Data Mining Practical Machine Learning Tools and Techniques

Service Management and Marketing: Managing the Moments of Truth in Service Competition

Gradient Boosted Decision Trees (GBDT’s) are a powerful tool for classification and regression tasks in Big Data. Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT’s in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have successfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that cast CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost’s effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.

/pdf/catboost-for-big-data-an-interdisciplinary-review-utp91h5i0y.pdf

CatBoost for big data: an interdisciplinary review

COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach

This paper thoroughly presents a comprehensive review of the so-called moth–flame optimization (MFO) and analyzes its main characteristics. MFO is considered one of the promising metaheuristic algorithms and successfully applied in various optimization problems in a wide range of fields, such as power and energy systems,
economic dispatch, engineering design, image processing and medical applications. This manuscript describes the available literature on MFO, including its variants and hybridization, the growth of MFO publications, MFO application areas, theoretical analysis and comparisons of MFO with other algorithms.
Conclusions focus on the current work on MFO, highlight its weaknesses, and suggest possible future research directions. Researchers and practitioners of MFO belonging to different fields, like the domains of optimization, medical, engineering, clustering and data mining, among others will benefit from this study.

Moth–flame optimization algorithm: variants and applications

An efficient novel approach for iris recognition based on stylometric features and machine learning techniques

In today's market circumstances, the fact that the number of jobs that are taking place with the help of information systems constantly growing is indisputable. Managers often know very little about the information system and in that circumstance it is very difficult to them to effectively perform control function and successfully manage information's. This paper explains the concept of information systems audit and methodologies used. IT governance and information systems audit is imposed as an imperative for successful business. To improve the management of IT in accordance with regulatory requirements, organizations are using best practice frameworks to facilitate the work. One of these frameworks for IT governance is Cobit, which provides guidelines on what can be done in an organization in terms of control activities, measurement and documentation of processes and operations.

IT audit in accordance with Cobit standard

A novel approach to steganography based on the properties of Catalan numbers and Dyck words

This paper presents implementation of the moth search algorithm adjusted for solving static drone location problem. The optimal location of drones is one of the most important issues in this domain, and it belongs to the group of NP-hard optimization. The objective of the model applied in this paper is to establish monitoring all targets with the least possible number of drones. For testing purposes, we used problem instance with 30 uniformly distributed targets in the network domain. According to the results of simulations, where moth search algorithm established full coverage of targets, this approach shows potential in dealing with this kind of problem.

Moth Search Algorithm for Drone Placement Problem

This work presents a method based on information-theoretic analysis of iris biometric that aims to extract homogeneous regions of high entropy. Successful extraction of these regions facilitates the development of effective systems for generation of cryptographic keys of lengths up to 400 bits per iris. At the same time, this approach allows for the application of simpler error correction codes with equal false accept rate levels, which reduces the overall complexity of this class of systems.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-bmt.2016.0061

Marko Šarac

Papers

An efficient novel approach for iris recognition based on stylometric features and machine learning techniques

IT audit in accordance with Cobit standard

A novel approach to steganography based on the properties of Catalan numbers and Dyck words

Moth Search Algorithm for Drone Placement Problem

Fuzzy commitment scheme for generation of cryptographic keys based on iris biometrics