interpretation. When patients are affected by lung diseases at home, they are unable to detect lung abnormalities on their own and delay treatment; when they are in the process of rescue and treatment of respiratory infectious diseases, in-ear stethoscopes are easily contaminated and cause nosocomial infections. Although stethoscopic sounds contain a wealth of information about health status, the lack of standardised collection methods, classification criteria and analysis tools has limited the objective analysis and application of stethoscopic sounds in practice. In this study, the data collection, arrangement and database design of the lung auscultation sound were carried out by using the unified auscultation sound collection equipment and process. The study used the software MetlabR2017a for data management and analysis to create a database of lung auscultation sounds in a healthy group and a group of patients with lung disease. A database of lung auscultation sounds was established for healthy groups and groups of patients with lung diseases. A standard set of classification of auscultatory tones, labelling specifications, audio characteristic signal parameters were developed. Building a system for storing, managing and analysing lung auscultation sound data to provide important data support for research related to the screening and monitoring of lung diseases and the translation of medical artificial intelligence applications. The study accumulated the experience of building an audio database of lung auscultation sounds, provided a useful reference for the management and analysis of the audio database, and laied the foundation for supporting the subsequent application of medical artificial intelligence-assisted auscultation in the screening and monitoring of lung diseases, which was of great medical value and practical application.

ABSTRACTRainfall prediction is one of the crucial stages of the watershed management process. In this research, A comparison of the performance among Monte Carlo and Thomas Fiering, linear regression (LR), multiple linear regression (MLR), and SVM optimized by Simulated Annealing (SVM-SA) is carried out for Monthly rainfall prediction. In addition, the efficiency of the input patterns to the models including single input-multiple output (SIMO), multiple input-multiple output (MIMO), single input-single output (SISO), multiple input-single output (MISO) patterns are investigated. For this purpose, the time series of 34 rain gauge stations in the Karkheh basin was used. The results showed that SISO, MISO, MIMO, SIMO, and Monte Carlo and Thomas Fiering models are ranked first to fifth respectively. By comparing the performance of the models, it can be found that there is no significant difference between the SVM-SA, LR, and MLR models, However, the LR model is a method for predicting monthly rainfall more easily than other methods. This method has fewer adjustable parameters than other models.

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.

Seasonal Autoregressive Integrated Moving Average is a statistical model designed to analyze and forecast data with that shows seasonal patterns and trends. Support Vector Machine (SVM) is a machine learning-based technique that can be used to forecast time series data. SVM uses the kernel tricks to overcome non-linearity problems, whereas The SARIMA model is well-suited for data that exhibit seasonal fluctuations that repeat over time. Lombok International Airport is the main gateway to West Nusa Tenggara and has become a symbol of tourism growth in the region. Time series analysis is a very useful tool in determining patterns and forecasting the number of international arrivals at Lombok International Airport within a certain period. This study aims to compare the SARIMA model and SVM which can read non-linear patterns in the number of international arrivals at Lombok International Airport. After obtaining the SARIMA and SVM models, the two models are evaluated using test data based on the smallest RMSE value. The SVM model with a linear kernel trick provides the smallest RMSE when compared to SARIMA with SVM RMSE is 238,655. While the best model in Seasonal ARIMA is SARIMA (3,1,0)(1,0,0)12, the forecasting results show SARIMA is better in the forecasting process for the next 10 months.

Abstract A growing number of industries have started to adapt to the circular economy since the concept's introduction. Therefore, in order to accurately evaluate the development level of circular economy, the circular economy prediction model based on support vector machine-Gaussian K-nearest neighbor is proposed. This model first uses the improved K-nearest neighbor algorithm based on Gaussian function to classify the index data of various levels, and then uses Support Vector Machine to make predictions based on relevant data. According to the experimental findings, the model's average prediction accuracy for each level of indicator was approximately 98.1%, 98.8%, 94.9%, and 95.9% for the levels of industrial development, resource consumption, ecological protection, and resource recycling and reuse, respectively. This prediction accuracy was higher than that of the multi-vector autoregressive model and the grey prediction model. The average prediction accuracy of the multi-vector autoregressive model, the grey prediction model, and the support vector machine-Gaussian K-nearest neighbor-based model in predicting the overall development level of the circular economy were about 94.3%, 96.2%, and 99.3%, respectively, with average recalls of about 86.6%, 87.7%, and 89.1%, and the average F1-measure was about 0.88, 0.89, and 0.92. Moreover, the average relative error based on the support vector machine-Gaussian K-nearest neighbour model was only approximately 0.6%, which was lower than the 3.7% and 2.8% for the multi-vector autoregressive model and the grey prediction model, respectively. Meanwhile, compared with the existing time series analysis techniques, the proposed SVM-Gaussian K nearest neighbor model fitted up to 0.95, which achieved good prediction performance. According to the above data, the support vector machine-Gaussian K-nearest neighbour model has the highest accuracy in predicting the amount of development of the circular economy.

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.

Although per capita beef consumption in Indonesia remains relatively low, the demand for beef has increased significantly over the past three decades. With domestic production unable to keep pace with this growing demand, the country has resorted to imports from other countries. This study aims to predict beef consumers and identify their characteristics in Indonesia using various algorithms combined with resampling techniques to handle an imbalanced dataset. A total of 33,361 observations were collected from the National Socioeconomic and Expenditure Survey conducted in 2021. Based on criteria of precision, sensitivity and F-score, oversampling produces higher accuracy than undersampling and no sampling. Among the six classifiers used to predict beef consumption in Indonesia, random forest and support vector machine outperformed others. Income level is identified as the most powerful factor for predicting beef consumption, followed by culture, house size, education level, age and consumption of poultry meat. These findings offer valuable insights for stakeholders involved in the beef industry, empowering them to devise more effective strategies. Furthermore, government can leverage these results to formulate macro policies pertaining to the beef industry, ensuring alignment with evolving consumer demand and fostering sustainable sectoral growth.

Sugarcane suffers from the increased frequency and severity of droughts and floods, negatively affecting growing conditions. Climate change has affected cultivation, and the growth dynamics have changed over the years. The identification of the development stages of sugarcane is necessary to reduce its vulnerability. Traditional methods are inefficient when detecting those changes, especially when estimating sugarcane maturity—a critical step in sugarcane production. Hence, the study aimed to develop a cost- and time-effective method to estimate sugarcane maturity using high spatial-resolution remote sensing data. Images were acquired using a drone. Field samples were collected and measured in the laboratory for brix and pol values. Normalized Difference Water Index, Green Normalized Difference Vegetation Index and green band were chosen (highest correlation with field samples) for further analysis. Random forest (RF), Support Vector Machine (SVM), and multi-linear regression models were used to predict sugarcane maturity using the brix and pol variables. The best performance was obtained from the RF model. Hence, the maturity index of the study area was calculated based on the RF model results. It was found that the field plot has not yet reached maturity for harvesting. The developed cost- and time-effective method allows temporal crop monitoring and optimizes the harvest time.

Exponential population increases are threatening food security, particularly in mountainous areas. One potential solution is dual-use intercropped agroforestry systems such as olive (Olea europaea)–maize (Zea mays), which may mitigate risk by providing multiple market sources (oil and grain) for smallholder producers. Several studies have conducted integrated agroforestry land suitability analyses; however, few studies have used machine learning (ML) algorithms to evaluate multiple variables (i.e., soil physicochemical properties and climatic and topographic data) for the selection of suitable rainfed sites in mountainous terrain systems. The goal of this study is therefore to identify suitable land classes for an integrated olive–maize agroforestry system based on the Food and Agriculture Organization (FAO) land suitability assessment framework for 1757 km2 in Khyber Pakhtunkhwa province, Pakistan. Information on soil physical and chemical properties was obtained from 701 soil samples, along with climatic and topographic data. After determination of land suitability classes for an integrated olive–maize-crop agroforestry system, the region was then mapped through ML algorithms using random forest (RF) and support vector machine (SVM), as well as using traditional techniques of weighted overlay (WOL). Land suitability classes predicted by ML techniques varied greatly. For example, the S1 area (highly suitable) classified through RF was 9%↑ than that of SVM, and 8%↓ than that through WOL. The area of S2 (moderately suitable) classified through RF was 18%↑ than that of SWM and was 17%↓ than the area classified through WOL; similarly, the S3 (marginally suitable) class area via RF was 27%↓ than that of SVM, and 45%↓ than the area classified through WOL. Conversely, the area of N2 (permanently not suitable class) classified through RF and SVM was 6%↑ than the area classified through WOL. Model performance was assessed through overall accuracy and Kappa Index and indicated that RF performed better than SVM and WOL. Crop suitability limitations of the study area included high elevation, slope, pH, and large gravel content. Results can be used for sustainable intensification in mountainous rainfed regions by expanding intercrop agroforestry systems in developing nations to close yield gaps.

Purpose – The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA). Design/methodology/approach – In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent. Findings – Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models. Research limitations/implications – In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions. Originality/value – In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

Purposes Aiming at the problem that Granger Causality (GC) analysis in EEG emotion recognition does not consider the interaction between nodes when constructing brain network, a method of constructing brain network by combining fuzzy cognitive map (FCM) and GC analysis is proposed. Methods First, on the basis of the correspondence between the structure of FCM and GC brain network, the GC brain network is modeled and improved by using the causal attributes among FCM nodes, and the FCM-GC brain network is constructed, by taking into account the cooperative interaction among nodes. Furthermore, in order to deeply integrate FCM with GC brain network, the spatial position information of EEG electrode channel is added to FCM training, and a new IFCM-GC brain network is constructed. On the basis of DEAP emotional EEG database, the features of IFCM-GC brain network are extracted, and the support vec tor machine is used as the recognition model. The average recognition rates in valence dimension and incentive dimension are 97.10% and 97.00%, respectively, which are more than 8% higher than the existing research on GC improvement. Findings The GC brain network constructed by this method takes into account the cooperative interaction among multiple nodes, and effectively improves the performance of emotion recognition system.

In the current era of digitalization, various activities are conducted using technology to aid their execution, including the democratic process scheduled for February 2024. The Komisi Pemilihan Umum (KPU) is utilizing a mobile-based application called Sirekap. During the previous presidential and vice-presidential elections, there were many pros and cons regarding the Sirekap application. A significant number of negative reviews were expressed by the public towards this application. This study employs the SVM algorithm to perform sentiment analysis of Sirekap application users. Before building the model, several steps were undertaken, including data labeling, data preprocessing, splitting the dataset into training and testing data, and performing transformations using Count Vectorizer. Evaluation of the SVM model results shows quite good performance with an accuracy of 81%. For the negative class, the precision and recall values are 87% and 85%, respectively, while for the positive class, the precision and recall values only reach 66% and 70%, indicating a need for improvement in the model's identification of the positive class. Five-fold cross-validation was performed with an average cross-validation score of 79.6% and a standard deviation of 2.14%, indicating the model's consistency across various training data subsets. These findings suggest that the SVM model can effectively perform text classification tasks. Based on the negative word cloud, it can be concluded that the Sirekap application still has many shortcomings affecting the democratic process in February 2024.

Internal security of the state is one of the prerequisites for sustainable development. To ensure the public safety and personal security of citizens, it is necessary to develop effective measures to reduce crime and prevent crime in the future. The starting point for the development and practical implementation of an effective strategy to combat crime or prevent certain crimes is criminological forecasting. Individual forecasting is aimed at determining the possibility of committing a crime (crimes) in the future by a certain person or group of persons. For risk assessment, the following are traditionally used machine learning models. Such models also provide qualitative assessments in the scientific prediction of the likelihood and possibilities of committing a repeat criminal offense. Knowledge gained from the application of machine learning algorithm, can provide justice authorities with anticipatory information that is essential for developing a general concept of combating crime. The development of applied models for crime analysis and forecasting can become a reliable tool to support decision-making in predicting likely criminal behavior in the future and ensuring the internal security of the state. In this paper, the results of the application are presented by the machine-learning algorithms Support Vector Machine (SVM) for assessment of the risk of recidivism of criminal offenses by persons who have already been convicted of such a crime in the past. The data set consisted of the 12,000 criminal defendants’ criminal profile information in Ukraine. The constructed classifier has a high precision (98.67%), recall (97.53%) and is qualitative (AUC is equal 0.981). The created SVM model can be applied to new data set to predict the risk of reoffending by convicted individuals in the future.

Eriocheir sinensis is an economically important aquatic animal. Its regulatory mechanisms underlying many biological processes are still vague due to the lack of systematic analysis tools. The protein–protein interaction network (PIN) is an important tool for the systematic analysis of regulatory mechanisms. In this work, a novel machine learning method, DGO-SVM, was applied to predict the protein–protein interaction (PPI) in E. sinensis, and its PIN was reconstructed. With the domain, biological process, molecular functions and subcellular locations of proteins as the features, DGO-SVM showed excellent performance in Bombyx mori, humans and five aquatic crustaceans, with 92–96% accuracy. With DGO-SVM, the PIN of E. sinensis was reconstructed, containing 14,703 proteins and 7,243,597 interactions, in which 35,604 interactions were associated with 566 novel proteins mainly involved in the response to exogenous stimuli, cellular macromolecular metabolism and regulation. The DGO-SVM demonstrated that the biological process, molecular functions and subcellular locations of proteins are significant factors for the precise prediction of PPIs. We reconstructed the largest PIN for E. sinensis, which provides a systematic tool for the regulatory mechanism analysis. Furthermore, the novel-protein-related PPIs in the PIN may provide important clues for the mechanism analysis of the underlying specific physiological processes in E. sinensis.

Abstract During their life cycle, buildings are subjected to damage that reduces their performance and can pose a significant threat to structural safety. This paper presents the results of research into the creation of a model for predicting damage intensity of buildings located in mining terrains. The basis for the research was a database of technical and mining impact data for 185 masonry residential buildings. The intensity of damage to buildings was negligible and ranged from 0 to 6%. The Convolutional Neural Network (CNN) methodology was used to create the model. The Support Vector Machine (SVM) methodology, which is commonly used for analysis of this type of issue, was used for comparisons. The resulting models were evaluated by comparing parameters such as accuracy, precision, recall, and F 1 score. The comparisons revealed only minor differences between the models. Despite the small range of damage intensity, the models created were able to achieve prediction results of around 80%. The SVM model had better results for training set accuracy, while the CNN model achieved higher values for F 1 score and average precision for the test set. The results obtained justify the adoption of the CNN methodology as effective in the context of predicting the damage intensity of masonry residential buildings located in mining terrains.

Pengoperasian pelabuhan secara dasar meliputi berbagai kegiatan pelayanan, salah satu proses pelayanan kegiatan di pelabuhan yaitu pelayanan pemanduan dan penundaan kapal. Salah satu langkah yang dibutuhkan dalam proses penetapan kapal tunda dalam pelayanan pemanduan dan penundaan kapal yaitu pemilihan konfigurasi Kapal Tunda. Penelitian ini menguji performa klasifikasi Support Vector Machine (SVM) dan Naïve Bayes Classifier (NBC) pada data Laporan Harian Gerakan Kapal (LHGK) di Pelabuhan Tanjung Priok selama periode 2021 untuk proses pemodelan dan evaluasi. Penelitian ini bertujuan untuk membuat modelan prediksi dalam penentuan konfigurasi Kapal Tunda, evaluasi hasil model prediksi untuk memilih konfigurasi kapal tunda di Pelabuhan Tanjung Priok. Dengan menerapkan model klasifikasi NBC dan SVM yang ditingkatkan dengan kernel Linier dan RBF, termasuk juga pemilihan fitur baik untuk SVM dan Naïve Bayes. Hasil uji perbandingan model prediksi antara SVM dan NBC menujukan bahwa klasifikasi SVM memberikan hasil yang paling optimal, yaitu menggunakan kernel linier pada nilai C=10, diperoleh akurasi sebesar 84,7%, recall sebesar 84,7%, F1-score sebesar 88,7%, dan akurasi sebesar 88,7%. Penelitian ini dimasa yang akan datang dapat dimanfaatkan dalam proses pengambilan keputusan dalam menentukan susunan konfigurasi Kapal Tunda oleh petugas pelabuhan.

Abstract To improve the current oil painting teaching mode in Chinese universities, this study combines deep learning technology and artificial intelligence technology to explore oil painting teaching. Firstly, the research status of individualized education and related research on image classification based on brush features are analyzed. Secondly, based on a convolutional neural network, mathematical morphology, and support vector machine, the oil painting classification model is constructed, in which the extracted features include color and brush features. Moreover, based on artificial intelligence technology and individualized education theory, a personalized intelligent oil painting teaching framework is built. Finally, the performance of the intelligent oil painting classification model is evaluated, and the content of the personalized intelligent oil painting teaching framework is explained. The results show that the average classification accuracy of oil painting is 90.25% when only brush features are extracted. When only color features are extracted, the average classification accuracy is over 89%. When the two features are extracted, the average accuracy of the oil painting classification model reaches 94.03%. Iterative Dichotomiser3, decision tree C4.5, and support vector machines have an average classification accuracy of 82.24%, 83.57%, and 94.03%. The training speed of epochs data with size 50 is faster than that of epochs original data with size 100, but the accuracy is slightly decreased. The personalized oil painting teaching system helps students adjust their learning plans according to their conditions, avoid learning repetitive content, and ultimately improve students' learning efficiency. Compared with other studies, this study obtains a good oil painting classification model and a personalized oil painting education system that plays a positive role in oil painting teaching. This study has laid the foundation for the development of higher art education.

With the progressive enhancement of social media, several people prefer posting their opinions on various social media instead of posting on radios, television or newspapers. The postings differ in dimensions and include various titles and comments. Nowadays, the formation of plagiarism is increasing tremendously which occurs by rewriting or repeating one’s work. There are many ways to detect plagiarism by browsing through the internet. The significant intention of this paper involves the detection of plagiarism in social media using four different phases, namely the data pre-processing phase, n-gram evaluation, similarity/distance computation analysis and the plagiarism detection phase. The pre-processing includes data cleaning processes, such as the removal of redundant data, upper case letters, noise, irrelevant punctuations and characterizing into a vector form. After pre-processing the data are fed for n-gram evaluation to develop a posting attribution system. Then finally, an ensemble support vector machine-based African vulture optimization (ESVM-AVO) approach is employed to detect plagiarism which signifies that the performance based on detection is enhanced and the execution time in obtaining a high rate of detection accuracy is very low. Finally, the performance evaluation and the comparative analysis are carried out to determine the performance of the proposed system.

To explore the muscle fatigue features of upper limb at different heights in overhead static work, an experiment was conducted to obtain the surface electromyography (sEMG) of subjects and their subjective fatigue state based on Borg CR-10 scale. The processing methods of time domain and frequency domain features of sEMG were studied and the multiclass support vector machine (SVM) was used to identify the state of muscle fatigue. By analyzing the muscular contribution, the correlation of subjective ratings and objective muscle fatigue features, ranking order of muscle fatigue accumulation, and muscular fatigue classification and identification, the results show that the muscles contribute above 10% on average are the biceps, deltoid and trapezius, and their cumulative contribution exceeds 70%; and the ranking orders of muscle fatigue accumulation in three heights are H3>H2>H1 for biceps and trapezius and H2>H3>H1 for deltoid; and with the time increase of overhand static operation, the muscle fatigue of upper limb gradually accumulates, resulting in the value of time domain features increases and the frequency domain features decreases, and their changes are consistent; and the accuracy of multiclass SVM is above 90% for identifying muscle fatigue of upper limb in overhead static work.

In order to solve the prediction problem of the roof settlement in the unsupported period of roadway excavation in the deep roadway engineering of coal mines, the support vector machine (SVM) tool of artificial intelligence was introduced. Combined with the sparrow search optimization algorithm (SSA), a prediction model of the roof settlement during its unsupported period based on SSA-SVM was proposed. Taking the displacement data during the roof’s unsupported period in the deep underground roadway excavation process of the Great Wall No.5 Mine in Inner Mongolia as a sample, six influencing factors including uniaxial compressive strength (UCS), rock integrity (RQD), ground stress, roadway width-span ratio, non-support roof time and artificial mining were selected. The comprehensive influence weights of the data were summarized through applicability, correlation and classification consistency evaluation. The accuracy of ten-fold cross-validation was used as the fitness function to train and test the SSA-SVM prediction model with different population numbers. The optimal parameter model of population number was selected by error correlation coefficient (RMSE, MAPE, R2), ROC curve, AUC ± Std, running time and standard deviation rate η. The model was applied to the 1902S return airway to predict the settlement of the roadway during the roof’s unsupported period, and compared with the actual mine pressure monitoring data of the roadway. The results show that the prediction performance of SSA-SVM model is better when the population number is 90. The RMSE of training samples is 0.0165, MAPE is 22.54%, and R2 is 0.8295. The RMSE of the test sample is 0.0156, MAPE is 22.37%, and R2 is 0.8490. The realism AUC reaches the maximum of 0.8467, and the standard deviation Std is the minimum of 0.0115. The shortest running time is 8.7239 s and the standard deviation rate is maintained at 0.12%. In the field application of the 1902S return airway, there is no large deviation between the predicted value and the actual value, which is maintained in the range of linear fitting y = 0.90x and y = 1.10x. Both the error correlation coefficient and AUC ± Std satisfy the requirements of test accuracy. The prediction effect of this model can provide an important guidance for subsequent support design and reinforcement of support measures.

Abstract The present study investigates the optimization of the steam gasification process for the conversion of palm oil waste into environmentally friendly energy, utilizing the catalytic properties of calcium oxide and coal bottom ash. The objective of our research is to investigate the enhancement of the conversion process by employing a machine-learning approach. Specifically, we utilize a support vector machine (SVM) to model and evaluate the impact of different operational parameters on the resulting gas mixture. One notable feature of this study involves the incorporation of an adaptive marine predator algorithm (AMPA) into the SVM framework, aiming to enhance the predicted precision and efficiency of the model. The primary focus of this study revolves around the development of an intelligent optimization framework that surpasses conventional machine learning techniques, hence providing a more dynamic and efficient strategy for process improvement. The SVM model’s performance, as assessed against experimental benchmarks, exhibits a notable degree of predictive accuracy and substantial concurrence with observed data. This increase in performance indicates that our methodology has the potential to make a significant contribution to the enhancement of renewable catalysts in gasification processes. The findings of this study could potentially have significant ramifications for the advancement of renewable energy production and the creation of intelligent systems in complicated industrial applications.

With the advent and subsequent explosion of the internet, global connectivity has been achieved, and is on the rise. This provides a host of advantages such as connectivity and communication, information broadcast and transmission, amongst others. This however introduces a new set of challenges: the safety and protection of these communication channels amongst them. Information has always been power, and the widespread mature of information only results in the widespread attempts to procure it, sometimes via illegal channels. In view of this, this research aims at detecting Crypto-ransomware and locker ransomware. Data was collected from an open repository and cleaned. The cleaned data was then split into tests, train sets and validation which was used to train a number of ML models based on the: Random Forest algorithm, Support Vector Machine (SVM) and Gradient boosting algorithm. Ransomware is one of the well-known ways and frequent use which cyber-attackers use in infecting their victims, either through phishing or drive download. Attackers will create an email pretending to be from a genuine resource and send it to their targeted victims. However, this research illustrated how to combat crypto-ransomware and locker ransomware. Implementing the machine learning algorithm, the system can detect ransomware under 30’s, giving computer users over 90% assurance of their system for ransomware free.

To solve the problems of high false alarm rate and leakage rate of coal mine gas and coal dust explosion disaster alarm methods, and improve the accuracy of coal mine gas and coal dust explosion perception, sound identification method of coal mine gas and coal dust explosion based on wavelet scattering transform was proposed: install mining sound pickup equipment in the critical monitoring area of coal mine underground, and collect equipment working sound and environmental sound in real time, the wavelet scattering coefficients were obtained from the collected sound by wavelet scattering transform, the wavelet scattering coefficients of the sound signal were constructed, the collected the 11-dimensional feature parameters consisting of small gradient dominance, large gradient dominance, energy, gray average, gradient average, gray mean square difference, gradient mean square difference, correlation, gray entropy, gradient entropy, mixing entropy were obtained by calculating the image gray gradient co-generation matrix of the wavelet scattering coefficient map, which constituted the feature vector characterizing the sound signal, and were input to the support vector machine for training to obtain the coal mine the 11-dimensional feature vectors were obtained by extracting the gray gradient covariance matrix of the wavelet scattering coefficient map of the sound signal to be measured, and bring it into the trained coal mine gas and coal dust explosion sound recognition model for sound recognition classification, it verified by experiments. The wavelet scattering coefficients of different sounds and their feature parameter distribution characteristics were analyzed, the wavelet scattering coefficients of gas and coal dust explosion sounds and their 11-dimensional feature vectors were significantly different from other sounds in the coal mine, the feasibility of the proposed feature extraction method was demonstrated. Support vector machine hyperparameter optimization experiments completed by Bayesian optimization to select hyperparameters that better fit the training model, and the recognition experimental results show that the recognition rate of the proposed method was 95.77%, which was significantly better than other comparison algorithms. It can meet the needs of coal mine gas and coal dust explosion recognition.

The COVID-19 pandemic has impacted health, economy, and society. Social distancing measures and quarantine policies have restricted economic activities, leading to downturns in COVID-19-affected regions and a subsequent rise in unemployment rates, particularly in urban areas. Concurrently, there has been a remarkable surge in the utilization of the X (Twitter) platform, with Indonesia ranking 6th globally in X (Twitter) users. This study aims to understand the diverse perspectives of society on unemployment and the factors influencing society's views on unemployment through sentiment analysis of X (Twitter) data. By analyzing 576,764 tweets from April 2020 to October 2023, tweets are categorized into positive, neutral, and negative classes. Classification model was built to classify tweet data by implementing TF-IDF for word weighting, and a pair of machine learning algorithms, Naïve Bayes and Support Vector Machine (SVM). Model evaluation yielded the highest accuracy of 81.5% using Naïve Bayes. The classification outcomes highlight prevalent negative perceptions of unemployment among Indonesians, totaling 50.03%. This research contributes to the literature by providing a large-scale analysis of social media data to uncover public sentiment trends and offering insights for policymakers to address unemployment and improve welfare.

This research aims to develop an automated classification system to accurately identify and classify Ulos batik patterns using K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) techniques. The method is based on computer vision technology and texture analysis using the Gray-Level Co-occurrence Matrix (GLCM). The dataset consists of 1,800 images of Ulos fabric categorized into six main motif classes. The preprocessing process involves converting images to grayscale and extracting features with GLCM. Two classification algorithms, K-NN and SVM, were used for modeling, with evaluation using confusion matrix metrics and Area Under Curve (AUC). Evaluation results show that the K-NN model has an accuracy of 82%, while SVM has an accuracy of 57%. The analysis also highlights the superiority of K-NN in distinguishing Ulos fabric patterns. This research contributes to cultural preservation and the development of the creative industry by introducing an effective automated classification system for Ulos fabric patterns.

The early detection of pests in plants and crops is essential for the production of good quality food. Computer vision techniques can be applied for the early detection of pests and which can minimise the pesticides used on the plants. Among many pests, white flies, mites, aphids and thrips are the most hazardous pests that affect the leaves. This paper presents an automated approach for the detection of different types of pests from leaf images of plants. The images of the plant leaves were acquired using a digital camera. Image pre-processing techniques, such as noise removal, filtering and contrast enhancement, are used for improving the quality of the images. Then, the k-means clustering method and global thresholding were used for segmenting the pests from the infected leaves. Textural features are extracted from those segmented images by statistical feature extraction methods. Artificial Neural Network (ANN) and Binary Support Vector Machine (SVM) classifiers were used to classify the unaffected leaf images from the pest affected leaf images and a multi-SVM classifier was used to identify the different types of pests.

To solve the problems of large prediction error, slow convergence speed, and poor generalisation ability of traditional models in predicting surface deformation of open-pit mine slopes, this paper proposes a new intelligent prediction model based on the Mayfly algorithm-optimised support vector machine (MA-SVM). In this method, the MA is used to optimise the SVM parameters to reduce the uncertainty of the model and avoid time-consuming parameter adjustment. To evaluate the proposed prediction model, real-world deformation data of the north slope of the Anjialing open-pit mine in Pingshuo city, China, are collected using the microdeformation monitoring radar and used to investigate the deformation prediction performance of the proposed method. The results of the analysis demonstrate that the proposed method is able to accurately predict the deformation of the surface of the mine slope and outperforms three existing popular methods, including SVM, genetic algorithm (GA)-SVM, and particle swarm optimisation (PSO)-SVM). The mean absolute error (MAE) of the proposed MA⁃SVM is 2.52 % while 6.56 %, 4.95 %, and 5.16 % for the SVM, GA-SVM, and PSO-SVM, respectively; the root mean square error (RMSE) of the proposed MA⁃SVM is 10.21 % while 30.79 %, 17.38 %, and 22.54 % for the other three methods. Because the proposed MA⁃SVM model is able to predict slope deformation using actual monitoring data, it is of practical importance in real-world applications for early warning on landslides of mine slopes.