In order to correct the deficiencies of intrusion detection technology, the entire computer and network security system are needed to be more perfect. This work proposes an improved k-means algorithm and an improved Apriori algorithm applied in data mining technology to detect network intrusion and security maintenance. The classical KDDCUP99 dataset has been utilized in this work for performing the experimentation with the improved algorithms. The algorithm’s detection rate and false alarm rate are compared with the (...) experimental data before the improvement. The outcomes of proposed algorithms are analyzed in terms of various simulation parameters like average time, false alarm rate, absolute error as well as accuracy value. The results show that the improved algorithm advances the detection efficiency and accuracy using the designed detection model. The improved and tested detection model is then applied to a new intrusion detection system. After intrusion detection experiments, the experimental results show that the proposed system improves detection accuracy and reduces the false alarm rate. A significant improvement of 90.57% can be seen in detecting new attack type intrusion detection using the proposed algorithm. (shrink)

Scalability, efficiency, and security had been a persistent problem over the years in data mining, several techniques had been proposed and implemented but none had been able to solve the problem of scalability, efficiency and security from cloud computing. In this research, we solve the problem scalability, efficiency and security in data mining over cloud computing by using a restful web services and combination of different technologies and tools, our model was trained by (...) using different machine learning algorithm, and finally using different validation and cross-validation methods to compare and validate our results. We build, train, test and deploy our model by restful API services which can be called through an endpoint, and with very high accuracy and huge success. (shrink)

This study explored the global cyberspace security issues, with the purpose of breaking the stereotype of people’s cognition of cyberspace problems, which reflects the relationship between interdependence and association. Based on the Apriori algorithm in association rules, a total of 181 strong rules were mined from 40 target websites and 56,096 web pages were associated with global cyberspace security. Moreover, this study analyzed support, confidence, promotion, leverage, and reliability to achieve comprehensive coverage of data. A total of (...) 15,661 sites mentioned cyberspace security-related words from the total sample of 22,493 professional websites, accounting for 69.6%, while only 735 sites mentioned cyberspace security-related words from the total sample of 33,603 non-professional sites, accounting for 2%. Due to restrictions of language, the number of samples of target professional websites and non-target websites is limited. Meanwhile, the number of selections of strong rules is not satisfactory. Nowadays, the cores of global cyberspace security issues include internet sovereignty, cyberspace security, cyber attack, cyber crime, data leakage, and data protection. (shrink)

In recent years, the network has become more complex, and the attacker’s ability to attack is gradually increasing. How to properly understand the network security situation and improve network security has become a very important issue. In order to study the method of extracting information about the security situation of the network based on cloud computing, we recommend the technology of knowledge of the network security situation based on the data extraction technology. It converts each (...) received cyber security event into a standard format that can be defined as multiple brochures, creating a general framework for the cyber security situation. According to the large nature of network security situation data, the Hadoop platform is used to extract aggregation rules, and perform model extraction, pattern analysis, and learning on a network security event dataset to complete network security situation rule mining, and establish a framework for assessing the state of network security. According to the results of the federal rule extraction, the level of network node security risk is obtained in combination with signal reliability, signal severity, resource impact, node protection level, and signal recovery factor. A simulation test is performed to obtain the intrusion index according to the source address of the network security alarm. Through the relevant experiments and analysis of the results, the attack characteristics obtained in this study were obtained after manually reducing the network security event in the 295 h window. The results show that after the security event is canceled, the corresponding window attack index decreases to 0, indicating that this method can effectively implement a network security situation awareness. The proposed technique allows you to accurately sense changes in network security conditions. (shrink)

Due to the complexity and versatility of network security alarm data, a cloud-based network security data extraction method is proposed to address the inability to effectively understand the network security situation. The information properties of the situation are generated by creating a set of spatial characteristics classification of network security knowledge, which is then used to analyze and optimize the processing of hybrid network security situation information using cloud computing technology and co-filtering technology. (...) Knowledge and information about the security situation of a hybrid network has been analyzed using cloud computing strategy. The simulation results show that a cyber security crash occurs in window 20, after which the protection index drops to window 500. The increase in the security index of 500 windows is consistent with the effectiveness of the concept of this document method, indicating that this document method can sense changes in the network security situation. Starting from the first attacked window, the defense index began to decrease. In order to simulate the added network defense, the network security events in the 295th time window were reduced in the original data, and the defense index increased significantly in the corresponding time period, which is consistent with the method perception results, which further verifies the effectiveness and reliability of this method on the network security event perception. This method provides high-precision knowledge of network security situations and improves the security and stability of cloud-based networks. (shrink)

Recently, there has been a heavy debate in the US about the government’s use of data mining in its fight against terrorism. Privacy concerns in fact led the Congress to terminate the funding of TIA, a program for advanced information technology to be used in the combat of terrorism. The arguments put forward in this debate, more specifically those found in the main report and minority report by the TAPAC established by the Secretary of Defense to examine the (...) TIA issue, will be analysed to trace the deeper roots of this controversy. This analysis will in turn be used as a test case to examine the adequacy of the usual theoretical frameworks for these kinds of issues, in particular the notion of privacy. Whereas the dominant theoretical framing of the notion of privacy turns around access to information, most of the core arguments in the debate do not fit in this kind of framework. The basic disagreements in the controversy are not about mere access, they involve both access and use. Furthermore, whereas the issue of access by itself refers to a more or less static situation, the real disagreements much more concern the organisational dynamics of the use of information, the mechanisms in the organisation that control these dynamics, and the awareness present within the organisation of the ‘social risks’ these dynamics represent. The bottom line question is whether the assessment of these gives sufficient reason for trust. (shrink)

Guided by the theories of system theory, synergetic theory, and other disciplines and based on fuzzy data mining algorithm, this article constructs a three-tier social security fund cloud audit platform. Firstly, the article systematically expounds the current situation of social security fund and social security fund audit, such as the technical basis of cloud computing and data mining. Combined with the actual work, the necessity and feasibility of building a cloud audit platform for (...) social security funds are analyzed. This article focuses on the construction of the cloud audit platform for social security funds. The general idea of using fuzzy data mining algorithm to build the social security fund audit cloud platform is to compress the knowledge contained in a large number of data into the weights between nodes and optimize the weights through the learning of the neural network system. Through the optimization function, the information contained in the neural network is stored in a few weights as far as possible. The main information is further highlighted by network clipping and removing weights that have little impact on the output. (shrink)

The power grid is an important connection between power sources and users, responsible for supplying and distributing electric energy to users. Modern power grids are widely distributed and large in scale, and their security faces new problems and challenges. Information entropy theory is an objective weighting method that compares the information order of each evaluation index to judge the weight value. With the wide application of entropy theory in various disciplines, the subject of introducing entropy into the power system (...) has been gradually concerned. This article aims to study the smart terminal security technology of the power grid perception layer based on information entropy data mining. This article analyzes its related methods and designs a smart terminal for the power grid. On this basis, a data analysis platform is built and a safety plan is designed. The result is that the average absolute error, root mean square error, average absolute percentage error, and mean square error of the platform's power load forecast are 1.58, 1.96, 8.2%, and 3.93, respectively. These error values are within the ideal range, and the data processing ability is strong. The packet loss rate of the adversary's eavesdropping was tested, and the average packet loss rates at locations a, b, c, and d were 1.05, 1.2, 1.81, and 2.2%, respectively. Data packets will be definitely lost, so the platform is highly secure. (shrink)

This chapter addresses the morality of two types of national security electronic surveillance (SIGINT) programs: the analysis of communication “metadata” and dragnet searches for keywords in electronic communication. The chapter develops a standard for assessing coercive government action based on respect for the autonomy of inhabitants of liberal states and argues that both types of SIGINT can potentially meet this standard. That said, the collection of metadata creates opportunities for abuse of power, and so judgments about the trustworthiness and (...) competence of the government engaging in the collection must be made in order to decide whether metadata collection is justified in a particular state. Further, the moral standard proposed has a reflexive element justifying adversary states’ intelligence collection against one another. Therefore, high-tech forms of SIGINT can only be justified at the cost of justifying cruder versions of signals intelligence collection practiced by a state’s less-advanced adversaries. (shrink)

Objective To compare assessments of the decision-making competencies of psychiatric inpatients as provided by physicians, nurses, relatives and an assessment tool.Methods This study was carried out at the psychiatry clinic of Kocaeli University Hospital from June 2007 to February 2008. The decision-making competence of the 83 patients who participated in the study was assessed by physicians, nurses, relatives and MacCAT-T.Results Of the 83 patients, the relatives of 73.8% of them, including the parents of 47.7%, were interviewed during the study. A (...) moderately good consistency between the competency assessments of the nurses versus those of the physicians, but a poor consistency between the assessments of the physicians and nurses versus those of the patients’ relatives, was determined. The differences in the competency assessment obtained with the MacCAT-T versus the evaluations of the physicians, nurses and patients’ relatives were statistically significant.Conclusions Our findings demonstrate those physicians, nurses and the patients’ relatives have difficulty in identifying patients lacking decision-making competence. Therefore, an objective competence assessment tool should be used along with the assessments of physicians and nurses, both of whom can provide clinical data, as well as those of relatives, who can offer insights into the patient's moral values and expectations. (shrink)

The paper discusses the problems in the detection of security information in legal and other administrative data. The authors analyse the prospects of the use of datamining in the solution of two key problems: abundance and indirectness of these data. Security research uses two kinds of data. The first one is scientific data, designed and gathered specially for the verification of certain security theories. They are the data of criminological, sociological, psychological surveys, (...) experimental data, etc. The second kind is data that are not designed for security research. Most of the data are the by-products of the national legal system, especially its criminal justice, of national and local institutions responsible for the maintenance of public order, and of other public agencies. All the data provide huge amounts of information destined to control and direct the activities of these institutions. There are two kinds of problems in the use of this information in security study. First, this information has to be ‘decoded’ from data describing the activities of related institutions. Second, data on security are lost in huge amounts of other public data. Thus, they have to be ‘mined’. The paper discusses the prospects of the modern information proceeding method—data-mining in the solution of both problems. A new concept of data mining as a meta-procedure is proposed. The object of this meta-procedure is supposed to be the integration of multiple current theories (for example, criminal security) and related statistical procedures. A general algorithm of such data-mining is proposed. (shrink)

Purpose The purpose of this paper is to review current practice in sharing and mining medical data revealing benefits, costs and ethical issues. Based on stakeholder perspectives and values, the authors create an ethical code to regulate the sharing and mining of medical information. Design/methodology/approach The framework is based on a review of academic, practitioner and legal research. Findings Owing to the inability of current safeguards to protect consumers from risks related to the disclosure of medical information, (...) the authors develop a framework for ethical sharing and mining of medical data, security, transparency, respect, accountability, community and quality, which espouses security, transparency, respect, accountability, community and quality as the basic tenets of ethical data sharing and mining practice. Research limitations/implications The STRACQ framework is an original, previously unpublished contribution that will require modification over time based on discussion and debate within and among the academy, medical community and public policymakers. Social implications The framework for sharing borrows from the Fair Credit Reporting Act, allowing the collection and dissemination of identified medical data but placing strict limitations on use. Following this framework, benefits of shared and mined medical data are freely available with appropriate safeguards for consumer privacy. Originality/value Mandates for adoption of electronic health-care records require an understanding of medical data mining. This paper presents a review of data mining techniques and reasons for engaging in the practice of identifying benefits, costs and ethical issues. The authors create an original framework, STRACQ, for ethical sharing and mining of medical information, allowing knowledge exploration while protecting consumer privacy. (shrink)

The relationship between students and higher education is seen to have become increasingly transactional. We approach the study of the student–HE relationship in a novel way, by focusing on students’ behaviour post-university, rather than on student narratives. Conceptually, the article builds on multidimensional views of student engagement and the differentiation between psychological transactional contracts – where students who achieve better academic results are more likely to donate – and relational contracts – where students donate more following engagement in social experiences. (...) Making use of longitudinal data on donation behaviour from over 50,000 alumni from an English university over two decades, we find that while students who get better degree outcomes are more likely to donate, the association between participation in social experiences and donations is much stronger. This questions prevalent transactional models of HE and underlines the limitations of purely consumerist views of the relation between students and higher education institutions, even in the marketized UK context. (shrink)

From ‘connecting the dots’ and finding ‘the needle in the haystack’ to predictive policing and data mining for counterinsurgency, security professionals have increasingly adopted the language and methods of computing for the purposes of prediction. Digital devices and big data appear to offer answers to a wide array of problems of (in)security by promising insights into unknown futures. This article investigates the transformation of prediction today by placing it within governmental apparatuses of discipline, biopower and (...) big data. Unlike disciplinary and biopolitical governmentality, we argue that prediction with big data is underpinned by the production of a different time/space of ‘between-ness’. The digital mode of prediction with big data reconfigures how we are governed today, which we illustrate through an analysis of how predictive policing actualizes between-ness as hotspots and near-real-time decisions. (shrink)

Increasing numbers of decisions about everyday life are made using algorithms. By algorithms we mean predictive models captured from historical data using data mining. Such models often decide prices we pay, select ads we see and news we read online, match job descriptions and candidate CVs, decide who gets a loan, who goes through an extra airport security check, or who gets released on parole. Yet growing evidence suggests that decision making by algorithms may discriminate people, (...) even if the computing process is fair and well-intentioned. This happens due to biased or non-representative learning data in combination with inadvertent modeling procedures. From the regulatory perspective there are two tendencies in relation to this issue: to ensure that data-driven decision making is not discriminatory, and to restrict overall collecting and storing of private data to a necessary minimum. This paper shows that from the computing perspective these two goals are contradictory. We demonstrate empirically and theoretically with standard regression models that in order to make sure that decision models are non-discriminatory, for instance, with respect to race, the sensitive racial information needs to be used in the model building process. Of course, after the model is ready, race should not be required as an input variable for decision making. From the regulatory perspective this has an important implication: collecting sensitive personal data is necessary in order to guarantee fairness of algorithms, and law making needs to find sensible ways to allow using such data in the modeling process. (shrink)

One of the crucial fields of study that is getting more attention is association rule mining. It is crucial to databases' knowledge discovery (KDD). KDD and association rule mining have a very broad use. It has evolved at a rapid rate over the past fifteen years. Association Rule Mining is a novel technology, although it is still in the discovery and development stages. Video is an illustration of interactive media data since it contains text, pictures, meta- (...) class='Hi'>data, visual, sound, and different kinds of data. It is frequently used for a variety of important likely reasons, such as security and reconnaissance, amusement, healthcare, educational endeavours, and sports. One of the main issues for the local data-mining research community is video data mining, which aims to find and comprehend fascinating examples from the large volume of video data. Video data mining is still in its infancy when compared to mining other sorts of data. It is pivotal to grasp and naturally mine the information from video databases considering the continually extending computerized libraries and video databases. Differentiating association rules between components in a significant video library is critical in the field of video data mining. As per continuous imaginative work, association rule mining is being utilized rapidly in various tasks, including perception, social affairs, broadcast news, sports, records, films, clinical data, as well as on the web and individual media assortments. This exploration tries to give a far-reaching method to association rule extraction from video databases. (shrink)

Biobanking in Europe has made major steps towards harmonization and shared standards for the collection and processing of data and samples stored in biobanks. Still, biobanks and researchers face substantial legal difficulties in the field of data protection and sample management. Data protection law was harmonized almost 15 years ago while rights in samples fall under the competence of the Member States of the EU. Despite the Data Protection Directive the field of data protection shows (...) a substantial degree of deviation as public health was excluded from the harmonization. Biobanks seem to have substantially fewer difficulties to cope with in terms of the legal requirements in the field of sample management. This paper discusses the legal framework, experiences of different biobanks in Europe and potential ways forward. It also highlights the need for a health economic analysis of the costs and benefits of privacy protection in Europe. At the moment, policymakers seem to build their decisions on an insufficient evidence base which underestimates the potential value of biobanks for European public health. Within the past few years little progress has been achieved with regards to the development of a unified legal framework in Europe, The diversity in the legal system is also reflected in the different approaches of ethics committees towards biobanking. To secure the responsible and effective use of data and samples, more efforts are needed to come up with pathways for a solution. (shrink)

"Data mining is not an invasion of privacy because access to data is only by machines, not by people": this is the argument that is investigated here. The current importance of this problem is developed in a case study of data mining in the USA for counterterrorism and other surveillance purposes. After a clarification of the relevant nature of privacy, it is argued that access by machines cannot warrant the access to further information, since the (...) analysis will have to be made either by humans or by machines that understand. It concludes that the current data mining violates the right to privacy and should be subject to the standard legal constraints for access to private information by people. (shrink)

Biobanking in Europe has made major steps towards harmonization and shared standards for the collection and processing of data and samples stored in biobanks. Still, biobanks and researchers face substantial legal difficulties in the field of data protection and sample management. Data protection law was harmonized almost 15 years ago while rights in samples fall under the competence of the Member States of the EU. Despite the Data Protection Directive the field of data protection shows (...) a substantial degree of deviation as public health was excluded from the harmonization. Biobanks seem to have substantially fewer difficulties to cope with in terms of the legal requirements in the field of sample management. This paper discusses the legal framework, experiences of different biobanks in Europe and potential ways forward. It also highlights the need for a health economic analysis of the costs and benefits of privacy protection in Europe. At the moment, policymakers seem to build their decisions on an insufficient evidence base which underestimates the potential value of biobanks for European public health. Within the past few years little progress has been achieved with regards to the development of a unified legal framework in Europe, The diversity in the legal system is also reflected in the different approaches of ethics committees towards biobanking. To secure the responsible and effective use of data and samples, more efforts are needed to come up with pathways for a solution. (shrink)

In this paper, it is argued that although data mining (DM) is being touted as a solution to many business problems and is basking in the glory of electronic business environments of today, as practiced currently, it reflects a preoccupation with short-run commercial applications and a neglect of the underlying theoretical issues. Although an argument can be made that theoretical precedence is not a necessary prerequisite for practical application or for commercial success, it can also be argued that (...) an exclusive reliance on data-driven and exploratory components of pattern recognition without a corresponding attention to the causal schemas underlying patterns is destined to limit the potential for DM to evolve into a long-term solution to business problems or into an intellectual discipline in its own right. This paper presents an overview of key features and assumptions in DM and examines some of the key practical, philosophical, and foundational issues in DM. (shrink)

Nowadays, the modern management is promoted to resolve the issue of unreliable information transmission and to provide work efficiency. The basic aim of the modern management is to be more effective in the role of the school to train talents and serve the society. This article focuses on the application of data mining (DM) in the development of information management system (IMS) in universities and colleges. DM provides powerful approaches for a variety of educational areas. Due to the (...) large amount of student information that can be used to design valuable patterns relevant to student learning behavior, research in the field of education is continuously expanding. Educational data mining can be used by educational institutions to assess student performance, assisting the institution in recognizing the student’s accomplishments. In DM, classification is a well-known technique that has been regularly used to determine student achievement. In this study, the process of DM and the application research of association rules is introduced in the development of IMS in universities and colleges. The results show that the curriculum covers the whole field and the minimum transaction support count be 2, minconf = 70%. The results also suggested that students who choose one course also tend to choose the other course. The application of DM theory in university information will greatly upsurge the data analysis capability of administrators and improve the management level. (shrink)

Web mining refers to the whole of data miningand related techniques that are used toautomatically discover and extract informationfrom web documents and services. When used in abusiness context and applied to some type ofpersonal data, it helps companies to builddetailed customer profiles, and gain marketingintelligence. Web mining does, however, pose athreat to some important ethical values likeprivacy and individuality. Web mining makes itdifficult for an individual to autonomouslycontrol the unveiling and dissemination of dataabout his/her private (...) life. To study thesethreats, we distinguish between `content andstructure mining' and `usage mining.' Webcontent and structure mining is a cause forconcern when data published on the web in acertain context is mined and combined withother data for use in a totally differentcontext. Web usage mining raises privacyconcerns when web users are traced, and theiractions are analysed without their knowledge.Furthermore, both types of web mining are oftenused to create customer files with a strongtendency of judging and treating people on thebasis of group characteristics instead of ontheir own individual characteristics and merits(referred to as de-individualisation). Althoughthere are a variety of solutions toprivacy-problems, none of these solutionsoffers sufficient protection. Only a combinedsolution package consisting of solutions at anindividual as well as a collective level cancontribute to release some of the tensionbetween the advantages and the disadvantages ofweb mining. The values of privacy andindividuality should be respected and protectedto make sure that people are judged and treatedfairly. People should be aware of theseriousness of the dangers and continuouslydiscuss these ethical issues. This should be ajoint responsibility shared by web miners (bothadopters and developers), web users, andgovernments. (shrink)

With the continuous progress and penetration of automated data collection technology, enterprises and organizations are facing the problem of information overload. The demand for expertise in data mining and analysis is increasing. Self-efficacy is a pivotal construct that is significantly related to willingness and ability to perform a particular task. Thus, the objective of this study is to develop an instrument for assessing self-efficacy in data mining and analysis. An initial measurement list was developed based (...) on the skills and abilities about executing data mining and analysis, and expert recommendations. A useful sample of 103 university students completed the online survey questionnaire. A 19-item four-factor model was extracted by exploratory factor analysis. Using the partial least squares-structural equation modeling technique, the model was cross-examined. The instrument showed satisfactory reliability and validity. The proposed instrument will be of value to researchers and practitioners in evaluating an individual’s abilities and readiness in executing data mining and analysis. (shrink)

Using a Rawlsian approach to analyze the ethical implications of data mining within three major codes of ethics used by American marketing firms, the author argues that marketers should re-conceptualize their business conduct, as defined in their individual codes of ethics, to incorporate a Rawlsian concern for society's least advantaged members. Rawls's concept of primary goods provides the framework for the argument that anonymity, a component of privacy, is vital for consumers whose autonomy is affected by data (...) mining. A combination of practical measures, ethical guidelines, and legislative protections are recommended for minimizing concerns about data mining, while still allowing for commercial advantages provided by the practice. (shrink)

High vocation education is an important foundation for China to cultivate high teaching quality and technical and skilled talents. In the new era, the acceleration of the development of modern vocational education is put in a more prominent position. It is proposed that we should adhere to moral education, closely combine this with the needs of technological change and industrial upgrading, constantly improve the quality of high vocational education teaching, and cultivate more technical and skilled talents with both political integrity (...) and ability for modernization construction. Under the background of social informatization, using artificial intelligent technology to solve these problems can play an important role for improving the teaching quality of high vocational education. This paper proposed a data mining approach in promoting student satisfaction with the teaching quality of high vocational education. We design a questionnaire for Students’ satisfaction with the teaching quality of basic entrepreneurship curriculum. We take the survey data of vocation education as an example and use mining technology analysis software to understand the current status of the teaching quality of basic entrepreneurship curriculum. The results determine the main factors affecting Students’ satisfaction with teaching quality. The results of this paper can be used in student management, education strategy, student education satisfaction, and teaching quality in high vocation college education, and to improve the teaching quality of fundamentals of entrepreneurship curriculum in high vocation education. (shrink)

This paper presents the NeoCrawler – a tailor-made webcrawler, which identifies and retrieves neologisms from the Internet and systematically monitors the use of detected neologisms on the web by means of weekly searches. It enables researchers to use the web as a corpus in order to investigate the dynamics of lexical innovation on a large-scale and systematic basis. The NeoCrawler represents an innovative web-mining tool which opens up new opportunities for linguists to tackle a number of unresolved and under-researched (...) issues in the field of lexical innovation. This paper presents the design as well as the most important characteristics of two modules, the Discoverer and the Observer, with regard to the usage-based study of lexical innovation and diffusion. (shrink)

In recent years, neuroscience has begun to transform itself into a “big data” enterprise with the importation of computational and statistical techniques from machine learning and informatics. In addition to their translational applications such as brain-computer interfaces and early diagnosis of neuropathology, these tools promise to advance new solutions to longstanding theoretical quandaries. Here I critically assess whether these promises will pay off, focusing on the application of multivariate pattern analysis (MVPA) to the problem of reverse inference. I argue (...) that MVPA does not inherently provide a new answer to classical worries about reverse inference, and that the method faces pervasive interpretive problems of its own. Further, the epistemic setting of MVPA and other decoding methods contributes to a potentially worrisome shift towards prediction and away from explanation in fundamental neuroscience. (shrink)

Motivation: • Breaking barriers in publishing demands a proactive attitude • Open data, open review and open dialogue in making social sciences plausible .

Data mining, as an essential part of artificial intelligence, is a powerful digital technology, which makes businesses predict future trends and alleviate the process of decision-making and enhancing customer experience along their digital transformation journey. This research provides a practical implication – a case study - to provide guidance on analyzing information and predicting repairs in home appliances after sales services business. The main benefit of this practical comparative study of various classification algorithms, by using the Weka tool, (...) is the analysis of information and the prediction of repairs in the home appliances after sales services business. The comparison of algorithms is performed considering different parameters, such as the mean absolute error, root mean square error, relative absolute error and root relative squared error, receiver operating characteristic area, accuracy, Matthews’s correlation coefficient, precision-recall curve, precision, F-measure, recall and statistical criteria. Five classification algorithms such as the Naive Bayes, J48, random forest, K-Nearest Neighbor, and logistic regression were implemented in the dataset. J48 has proved to provide the best accuracy and the lowest error among the other examined algorithms applied to a home appliances after sales services dataset to predict repairs based on product guarantee period. The extracted information and results of an after sales services business by using data mining techniques prove to alleviate the process of streamlining decision-making and provide reliable predictions, especially for the customers, as well as increase businesses’ efficiency along their digital transformation journey. (shrink)

This essay traces the complex constellation of ideas that informs Anders's turn to the generalizing expression ‘the human’ in his postwar work. It mobilizes the properties of radioactive material and digital data, which are both curiously imperceptible to our senses, to discuss Anders’s insistence on the universalizing pronoun `we' and assess its significance in the contemporary world. To do so, it aligns Anders's work with current debates about the Anthropocene and critiques of the use of the term ‘the human’ (...) in postcolonial settings. (shrink)

Student performance prediction aims to evaluate the grade that a student will reach before enrolling in a course or taking an exam. This prediction problem is a kernel task toward personalized education and has attracted increasing attention in the field of artificial intelligence and educational data mining. This paper provides a systematic review of the SPP study from the perspective of machine learning and data mining. This review partitions SPP into five stages, i.e., data collection, (...) problem formalization, model, prediction, and application. To have an intuition on these involved methods, we conducted experiments on a data set from our institute and a public data set. Our educational dataset composed of 1,325 students, and 832 courses was collected from the information system, which represents a typical higher education in China. With the experimental results, discussions on current shortcomings and interesting future works are finally summarized from data collections to practices. This work provides developments and challenges in the study task of SPP and facilitates the progress of personalized education. (shrink)

Health‐related data uses and data sharing have been in the spotlight for a while. Since the beginning of the big data era, massive data mining and its inherent possibilities have only increased the debate about what the limits are. Data governance is a relevant aspect addressed in ethics guidelines. In this context, the European project BRIDGE Health (BRidging Information and Data Generation for Evidence‐based Health policy and research) strove to achieve a comprehensive, integrated (...) and sustainable EU health‐information system. One of the aims of the project was to evaluate the requirements to construct a data‐linkage infrastructure for the secure management of health information. In a blueprint provided for this infrastructure, the topics ethics and the intimately related governance occupied a whole section, where the recent ethics guidelines by the Council for International Organizations of Medical Sciences (CIOMS) and the World Medical Association (WMA) were referenced. We explore what has changed in the latest versions of the ethics documents adopted by CIOMS and WMA regarding the management of health data and human tissues, the appropriateness of their application in new forms of research and infrastructures as the proposed in the BRIDGE Health project, and whether society should be so concerned about this topic, in the digital era of social exchange. (shrink)

This paper explores the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of social network sites users. It applies a data mining algorithm to a real dataset to provide empirically-based evidence of the ease with which characteristics about the SNS users can be discovered and used in a way that could invade their privacy. One major contribution of this article is the use of the (...) decision forest data mining algorithm to the context of SNS, which does not only build a decision tree but rather a forest allowing the exploration of more logic rules from a dataset. One logic rule that SysFor built in this study, for example, revealed that anyone having a profile picture showing just the face or a picture showing a family is less likely to be lonely. Another contribution of this article is the discussion of the implications of the data mining problem for governments, businesses, developers and the SNS users themselves. (shrink)

The present study examines certain challenges that KDD (Knowledge Discovery in Databases) in general and data mining in particular pose for normative privacy and public policy. In an earlier work (see Tavani, 1999), I argued that certain applications of data-mining technology involving the manipulation of personal data raise special privacy concerns. Whereas the main purpose of the earlier essay was to show what those specific privacy concerns are and to describe how exactly those concerns have (...) been introduced by the use of certain KDD and data-mining techniques, the present study questions whether the use of those techniques necessarily violates the privacy of individuals. This question is considered vis-à-vis a recent theory of privacy advanced by James Moor (1997). The implications of that privacy theory for a data-mining policy are also considered. (shrink)

In recent years, with the development of construction industry, more scientific, systematic, fast, and intelligent calculation methods are needed to coordinate urban development and fierce market competition, and mathematical algorithm library plays an important role in artificial intelligence. Therefore, the author uses computer mathematical algorithm and extension theory to study and analyze the residential building design and intelligent data mining. It is found that the research of the computer-aided expression method of extension building planning is mainly the expression (...) of the input and output system of extension building planning. It includes knowledge representation, system outline design, system flow, and interface expression based on the mathematical database. (shrink)

Education at all levels is increasingly augmented and enhanced by data mining and analytics, catalysed by the growing prevalence of automated distance learning. With an unprecedented capacity to scale both horizontally and vertically, data mining and analytics are set to be a transformative part of the future of education. We reflect on the assumptions behind data mining and the potential consequences of learning analytics, with reference to an issue brief prepared for the U.S. Department (...) of Education entitled Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics. We argue that the associated gains conceal subtle, but important risks. Data-ism, an underpinning paradigm, assigns unjustified veracity to data-driven science and the application of personalised analytics may compromise individual privacy, agency and inventiveness. This holds serious ethical implications, particularly when considering the impact on minors, rendering wholesale adoption premature. (shrink)

In order to improve education and teaching methods and meet the “heart” needs of college students in the era of big data, this paper analyzes the application of data mining technology in college mental health education, and introduces database technology and decision tree algorithm to support college mental health work. This process verifies the feasibility of this kind of system with the help of an example. Using the test standards outlined in this document, 1.5 previous test tasks (...) were completed within the timeframe. During the system test, the error rate was 14% and the number of tests was 7%.However, the error rate in the development stage is 11%, which is lower than 19% of the old version. The error rate in the acceptance stage is 14%, which is lower than 5% of the old version. That is to say, most of the errors were found in time in the system analysis and design stage. 14% of the problems found in the development stage are basically small problems in the interface display, which do not need major changes. However, the old version also includes design defects found in the development stage, and only large-scale rewriting of the involved modules. In the research process, the work of mental health in Colleges and universities has been promoted. At this time, the law of psychological changes of college students has been summarized. Therefore, the support of data mining technology can better meet the needs of mental health education in Colleges and universities. (shrink)

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to (...) be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes. (shrink)

This article starts with the analysis of the existing electronic commerce system, summarizes its characteristics, and analyzes and solves its existing problems. Firstly, the characteristics of the relational database My Structured Query Language and the distributed database HBase are analyzed, their respective advantages and disadvantages are summarized, and the advantages and disadvantages of each are taken into account when storing data. My SQL is used to store structured business data in the system, while HBase is used to store (...) unstructured data such as pictures. These two storage mechanisms together constitute a data storage subsystem. Secondly, considering the large amount of data in the e-commerce system and the complex calculation of the data mining algorithm, this paper uses MapReduce to realize the parallelization of the data mining algorithm and builds a Hadoop-based commodity recommendation subsystem on this basis. We use JavaEE technology to design a full-featured web mall system. Finally, based on the impact of cloud computing, mobile e-commerce is analyzed, including relevant theories, service mode, architecture, core technology, and the application in e-commerce, which can realize e-commerce precision marketing, find the optimal path of logistics, and take effective security measures to avoid transaction risks. This method can avoid the disadvantages of the traditional e-commerce, where large-scale data cannot be processed in a timely manner, realize the value of mining data behind, and realize the precision marketing of e-commerce enterprises. (shrink)

Preventive vaccination is one of the greatest successes of modern medicine. The SARS-CoV-2 epidemic, during which vaccination is the main method of prevention against death and severe disease, gave rise to a resurgence of anti-vaccine movements. The aim of this study was to analyse the attitudes of students towards vaccination and the COVID-19 pandemic. The statistical analysis was performed with the use of the following data-mining methods: correspondence analysis and basket analysis. The obtained results show that students of (...) medicine are characterized with the highest level of knowledge. Students of other medical faculties, on the other hand, have a significantly less uniform views, as do students of non-medical faculties. (shrink)

International large-scale assessments, such as PISA, provide structured and static data. However, due to its extensive databases, several researchers place it as a reference in Big Data in Education. With the goal of exploring which factors at country, school and student level have a higher relevance in predicting student performance, this paper proposes an Educational Data Mining approach to detect and analyze factors linked to academic performance. To this end, we conducted a secondary data analysis (...) and built decision trees (C4.5 algorithm) to obtain a predictive model of school performance. Specifically, we selected as predictor variables a set of socioeconomic, process and outcome variables from PISA 2018 and other sources (World Bank, 2020). Since the unit of analysis were schools from all the countries included in PISA 2018 (n= 21,903), student and teacher predictor variables were imputed to the school database. Based on the available student performance scores in Reading, Math, and Science, we applied k-means clustering to obtain a categorized (three categories) target variable of global school performance. Results show the existence of two main branches in the decision tree, split according to the schools’ mean socioeconomic status (SES). While performance in high-SES schools is influenced by educational factors such as metacognitive strategies or achievement motivation, performance in low-SES schools is affected in greater measure by country-level socioeconomic indicators such as GDP, and individual educational indicators are relegated to a secondary level. Since these evidences are in line and delve into previous research, this work concludes by analyzing its potential contribution to support the decision making processes regarding educational policies. (shrink)

Pharmaceutical companies have long relied on direct marketing of their drugs to physicians through one-on-one meetings with sales representatives. This practice of “detailing” is substantial in its costs and its number of participants. Every year, pharmaceutical companies spend billions of dollars on millions of visits to physicians by tens of thousands of sales representatives.Critics have argued that drug detailing results in sub-optimal prescribing decisions by physicians, compromising patient health and driving up spending on medical care. In this view, physicians often (...) are unduly influenced both by marketing presentations that do not accurately reflect evidence from the medical literature and by the gifts that sales representatives deliver in conjunction with their presentations. (shrink)

This essay examines issues involving personal privacy and informed consent that arise at the intersection of information and communication technology and population genomics research. I begin by briefly examining the ethical, legal, and social implications program requirements that were established to guide researchers working on the Human Genome Project. Next I consider a case illustration involving deCODE Genetics, a privately owned genetics company in Iceland, which raises some ethical concerns that are not clearly addressed in the current ELSI guidelines. The (...) deCODE case also illustrates some ways in which an ICT technique known as data mining has both aided and posed special challenges for researchers working in the field of population genomics. On the one hand, data-mining tools have greatly assisted researchers in mapping the human genome and in identifying certain “disease genes” common in specific populations. On the other hand, this technology has significantly threatened the privacy of research subjects participating in population genomics studies, who may, unwittingly, contribute to the construction of new groups that put those subjects at risk for discrimination and stigmatization. In the final section of this paper I examine some ways in which the use of data mining in the context of population genomics research poses a critical challenge for the principle of informed consent, which traditionally has played a central role in protecting the privacy interests of research subjects participating in epidemiological studies. (shrink)