https://cyberleninka.org/article/n/1071663.pdf

Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method

Intrusion detection has gain a broad attention and become a fertile field for several researches, and still being the subject of widespread interest by researchers. The intrusion detection community still confronts difficult problems even after many years of research. Reducing the large number of false alerts during the process of detecting unknown attack patterns remains unresolved problem. However, several research results recently have shown that there are potential solutions to this problem. Anomaly detection is a key issue of intrusion detection in which perturbations of normal behavior indicates a presence of intended or unintended induced attacks, faults, defects and others. This paper presents an overview of research directions for applying supervised and unsupervised methods for managing the problem of anomaly detection. The references cited will cover the major theoretical issues, guiding the researcher in interesting research directions.

/pdf/machine-learning-techniques-for-anomaly-detection-an-2gpkeyiov5.pdf

Machine Learning Techniques for Anomaly Detection: An Overview

The increasing miniaturisation of animal-tracking technology has made it possible to gather exceptionally detailed machine-sensed data on the social dynamics of almost entire populations of individuals, in both terrestrial and aquatic study systems. Here, we review important issues concerning the collection of such data, and their processing and analysis, to identify the most promising approaches in the emerging field of 'reality mining'. Automated technologies can provide data sensing at time intervals small enough to close the gap between social patterns and their underlying processes, providing insights into how social structures arise and change dynamically over different timescales. Especially in conjunction with experimental manipulations, reality mining promises significant advances in basic and applied research on animal social systems.

/pdf/reality-mining-of-animal-social-systems-295lzahruh.pdf

Reality mining of animal social systems.

Machine learning approach for automated screening of malaria parasite using light microscopic images

Supplier selection (SS) is considered a sophisticated, application-oriented, decision-making (DM) problem and has received considerable attention. In the past two decades, DM theories and techniques continue to be incorporated into and contribute to the development of SS applications. Maintaining the pace of the rapid transitions in this field, this paper systematically reviews the relevant articles published between 2013 and 2018. Articles that orient various DM techniques are selected and analyzed under a well-established framework. State-of-the-art developments in the adoption of DM techniques are summarized in a SS process. We pay particular attention to promising directions that can dominate future research in this field. This paper further extends the history of several interacting fields, including big data and economic theories, toward methodological rather than application dimensions. The potential of such fields for SS is discussed from an interdisciplinary perspective.

Decision-making techniques in supplier selection: Recent accomplishments and what lies ahead

Data Mining : Concept and Techniques

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Data Mining : Concept and Techniques