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This work presents a method for extracting vessel arrival times and arrival processes from Automatic Identification System (AIS) data. This work employs the methodology presented by Mitchell and Scully (2014) for inferring tidal elevation at the time of vessel movement and calculating the tidal dependence (TD) parameter to 23 U.S. port areas for the years 20122014. Tidal prediction stations and observation reference lines are catalogued for considered ports. AIS data obtained from the U.S. Coast Guard, and 6-minute tide predictions, obtained from the National Oceanographic and Atmospheric Administration, are used to rank relative tidal dependence for arriving cargo and tank vessel traffic in studied locations. Results include relevant tide range and elevation threshold observations for each year and location studied. AIS-derived arrival processes, including arrival frequency, arrival rate, and interarrival time are visualized using several techniques with comparative discussion between ports to highlight implications for understanding seasonal traffic trends or port resiliency. The ports with the highest and lowest TD value, Portland, ME, and Los Angeles, CA, respectively, are discussed with regard to weekly arrival patterns and interarrival time. Cargo composition and value obtained through the Channel Portfolio Tool is also considered.

This article is from BMC Research Notes , volume 4 . Abstract Background: Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results: Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5. Conclusions: When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing.

The dataset, code and read.me file for Unraveling the Skillsets of Data Scientists: Text Mining Analysis of Dutch University Master Programs in Data Science and Artificial Intelligence

The dataset, code and read.me file for Unraveling the Skillsets of Data Scientists: Text Mining Analysis of Dutch University Master Programs in Data Science and Artificial Intelligence

This article is from BMC Medical Research Methodology , volume 14 . Abstract Background: The randomized controlled study is the gold-standard research method in biomedicine. In contrast, the validity of a (nonrandomized) observational study is often questioned because of unknown/unmeasured factors, which may have confounding and/or effect-modifying potential. Methods: In this paper, the author proposes a perturbation test to detect the bias of unmeasured factors and a perturbation adjustment to correct for such bias. The proposed method circumvents the problem of measuring unknowns by collecting the perturbations of unmeasured factors instead. Specifically, a perturbation is a variable that is readily available (or can be measured easily) and is potentially associated, though perhaps only very weakly, with unmeasured factors. The author conducted extensive computer simulations to provide a proof of concept. Results: Computer simulations show that, as the number of perturbation variables increases from data mining, the power of the perturbation test increased progressively, up to nearly 100%. In addition, after the perturbation adjustment, the bias decreased progressively, down to nearly 0%. Conclusions: The data-mining perturbation analysis described here is recommended for use in detecting and correcting the bias of unmeasured factors in observational studies.

Every organization needs an evaluation system in order to be aware of the level of performance and desirability of its units. It is more important for agricultural companies, including agro-industries. In this study, 20 sugarcane harvesting units were selected. After modeling based on input-oriented CCR and BCC models, efficiency values for sugarcane harvesting units were calculated and the CART decision tree was used to extract rules to predict the efficiency of these units. The results of a study of 20 sugarcane harvesting units in the CCR model showed that 6 units had an efficient score and 14 units had an inefficient score, and their technical efficiency score was in the range of 0.73-0.95. The results of the BCC model study also showed that out of a total of 20 sugarcane harvesting units, 8 units had efficient scores. As can be seen, in the BCC model, more units are introduced as efficient units and there is less dispersion between inefficient units. Also, the distribution of efficient units in the BCC model is less than the CCR model. The average technical efficiency, pure technical efficiency, and scale efficiency were 93%, 88%, and 93%, respectively. Also, the accuracy of the decision tree model for technical efficiency and pure technical efficiency was 86% and 93%, respectively.

Data survey on the elderly health condition in each year aimed to investigate the performance result on the elderly health care and to evaluate the elderly’s health and health promotion. Thus, in analyzing the data, it mainly relied on the mining data technique for the evaluating health condition. This study presented the data analysis by clustering method. Then, the data was taken from each group to find the association rule. The analysis results showed that the elderly’s health condition data could be classified into four different groups; cluster 1 (25%) were male elderly with high blood pressure and smoking cigarette, cluster 2 (25%) were female elderly with no the congenital disease but the result from the eye sight examination, it was found that they were long-sighted, cluster 3 (24%) were female elderly with no the congenital disease but having the insomnia and osteoarthritis and cluster 4 (26%) were female elderly with high blood pressure and diabetes. It also indicated that each group had the rule showing the correlation between the data in each group having the minimum value of confidence at 0.8 and the minimum value of support not less than 0.5. 

Data survey on the elderly health condition in each year aimed to investigate the performance result on the elderly health care and to evaluate the elderly’s health and health promotion. Thus, in analyzing the data, it mainly relied on the mining data technique for the evaluating health condition. This study presented the data analysis by clustering method. Then, the data was taken from each group to find the association rule. The analysis results showed that the elderly’s health condition data could be classified into four different groups; cluster 1 (25%) were male elderly with high blood pressure and smoking cigarette, cluster 2 (25%) were female elderly with no the congenital disease but the result from the eye sight examination, it was found that they were long-sighted, cluster 3 (24%) were female elderly with no the congenital disease but having the insomnia and osteoarthritis and cluster 4 (26%) were female elderly with high blood pressure and diabetes. It also indicated that each group had the rule showing the correlation between the data in each group having the minimum value of confidence at 0.8 and the minimum value of support not less than 0.5. 

In this competitive scenario of the educational system, the higher education institutes use data mining tools and techniques for academic improvement of the student performance and to prevent drop out. The authors collected data from three colleges of Assam, India. The data consists of socio-economic, demographic as well as academic information of three hundred students with twenty-four attributes. Four classification methods, the J48, PART, Random Forest and Bayes Network Classifiers were used. The data mining tool used was WEKA. The high influential attributes were selected using the tool. The internal assessment attribute in the continuous evaluation process makes the highest impact in the final semester results of the students in our dataset. The results showed that random forest outperforms the other classifiers based on accuracy and classifier errors. Apriori algorithm was also used to find the association rule mining among all the attributes and the best rules were also displayed.

In this competitive scenario of the educational system, the higher education institutes use data mining tools and techniques for academic improvement of the student performance and to prevent drop out. The authors collected data from three colleges of Assam, India. The data consists of socio-economic, demographic as well as academic information of three hundred students with twenty-four attributes. Four classification methods, the J48, PART, Random Forest and Bayes Network Classifiers were used. The data mining tool used was WEKA. The high influential attributes were selected using the tool. The internal assessment attribute in the continuous evaluation process makes the highest impact in the final semester results of the students in our dataset. The results showed that random forest outperforms the other classifiers based on accuracy and classifier errors. Apriori algorithm was also used to find the association rule mining among all the attributes and the best rules were also displayed.

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In this study paper, the feasibility of constructing a complete smart system for anticipating electrical power consumption is created, as electricity's market share is expected to expand over the future decades. Smart grids and smart meters will help utility companies and their customers soon. New services and businesses in energy management need software development and data analytics skills. New services and enterprises are competitive. The project's electricity consumers are categorized by their hourly power usage percentage. This classification was done using data mining (five algorithms in specific) and data analysis theory. This division aims to help each group minimize energy use and expenditures, encourage energy-saving activities, and promote consumer involvement by giving tailored guidance. The intended segmentation is done through an iterative process using a computer classification computation, post-analysis, and data mining with visualization and statistical methodologies.

In this study paper, the feasibility of constructing a complete smart system for anticipating electrical power consumption is created, as electricity's market share is expected to expand over the future decades. Smart grids and smart meters will help utility companies and their customers soon. New services and businesses in energy management need software development and data analytics skills. New services and enterprises are competitive. The project's electricity consumers are categorized by their hourly power usage percentage. This classification was done using data mining (five algorithms in specific) and data analysis theory. This division aims to help each group minimize energy use and expenditures, encourage energy-saving activities, and promote consumer involvement by giving tailored guidance. The intended segmentation is done through an iterative process using a computer classification computation, post-analysis, and data mining with visualization and statistical methodologies.

BACKGROUND AND OBJECTIVES: Data mining is known as a process of discovering patterns in large datasets through a combination of statistical tools and techniques. In recent years, data mining and its applications in different businesses have increasingly grown. Insurance industry is one of the data-driven businesses whose survival is so dependent on satisfying customers besides achieving the highest benefit. Information or data is a vital asset of the insurance industry ;accordingly, using data mining techniques to discover patterns behind large datasets is a need. Having seen the increasingly high rate of information technology and recorded data in data-driven businesses, lots of industries like the insurance industry have been urged to use state-of-the-art data mining techniques to turn raw data into useful information using Big Data Analytics. METHODS: Looking at the current research on data mining applications in the insurance industry proves the fact that we should recognize the state-of-the art techniques in data mining and set new strategies to focus on Big Data Analytics more. Big Data Analytics consists of the algorithms which are more efficient and less time-consuming so it can help to identify patterns and rules in complex datasets. For this purpose, this paper presents a comprehensive literature review regarding the usage of data mining techniques in the insurance industry by the scientometrics approach. For this purpose, first we searched and gathered bibliometrics files of recent researches from Web of Science and Scopus into four different scenarios. In each scenario, we looked up for different keywords regarding “Data Mining”, “Insurance Industry”, and “Risk Management” to make sure that all the results would be specifically focused on the research topic. Then, we used R programming software to analyze the results of each scenario based on keywords co-occurrence in the given research. FINDINGS: The results of keywords co-occurrence and a word cloud of recent research confirm that insurance companies should focus on Big Data Analytics instead of traditional data processing to get information systematically from too large or complex datasets. Big Data Analytics has been used for several years, but in recent years many data-driven businesses, like the insurance industry, have used its techniques associated with risk and risk factor identification. Risk management in the insurance industry has been widely considered in recent researches. Therefore, in this paper, some high-ranked journals and the most significant researches have been identified and recommended in order to pave the way for future researches in this field. CONCLUSION: We hope that the comprehensive literature review provided in this paper can help the researchers to focus on the relative journals and researches published then get into more details. For this purpose, the lists of all journals and conferences besides the most cited researches are provided in the experimental section of this paper. Also, the ranking list of different countries from all around the world related to data mining and Big Data Analytics in the insurance industry is presented. The results show that Iran is the 15 th  country that uses data mining techniques and it is the 17 th  country in the world focusing on risk management in the insurance industry.

BACKGROUND AND OBJECTIVES: Data mining is known as a process of discovering patterns in large datasets through a combination of statistical tools and techniques. In recent years, data mining and its applications in different businesses have increasingly grown. Insurance industry is one of the data-driven businesses whose survival is so dependent on satisfying customers besides achieving the highest benefit. Information or data is a vital asset of the insurance industry ;accordingly, using data mining techniques to discover patterns behind large datasets is a need. Having seen the increasingly high rate of information technology and recorded data in data-driven businesses, lots of industries like the insurance industry have been urged to use state-of-the-art data mining techniques to turn raw data into useful information using Big Data Analytics. METHODS: Looking at the current research on data mining applications in the insurance industry proves the fact that we should recognize the state-of-the art techniques in data mining and set new strategies to focus on Big Data Analytics more. Big Data Analytics consists of the algorithms which are more efficient and less time-consuming so it can help to identify patterns and rules in complex datasets. For this purpose, this paper presents a comprehensive literature review regarding the usage of data mining techniques in the insurance industry by the scientometrics approach. For this purpose, first we searched and gathered bibliometrics files of recent researches from Web of Science and Scopus into four different scenarios. In each scenario, we looked up for different keywords regarding “Data Mining”, “Insurance Industry”, and “Risk Management” to make sure that all the results would be specifically focused on the research topic. Then, we used R programming software to analyze the results of each scenario based on keywords co-occurrence in the given research. FINDINGS: The results of keywords co-occurrence and a word cloud of recent research confirm that insurance companies should focus on Big Data Analytics instead of traditional data processing to get information systematically from too large or complex datasets. Big Data Analytics has been used for several years, but in recent years many data-driven businesses, like the insurance industry, have used its techniques associated with risk and risk factor identification. Risk management in the insurance industry has been widely considered in recent researches. Therefore, in this paper, some high-ranked journals and the most significant researches have been identified and recommended in order to pave the way for future researches in this field. CONCLUSION: We hope that the comprehensive literature review provided in this paper can help the researchers to focus on the relative journals and researches published then get into more details. For this purpose, the lists of all journals and conferences besides the most cited researches are provided in the experimental section of this paper. Also, the ranking list of different countries from all around the world related to data mining and Big Data Analytics in the insurance industry is presented. The results show that Iran is the 15 th  country that uses data mining techniques and it is the 17 th  country in the world focusing on risk management in the insurance industry.

This article is from Database: The Journal of Biological Databases and Curation , volume 2013 . Abstract Knowledge representation of the role of phosphorylation is essential for the meaningful understanding of many biological processes. However, such a representation is challenging because proteins can exist in numerous phosphorylated forms with each one having its own characteristic protein–protein interactions (PPIs), functions and subcellular localization. In this article, we evaluate the current state of phosphorylation event curation and then present a bioinformatics framework for the annotation and representation of phosphorylated proteins and construction of phosphorylation networks that addresses some of the gaps in current curation efforts. The integrated approach involves (i) text mining guided by RLIMS-P, a tool that identifies phosphorylation-related information in scientific literature; (ii) data mining from curated PPI databases; (iii) protein form and complex representation using the Protein Ontology (PRO); (iv) functional annotation using the Gene Ontology (GO); and (v) network visualization and analysis with Cytoscape. We use this framework to study the spindle checkpoint, the process that monitors the assembly of the mitotic spindle and blocks cell cycle progression at metaphase until all chromosomes have made bipolar spindle attachments. The phosphorylation networks we construct, centered on the human checkpoint kinase BUB1B (BubR1) and its yeast counterpart MAD3, offer a unique view of the spindle checkpoint that emphasizes biologically relevant phosphorylated forms, phosphorylation-state–specific PPIs and kinase–substrate relationships. Our approach for constructing protein phosphorylation networks can be applied to any biological process that is affected by phosphorylation.Database URL:http://www.yeastgenome.org/

Dataset consists of seven cyclists, who upload their activities to Strava and Garmin Connect profiles. Typically, these activities can be downloaded as a GPX format, which basically presents an XML format. Following features of each training can be extracted: GPS location, elevation, duration, distance, heart rate and even power.

In recent years, intrusion detection systems have gained wide acceptance within both government and commercial organizations. A number of intrusion detection tools are commercially available and are being routinely used as part of the protection of network and computer systems. There are several limitations to the present generation of the intrusion detection systems: these tools detect only those attacks that are already known, generate too many false positives, and operation of these tools is too labor intensive. To overcome these problems, we developed methods and tools that can be used by the system security officer to understand the massive amount of data that is being collected by the intrusion detection systems, analyze the data, and determine the importance of an alarm. Report divided into three parts. Part I describes a network intrusion detection system, called Audit Data Analysis and Mining (ADAM), which employs a series of data mining techniques including association rules, classification techniques, and pseudo-Bayes estimators to detect attacks using the network audit trail data. Part II shows how to build attack scenarios by explicitly including network vulnerability/exploit relationships in the model. Part III provides a complete list of publications resulting from this effort and successfully licensed the resulting technology to a company called Secure Decisions and filed for four patents.

COVID-19 is a disease caused by coronavirus. 'CO' stands for corona, 'VI' for virus, and 'D' for disease. Formerly, this disease was referred to as '2019 novel coronavirus. The data mining is the best tools for analyzing and predicting the hidden information with the help of pre-existing dataset. The covid analysis and prediction for consider different related parameters namely name of the states, total cases, today cases, active cases, discharged cases, today discharged cases, overall death and today deaths. In this paper, taking consideration into analyzing and predicting covid dataset using statistical techniques namely regression model. Numerical illustrations also provide to prove the results and discussions

COVID-19 is a disease caused by coronavirus. 'CO' stands for corona, 'VI' for virus, and 'D' for disease. Formerly, this disease was referred to as '2019 novel coronavirus. The data mining is the best tools for analyzing and predicting the hidden information with the help of pre-existing dataset. The covid analysis and prediction for consider different related parameters namely name of the states, total cases, today cases, active cases, discharged cases, today discharged cases, overall death and today deaths. In this paper, taking consideration into analyzing and predicting covid dataset using statistical techniques namely regression model. Numerical illustrations also provide to prove the results and discussions

COVID-19 is a disease caused by coronavirus. 'CO' stands for corona, 'VI' for virus, and 'D' for disease. Formerly, this disease was referred to as '2019 novel coronavirus. The data mining is the best tools for analyzing and predicting the hidden information with the help of pre-existing dataset. The covid analysis and prediction for consider different related parameters namely name of the states, total cases, today cases, active cases, discharged cases, today discharged cases, overall death and today deaths. In this paper, taking consideration into analyzing and predicting covid dataset using statistical techniques namely regression model. Numerical illustrations also provide to prove the results and discussions

COVID-19 is a disease caused by coronavirus. 'CO' stands for corona, 'VI' for virus, and 'D' for disease. Formerly, this disease was referred to as '2019 novel coronavirus. The data mining is the best tools for analyzing and predicting the hidden information with the help of pre-existing dataset. The covid analysis and prediction for consider different related parameters namely name of the states, total cases, today cases, active cases, discharged cases, today discharged cases, overall death and today deaths. In this paper, taking consideration into analyzing and predicting covid dataset using statistical techniques namely regression model. Numerical illustrations also provide to prove the results and discussions

This work presents a method for extracting vessel arrival times and arrival processes from Automatic Identification System (AIS) data. This work employs the methodology presented by Mitchell and Scully (2014) for inferring tidal elevation at the time of vessel movement and calculating the tidal dependence (TD) parameter to 23 U.S. port areas for the years 20122014. Tidal prediction stations and observation reference lines are catalogued for considered ports. AIS data obtained from the U.S. Coast Guard, and 6-minute tide predictions, obtained from the National Oceanographic and Atmospheric Administration, are used to rank relative tidal dependence for arriving cargo and tank vessel traffic in studied locations. Results include relevant tide range and elevation threshold observations for each year and location studied. AIS-derived arrival processes, including arrival frequency, arrival rate, and interarrival time are visualized using several techniques with comparative discussion between ports to highlight implications for understanding seasonal traffic trends or port resiliency. The ports with the highest and lowest TD value, Portland, ME, and Los Angeles, CA, respectively, are discussed with regard to weekly arrival patterns and interarrival time. Cargo composition and value obtained through the Channel Portfolio Tool is also considered.

The aim of this work is the use of modular artificial neural networks (ANN) for data mining (Data Mining) and forecasting of various processes in the field of ecology and environmental protection, as well as the comparison of the results of the proposed model with the results of other data analysis methods (the methods of mathematical modeling and mathematical statistics)

The aim of this work is the use of modular artificial neural networks (ANN) for data mining (Data Mining) and forecasting of various processes in the field of ecology and environmental protection, as well as the comparison of the results of the proposed model with the results of other data analysis methods (the methods of mathematical modeling and mathematical statistics)

The aim of this work is the use of modular artificial neural networks (ANN) for data mining (Data Mining) and forecasting of various processes in the field of ecology and environmental protection, as well as the comparison of the results of the proposed model with the results of other data analysis methods (the methods of mathematical modeling and mathematical statistics)

The aim of this work is the use of modular artificial neural networks (ANN) for data mining (Data Mining) and forecasting of various processes in the field of ecology and environmental protection, as well as the comparison of the results of the proposed model with the results of other data analysis methods (the methods of mathematical modeling and mathematical statistics)

The aim of this work is the use of modular artificial neural networks (ANN) for data mining (Data Mining) and forecasting of various processes in the field of ecology and environmental protection, as well as the comparison of the results of the proposed model with the results of other data analysis methods (the methods of mathematical modeling and mathematical statistics)

Dataset consists of the data produced by nine cyclists. Data were directly exported from their Strava or Garmin Connect accounts. Data format of sport's activities could be written in GPX or TCX form, which are basically the XML formats adapted to specific purposes. From each dataset, many following information can be obtained: GPS location, elevation, duration, distance, average and maximal heart rate, while some workouts include also data obtained from power meters.

Dataset consists of the data produced by nine cyclists. Data were directly exported from their Strava or Garmin Connect accounts. Data format of sport's activities could be written in GPX or TCX form, which are basically the XML formats adapted to specific purposes. From each dataset, many following information can be obtained: GPS location, elevation, duration, distance, average and maximal heart rate, while some workouts include also data obtained from power meters.

Dataset consists of the data produced by nine cyclists. Data were directly exported from their Strava or Garmin Connect accounts. Data format of sport's activities could be written in GPX or TCX form, which are basically the XML formats adapted to specific purposes. From each dataset, many following information can be obtained: GPS location, elevation, duration, distance, average and maximal heart rate, while some workouts include also data obtained from power meters.

Unsupervised machine learning is one of the accepted platforms for applying a broad data analytics challenge that involves the way to identify secret trends, unexplained associations, and other significant data from a wide dispersed dataset. The precise yield estimate for the various crops involved in the planning is a critical problem for agricultural planning. To achieve realistic and effective solutions to this problem, data mining techniques are an essential approach. Applying distplot combined with kernel density estimate (KDE) in this paper to visualize the probability density of disseminated datasets of vast crop deals for crop planning. This paper focuses on analyzing and segmenting agricultural data and determining optimal parameters to maximize crop yield using data mining techniques such as K-means clustering and principal component analysis (PCA).

The foundation of this study is based on the Kano model. This model was initially used by making a questionnaire survey. But now a days due to vast amount of data and opinions available via the internet on the World Wide Web the model can be changed. In this study data from an e commerce site has been collected and has undergone preprocessing, sentiment analysis and then Kano evaluation to understand how to satisfy a customer. The use of the said model overcomes the disadvantages of the sentiment analysis. In this study a combination of sentiment analysis and Kano model has been done on online comment data giving us picture of how to discover the demands of a customer and also how to satisfy him. Nilza Angmo | Er. Vandana "Study and Analysis of Online Comment Data Mining and Kano Model Research" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38528.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/38528/study-and-analysis-of-online-comment-data-mining-and-kano-model-research/nilza-angmo

The Data Mining project seeks to bring the capability of data visualization to NASA anomaly and problem reporting systems for the purpose of improving data trending, evaluations, and analyses. Currently NASA systems are tailored to meet the specific needs of its organizations. This tailoring has led to a variety of nomenclatures and levels of annotation for procedures, parts, and anomalies making difficult the realization of the common causes for anomalies. Making significant observations and realizing the connection between these causes without a common way to view large data sets is difficult to impossible. In the first phase of the Data Mining project a portal was created to present a common visualization of normalized sensitive data to customers with the appropriate security access. The tool of the visualization itself was also developed and fine-tuned. In the second phase of the project we took on the difficult task of searching and analyzing the target data set for common causes between anomalies. In the final part of the second phase we have learned more about how much of the analysis work will be the job of the Data Mining team, how to perform that work, and how that work may be used by different customers in different ways. In this paper I detail how our perspective has changed after gaining more insight into how the customers wish to interact with the output and how that has changed the product.

In the ever-evolving landscape of the telecommunications industry, where customer churn poses a significant challenge, the role of data mining in predicting and mitigating churn has become paramount. Concurrently, the telecommunications sector is also grappling with the relentless menace of fraud, requiring rapid detection and prevention measures. This research paper presents a comprehensive comparative analysis of serial and parallel data mining approaches for customer churn prediction within the telecom sector. In the first section, we clarify the key approaches and techniques used in data mining for predicting customer turnover, including logistic regression, decision trees, random forests, and neural networks. Serial data mining is investigated with its inherent limits in terms of processing time, scalability, and real-time applicability, which is often done on a single processor core. On the other hand, a detailed analysis of parallel data mining, made possible by multi-core architectures or distributed computing clusters, is presented. We emphasize the potential advantages of parallel processing, such as more computational resources, faster processing, scalability, and real-time capabilities. The paper explores the nuances of parallel data mining implementation in the context of telecommunications data, highlighting the difficulties and expenses involved in establishing and maintaining a parallel infrastructure. The study examines how quick fraud detection and fraud prevention can be accomplished by utilizing parallel data mining’s real-time capabilities. Real-time applications for fraud prevention include proactive customer service, proactive pricing schemes, network quality monitoring, and personalized advice. Performance parameters, such as accuracy, precision, recall, and F1-score, are tested using real-world telecom datasets for the comparison study. The conclusions of this investigation provide light on the usefulness of serial and parallel methods for predicting client attrition. We also look into how these prediction models’ impact on fraud detection and prevention may spread. In conclusion, this research contributes valuable insights into the practicality and efficacy of serial and parallel data mining approaches for customer churn prediction in telecom, with a specific focus on their implications for fraud detection and prevention. The findings provide a roadmap for telecom companies seeking to optimize their data-driven strategies for customer retention and fraud mitigation in the era of big data and advanced analytics.

In the ever-evolving landscape of the telecommunications industry, where customer churn poses a significant challenge, the role of data mining in predicting and mitigating churn has become paramount. Concurrently, the telecommunications sector is also grappling with the relentless menace of fraud, requiring rapid detection and prevention measures. This research paper presents a comprehensive comparative analysis of serial and parallel data mining approaches for customer churn prediction within the telecom sector. In the first section, we clarify the key approaches and techniques used in data mining for predicting customer turnover, including logistic regression, decision trees, random forests, and neural networks. Serial data mining is investigated with its inherent limits in terms of processing time, scalability, and real-time applicability, which is often done on a single processor core. On the other hand, a detailed analysis of parallel data mining, made possible by multi-core architectures or distributed computing clusters, is presented. We emphasize the potential advantages of parallel processing, such as more computational resources, faster processing, scalability, and real-time capabilities. The paper explores the nuances of parallel data mining implementation in the context of telecommunications data, highlighting the difficulties and expenses involved in establishing and maintaining a parallel infrastructure. The study examines how quick fraud detection and fraud prevention can be accomplished by utilizing parallel data mining’s real-time capabilities. Real-time applications for fraud prevention include proactive customer service, proactive pricing schemes, network quality monitoring, and personalized advice. Performance parameters, such as accuracy, precision, recall, and F1-score, are tested using real-world telecom datasets for the comparison study. The conclusions of this investigation provide light on the usefulness of serial and parallel methods for predicting client attrition. We also look into how these prediction models’ impact on fraud detection and prevention may spread. In conclusion, this research contributes valuable insights into the practicality and efficacy of serial and parallel data mining approaches for customer churn prediction in telecom, with a specific focus on their implications for fraud detection and prevention. The findings provide a roadmap for telecom companies seeking to optimize their data-driven strategies for customer retention and fraud mitigation in the era of big data and advanced analytics.

In the ever-evolving landscape of the telecommunications industry, where customer churn poses a significant challenge, the role of data mining in predicting and mitigating churn has become paramount. Concurrently, the telecommunications sector is also grappling with the relentless menace of fraud, requiring rapid detection and prevention measures. This research paper presents a comprehensive comparative analysis of serial and parallel data mining approaches for customer churn prediction within the telecom sector. In the first section, we clarify the key approaches and techniques used in data mining for predicting customer turnover, including logistic regression, decision trees, random forests, and neural networks. Serial data mining is investigated with its inherent limits in terms of processing time, scalability, and real-time applicability, which is often done on a single processor core. On the other hand, a detailed analysis of parallel data mining, made possible by multi-core architectures or distributed computing clusters, is presented. We emphasize the potential advantages of parallel processing, such as more computational resources, faster processing, scalability, and real-time capabilities. The paper explores the nuances of parallel data mining implementation in the context of telecommunications data, highlighting the difficulties and expenses involved in establishing and maintaining a parallel infrastructure. The study examines how quick fraud detection and fraud prevention can be accomplished by utilizing parallel data mining’s real-time capabilities. Real-time applications for fraud prevention include proactive customer service, proactive pricing schemes, network quality monitoring, and personalized advice. Performance parameters, such as accuracy, precision, recall, and F1-score, are tested using real-world telecom datasets for the comparison study. The conclusions of this investigation provide light on the usefulness of serial and parallel methods for predicting client attrition. We also look into how these prediction models’ impact on fraud detection and prevention may spread. In conclusion, this research contributes valuable insights into the practicality and efficacy of serial and parallel data mining approaches for customer churn prediction in telecom, with a specific focus on their implications for fraud detection and prevention. The findings provide a roadmap for telecom companies seeking to optimize their data-driven strategies for customer retention and fraud mitigation in the era of big data and advanced analytics.

In the ever-evolving landscape of the telecommunications industry, where customer churn poses a significant challenge, the role of data mining in predicting and mitigating churn has become paramount. Concurrently, the telecommunications sector is also grappling with the relentless menace of fraud, requiring rapid detection and prevention measures. This research paper presents a comprehensive comparative analysis of serial and parallel data mining approaches for customer churn prediction within the telecom sector. In the first section, we clarify the key approaches and techniques used in data mining for predicting customer turnover, including logistic regression, decision trees, random forests, and neural networks. Serial data mining is investigated with its inherent limits in terms of processing time, scalability, and real-time applicability, which is often done on a single processor core. On the other hand, a detailed analysis of parallel data mining, made possible by multi-core architectures or distributed computing clusters, is presented. We emphasize the potential advantages of parallel processing, such as more computational resources, faster processing, scalability, and real-time capabilities. The paper explores the nuances of parallel data mining implementation in the context of telecommunications data, highlighting the difficulties and expenses involved in establishing and maintaining a parallel infrastructure. The study examines how quick fraud detection and fraud prevention can be accomplished by utilizing parallel data mining’s real-time capabilities. Real-time applications for fraud prevention include proactive customer service, proactive pricing schemes, network quality monitoring, and personalized advice. Performance parameters, such as accuracy, precision, recall, and F1-score, are tested using real-world telecom datasets for the comparison study. The conclusions of this investigation provide light on the usefulness of serial and parallel methods for predicting client attrition. We also look into how these prediction models’ impact on fraud detection and prevention may spread. In conclusion, this research contributes valuable insights into the practicality and efficacy of serial and parallel data mining approaches for customer churn prediction in telecom, with a specific focus on their implications for fraud detection and prevention. The findings provide a roadmap for telecom companies seeking to optimize their data-driven strategies for customer retention and fraud mitigation in the era of big data and advanced analytics.

Iceland Authorities Hold Two Suspects Over Bitcoin Mining Theft Indian Cryptocurrency Analysis, Tools, Tutorials, Data And News

This paper discusses data mining--an end-to-end (ETE) data analysis tool that is used by researchers in higher education. It also relates data mining and other software programs to a brand new concept called "Knowledge Management." The paper culminates in the Tier Knowledge Management Model (TKMM), which seeks to provide a stable structure with which to organize the plethora of established and nascent technologies. Data mining is a knowledge discovery process to reveal patterns and relationships in data via high-powered data modeling procedures. The field is in the process of being harmonized with statistics to provide researchers with a richer and more unified palate of analysis tools. The birth of data mining, however, has not completed the road map for research in higher education. With the development in data warehousing and data mining, the landscape for knowledge management has greatly changed. After extensive research and based on actual experience, a model for managing knowledge for research and planning is proposed to be the Tiered Knowledge Management Model (TKMM). A roadmap like TKMM may help guide the efforts for researchers to update their skills and choose the right tool. For example, the Project Management model explains what tool is best for which project. Addendum describes five steps to successful data mining. (Contains 13 references.) (JA)

This paper discusses data mining--an end-to-end (ETE) data analysis tool that is used by researchers in higher education. It also relates data mining and other software programs to a brand new concept called "Knowledge Management." The paper culminates in the Tier Knowledge Management Model (TKMM), which seeks to provide a stable structure with which to organize the plethora of established and nascent technologies. Data mining is a knowledge discovery process to reveal patterns and relationships in data via high-powered data modeling procedures. The field is in the process of being harmonized with statistics to provide researchers with a richer and more unified palate of analysis tools. The birth of data mining, however, has not completed the road map for research in higher education. With the development in data warehousing and data mining, the landscape for knowledge management has greatly changed. After extensive research and based on actual experience, a model for managing knowledge for research and planning is proposed to be the Tiered Knowledge Management Model (TKMM). A roadmap like TKMM may help guide the efforts for researchers to update their skills and choose the right tool. For example, the Project Management model explains what tool is best for which project. Addendum describes five steps to successful data mining. (Contains 13 references.) (JA)

This paper discusses data mining--an end-to-end (ETE) data analysis tool that is used by researchers in higher education. It also relates data mining and other software programs to a brand new concept called "Knowledge Management." The paper culminates in the Tier Knowledge Management Model (TKMM), which seeks to provide a stable structure with which to organize the plethora of established and nascent technologies. Data mining is a knowledge discovery process to reveal patterns and relationships in data via high-powered data modeling procedures. The field is in the process of being harmonized with statistics to provide researchers with a richer and more unified palate of analysis tools. The birth of data mining, however, has not completed the road map for research in higher education. With the development in data warehousing and data mining, the landscape for knowledge management has greatly changed. After extensive research and based on actual experience, a model for managing knowledge for research and planning is proposed to be the Tiered Knowledge Management Model (TKMM). A roadmap like TKMM may help guide the efforts for researchers to update their skills and choose the right tool. For example, the Project Management model explains what tool is best for which project. Addendum describes five steps to successful data mining. (Contains 13 references.) (JA)

This paper discusses data mining--an end-to-end (ETE) data analysis tool that is used by researchers in higher education. It also relates data mining and other software programs to a brand new concept called "Knowledge Management." The paper culminates in the Tier Knowledge Management Model (TKMM), which seeks to provide a stable structure with which to organize the plethora of established and nascent technologies. Data mining is a knowledge discovery process to reveal patterns and relationships in data via high-powered data modeling procedures. The field is in the process of being harmonized with statistics to provide researchers with a richer and more unified palate of analysis tools. The birth of data mining, however, has not completed the road map for research in higher education. With the development in data warehousing and data mining, the landscape for knowledge management has greatly changed. After extensive research and based on actual experience, a model for managing knowledge for research and planning is proposed to be the Tiered Knowledge Management Model (TKMM). A roadmap like TKMM may help guide the efforts for researchers to update their skills and choose the right tool. For example, the Project Management model explains what tool is best for which project. Addendum describes five steps to successful data mining. (Contains 13 references.) (JA)

This article is from Journal of Research in Medical Sciences : The Official Journal of Isfahan University of Medical Sciences , volume 19 . Abstract Background:: selecting the correct statistical test and data mining method depends highly on the measurement scale of data, type of variables, and purpose of the analysis. Different measurement scales are studied in details and statistical comparison, modeling, and data mining methods are studied based upon using several medical examples. We have presented two ordinal–variables clustering examples, as more challenging variable in analysis, using Wisconsin Breast Cancer Data (WBCD). Ordinal-to-Interval scale conversion example:: a breast cancer database of nine 10-level ordinal variables for 683 patients was analyzed by two ordinal-scale clustering methods. The performance of the clustering methods was assessed by comparison with the gold standard groups of malignant and benign cases that had been identified by clinical tests. Results:: the sensitivity and accuracy of the two clustering methods were 98% and 96%, respectively. Their specificity was comparable. Conclusion:: by using appropriate clustering algorithm based on the measurement scale of the variables in the study, high performance is granted. Moreover, descriptive and inferential statistics in addition to modeling approach must be selected based on the scale of the variables.

This article is from Journal of Research in Medical Sciences : The Official Journal of Isfahan University of Medical Sciences , volume 19 . Abstract Background:: selecting the correct statistical test and data mining method depends highly on the measurement scale of data, type of variables, and purpose of the analysis. Different measurement scales are studied in details and statistical comparison, modeling, and data mining methods are studied based upon using several medical examples. We have presented two ordinal–variables clustering examples, as more challenging variable in analysis, using Wisconsin Breast Cancer Data (WBCD). Ordinal-to-Interval scale conversion example:: a breast cancer database of nine 10-level ordinal variables for 683 patients was analyzed by two ordinal-scale clustering methods. The performance of the clustering methods was assessed by comparison with the gold standard groups of malignant and benign cases that had been identified by clinical tests. Results:: the sensitivity and accuracy of the two clustering methods were 98% and 96%, respectively. Their specificity was comparable. Conclusion:: by using appropriate clustering algorithm based on the measurement scale of the variables in the study, high performance is granted. Moreover, descriptive and inferential statistics in addition to modeling approach must be selected based on the scale of the variables.

This article is from Hepatitis Monthly , volume 13 . Abstract Background: Chronic HCV represents one of the common causes of chronic liver disease worldwide with Egypt having the highest prevalence, namely genotype 4. Interleukin IL-28B gene polymorphism has been shown to relate to HCV treatment response, mainly in genotype1.Objectives: We aim to evaluate the predictive power of the rs12979860 IL28B SNP and its protein for treatment response in genotype 4 Egyptian patients by regression analysis and decision tree analysis.Patients and Methods: The study included 263 chronic HCV Egyptian patients receiving peg-interferon and ribavirin therapy. Patients were classified into 3 groups; non responders (83patients), relapsers (76patients) and sustained virological responders (104 patients). Serum IL 28 B was performed, DNA was extracted and analyzed by direct sequencing of the SNP rs 12979860 of IL28B gene.Results: CT, CC and TT represented 56 %, 25 % and 19% of the patients, respectively. Absence of C allele (TT genotype) was significantly correlated with the early failure of response while CC was associated with sustained virological response. The decision tree showed that baseline alpha fetoprotein (AFP ≤ 2.68 ng/ml) was the variable of initial split (the strongest predictor of response) confirmed by regression analysis. Patients with TT genotype had the highest probability of failure of response.Conclusions: Absence of the C allele was significantly associated with failure of response. The presence of C allele was associated with a favorable outcome. AFP is a strong baseline predictor of HCV treatment response. A decision tree model is useful for predicting the probability of response to therapy.

Perspective Data Analysis and Mining Algorithm for Interior Art Design from the Perspective of Virtual Metadata-Assisted 3D Modeling

Perspective Data Analysis and Mining Algorithm for Interior Art Design from the Perspective of Virtual Metadata-Assisted 3D Modeling

Data analysis and data mining are concerned with unsupervised pattern finding and structure determination in data sets. The data sets themselves are explicitly linked as a form of representation to an observational or otherwise empirical domain of interest. "Structure" has long been understood as symmetry which can take many forms with respect to any transformation, including point, translational, rotational, and many others. Beginning with the role of number theory in expressing data, we show how we can naturally proceed to hierarchical structures. We show how this both encapsulates traditional paradigms in data analysis, and also opens up new perspectives towards issues that are on the order of the day, including data mining of massive, high dimensional, heterogeneous data sets. Linkages with other fields are also discussed including computational logic and symbolic dynamics. The structures in data surveyed here are based on hierarchy, represented as p-adic numbers or an ultrametric topology.

Today the world has already acknowledged as a global village by the inter-net which has technologically evolved into a significant performance instrument for individuals, businesses, and countries seeking to achieve betterment. This study is based on data mining techniques to predict the satisfaction level of internet users from the context of Bangladesh. After conducting a public survey with 18 questions, we were able to acquire 451 responses from participants. Data for user satisfaction was associated with end-user characteristics including certain getting high speed, internet packages, cable type of Wi-Fi connection with targeting various age groups and occupations. The research's most key conceptual breakthrough was the reliability of magnitude predictions of user satisfaction level based on their experience with internet use. The empirical findings indicate that people in Bangladesh have high expectations in existing internet technology, and they are very dissatisfied with their facilities of internet use and to measure satisfaction level related with monthly limit of the Wi-Fi packages and the elements affecting internet speed. Several classifier models were applied to our dataset and among them, Random Forest (RF) performance reaches the top position with 91.53% accuracy.