11 research outputs found

    Clustering Cum Polar Coordinate Feature Transformation (C-PCFT) Approach to Identify Pores in Carbonate Rocks

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    Most of the world’s oil reserves and natural gas are stored within carbonate rock’s pores and fractures. Pores and fractures are quite popular for predicting the amount of petroleum under an adequate trap condition. Hence, their petrophysical properties, such as shape and size, are paramount for accurately predicting the reservoir’s state and condition. Current modelling techniques are mostly based on manual and expert judgement which are time-consuming and cost-intensive. In this study, we devised a robust and scalable image processing framework that uses the combination of pixel-based clustering approach with a polar coordinate feature transformation technique to intelligently identify the pores of carbonate rock samples. We reported that such a method can be effective in detecting pores of different shapes and sizes in an automated fashion. We rigorously tested the proposed method on the computed tomography-scanned micro-images of a carbonate rock sample, and the results demonstrate improved identification accuracy of the proposed method than the current deep learning counterparts. Another key advantage compared to deep learning methods, the proposed method does not require extensive training on data, which saves time and effort without being computationally too expensive

    A Compromise Programming for Multi-Objective Task Assignment Problem

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    The problem of scheduling is an area that has attracted a lot of attention from researchers for many years. Its goal is to optimize resources in the system. The lecturer’s assigning task is an example of the timetabling problem, a class of scheduling. This study introduces a mathematical model to assign constrained tasks (the time and required skills) to university lecturers. Our model is capable of generating a calendar that maximizes faculty expectations. The formulated problem is in the form of a multi-objective problem that requires the trade-off between two or more conflicting objectives to indicate the optimal solution. We use the compromise programming approach to the multi-objective problem to solve this. We then proposed the new version of the Genetic Algorithm to solve the introduced model. Finally, we tested the model and algorithm with real scheduling data, including 139 sections of 17 subjects to 27 lecturers in 10 timeslots. Finally, a web application supports the decision-maker to visualize and manipulate the obtained results

    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

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    In data analysis, recognizing unusual patterns (outliers’ analysis or anomaly detection) plays a crucial role in identifying critical events. Because of its widespread use in many applications, it remains an important and extensive research brand in data mining. As a result, numerous techniques for finding anomalies have been developed, and more are still being worked on. Researchers can gain vital knowledge by identifying anomalies, which helps them make better meaningful data analyses. However, anomaly detection is even more challenging when the datasets are high-dimensional and multivariate. In the literature, anomaly detection has received much attention but not as much as anomaly detection, specifically in high dimensional and multivariate conditions. This paper systematically reviews the existing related techniques and presents extensive coverage of challenges and perspectives of anomaly detection within high-dimensional and multivariate data. At the same time, it provides a clear insight into the techniques developed for anomaly detection problems. This paper aims to help select the best technique that suits its rightful purpose. It has been found that PCA, DOBIN, Stray algorithm, and DAE-KNN have a high learning rate compared to Random projection, ROBEM, and OCP methods. Overall, most methods have shown an excellent ability to tackle the curse of dimensionality and multivariate features to perform anomaly detection. Moreover, a comparison of each algorithm for anomaly detection is also provided to produce a better algorithm. Finally, it would be a line of future studies to extend by comparing the methods on other domain-specific datasets and offering a comprehensive anomaly interpretation in describing the truth of anomalies

    Deep Reinforcement Learning for Anomaly Detection: A Systematic Review

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    Anomaly detection has been used to detect and analyze anomalous elements from data for years. Various techniques have been developed to detect anomalies. However, the most convenient one is Machine learning which is performing well but still has limitations for large-scale unlabeled datasets. Deep Reinforcement Learning (DRL) based techniques outperform the existing supervised or unsupervised and other alternative techniques for anomaly detection. This study presents a Systematic Literature Review (SLR), which analyzes DRL models that detect anomalies in their application. This SLR aims to analyze the DRL frameworks for anomaly detection applications, proposed DRL methods, and their performance comparisons against alternative methods. In this review, we have identified 32 research articles published from 2017–2022 that discuss DRL techniques for various anomaly detection applications. After analyzing the selected research articles, this paper presents 13 different applications of anomaly detection found in the selected research articles. We identified 50 different datasets applied in experiments on anomaly detection and demonstrated 17 distinct DRL models used in the selected papers to detect anomalies. Finally, we analyzed the performance of these DRL models and reviewed them. Additionally, we observed that detecting anomalies using DRL frameworks is a promising area of research and showed that DRL had shown better performance for anomaly detection where other models lack. Therefore, we provide researchers with recommendations and guidelines based on this review