Managing the ‘information deluge’: How text mining and machine learning are changing systematic review methods

The AAAI-99 Workshop Program (a part of the sixteenth national conference on artificial intelligence) was held in Orlando, Florida. The program included 16 workshops covering a wide range of topics in AI. Each workshop was limited to approximately 25 to 50 participants. Participation was by invitation from the workshop organizers. The workshops were Agent-Based Systems in the Business Context, Agents' Conflicts, Artificial Intelligence for Distributed Information Networking, Artificial Intelligence for Electronic Commerce, Computation with Neural Systems Workshop, Configuration, Data Mining with Evolutionary Algorithms: Research Directions (Jointly sponsored by GECCO-99), Environmental Decision Support Systems and Artificial Intelligence, Exploring Synergies of Knowledge Management and Case-Based Reasoning, Intelligent Information Systems, Intelligent Software Engineering, Machine Learning for Information Extraction, Mixed-Initiative Intelligence, Negotiation: Settling Conflicts and Identifying Opportunities, Ontology Management, and Reasoning in Context for AI Applications.

This dissertation contributes with novel predictive modelling approaches to data-driven computational psychiatry and offers alternative analyses frameworks to the standard statistical analyses in psychiatric research. In particular, this document advances research in medical data mining, especially psychiatry, via two phases. In the first phase, this document promotes research by proposing synergistic machine learning and statistical approaches for detecting patterns and developing predictive models in clinical psychiatry data to classify diseases, predict treatment outcomes or improve treatment selections. In particular, these data-driven approaches are built upon several machine learning techniques whose predictive models have been pre-processed, trained, optimised, post-processed and tested in novel computationally intensive frameworks. In the second phase, this document advances research in medical data mining by proposing several novel extensions in the area of data classification by offering a novel decision tree algorithm, which we call PIDT, based on parameterised impurities and statistical pruning approaches toward building more accurate decision trees classifiers and developing new ensemblebased classification methods. In particular, the experimental results show that by building predictive models with the novel PIDT algorithm, these models primarily led to better performance regarding accuracy and tree size than those built with traditional decision trees. The contributions of the proposed dissertation can be summarised as follow. Firstly, several statistical and machine learning algorithms, plus techniques to improve these algorithms, are explored. Secondly, prediction modelling and pattern detection approaches for the first-episode psychosis associated with cannabis use are developed. Thirdly, a new computationally intensive machine learning framework for understanding the link between cannabis use and first-episode psychosis was introduced. Then, complementary and equally sophisticated prediction models for the first-episode psychosis associated with cannabis use were developed using artificial neural networks and deep learning within the proposed novel computationally intensive framework. Lastly, an efficient novel decision tree algorithm (PIDT) based on novel parameterised impurities and statistical pruning approaches is proposed and tested with several medical datasets. These contributions can be used to guide future theory, experiment, and treatment development in medical data mining, especially psychiatry.

Machine learning, data mining, and several related research areas are concerned with methods for the automated induction of models and the extraction of patterns from realistic data. Fuzzy classification is one of the important applications in fuzzy set where values ranges between 0 and 1 .Main objective of the fuzzy logic and fuzzy set to find a set of fuzzy rules that form a classification model. The set is major advantage of using fuzzy rules for classification applications to maintain transparency as well as high accuracy rate. Fuzzy pattern tree induction introduced as a novel for machine learning method for classification. Pattern tree is a hierarchical tree-like structure, whose inner nodes are marked with generalized (fuzzy) logical operators and whose leaf nodes are associated with fuzzy predicates on input attributes. Pattern tree make use of different aggregation including t-norms and t-conorms. There are two types of Fuzzy Pattern trees: (i) Bottom up induction: Information propagates from bottom to top. (ii) Top down induction: Information propagates from top to down. In this paper performance analysis and comparative study of above both approaches using number of parameters like accuracy rate, model size is mentioned. The paper is divided into five sections: section 1 is introduction of Fuzzy pattern tree, section 2 describes types of fuzzy pattern tree schemes and section 3 presents their comparative performance, section 4 does the conclusion. Section 5 includes future scope.

Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g. point-dimension, observation-variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: CASE: Exploiting the programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, where as the SPJ method does not. In a relational database, especially with normalized tables, a significant effort is required to prepare a summary data set that can be used as input for a data mining or statistical algorithm. Most algorithms require as input a data set with a horizontal layout, with several Records and one variable or dimension per column. That is the case with models like clustering, classification, regression and PCA; consult. Each research discipline uses different terminology to describe the data set. In data mining the common terms are point-dimension. Statistics literature generally uses observation-variable. Machine learning research uses instance-feature. This article introduces a new class of aggregate functions that can be used to build data sets in a horizontal layout (denormalized with aggregations), automating SQL query writing and extending SQL capabilities. We show evaluating horizontal aggregations is a challenging and interesting problem and we introduced alternative methods and optimizations for their efficient evaluation. II. MOTIVATION As mentioned above, building a suitable data set for data mining purposes is a time- consuming task. This task generally requires writing long SQL statements or customizing SQL Code if it is automatically generated by some tool. There are two main ingredients in such SQL code: joins and aggregations; we focus on the second one. The most widely- known aggregation is the sum of a column over groups of rows. Some other aggregations return the average, maximum, minimum or row count over groups of rows. There exist many aggregations functions and operators in SQL. Unfortunately, all these aggregations have limitations to build data sets for data mining purposes. The main reason is that, in general, data sets that are stored in a relational database (or a data warehouse) come

As the number of patients treated in-home are increasing exponentially mainly in countries such as Japan, US A and Europe etc., these people often enter into a critical situation that may require help immediately (e.g. when facing an accident, or becoming depressed). Researches and Advances in computing areas and the Internet of Things (IOT) have provided efficient and cheap equipments including wireless communication and cameras, such as smart phones or embedded devices that enable the deployment of Health S mart Homes (HS H) that can provide medical treatment of patients in their homes. The images captured from these cameras can help nurses or caretakers of patients to provide timely help. The use of such patient images and emotional detection to assist patients and elderly people within home is provided in this article. Internet of Things and Cloud computing can work together, which can address to the Big Data problems. Big data is actually a term used for a huge data set that performs operations like storage of data, analysis, sharing, transfer, predictive analysis; updating etc. data set can grow rapidly. Data sets have different analytics on data that involve process of inspecting, transformation and modeling of data so as to discover new information, and to reach on some particular decision. There are some particular data analysis techniques from which data mining is a popular one which focuses on modeling and discovery of new facts. The discovered knowledge helps in predictive analysis purposes as well as text analysis. Further on data mining is performed on data set which involves discovery of patterns by applying different operations at databases, and using artificial intelligence, machine learning, statics etc. Keywords: Cloud computing, IOT, AI, face recognition, big data, data mining.

This thesis describes a data provenance framework and other associated frameworks for utilizing provenance for data quality and reproducibility. We first identify the requirements for the design of a comprehensive provenance framework which can be applicable to various applications, supports a rich set of provenance metadata, and is interoperable with other provenance management systems. We then design and develop a provenance framework, called SimP, addressing such requirements. Next, we present four prominent applications and investigate how provenance data can be beneficial to such applications. The first application is the quality assessment of access control policies. Towards this, we design and implement the ProFact framework which uses provenance techniques for collecting comprehensive data about actions which were either triggered due to a network context or a user (i.e., a human or a device) action. Provenance data are used to determine whether the policies meet the quality requirements. ProFact includes two approaches for policy analysis: structure-based and classification-based. For the structure-based approach, we design tree structures to organize and assess the policy set efficiently. For the classification-based approach, we employ several classification techniques to learn the characteristics of policies and predict their quality. In addition, ProFact supports policy evolution and the assessment of its impact on the policy quality. The second application is workflow reproducibility. Towards this, we implement ProWS which is a provenance-based architecture for retrieving workflows. Specifically, ProWS transforms data provenance into workflows and then organizes data into a set of indexes to support efficient querying mechanisms. ProWS supports composite queries on three types of search criteria: keywords of workflow tasks, patterns of workflow structure, and metadata about workflows (e.g., how often a workflow was used). The third application is the access control policy reproducibility. Towards this, we propose a novel framework, Polisma, which generates attribute-based access control policies from data, namely from logs of historical access requests and their corresponding decisions. Polisma combines data mining, statistical, and machine learning techniques, and capitalizes on potential context information obtained from external sources (e.g., LDAP directories) to enhance the learning process. The fourth application is the policy reproducibility by utilizing knowledge and experience transferability. Towards this, we propose a novel framework, FLAP, which transfer attribute-based access control policies between different parties in a collaborative environment, while considering the challenges of minimal sharing of data and support policy adaptation to address conflict. All frameworks are evaluated with respect to performance and accuracy.

The focus of this thesis is fast and robust adaptations of logistic regression (LR) for data mining and high-dimensional classification problems. LR is well-understood and widely used in the statistics, machine learning, and data analysis communities. Its benefits include a firm statistical foundation and a probabilistic model useful for “explaining” the data. There is a perception that LR is slow, unstable, and unsuitable for large learning or classification tasks. Through fast approximate numerical methods, regularization to avoid numerical instability, and an efficient implementation we will show that LR can outperform modern algorithms like Support Vector Machines (SVM) on a variety of learning tasks. Our novel implementation, which uses a modified iteratively re-weighted least squares estimation procedure, can compute model parameters for sparse binary datasets with hundreds of thousands of rows and attributes, and millions or tens of millions of nonzero elements in just a few seconds. Our implementation also handles real-valued dense datasets of similar size.

Indonesian EITI (Extractive Industries Transparency Initiative) is an organization under Ministry of Economic Coordination which used to increase the transparency of extractive industry in Indonesia. EITI Indonesia manage a lot of data about mining, one of the managed data is data Mining Business License in Indonesia. The data has many records that require large storage allocation and difficult process data that is used by the EITI for decision making. . This data Mining Business License will be used for the processing of data mining that aims to help look for interesting patterns to determine a learning and two itemsets (attributes) that exist. Application Data Mining with Emerging Patterns Supervised methods will be used as a solution for managing data such large, so it is easy to produce a decision in the form of an interesting pattern information to determine the transparency of mining license in Indonesia. System development methods using CRISP-DM. The design of data mining applications using the programming language Java, NetBeans and MySQL database tools used to build a technology Supervised Emerging Patterns in multi-attribute decision making.

Kernel based techniques have shown outstanding success in data mining and machine learning in the recent past. Many optimization problems of kernel based methods suffer from multiple local optima. ...

It is seen that distance education environments are more frequently used due to the progress of internet today. The data acquired from these environments are commonly irregular journal records and text-based communication messages. Digital mining applications are used in order to convert these irregular data into regular, and cleaning out the information stacks. The purpose of this research is to reveal the usage of the types of digital mining application, which are being applied within the distance education environments. For this purpose, a situation-based study was carried out which was related to the subjects of data mining, web mining and text mining, being used in the distance education environments. As a result of the research, a relationship was determined between the types of digital mining. Key words: Text mining, data mining, web mining.

There is global attention on new data analytic methods. Data scraping (a typical first step for advanced data analytics), text and data mining (TDM, the extraction of knowledge from data) and machine learning (ML, often also simply referred to as Artificial Intelligence or AI) are seen as critical technologies. The legal issues involved in the regulation of data range from privacy and data protection (such as the GDPR) to proprietary approaches (such as copyright, database rights, or proposed new rights in data themselves). This paper focusses on one specific intervention, the introduction of two exceptions for text and data mining in the Directive on Copyright in the Digital Single Market (CDSM). Art. 3 is a mandatory exception for text and data mining (TDM) for the purposes of scientific research; Art. 4 permits text and data mining by anyone but with rightsholders able to “contract-out” (Art. 4), for example preventing TDM use of publicly available online content by “machine-readable means”. We trace the context of using the lever of copyright law to enable emerging technologies and support innovation. Within the EU copyright intervention, elements that may underpin a transparent legal framework for AI are identified, such as the possibility of retention of (permanent) copies for further verification. On the other hand, we identify several pitfalls, including an excessively broad definition of TDM which makes the entire field of data-driven AI development dependent on an exception. We analyse the implications of limiting the scope of the exceptions to the right of reproduction (which leaves the communication of research results in a grey zone). We also argue that the limitation of Art. 3 to certain beneficiaries remains problematic; and that the requirement of lawful access is difficult to operationalize. In conclusion, we argue that there should be no need for a TDM exception for the act of extracting informational value from protected works. The EU’s CDSM provisions paradoxically may favour the development of biased AI systems due to price and accessibility conditions for accessing training data that offer the wrong incentives. We also identify some old and new areas of the EU acquis which will play a crucial role in the future relationship of EU copyright law with technological innovation.

Like many construction industry professions, quantity surveying (QS) has been around for many years and has undergone many changes to reflect developments within the wider industry and society. The proliferation of computers into the design process starting from the 1980’s leading up to the rise of Building Information Modelling has particularly led to significant changes in the design and construction landscape. In the UK for instance, the proliferation of BIM and possible demise of traditional Bill of Quantities, with the concurrent rise of smart buildings/cities with exploitation of Big Data (BD), artificial intelligence (AI) and Machine Learning (ML). It implies that QS practices need to reflect on emerging products and services that can promote construction quality and productivity as well as their own professional development. With the decline of traditional QS roles and increased focus on speed of construction, there may be opportunities for different roles for quantity surveyors when dealing with data-driven needs of advanced clients such as BIM managers and Project managers. Additionally, there is need to improve the market value for traditional QS practices when dealing with less innovative clients with less time constraints, which inadvertently contributes to a skills gap which will allow practices to charge more for the traditional services. This study is an exploratory research based on secondary data, which is aim at understanding BIM adoption and related technical advancements that represent innovative and emerging roles for QS professionals to meet the growing demands in the industry. The findings will ignite and support the need for changes in practice, professional education as well as attitudinal behaviour required toward the UK’s Construction 2025 goals. This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Applied Science University Journal of Natural Sciences following peer review. The definitive publisher-authenticated version Seidu et al (2020) Innovative Changes in Quantity Surveying Practice through BIM, Big Data, Artificial Intelligence and Machine Learning, is available online at: DOI:10.18576/asuj/040105

Most team sports are characterized by positions to which the players in a team are assigned. The goal of this classification is to attribute specific responsibilities during a game. Moreover, the same classification drives the buying and selling of players according to team managers and coaches strategies. The existing positions - often defined a long time ago - tend to reflect traditional points of view about the game and sometimes they are no longer well-suited to the new concepts arisen with the evolution of the way of playing. This paper focuses on basketball and aims at describing new roles of players during the game, by means of the analysis of players' performance statistics with data mining and machine learning tools. In detail, self-organizing maps and fuzzy clustering procedures are adopted in tandem to define groups of players with similar way of playing. The results show that, when considering the modern basketball players' statistics, classical positions are not able to fully represent their way of playing, and a new set of 5 roles emerges as a meaningful classification of players' characteristics.

SummaryTo summarize current excellent research in the field of bioinformatics.Synopsis of the articles selected for the IMIA Yearbook 2006. Results: Current research in the field of bioinformatics clearly shows ongoing unification of experimental findings and clinical outcomes. Microarray data, gene sequences and clinical data are more and more perceived as different but related facets of one entity. Significant work is done in the area of text and data mining in order to bring together patient data and biochemical phenomena by means of ontologies. A strong trend in the clinical field is performance of exhaustive studies on DNA material derived from patients that suffer from diseases that are already known to be inherited. Examination of appropriate methods covers data and text mining, ontologies as well as machine learning and classification.The best paper selection of articles on bioinformatics shows examples of excellent research on methods used for studying inherited diseases and their underlying genetic dispositions. Clinical studies, inclusion of experimental findings like microarray data, and of knowledge representation formats all lead to a better understanding the linkage between gene sequences, biological functions and clinical findings in the form of healthy state or physiological disorders.

Bidirectional Human-Robot Learning: Imitation and Skill Improvement