Mining Procedures Research Articles

Intrusion Detection System Based on Evolving Rules for Wireless Sensor Networks

Human care services, as one of the classical Internet of things applications, enable various kinds of things to connect with each other through wireless sensor networks (WSNs). Owing to the lack of physical defense devices, data exchanged through WSNs such as personal information is exposed to malicious attacks. Therefore, intrusion detection is urgently needed to actively defend against such attacks. Intrusion detection as a data mining procedure cannot control the size of rule sets and distinguish the similarity between normal and intrusion network behaviors. Therefore, in this paper, an evolving mechanism is introduced to extract the rules for intrusion detection. To extract diversified rules as well as control the quantity of rulesets, the extracted rules are examined according to the distance between the rules in the rule set of the same class and the rules in the rule set of different classes. Thereby, it alleviates the problem that the quantity of rules expands unexpectedly with the evolving genetic network programming. The simulations are conducted on a benchmark intrusion dataset, and the results show that the proposed method provides an effective solution to evolve the class association rules and improves the intrusion detection performance.

Strength Characteristics and the Reaction Mechanism of Stone Powder Cement Tailings Backfill

Stone powder cement (SPC) is widely used as a novel cement substitute material in concrete for its good gelling performance and low cost. In order to reduce the backfilling cost and assess the potential of SPC backfilling materials, a series of experiments were conducted to analyze the strength and hydration reaction mechanism of stone powder cement tailings backfill (SPCTB). The analysis was based on SPC and tailings, which were used as the gelling agent and the aggregate, respectively. The results showed that the strength of the backfill was greatly reduced at an early stage and slightly reduced in the final stages. The stone powder content was less than 15%, which met the requirement of mining procedure. The addition of stone powder reduced the content of adsorbed water and capillary water in the early stages, while it increased in the middle stages. The SiO2 contained in stone powder reacted with the hydration products at later stages, which is the reason why the growth of strength is rapid between the groups with the addition of stone powder. The addition of stone powder improved the microstructure of backfill and produced a denser three‐dimensional (3D) network structure; however, the plane porosities of Groups A and B gradually increased with the increase in the content of stone powder. The cement powder mixed appropriately with the stone power could meet the strength requirement and reduce the cost of backfilling materials.

The use of additional evidence in mining usercreated descriptions for content structural design

The use of a text mining approach for full automatic taxonomy creation for content management has proven with serious limitations. The high level semantics indicating relevant association of entities among the documents are often not explored. This study introduces a feasible method that allows identifying high level semantics into text mining procedures while providing for appropriate levels of document descriptions to support access and discoverability. Due to the effectiveness of categorization and adequacy of the structure created can be better determined by humans who are familiar to the documents, qualitative inquiry rather than a purely experimental design was applied. The study collected the data and run the text mining analysis with text analysis, clustering and topic extraction. Two examples show how to develop a faceted classification structure to support digital collection access and navigation using the method. The study indicates that the text-mining method supports taxonomy creation with more efficiency and accuracy when human domain and application knowledge are captured during data collection and text mining processing. The proposed method of taxonomy creation would support the creation of new knowledge.

Intelligent Web-based English E-learning tutor with Data Mining technique

With the transmission of instructive information mining field, it is in effect progressively associated with various research ranges, for example, versatile and smart electronic mentors, savvy instructive applications and other pleasing on the web instructive information mining frameworks. The utilizations of instructive information mining consider the framework scholarly perspectives, the scholastic foundation, and the learner's order. This paper proposes another versatile e-learning framework. The proposed framework incorporates an outstanding canny electronic English e-learning mentor with information mining procedures. Likewise, the information mining strategies are utilized as a part of request to bunch understudies’ learning styles as per Jackson's learning styles. A definitive objective of the proposed framework is to decide the best showing pattern for every learner. The proposed system can be made available through the web everywhere as well as Every Time (EWET). It also offers adaptive facilities such as learning videos, adaptive presentations, and quizzes for the students. Moreover, it helps both teachers and students to follow the best learning process and achieve the highest academic rates. The results show that the highest student's achievement pattern is the pattern (Speaking-Reading-Grammar-Writing) with score of at least 87.4%.

Applying Decision Tree for Prognosis of Diabetes Mellitus

The main aim of this work is to design an efficient data mining procedure for prognosis of diabetes by extracting knowledge form the historical medical records. The data was obtained from leading diabetic diagnostic centres of Srinagar (J&K). The data set obtained contains the record of almost all age groups of population. The main focus was on type 2 diabetes, as it is the most common type affecting nearly 90% of the diagnosed population. The data set contained record of 734 patients. After proper scrutiny of the data, decision tree classifier was applied on it using Waikato environment for knowledge analysis's J48 decision tree classifier to develop model. The model achieved an accuracy of 92.5068%.

Brand strategies in social media in hospitality and tourism

PurposeThis paper aims to present an automated literature analysis to unveil the drivers for incorporating social media in tourism and hospitality brand strategies.Design/methodology/approachTo gather relevant literature, Google Scholar was queried with “brand”/“branding” and “social media” for articles in ten top-ranked tourism and hospitality journals, resulting in a total of 479 collected articles. The methodology adopted for the analysis is based on text mining and topic modeling procedures. The topics discovered are characterized by terms belonging to a dictionary previously compiled and provide a segmentation of the articles in coherent sets of the literature.FindingsMost of the 213 articles that encompass a strong relation between social media and branding are mentioning mainly brand building stages. A large research gap was found in hospitality and tourism considering that, besides advertising, no topic was discovered related to known brand strategies such as co-branding or franchising.Practical implicationsThe present analysis concludes that specialized tourism and hospitality literature needs to keep pace with research that is being conducted on a wide range of industries to assess the influence of social media.Originality/valueThe automated analysis approach used has no precedent in tourism and hospitality research. By including an innovative topical concept map, it led to identifying and summarizing the topics, providing a clear picture on the findings. This study calls for research by specialized tourism and hospitality publications, eventually leading to special issues on this vibrant subject.

Prediction of heart disease using apache spark analysing decision trees and gradient boosting algorithm

Numerous destructive things influence the working arrangement of human body as hypertension, smoking, obesity, inappropriate medication taking which causes many contrasting diseases as diabetes, thyroid, strokes and coronary diseases. The impermanence and horribleness of the environment situation is also the reason for the coronary disease. The structure of Apache start relies on the evolution which requires gathering of the data. To break down the significance of use programming focused on data structure the Apache stop ought to be utilized and it gives various central focuses as it is fast in light as it uses memory worked in preparing. Apache Spark continues running on dispersed environment and chops down the data in bunches giving a high profitability rate. Utilizing mining procedure as a part of the determination of coronary disease has been exhaustively examined indicating worthy levels of precision. Decision trees, Neural Network, Gradient Boosting Algorithm are the various apache spark proficiencies which help in collecting the information.

Fully Mechanized Longwall Mining in an Underground Bauxite Deposit: A Case Study

Based on the boundary surfaces of bauxite deposit modelled by the two-dimensional biharmonic spline interpolation, it was ascertained that the fully mechanized longwall mining of bauxite is feasible in terms of lithological characteristics, orebed occurrence, physical–mechanical properties of ore–rock, faults, Karst caves, permeable fissures, etc. Furthermore, a comprehensive target function consisting of waste-rock interfused ratio and ore loss ratio was used to obtain the optimal cutting height of shearer by a virtual mining procedure that was proposed to numerically perform the fully mechanized longwall mining. For the 1102 longwall mining panel in Wachangping Bauxite Mine, the optimal cutting height of shearer ranges from 1.16 to 2.32 m supplied from two 1.16-m-diameter cutting drums, which meets the requirement of low-waste and high-recovery mining to achieve high-grade and low-loss exploitations of underground bauxite. These efforts can improve the application and popularization of fully mechanized longwall mining in non-coal underground mines.

Identification of future signal based on the quantitative and qualitative text mining: a case study on ethical issues in artificial intelligence

To foresee the advent of new technologies and their socio-economic impact is a necessity for academia, governments and private enterprises as well. In the future studies, the identification of future signal is one of the renowned techniques for analysis of trends, emerging issue, and gaining future insights. In the Big Data era, recent scholars have proposed using a text mining procedure focusing upon web data such as new social media and academic papers. However, the detection of future signals is still under a developing area of research, and there is much to improve existing methodology as well as developing theoretical foundations. The present study reviews previous literature on identifying emerging issue based on the weak signal detection approach. Then the authors proposed a revised framework that incorporate quantitative and qualitative text mining for assessing the strength of future signals. The authors applied the framework to the case study on the ethical issues of artificial intelligence (hereafter AI). From EBSCO host database, the authors collected text data covering the ethical issues in AI and conducted text mining analysis. Results reveal that emerging ethical issues can be classified as strong signal, weak signal, well-known but not so strong signal, and latent signal. The revised methodology will be able to provide insights for government and business stakeholders by identifying the future signals and their meanings in various fields.

Analysis of Risk Factors for Ganciclovir-induced Neutropenia Using a Data Mining Procedure

Analysis of Risk Factors for Ganciclovir-induced Neutropenia Using a Data Mining Procedure

Ancestors\u2019 dietary patterns and environments could drive positive selection in genes involved in micronutrient metabolism\u2014the case of cofactor transporters

BackgroundDuring evolution, humans colonized different ecological niches and adopted a variety of subsistence strategies that gave rise to diverse selective pressures acting across the genome. Environmentally induced selection of vitamin, mineral, or other cofactor transporters could influence micronutrient-requiring molecular reactions and contribute to inter-individual variability in response to foods and nutritional interventions.MethodsA comprehensive list of genes coding for transporters of cofactors or their precursors was built using data mining procedures from the HGDP dataset and then explored to detect evidence of positive genetic selection. This dataset was chosen since it comprises several genetically diverse worldwide populations whom ancestries have evolved in different environments and thus lived following various nutritional habits and lifestyles.ResultsWe identified 312 cofactor transporter (CT) genes involved in between-cell or sub-cellular compartment distribution of 28 cofactors derived from dietary intake. Twenty-four SNPs distributed across 14 CT genes separated populations into continental and intra-continental groups such as African hunter-gatherers and farmers, and between Native American sub-populations. Notably, four SNPs were located in SLC24A3 with one being a known eQTL of the NCKX3 protein. ConclusionsThese findings could support the importance of considering individual’s genetic makeup along with their metabolic profile when tailoring personalized dietary interventions for optimizing health.

Liquefaction Prediction Using Rough Set Theory

Evaluation of liquefaction is one of the most important issues in geotechnical engineering. Liquefaction prediction depends on many factors and the relationship between these factors is non-linear and complex. Different methods have been proposed by different authors for liquefaction prediction. These methods are mostly based on statistical approaches and neural network.In this paper a new approach based on Rough Set data mining procedure is presented for liquefaction prediction. The Rough set theory is a mathematical approach for analysis of imperfect knowledge or unclear description of objects. In this approach the decision rules are derived from conditional attributes in Rough Set analysis and the results are compared with actual field observations. The results of this study indicate that using this method can be helpful for liquefaction prediction and can reduce unnecessary costs in site investigation process.

Tweeting about public transit — Gleaning public perceptions from a social media microblog

Capturing public perceptions regarding transit systems is an essential part of creating a just and equitable service. Gathering such perceptions also holds the potential for identifying ways of increasing ridership and for identifying sources of transport-related social exclusion. Traditionally, these perceptions have been obtained from public participatory forums or via surveys on users’ opinions. However, increasingly, the use of alternative methods of obtaining public perceptions has included media and social media sources. Twitter is one such social media source and it is used in this research to obtain public opinions regarding a bus rapid transit system (BRT). Rather than relying upon purely automated data mining techniques for analyzing Twitter feeds, a traditional qualitative research design is used featuring a two-step process. First, a text mining procedure is used on the Twitter feeds filtered out by keywords relevant to the transit system, and secondly, a structured content analysis is applied to the entries. This method is applied to a case study on a Bus Rapid Transit system in Cali, Colombia. The results highlight concerns with safety, problems with the system’s infrastructure, and behavioral issues on the bus as primary points of discussion.

A data mining approach for identifying pathway-gene biomarkers for predicting clinical outcome: A case study of erlotinib and sorafenib.

A novel data mining procedure is proposed for identifying potential pathway-gene biomarkers from preclinical drug sensitivity data for predicting clinical responses to erlotinib or sorafenib. The analysis applies linear ridge regression modeling to generate a small (N~1000) set of baseline gene expressions that jointly yield quality predictions of preclinical drug sensitivity data and clinical responses. Standard clustering of the pathway-gene combinations from gene set enrichment analysis of this initial gene set, according to their shared appearance in molecular function pathways, yields a reduced (N~300) set of potential pathway-gene biomarkers. A modified method for quantifying pathway fitness is used to determine smaller numbers of over and under expressed genes that correspond with favorable and unfavorable clinical responses. Detailed literature-based evidence is provided in support of the roles of these under and over expressed genes in compound efficacy. RandomForest analysis of potential pathway-gene biomarkers finds average treatment prediction errors of 10% and 22%, respectively, for patients receiving erlotinib or sorafenib that had a favorable clinical response. Higher errors were found for both compounds when predicting an unfavorable clinical response. Collectively these results suggest complementary roles for biomarker genes and biomarker pathways when predicting clinical responses from preclinical data.

PREDICTING NORMATIVE AND PROBLEMATIC FAMILY PATHWAYS TO THE TRANSITION TO SIBLINGHOOD: COMMENTARY ON VOLLING ET AL.'S MONOGRAPH

Volling et al.'s monograph provides a rich, thoughtful, and rigorous account of how the transition to siblinghood is experienced by the first-born child and the family. In their comprehensive longitudinal study, they followed 241 families from the prenatal period before the second-born's birth until this child was 12-months old. Siblings are a critical, but understudied, relationship in children's development; the challenges posed in researching sibling dynamics in the context of the family are discussed. Prior psychodynamic and developmental research literature is critiqued, which places the current study into perspective and indicates the important theoretical frameworks (i.e., developmental psychopathology and developmental ecological systems) employed by Volling et al. to advance our understanding of this critical transition in the life of the family. The longitudinal study design, sample characteristics, identification of possible trajectories of adjustment (or not) to the birth of the sibling, and selection of family and child variables are addressed. The sophisticated statistical methods (Growth Mixture Modeling and data mining procedures) employed to predict child adjustment in association with parenting variables over time and sibling relationship quality at 12 months identified low- and high-risk trajectories on the seven subscales of the Child Behavior Check List (CBCL). This afforded a nuanced investigation of a variety of potentially problematic child behaviors (e.g., aggression, withdrawal, negative emotionality, somatic problems) in association with parenting behaviors. A final discussion included study limitations, significant strengths, and implications for clinicians and other professionals. The study's conclusion is that most children and families are resilient, take the birth of a sibling in their stride, and do not exhibit empirical evidence of a developmental crisis, as argued by earlier psychodynamic authors.

I. INTRODUCTION: UNDERSTANDING THE TRANSITION TO SIBLINGHOOD FROM A DEVELOPMENTAL PSYCHOPATHOLOGY AND ECOLOGICAL SYSTEMS PERSPECTIVE.

The birth of an infant sibling is a common occurrence in the lives of many toddler and preschool children. Early childhood is also a time for the emergence of disruptive behavior problems that may set the stage for later problem behaviors. The current study examined individual differences in young children’s behavioral and emotional adjustment after the birth of a sibling in an effort to uncover developmental trajectories reflecting sudden and persistent change (maladaptation), adjustment and adaptation (resilience), gradual linear increases, and no change (stability and continuity). Growth mixture modeling (GMM) was conducted with a sample of 241 families expecting their second child using a longitudinal research design across the first year after the sibling’s birth (prenatal, 1, 4 8 and 12 months) on seven syndrome scales of the Child Behavior Checklist (CBCL 1.5–5:(Achenbach & Rescorla, 2000): aggression, attention problems, anxiety/depression, emotional reactivity, withdrawal, somatic complaints, and sleep problems. For all scales, multiple classes describing different trajectory patterns emerged that reflected predominantly intercept differences; children high on problem behavior after the birth were those high before the birth. There was no evidence of a sudden, persistent maladaptive response indicating children underwent a developmental crisis for any of the problem behaviors examined. Most children were low on all problem behaviors examined and showed little change or actually declined in problem behaviors over time, although some children did experience more pronounced changes in the borderline clinical or clinical range. Only in the case of aggressive behavior was there evidence of an Adjustment and Adaptation Response showing a sudden change (prenatal to 1 month) that subsided by 4 months, suggesting that some young children react to stressful life events but adapt quickly to these changing circumstances. Further, children’s withdrawal revealed a curvilinear, quadratic path, suggesting children both increased and decreased in their withdrawal over time. Guided by a developmental ecological systems framework, we employed data mining procedures to uncover the child, parent, and family variables that best discriminated the different trajectory classes and found that children’s temperament, coparenting, parental self-efficacy, and parent-child attachment relationships were prominent in predicting children’s adjustment after the birth of an infant sibling. Finally, when trajectory classes were used to predict sibling relationship quality at 12 months, children high on aggression, attention problems, and emotional reactivity in the year after the birth engaged in more conflict and less positive involvement with the infant sibling at the end of the first year.

Big Data in Experimental Mechanics and Model Order Reduction: Today’s Challenges and Tomorrow’s Opportunities

Since the turn of the century experimental solid mechanics has undergone major changes with the generalized use of images. The number of acquired data has literally exploded and one of today’s challenges is related to the saturation of mining procedures through such big data sets. With respect to digital image/volume correlation one of tomorrow’s pathways is to better control and master this data flow with procedures that are optimized for extracting the sought information with minimum uncertainties and maximum robustness. In this paper emphasis is put on various hierarchical identification procedures. Based on such structures a posteriori model/data reductions are performed in order to ease and make the exploitation of the experimental information far more efficient. Some possibilities related to other model order reduction techniques like the proper generalized decomposition are discussed and new opportunities are sketched.

Big Data: Technologies, Challenges, Data Analytics and Management: A Review

Internet assumes an essential part in giving different learning sources to the world, which encourages numerous applications to give quality support of the customers. As the years go on the web is over-burden with parcel of data and it turns out to be difficult to extricate the applicable data from the web. This offers path to the advancement of the Big Data and the volume of the information continues expanding quickly step by step. Enormous Data has increased much consideration from the scholarly world and the IT business. In the advanced and figuring world, data is produced and gathered at a rate that quickly surpasses the limit go. Data mining procedures are utilized to locate the concealed data from the huge information. This Technique is utilized store, oversee, and investigate high speed of information and this information can be in any shape organized or unstructured frame. It is hard to handle substantial volume of information utilizing information base strategy like RDBMS. From one perspective, Big Data is amazingly important to deliver efficiency in organizations and transformative achievements in logical controls, which give us a considerable measure of chances to make incredible advances in many fields. There is most likely the future rivalries in business profitability and advances will without a doubt merge into the Big Data investigations. Then again, Big Data likewise emerges with many difficulties, for example, troubles in information catch, information stockpiling, information investigation and information perception. In this paper we concentrate on the audit of Big Data, its information order techniques and the way it can be mined utilizing different mining strategies.

A Survey on Privacy Preserving Data mining

Data mining objectives to seek out valuable patterns from big quantity of data. These patterns signify information and are conveyed in clusters or organization ideas. The abilities learned through fully knowledge mining approaches may contain confidential information about persons or trade. Upkeep of secrecy is a gigantic aspect of information mining also as a result be taught of attaining some information mining ambitions without dropping the secrecy of the individuals .The assessment of privacy preserving data mining (PPDM) algorithms must don’t forget the penalties of those algorithms in mining the outcome along with retaining privacy. Inside the constraints of privateness, a couple of ways have been introduced however nonetheless this branch of exploration is in its early life .The success of privateness preserving data mining procedures is measured in phrases of its efficiency, data utility, degree of uncertainty or resistance to data mining procedures and so on. Nevertheless no privateness maintaining algorithm exists that outperforms all others on all feasible standards. Rather, an algorithm could participate in better than one other on one exact criterion. So, the aim of this paper is to show the current situation of privacy preserving knowledge mining framework and tactics.

A comprehensive cluster and classification mining procedure for daily stock market return forecasting

Data mining and big data analytic techniques are playing an important role in many application fields, including the financial markets. However, only few studies have focused on predicting daily stock market returns, and among these studies, the data mining procedures utilized are either incomplete or inefficient. This paper presents a comprehensive data mining process to forecast the daily direction of the S&P 500 Index ETF (SPY) return based on 60 financial and economical features. The fuzzy c-means method (FCM) is initially used to cluster the preprocessed data. A principal component analysis (PCA) is applied next to the entire data set and each of seven clusters. The dimension of the entire cleaned data set is then reduced according to the combining results from the entire data set and each cluster. Corresponding to different levels of the dimensionality reduction, twelve new data sets are generated from the entire cleaned data. Artificial neural networks (ANNs) and logistic regression models are then used with the twelve transformed data sets for classification in order to forecast the daily direction of future market returns and indicate the efficiency of dimensionality reduction with PCA. A group of hypothesis tests are performed over the classification and simulation results to show that the ANNs give significantly higher classification accuracy than logistic regression, and that the trading strategies guided by the comprehensive cluster and classification mining procedure based on PCA and ANNs gain higher risk-adjusted profits than the comparison benchmarks, as well as those strategies guided by the forecasts based on PCA and logistic regression models.