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Ren, Qiubing, Mingchao Li, Mengxi Zhang, Yang Shen, and Wen Si. "Prediction of Ultimate Axial Capacity of Square Concrete-Filled Steel Tubular Short Columns Using a Hybrid Intelligent Algorithm." Applied Sciences 9, no. 14 (July 12, 2019): 2802. http://dx.doi.org/10.3390/app9142802.
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It is crucial to study the axial compression behavior of concrete-filled steel tubular (CFST) columns to ensure the safe operation of engineering structures. The restriction between steel tubular and core concrete in CFSTs is complex and the relationship between geometric and material properties and axial compression behavior is highly nonlinear. These challenges have prompted the use of soft computing methods to predict the ultimate bearing capacity (abbreviated as Nu) under axial compression. Taking the square CFST short column as an example, a mass of experimental data is obtained through axial compression tests. Combined with support vector machine (SVM) and particle swarm optimization (PSO), this paper presents a new method termed PSVM (SVM optimized by PSO) for Nu value prediction. The nonlinear relationship in Nu value prediction is efficiently represented by SVM, and PSO is used to select the model parameters of SVM. The experimental dataset is utilized to verify the reliability of the PSVM model, and the prediction performance of PSVM is compared with that of traditional design methods and other benchmark models. The proposed PSVM model provides a better prediction of the ultimate axial capacity of square CFST short columns. As such, PSVM is an efficient alternative method other than empirical and theoretical formulas.
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Arsioli, B., and P. Dedin. "Machine learning applied to multifrequency data in astrophysics: blazar classification." Monthly Notices of the Royal Astronomical Society 498, no. 2 (August 17, 2020): 1750–64. http://dx.doi.org/10.1093/mnras/staa2449.
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ABSTRACT The study of machine learning (ML) techniques for the autonomous classification of astrophysical sources is of great interest, and we explore its applications in the context of a multifrequency data-frame. We test the use of supervised ML to classify blazars according to its synchrotron peak frequency, either lower or higher than 1015 Hz. We select a sample with 4178 blazars labelled as 1279 high synchrotron peak (HSP: $\rm \nu$-peak > 1015 Hz) and 2899 low synchrotron peak (LSP: $\rm \nu$-peak < 1015 Hz). A set of multifrequency features were defined to represent each source that includes spectral slopes ($\alpha _{\nu _1, \nu _2}$) between the radio, infra-red, optical, and X-ray bands, also considering IR colours. We describe the optimization of five ML classification algorithms that classify blazars into LSP or HSP: Random forests (RFs), support vector machine (SVM), K-nearest neighbours (KNN), Gaussian Naive Bayes (GNB), and the Ludwig auto-ML framework. In our particular case, the SVM algorithm had the best performance, reaching 93 per cent of balanced accuracy. A joint-feature permutation test revealed that the spectral slopes alpha-radio-infrared (IR) and alpha-radio-optical are the most relevant for the ML modelling, followed by the IR colours. This work shows that ML algorithms can distinguish multifrequency spectral characteristics and handle the classification of blazars into LSPs and HSPs. It is a hint for the potential use of ML for the autonomous determination of broadband spectral parameters (as the synchrotron ν-peak), or even to search for new blazars in all-sky data bases.
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Yang, Jincheng, Ning Li, Weilong Lin, Liming Shi, Ming Deng, Qin Tong, and Wenjing Yang. "Machine Learning for Predicting Hyperglycemic Cases Induced by PD-1/PD-L1 Inhibitors." Journal of Healthcare Engineering 2022 (August 19, 2022): 1–12. http://dx.doi.org/10.1155/2022/6278854.
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Objective. Immune checkpoint inhibitors, such as programmed death-1/ligand-1 (PD-1/L1), exhibited autoimmune-like disorders, and hyperglycemia was on the top of grade 3 or higher immune-related adverse events. Machine learning is a model from past data for future data prediction. From post-marketing monitoring, we aimed to construct a machine learning algorithm to efficiently and rapidly predict hyperglycemic adverse reaction in patients using PD-1/L1 inhibitors. Methods. In original data downloaded from Food and Drug Administration Adverse Event Reporting System (US FAERS), a multivariate pattern classification of support vector machine (SVM) was used to construct a classifier to separate adverse hyperglycemic reaction patients. With correct core SVM function, a 10-fold 3-time cross validation optimized parameter value composition in model setup with R language software. Results. The SVM prediction model was set up from the number type/number optimization method, as well as the kernel and type of “rbf” and “nu-regression” composition. Two key values (nu and gamma) and case number displayed high adjusted r2 in curve regressions ( n u = 0.5649 × e − case / 6984 , gamma = 9.005 × 10 − 4 × case − 4.877 × 10 − 8 × case 2 ). This SVM model with computable parameters greatly improved the assessing indexes (accuracy, F1 score, and kappa) as well as coequal sensitivity and the area under the curve (AUC). Conclusion. We constructed an effective machine learning model based on compositions of exact kernels and computable parameters; the SVM prediction model can noninvasively and precisely predict hyperglycemic adverse drug reaction (ADR) in patients treated with PD-1/L1 inhibitors, which could greatly help clinical practitioners to identify high-risk patients and perform preventive measurements in time. Besides, this model setup process provided an analytic conception for promotion to other ADR prediction, such ADR information is vital for outcome improvement by identifying high-risk patients, and this machine learning algorithm can eventually add value to clinical decision making.
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Кузьменко, О., С. Миненко, К. Гриценко, and B. Яценко. "ЗАСТОСУВАННЯ МЕТОДІВ МАШИННОГО НАВЧАННЯ ДЛЯ СТАТИСТИЧНОГО АНАЛІЗУ ТА ПРОГНОЗУВАННЯ КІБЕРСПОРТИВНОЇ ГАЛУЗІ." MEASURING AND COMPUTING DEVICES IN TECHNOLOGICAL PROCESSES, no. 1 (May 27, 2021): 126–32. http://dx.doi.org/10.31891/2219-9365-2021-67-1-18.
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У статті розглянуто динаміку та поведінку кіберспортивної індустрії на світовому рівні та стан кіберспорту як індустрії в Україні. Визначено основні досягнення кіберспортивної сфери України. Проведено статистичний аналіз доходу кіберспортивної індустрії, сукупної аудиторії кіберспортивних ігор, постійних та пересічних глядачів змагань на основі аналізу варіації, моди, показників асиметрії та ексцесу розподілу. Для досягнення цілей дослідження було використано метод експоненційного згладження та метод опорних векторів. SVM є методом машинного навчання, який використовується для вирішення задач класифікації та регресії. Як і для класичної моделі регресії основою підходу є знаходження функції підгонки емпіричних даних. Обрані методи дозволили підготувати дані для аналізу та побудувати регресійні SVM-моделі з ядром на основі радіально-базисних функцій. Побудовані моделі для доходу кіберспорту та пересічних глядачів кіберспорту мають тип epsilon-SVM, а для світової аудиторії кіберспорту та постійних глядачів кіберспорту – nu-SVM. Доведена адекватність побудованих моделей на основі аналізу залишків моделі. Здійснено прогнозування вхідних показників. Визначено, що до 2025 року очікується постійне зростання доходу від кіберспортивної діяльності, що означає постійний розвиток та вдосконалення супутньої до кіберспорту інфраструктури. Визначено важливість та необхідність державної підтримки розвитку кіберспорту на всіх рівнях: від організації турнірних площадок до проведення регіональних, шкільних, аматорських турнірів. Отримані результати можуть бути використані Федерацією кіберспорту України, кіберспортивними організаціями, дослідниками для обгрунтування необхідності розвитку кіберспорту в Україні.
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Awtoniuk, Michał, Marcin Daniun, Kinga Sałat, and Robert Sałat. "Impact of feature selection on system identification by means of NARX-SVM." MATEC Web of Conferences 252 (2019): 03012. http://dx.doi.org/10.1051/matecconf/201925203012.
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Support Vector Machines (SVM) are widely used in many fields of science, including system identification. The selection of feature vector plays a crucial role in SVM-based model building process. In this paper, we investigate the influence of the selection of feature vector on model’s quality. We have built an SVM model with a non-linear ARX (NARX) structure. The modelled system had a SISO structure, i.e. one input signal and one output signal. The output signal was temperature, which was controlled by a Peltier module. The supply voltage of the Peltier module was the input signal. The system had a non-linear characteristic. We have evaluated the model’s quality by the fit index. The classical feature selection of SVM with NARX structure comes down to a choice of the length of the regressor vector. For SISO models, this vector is determined by two parameters: nu and ny. These parameters determine the number of past samples of input and output signals of the system used to form the vector of regressors. In the present research we have tested two methods of building the vector of regressors, one classic and one using custom regressors. The results show that the vector of regressors obtained by the classical method can be shortened while maintaining the acceptable quality of the model. By using custom regressors, the feature vector of SVM can be reduced, which means also the reduction in calculation time.
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Byeon, Haewon. "Predicting the Severity of Parkinson’s Disease Dementia by Assessing the Neuropsychiatric Symptoms with an SVM Regression Model." International Journal of Environmental Research and Public Health 18, no. 5 (March 4, 2021): 2551. http://dx.doi.org/10.3390/ijerph18052551.
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In this study, we measured the convergence rate using the mean-squared error (MSE) of the standardized neuropsychological test to determine the severity of Parkinson’s disease dementia (PDD), which is based on support vector machine (SVM) regression (SVR) and present baseline data in order to develop a model to predict the severity of PDD. We analyzed 328 individuals with PDD who were 60 years or older. To identify the SVR with the best prediction power, we compared the classification performance (convergence rate) of eight SVR models (Eps-SVR and Nu-SVR with four kernel functions (a radial basis function (RBF), linear algorithm, polynomial algorithm, and sigmoid)). Among the eight models, the MSE of Nu-SVR-RBF was the lowest (0.078), with the highest convergence rate, whereas the MSE of Eps-SVR-sigmoid was 0.110, with the lowest convergence rate. The results of this study imply that this approach could be useful for measuring the severity of dementia by comprehensively examining axial atypical features, the Korean instrumental activities of daily living (K-IADL), changes in rapid eye movement sleep behavior disorder (RBD), etc. for optimal intervention and caring of the elderly living alone or patients with PDD residing in medically vulnerable areas.
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Bachu, Anil Kumar, Kranthi Kumar Reddy, and Lelitha Vanajakshi. "BUS TRAVEL TIME PREDICTION USING SUPPORT VECTOR MACHINES FOR HIGH VARIANCE CONDITIONS." Transport 36, no. 3 (August 20, 2021): 221–34. http://dx.doi.org/10.3846/transport.2021.15220.
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Real-time bus travel time prediction has been an interesting problem since past decade, especially in India. Popular methods for travel time prediction include time series analysis, regression methods, Kalman filter method and Artificial Neural Network (ANN) method. Reported studies using these methods did not consider the high variance situations arising from the varying traffic and weather conditions, which is very common under heterogeneous and lane-less traffic conditions such as the one in India. The aim of the present study is to analyse the variance in bus travel time and predict the travel time accurately under such conditions. Literature shows that Support Vector Machines (SVM) technique is capable of performing well under such conditions and hence is used in this study. In the present study, nu-Support Vector Regression (SVR) using linear kernel function was selected. Two models were developed, namely spatial SVM and temporal SVM, to predict bus travel time. It was observed that in high mean and variance sections, temporal models are performing better than spatial. An algorithm to dynamically choose between the spatial and temporal SVM models, based on the current travel time, was also developed. The unique features of the present study are the traffic system under consideration having high variability and the variables used as input for prediction being obtained from Global Positioning System (GPS) units alone. The adopted scheme was implemented using data collected from GPS fitted public transport buses in Chennai (India). The performance of the proposed method was compared with available methods that were reported under similar traffic conditions and the results showed a clear improvement.
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Song, X., G. Cherian, and G. Fan. "A<tex>$nu$</tex>-Insensitive SVM Approach for Compliance Monitoring of the Conservation Reserve Program." IEEE Geoscience and Remote Sensing Letters 2, no. 2 (April 2005): 99–103. http://dx.doi.org/10.1109/lgrs.2005.846007.
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Bashir, Komal, Mariam Rehman, and Mehwish Bari. "Detection and Classification of Rice Diseases: An Automated Approach Using Textural Features." January 2019 38, no. 1 (January 1, 2019): 239–50. http://dx.doi.org/10.22581/muet1982.1901.20.
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Image processing techniques are widely used for the detection and classification of diseases for various plants. The structure of the plant and appearance of the disease on the plant pose a challenge for image processing. This research implements SVM (Support Vector Machine) based image-processing approach to analyze and classify three of the rice crop diseases. The process consists of two phases, i.e. training phase and disease prediction phase. The approach identifies disease on the leaf using trained classifier. The proposed research work optimizes SVM parameters (gamma, nu) for maximum efficiency. The results show that the proposed approach achieved 94.16% accuracy with 5.83% misclassification rate, 91.6% recall rate and 90.9% precision. These findings were compared with image processing techniques discussed in review of literature. The results of comparison conclude that the proposed methodology yields high accuracy percentage as compared to the other techniques. The results obtained can help the development of an effective software solution by incorporating image processing and collaboration features. This may facilitate the farmers and other bodies in effective decision making to efficiently protect the rice crops from substantial damage. While considering the findings of this research, the presented technique may be considered as a potential solution for adding image processing techniques to KM (Knowledge Management) systems.
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ACHARKI, Siham, Pierre Louis FRISON, Mina AMHARREF, Hanna KHOJ, and Samed BERNOUSSI. "Complémentarité des images optiques SENTINEL-2 avec les images radar SENTINEL-1 et ALOS-PALSAR-2 pour la cartographie de la couverture végétale : application à une aire protégée et ses environs au Nord-Ouest du Maroc via trois algorithmes d’apprentissage automatique." Revue Française de Photogrammétrie et de Télédétection 223 (November 29, 2021): 143–58. http://dx.doi.org/10.52638/rfpt.2021.599.
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Dans cet article, nous évaluons les performances de classification de trois algorithmes non paramétriques (kNN, RF et SVM) en utilisant les données multi-temporelles de trois satellites (Sentinel-1, Alos-Palsar-2 et Sentinel-2) et de leurs combinaisons. La zone d'étude choisie se caractérise par un climat méditerranéen subhumide et une topographie très accidentée qui rend la classification d’occupation du sol particulièrement difficile. En outre, elle contient une aire protégée nommée Jbel Moussa et présente une diversité biologique exceptionnelle. Afin de suivre le couvert végétal de cette dernière, nous avons acquis et prétraités les images satellitaires optiques et radar pour la période du 1er janvier au 31 décembre 2017. Ensuite, nous avons combiné les trois satellites, soit douze scénarios produits. Des cartes de classifications illustrent notre approche. Un total de trente-six classifications a été obtenu, en se basant sur sept classes : eau, bâtiment et infrastructures, sol nu, végétation peu dense, prairies, forêt peu dense et forêt dense. Les résultats ont montré que pour tous les scénarios, la précision globale la plus élevée a été produite par RF (53,03%-93,06%), suivie de kNN (49,16%-89,63%), tandis que SVM (47,86%-86,08%) a produit la précision de classification la plus faible. L'étude a également montré une similitude entre les performances de la combinaison des trois satellites et celles de Sentinel-2 seul. Les estimations de la superficie pour les différentes classes vont de 0,85 km2 (0,11% de la zone d'étude) à 326,84 km2 (41,31% de la zone d'étude)
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Zhang, Yingtao, Guangming Lv, Yaguan Li, Zirong Tang, and Zhenguo Nie. "The Design of Reflected Laser Intensity Testing System and Application of Quality Inspection for Laser Cladding Process." Machines 10, no. 10 (September 20, 2022): 821. http://dx.doi.org/10.3390/machines10100821.
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Laser cladding is one of the critical technologies for additive manufacturing and rapid repair. Improving cladding performance by materials and process parameters is the leading research direction, but defects and instability of quality in the cladding process are inevitable. Therefore, it is necessary to study which factors are related to quality. In this paper, a new detection method is proposed to measure the radiation intensity of the reflected laser, laser scanning displacement, and temperature of the substrate while cladding. The characteristic values corresponding to the position of the cladding spots are extracted, the cladding quality is preliminarily evaluated and graded, and the correlation between them is verified with the method of machine learning nu-SVM. The results show that the accuracy of the model trained by 300 groups of data to predict the quality grades is 78.74%, which indicates that there is a strong correlation between these process variables and the cladding quality, and this method is feasible for the quality evaluation and control of the cladding process.
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Devianti, Devianti, Sufardi Sufardi, Yusmanizar Yusmanizar, Herbert Hasudungan Siahaan, and Agustami Sitorus. "A Vis-NIRs Calibration Model for the Prediction of Myristicin and Alpha-Pinene on Nutmeg: A Comparison Study of PLSR Algorithm and Machine Learning Algorithm." Mathematical Modelling of Engineering Problems 9, no. 6 (December 31, 2022): 1583–88. http://dx.doi.org/10.18280/mmep.090618.
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Determination of the myristicin and alpha-pinene content of nutmeg is still constrained by the extended testing time in the laboratory, which is expensive and is carried out destructively. In addition, non-destructive testing using spectroscopy often faces problems in building models that only rely on algorithms that perform linearly, such as PCR and PLSR. Therefore, the present study studied Vis-NIR (381-1065 nm) as a fast, inexpensive, and non-destructive mechanism to determine the myristicin and alpha-pinene of nutmeg fruits from Aceh, Indonesia. Two algorithms commonly used in spectral data processing, partial least squares regression (PLSR) and machine learning represented by a support vector machine (SVM), were employed and compared to predict myristicin and alpha-pinene in nutmeg fruits. The chemical reference parameters (myristicin and alpha-pinene) were measured using gas chromatography mass spectrometry (GC-MS). Standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing were involved as spectra enhancement before the prediction models outcome. The results show that the kernel of the radial basis function (RBF) kernel n-SVM algorithm is better than PLSR for myristicin prediction with gamma (g), c, and nu (n) of 0.1, 1.0, and 0.99, respectively. Also, the e-SVM algorithm by RBF kernel is better than PLSR for the prediction of alpha-pinene in nutmeg fruits with gamma (g), c, and epsilon (e) compositions of 0.01, 10, 0.1, respectively. The coefficient correlation of calibration (rc) and coefficient determination of prediction (Rp2), the root means square error of calibration (RMSEC) and prediction (RMSEP), and the ratio (RPD) for the prediction of myristicin were 0.992, 0.986, 0.941%, 1.325% and 8.348, respectively. The coefficient correlation of calibration (rc) and coefficient determination of prediction (Rp2), the root mean square error of calibration (RMSEC) and prediction (RMSEP), and the ratio of prediction to deviation ratio (RPD) for the prediction of alpha-pinene were 0.976, 0.979, 0.305%, 0.317% and 6.826, respectively. In general, the results satisfactorily indicate that Vis-NIRS, with the appropriate algorithm, has promising results in determining myristicin and alpha-pinene on nutmeg from Aceh, Indonesia, as nondestructive measurement.
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Hamidi, Omid, Hamed Abbasi, and Hamid Mirhashemi. "Analysis of the Response of Urban Water Consumption to Climatic Variables: Case Study of Khorramabad City in Iran." Advances in Meteorology 2021 (March 18, 2021): 1–14. http://dx.doi.org/10.1155/2021/6615152.
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Iran is located in a dry climate belt. Such conditions have made the supply of urban water resources one of the most fundamental management challenges. The amount of water consumed in a city is affected by the weather conditions greatly such that as the weather changes, the amount of water consumed changes as well. In this study, several models including zero-order Pearson’s correlation coefficient, first-order Pearson’s correlation, generalized additive model (GAM), generalized linear model (GLM), support vector machine (SVM-Nu), and simplex optimization algorithm were used in order to identify linear/nonlinear reactions of monthly water consumption to the individual and combined associations of meteorological variables (temperature, air pressure, and relative humidity) in Khorramabad city. Zero-order and first-order correlations showed that, by controlling the air temperature, the effect of pressure and relative humidity on changes in water consumption increase. On the other hand, both individual and combined GAM models showed the same result in the nonlinear response of water consumption to the changes in relative humidity and air pressure. The spline method also revealed that, by eliminating the effect of air temperature, the nonlinear reaction of water consumption to changes in pressure and relative humidity was increasing, and by eliminating the effects of the relative humidity and air pressure, the nonlinear reaction of water consumption to the air temperature was intensified. In general, by decreasing the air pressure and temperature, the amount of urban household water consumption decreases drastically. These conditions are generally provided by entering low-pressure systems.
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Ikeda, Kazushi, and Noboru Murata. "Geometrical Properties of Nu Support Vector Machines with Different Norms." Neural Computation 17, no. 11 (November 1, 2005): 2508–29. http://dx.doi.org/10.1162/0899766054796897.
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By employing the L1 or L∞ norms in maximizing margins, support vector machines (SVMs) result in a linear programming problem that requires a lower computational load compared to SVMs with the L2 norm. However, how the change of norm affects the generalization ability of SVMs has not been clarified so far except for numerical experiments. In this letter, the geometrical meaning of SVMs with the Lp norm is investigated, and the SVM solutions are shown to have rather little dependency on p.
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Juita S, J. Ratna, Hetti Hidayati, and Alfian Akbar Gozali. "Electronic Product Feature-Based Sentiment Analysis Using Nu-SVM Method." International Journal on Information and Communication Technology (IJoICT) 1, no. 1 (March 9, 2016). http://dx.doi.org/10.21108/ijoict.2015.11.4.
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<p>Sentiment in a product online review is useful and influence decision-making a person may take in buying any product as well as that of organization in determining the number of product to produce. In an opinion, reviewer may provide positive and negative reviews at the same time that can be ambiguous. This is because opinion targets are often not the product as a whole; instead they are only part of a product called as feature, which have advantages and disadvantages based on the reviewers point of view. In this paper, the goal is to produce sentiment of a mobile phone opinion based on its feature. Opinion data used in this thesis are in English taken from www.cnet.com. Feature extraction is conducted by searching for phrases that match the dependency relation template, which is followed by feature filtering. The sentiment identification, positive and negative probability value, as well as target class label of the data preparation become the Nu SVM classifier input parameters. In the study of NU SVM, some data are treated as unlabeled data. The evaluation towards sentiment identification obtained from the study shows F1 Measure of 86.25% for positive class and 77.71% for negative class. The accuracy for feature identification, however, is 82%.</p>
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Mamat, Naeimah, Siti Fatin Mohd Razali, and Fatimah Bibi Hamzah. "Enhancement of water quality index prediction using support vector machine with sensitivity analysis." Frontiers in Environmental Science 10 (January 10, 2023). http://dx.doi.org/10.3389/fenvs.2022.1061835.
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For more than 25 years, the Department of Environment (DOE) of Malaysia has implemented a water quality index (WQI) that uses six key water quality parameters: dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), pH, ammoniacal nitrogen (AN), and suspended solids (SS). Water quality analysis is an essential component of water resources management that must be properly managed to prevent ecological damage from pollution and to ensure compliance with environmental regulations. This increases the need to define an efficient method for WQI analysis. One of the major challenges with the current calculation of the WQI is that it requires a series of sub-index calculations that are time consuming, complex, and prone to error. In addition, the WQI cannot be calculated if one or more water quality parameters are missing. In this study, the optimization method of WQI was developed to address the complexity of the current process. The potential of data-driven modeling, i.e., Support Vector Machine (SVM) based on Nu-Radial basis function with 10-fold cross-validation, was developed and explored to improve the prediction of WQI in Langat watershed. A thorough sensitivity analysis under six scenarios was also conducted to determine the efficiency of the model in WQI prediction. In the first scenario, the model SVM-WQI showed exceptional ability to replicate the DOE-WQI and obtained statistical results at a very high level (correlation coefficient, r > 0.95, Nash Sutcliffe efficiency, NSE >0.88, Willmott’s index of agreement, WI > 0.96). In the second scenario, the modeling process showed that the WQI can be estimated without any of the six parameters. It can be seen that the parameter DO is the most important factor in determining the WQI. The pH is the factor that affects the WQI the least. Moreover, scenarios three to six show the efficiency of the model in terms of time and cost by minimizing the number of variables in the input combination of the model (r > 0.6, NSE >0.5 (good), WI > 0.7 (very good)). In summary, the model will greatly improve and accelerate data-driven decision making in water quality management by making data more accessible and attractive without human intervention.