Combined Sampling Method Research Articles

Research on Hard Rock Pillar Stability Prediction Based on SABO-LSSVM Model

The increase in mining depth necessitates higher strength requirements for hard rock pillars, making mine pillar stability analysis crucial for pillar design and underground safety operations. To enhance the accuracy of predicting the stability state of mine pillars, a prediction model based on the subtraction-average-based optimizer (SABO) for hyperparameter optimization of the least-squares support vector machine (LSSVM) is proposed. First, by analyzing the redundancy of features in the mine pillar dataset and conducting feature selection, five parameter combinations were constructed to examine their effects on the performance of different models. Second, the SABO-LSSVM prediction model was compared vertically with classic models and horizontally with other optimized models to ensure comprehensive and objective evaluation. Finally, two data sampling methods and a combined sampling method were used to correct the bias of the optimized model for different categories of mine pillars. The results demonstrated that the SABO-LSSVM model exhibited good accuracy and comprehensive performance, thereby providing valuable insights for mine pillar stability prediction.

Modern methods for determining heavy metals in soil

This article discusses the problem of heavy metal detection in soil and its impact on vegetation. Based on the experience of foreign and domestic research, this article discusses global fundamental problems and challenges, modern methods of heavy metal detection, as well as prospects for further research and new challenges facing the scientific community. The aim of the study is to identify modern and established methods for the detection of heavy metals in soil, such as spectral analysis methods and reflectance spectra of plant parts. The review summarizes the results of experimental studies confirming the effectiveness of the combined sampling and spectrometry method for estimating the concentration of heavy metals in soil, as well as the feasibility of using plant reflectance spectra to measure pollution. World experience confirms the expediency of using spectral approaches to determine heavy metals in soil and analyze their impact on vegetation. The results of the research have practical application in the field of ecology, agriculture and nature protection, allow effectively controlling the level of heavy metal pollution and taking measures for its elimination.

Bayesian updating model of failure probability function and its solution

When new observations are collected, the failure probability function (FPF) should be calibrated to match the new observations. However, there is a lack of FPF updating model and corresponding solution at present. Therefore, this paper devotes to the corresponding research. The main contribution of this paper includes two aspects. The first is constructing a Bayesian updating model of FPF, and the second is proposing two methods, i.e., combination sampling (CS) method and combination importance sampling (CIS) method, to solve the FPF updating model. In the constructed FPF updating model, the likelihood function, which approximately describes the probability of the observation error, is combined with the prior information to calibrate the prior FPF by Bayesian theory. And by sharing the sample information of the constructed CS density or the CIS density, the complete FPF can be updated through a single simulation run. Moreover, to enhance the efficiency of the CS and CIS, the adaptive Kriging model of performance function is nested in the CS and CIS methods. Five examples show that the two proposed methods need much less computational cost than the competitive methods under the similar accuracy of calibrating FPF, and the proposed CIS method is more efficient than the proposed CS method.

A double sampling ridge penalized likelihood ratio control charting method with variable sample size for Phase II monitoring of high-dimensional covariance matrix

Today, manufacturers face two challenges: the financial limitation of sampling and the large number of study quality characteristics. In such situations, the problem dimension is usually larger than the sample size, which is known in the literature of statistical quality control as a high dimensional process. The use of conventional multivariate charts to monitor the covariance matrix of such processes is not possible for two reasons: (1) the non-reversibility of the sample covariance matrix, (2) the possibility of the occurrence of sparse shifts in which only a few elements of the covariance matrix are affected. This paper focuses on Phase II monitoring of the covariance matrix of high-dimensional processes based on integrating the ridge penalized likelihood ratio (RPLR) statistic and the combined DS-VSS sampling method, in which DS and VSS features are used concurrently. The performance of the proposed DS-VSS RPLR control chart is compared with the RPLR one in terms of three metrics of the average run length, standard deviation of the run length, and expected value of sample size under three types of joint diagonal/off-diagonal, diagonal and off-diagonal shift patterns. The results show that the DS-VSS scheme is more sensitive than the RPLR in detecting both sparse and non- sparse changes.

Relationship between risk level, risk perception and preventive behaviors of myocardial infarction and stroke in people aged 30 to 60 years of Jiroft city, south of Iran in 2022

Background: Among non-communicable diseases, myocardial infarction (MI) and stroke are the most common cause of death in the world. The current study aimed to investigate the relationship between risk level and preventive behaviors of heart in people aged 30 to 60 years of Jiroft city in 2022.
 Methods: This research was a cross-sectional study. The population under study is 383 middle-aged individuals (30-60 year) of Jiroft city with the combined sampling method. The data collection tools was reseacher made, included demographic questions, risk perception questionnaires, and preventive behavior questions for MI and stroke. Information was analyzed by SPSSv21 software and further analyzed with descriptive statistics tests, chi-square test, independent t-test, one-way ANOVA, and Pearson correlation test at level 0.05.
 Results: The participants' average age was 41.26 ± 8.32 years. The highest percentage (51.4%) of middle-aged individuals had a risk level between 10 and 20% for cardiovascular diseases. The study investigated a significant correlation (p < 0.01) between risk level, risk perception, and preventive behaviors for MI and stroke. Smoking and physical activity failed to reveal a significant correlation (p > 0.05), but other dimensions of preventive behaviors correlated positively and significantly with the total score of preventive behaviors (p < 0.01).
 Conclusion: According to study findings, risk perception plays a significant role in influencing preventive behaviors for MI and stroke. It is recommended to conduct educational interventions to enhance understanding of the actual risk of MI and stroke, particularly among individuals with a high-risk assessment level. This would encourage the adoption of preventive behaviors in this population.

Imbalanced Multiclass Data Classification Using Combined Data Sampling and Deep Learning Method

Multiclass Classification for finding pattern refers to classifying each data to part of the classes or labels that are generally more than two. The foremost challenge in classifying is with imbalanced data that have large portion data known to be majority class, and small portion known as minority class that leads to poor understanding of samples and less accurate results. The existing works discussed Random Upsampling, Random Downsampling, SMOTE methods individually with FeedForward Neural Network and found Random Oversampling gave better results .However, it generates more duplicate data and has less accuracy. Hence , this research work put forward Combined Random Over-Under Sampling approach that was preprocessed prior with Replacing Missing value with mean, Feature selection, Noise Filtering. Meanwhile this work extends the existing FeedForward Neural Network to Deep Learning . The proposed work is implemented in Rapidminer tool, assessed with appropriate evaluation measures for training and testing data individually.

PENGARUH TINGKAT PENDIDIKAN, PENDAPATAN, DAN STATUS KETENAGAKERJAAN TERHADAP FERTILITAS MELALUI USIA KAWIN PERTAMA DI KECAMATAN MENGWI

The objectives to be achieved in this study are 1) to analyze the effect of education level, income, and employment status on age at first marriage in Mengwi sub-district, Badung district, 2) to analyze the effect of education level, income, and employment status on fertility in Mengwi sub-district, district Badung, and 3) Analyzing the indirect effect of education level, income, and employment status on fertility through age at first marriage in Mengwi District, Badung Regency. Respondents in this study were couples of childbearing age as many as 123 samples with a purposive combined accidental sampling method. The data analysis technique used path analysis. The results of the study stated that 1) Education level, income, and employment status had a significant effect on age at first marriage in Mengwi District, Badung Regency, 2) Education level and income had a significant effect on fertility, while employment status had no significant effect on fertility in Mengwi District, Badung Regency, and 3) Education level, income, and employment status affect fertility indirectly through age at first marriage in Mengwi District, Badung Regency

Identifying strategies for adaptation of rural women to climate variability in water scarce areas

The current research aims to identify adaptation strategies adopted by rural women of Fars province in dealing with climate variability. This study is applied in terms of purpose, descriptive-correlational in terms of methodology, and field type in terms of data collection. The statistical population were rural women who were members of agricultural households in Fars province, which have been affected by climatic variability in four climatic zones: cold, mild, hot, and very hot. This study was conducted using survey method. A structured questionnaire was designed and its face and content validities were verified using experts' opinions. The size of the studied sample was estimated using Cochran's formula. For sampling, a combined stratified and cluster sampling method was used. Data were collected from 406 respondents from 32 villages in eight counties of Fars province. Then, the data was analyzed. In total, 43 adaptation strategies of rural women were classified in the form of four categories of strategies for development and diversification of income sources, cost management, social capital management, and environmental stress management. The most important adaptation strategy used by rural women against climatic changes has been the cost management strategy. The use of facilities and loans is the most important strategy among the cost management strategies that can lead to reducing the negative effects of climate variability on rural women. Then, the strategy of development and diversification of income sources, management of social capital, and management of environmental tensions were placed in the second to fourth places of adaptation strategies used by rural women under climate variability. The results of this study indicate that the rural women of Fars province are not only passive victims of climate changes, but they act actively and hope to adapt to sudden climate changes. Using prudent strategies, they employ their experience and expertise to reduce the adverse effects of climate variability.

Prediction of the Prevalence of Hypertension and Associated Risk Factors in Rwanda Using Gibbs Sampling Method.

In Rwanda, the prevalence of hypertension was 15.3% in 2015. At present, there are no accurate predictions of the prevalence of hypertension and its trend over time in Rwanda to assist decision makers in making plans for prevention and more effective interventions. This study used the Gibbs sampling method in combination with the Markov Chain Monte Carlo approach to predict the prevalence of hypertension and its associated risk factors in Rwanda over a period of ten years. The data were from World Health Organization (WHO) reports. The findings showed that the prevalence of hypertension is estimated to reach 17.82% in 2025, with tobacco use, being overweight or obese, and other risk factors having a respective prevalence of 26.26%, 17.13%, 4.80%, and 33.99%, which shows the increase and, therefore, measures for prevention to be taken. Therefore, to prevent and reduce the prevalence of this disease, the government of Rwanda should take appropriate measures to promote a balanced diet and physical exercise.

The Plight of Athletic Training Graduates in a Third World Country: An Obscure and Unregulated Profession in Jamaica

Background: Sports treatment and rehabilitation in Jamaica appears to be a costly struggle for athletes of all ages, with very few facilities and specialists on the island. However, the introduction of the athletic training (AT) profession can bridge the gap in sports treatment and rehabilitation by creating a solid foundation for athletes, especially at the high school level. After 12 years of establishing the course of study in Jamaica, athletic trainers (ATs) are still not recognized and are unable to practice. Purpose: The purpose of this study was to highlight the aspirations of the athletic training profession in Jamaica. Methods: The study was conducted using an explanatory case study design that used an open-ended survey to collect data from AT graduates. A total of 20 participants were selected using a combined sampling method that utilized purposive and snowball sampling techniques. Google forms were used to create the open-ended questions and a content analysis method that utilized multi-analyst coding was used to establish relevant themes and categories from the responses. Results: The results of the study indicated that graduates are fnding it extremely difcult to get jobs as athletic trainers, due to the lack of awareness, promotion, and ambiguity of AT in Jamaica. Despite the challenges, graduates believed that a national AT association is crucial for seeking licensure and promoting awareness of the profession in Jamaica. Conclusion: If recognized, ATs can signifcantly help to boost sports treatment in Jamaica and contribute to greater sporting performances. Keywords: allied health professionals, athletic trainers, developing profession.

Short pulses from a gain-switched quantum cascade laser

The picosecond carrier dynamics observed in quantum cascade lasers (QCLs) poses a fundamental obstacle for the formation of intracavity pulses. On the other hand, the ultrafast gain response makes the QCL ideally suited for high frequency modulation of its pump current. In this work, we leverage this property and use short electrical excitations to generate 33 ps optical pulses with up to Watt level peak power. We ensure lasing on a single resonator mode via optical injection seeding. The resulting pulses are characterized in both time and frequency domains using an optical sampling method in combination with a spectrometer. We interpret the obtained results using laser rate equations. The results presented in this work open new pathways for the generation of highly tunable, high power mid-infrared pulses from a monolithic source.

A Proactive Explainable Artificial Neural Network Model for the Early Diagnosis of Thyroid Cancer

Early diagnosis of thyroid cancer can reduce mortality, and can decrease the risk of recurrence, side effects, or the need for lengthy surgery. In this study, an explainable artificial neural network (EANN) model was developed to distinguish between malignant and benign nodules and to understand the factors that are predictive of malignancy. The study was conducted using the records of 724 patients who were admitted to Shengjing Hospital of China Medical University. The dataset contained the patients’ demographic information, nodule characteristics, blood test findings, and thyroid characteristics. The performance of the model was evaluated using the metrics of accuracy, sensitivity, specificity, F1 score, and area under the curve (AUC). The SMOTEENN combined sampling method was used to correct for a significant imbalance between malignant and benign nodules in the dataset. The proposed model outperformed a baseline study, with an accuracy of 0.99 and an AUC of 0.99. The proposed EANN model can assist health care professionals by enabling them to make effective early cancer diagnoses.

Optimizing spatial interpolation method and sampling number for predicting cadmium distribution in the largest shallow lake of North China

Cadmium (Cd) pollution has been widely recognized in lake ecosystems. Although the accurate prediction of the spatial distributions of Cd in lakes is important for controlling Cd pollution, the traditional monitoring methods of setting discrete and limited sampling points cannot actually reflect the continuous spatial distribution characteristics of Cd. In this study, we set up 93 sampling points in Baiyangdian Lake (BYDL), and collected surface water, overlying water and sediment samples from each sampling point. Cd contents were measured to predict their spatial distributions in different environmental components by three interpolation methods, inverse distance weighted (IDW), radial basis function (RBF) and ordinary kriging (OK), and the effects of different sampling numbers on the interpolation accuracy were also assessed to optimize the interpolation method and sampling number. The results showed that the interpolation accuracy of IDW decreased with increasing power values. The best basis function for RBF was IMQ, and the best semivariogram models for OK were the spherical model and stable model. The best interpolation method for the waters and sediments was RBF-IMQ compared with OK and IDW. Within the sampling number range of 50–93, the interpolation accuracy for Cd in surface water increased with the increase in sampling number. Comparatively, the interpolation accuracy was the highest for overlying water and sediments when the sampling number was 60. The findings of this work provide a combined sampling and spatial interpolation method for monitoring the spatial distribution and pollution levels of Cd in the waters and sediments of shallow lakes.

Efforts to eliminate schistosomiasis in Hubei province, China: 2005–2018

BackgroundThe Hubei province is one of the most schistosomiasis-epidemic-prone provinces in China. A series of strategies were adopted by the government to curb the rebound schistosomiasis endemic status that has prevailed since the early 2000s. This study aimed to elucidate the trends of schistosomiasis transmission and to appraise the effectiveness of the integrated control strategy in lake and marshland areas. MethodsSurveillance data of schistosomiasis in the Hubei province between 2005 and 2018 were analyzed, including conventional health control measures, integrated strategies, and measures that focused on the infection source. According to the local annual plan for schistosomiasis control in endemic counties, previous measures were human and snail control and surveillance. Residents aged 6–65 years were screened by an immunological detection method called indirect hemagglutination assay (IHA) after the transmission season each year. All residents who tested positive were then asked to provide a fecal sample for examination by the miracidium hatching technique (MHT) to detect the presence of schistosomes. Moreover, systematic snail surveys were conducted as a part of the combined environmental sampling method. The latter included integrated strategies and measures that focused on the infection source. Bovine stool samples were also collected and concurrently assessed using the MHT by the agriculture department, river-hardening slope protection was constructed by the water conservancy department, and forestation promotion was conducted by the forest department. The effectiveness of the integrated control strategy was assessed using two indicators of resident and livestock infection rates and three indicators of snail epidemics across all endemic areas. ResultsFrom 2005 to 2018, a total of 28. 46 million and 2. 05 million residents were assessed by immunological (IHA) and etiological (MHT) detection techniques, respectively. Snail surveys and molluscicide application were performed in 2. 26 hectares and 0. 37 hectares, respectively. Moreover, 2. 60 million bovines were assessed by etiological detection techniques (MHT). The river-hardening slope protection project was implemented in 503 places, and 46 thousand hectares in endemic areas underwent environmental modification. Forestation was implemented at an area of 0. 15 million hectares. Between 2005 and 2018, the epidemic indicators, including resident and livestock infection rates and the infested areas and infection rate of snails, all presented downward trends. The resident infection rate decreased from 3. 78% in 2005 to 0% in 2016, which persisted through 2018. The livestock infection rate decreased from 5. 63% in 2005 to 0% in 2013, which also persisted through 2018. From 2005 to 2018, the snail-inhabited area was slightly reduced, but the area of infected snails decreased to 0 in 2012; this persisted through 2018. All counties met the goal for schistosomiasis infection control, transmission control, and disruption of schistosomiasis activity in 2008, 2013, and 2018 separately. That means the goal has been achieved in each stage. ConclusionsThe decline of the schistosomiasis epidemic rate demonstrates that the Chinese government was successful in meeting its public health goal in Hubei province. In the next decade, precision interventions must be implemented in endemic counties with a relatively low epidemic status to achieve the goals of the Outline of the Healthy China 2030 Plan. A similar strategy can be applied in other countries to eliminate schistosomiasis globally.

Analytical study of the economic effects of sports for seniors in Iran

The aim of this study was to analyze the economic effects of elderly sports in Iran. The present study was qualitative. The research is based on grounded theory applying the emerging method attributed to Glaser (1992). Professors, of sports management, sociology, psychology, municipal managers and researchers (with at least two scientific research articles in the field of research) constituted the population of the current study. Since the purpose of qualitative sampling is information saturation, the combined theoretical sampling method of the improbably purposeful type was used until the theoretical saturation was achieved. A total of 15 participants were recruited as sample for the present study. Acceptability, transferability and verifiability indices were used to determine validity. In order to evaluate the reliability of the interviews, the method of intra-subject agreement of two coders (evaluators) was used. Findings of the present study revealed that the mean age of the research participants was 48 years. Out of all, 75% participants were males. The average career background of the participants was 22.5 years and 80% of them held a Ph.D. degree. The results showed that the economic effects of elderly sports were divided into two categories: micro and macro. The macro level consisted of the two concepts of increasing GDP (5 signs) and private sector investment (3 signs). The micro level consisted of the two concepts of quantitative and qualitative improvement of production inputs (6 signs) and reduction of treatment costs (8 signs).

Assessing the Impact of the COVID-19 Pandemic in Jamaica: Physical Activity Participation, Weight Gain, and the Mental Health Perception

The purpose of this research was to assess the impact of the COVID-19 pandemic on physical activity (PA) participation, weight gain, and the mental health perception in Jamaica. A quantitative approach was applied using a cross-sectional survey design to collect data. The research surveyed 836 Jamaicans from all 14 parishes using a structured questionnaire via Google Forms. The research used a combined sampling method that included convenience and snowball techniques to survey participants ages 12-71 years. The results indicated that the level of PA participation during the COVID-19 pandemic was 73.3%. However, 87.2% of the respondents engaged in less than 150 minutes of PA per week, while 96.1% of the respondents participated in low intensity and short durations of PA below the recommended PA guidelines set out by the WHO as the very minimum needed to provide health benefits to people. Additionally, the government restrictions impacted eating habits and weight gain for majority of the respondents. Stress and depression were the most perceived mental impact of the COVID-19 pandemic on the participants.

Prevalence and Risk Factors of Chronic Obstructive Pulmonary Disease in Kashi Region, Northwestern China.

ObjectiveTo investigate the prevalence and the risk factors of COPD in the Uyghur population in the Kashi region.MethodsFrom August 2018 to March 2019, we recruited participants of Uyghur ethnicity and aged ≥40 years old from the Kashi region using a combined cluster sampling and random sampling method. We collected potential risk factors using questionnaire, and conduced lung function using a portable pulmonary function instrument.ResultsA total of 2963 participants were included in this analysis, of whom 1268 were males and 1695 were females. There were 504 participants with COPD, generating a prevalence of 17.01%. With the increase of age, the prevalence of COPD in different genders increased significantly. The results of different regions were χ2= 627.89, p < 0.01, indicating significant differences in the prevalence in different regions. Among them, based on the existing survey data, it is speculated that Shache county has the highest crude prevalence, but the sample size needs to be further expanded. The participants with high age, smoking, lower BMI, high waist circumference, systolic blood pressure, fried cooking and barbecue share more COPD than those who partake of fruits and vegetables in this study.ConclusionThe prevalence of COPD among the Uyghur population in the Kashi region is higher than the national rural average. Among them, high age, smoking, low BMI, high waist circumference, high systolic blood pressure, cooking methods that may be stir-fried and deep-fried barbecue are risk factors for COPD, and vegetable and fruit intake may be a protective factor for COPD.

Outcome of Severe Vaso-Occlusive Crisis in Sickle Cell Disease Adults Admitted to Referral Centers in Africa and Europe. Introduction of Machine Learning Methods to Improve the Presev Score

Introduction: Sickle Cell Disease (SCD) is one of the most common genetic disease worldwide. The Acute Chest Syndrome (ACS) is a leading cause of death for SCD patients. The PRESEV1 study was set to produce a predictive score to assess the risk of an ACS development (Bartolucci et al., 2016). PRESEV2 was an international, multicenter prospective confirmatory study to validate the PRESEV score. This study aims at improving these predictions with the addition of a machine learning (ML) method. Patients and methods: Included patients follow PRESEV1 and PRESEV2 studies 'rules. The dataset thus contains 97 patients who developed an ACS episode (18.3%) against 434 patients who did not (81.7%). To compute the PRESEV score, we firstly used the method developed previously with the following variables as input: leukocytes, reticulocytes, hemoglobin levels and cervical spine pain. This method is based on a decision tree with fixed rules and is referred to as the decision tree method throughout this abstract. Secondly we used a ML method using a combined sampling method named SMOTEENN to balance the data and a C-Support Vector Classification (SVC) with fixed parameters to predict the score. This method produces a probability, with a threshold of 0.2, under which the patient is predicted to declare an ACS. We considered the dataset composed of PRESEV1 dataset and 80 percent of PRESEV2 with a randomly choice. The test dataset is thus composed of the remaining 20 percent of PRESEV2. This technique of random choice allowed us to use a 50-cross-validation and compute with Python an average score and a standard deviation (std). In order to allow comparison of the developed score with or without the addition of the ML method, rates were calculated by adding the weight of ACS representation in the dataset. Results: Among all parameters analyzed, the SVC method considered the following variables for calculation of the score: leukocytes, LDH, urea, reticulocytes and hemoglobin levels. A hundred and two adult patients with a severe VOC requiring hospitalization were included. Out of this pool of patients, 26 (25.5%) were predicted with a low risk of developing an ACS episode (SVC method). Sensibility and specificity were of 94.7% and 26.8%, respectfully. The negative predictive value (NPV) was of 95.8% and the positive predictive value (PPV) of 22.4%. Results are resumed in table 1. When compared to the PRESEV score (decision tree method), 44 patients out of 372 were identified with a low risk score (11.8%), Discussion and Conclusion: While the addition of a ML method did not allow the improvement of the sensibility or the NPV of the PRESEV score, it improved both the specificity and the PPV. The addition of artificial intelligence thus provides a better prediction with a higher percentage of "low-risk" patients. As highlighted in the international PRESEV study, this score could represent a useful tool for physicians in hospital settings, with limited beds. While the PRESEV score could allow a better management of "low risk" patients on one side, the identification of "high-risk" patients could also represent a serious advantage to physicians, as it could improve the feasibility of clinical trials for the prevention of this lethal complication in SCD patients. Disclosures Bartolucci: Innovhem: Other; Novartis: Research Funding; Roche: Consultancy; Bluebird: Consultancy; Emmaus: Consultancy; Bluebird: Research Funding; Addmedica: Research Funding; AGIOS: Consultancy; Fabre Foundation: Research Funding; Novartis: Consultancy; ADDMEDICA: Consultancy; HEMANEXT: Consultancy; GBT: Consultancy.

Teaching Challenges in COVID-19 Scenery: Teams Platform-Based Student Satisfaction Approach

At the onset of the crisis caused by COVID-19, the Mexican education system chose to join the global context and suspend face-to-face classes for all educational levels. For the continuity of educational processes, a transition from a traditional educational model (face-to-face) to emergency remote teaching (ERT) was made through virtual learning platforms and learning management system (LMS) schemes. Universidad del Valle de Mexico (UVM), in a collaboration agreement with Microsoft Co., chose to use Teams to continue its educational process. In this work, we intend to identify the factors that can be taken into account regarding the level of student satisfaction in the teaching–learning process in ERT using Teams, and validate the established educational strategy. Statistical analysis was carried out to analyze the academic environment for these scenario changes while considering knowledge assessment, and competencies achievement. A combined sampling method was applied with convenience and statistical analysis. The main results established significant percentages, where more than 60% of the students surveyed were manifested in the use of the teams and the organization of the class sessions by the teachers, and the activities developed. Using the Cronbach’s Alpha coefficient, the reliability of the data collection instruments was determined. The correlations of each of the survey questions were calculated to determine the relationship between themselves and the total answers, giving results similar to those obtained through data science tools. Taking advantage of the situation, data science tools were applied to compare the results with obtained values from RapidMiner software in the correlation of factors in of 0.440, 0.384, 0.246, 0.048 and 0.384.

PENGARUH CITRA PASAR TRADISIONAL TERHADAP KEBERHASILAN CITY BRANDING KOTA SOLO

The positive image of the city plays a strong role in determining the success of city branding. The positive image of the city makes it easier to form competitive advantages. These city images can be communicated in various ways, and one of them through culture. This research was conducted to determine the influence of the image of traditional markets as a part of the culture of Solo in attracting residents and tourists to stay and visit. Multiple linear regression is used to test and find traditional market image factors that influence the success of city branding. The study used 120 samples consisting of residents and tourists. Sampling uses the combined sampling method. The results of the study show that there is an influence between the traditional market image on the success of city branding. Traditional markets have been proven to be able to influence the formation of a positive image of Solo although it is not the only determinant of the success of city branding. Traditional market condition and traditional market reputation are significant factors in attracting residents and tourists to stay and visit Solo.