Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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  • Article
    Citation - WoS: 37
    Citation - Scopus: 35
    Performance Prediction and Adaptation for Database Management System Workload Using Case-Based Reasoning Approach
    (Pergamon-Elsevier Science Ltd, 2018-07) Raza, Basit; Kumar, Yogan Jaya; Malik, Ahmad Kamran; Anjum, Adeel; Faheem, Muhammad
    Workload management in a Database Management System (DBMS) has become difficult and challenging because of workload complexity and heterogeneity. During and after execution of the workload, it is hard to control and handle the workload. Before executing the workload, predicting its performance can help us in workload management. By knowing the type of workload in advance, we can predict its performance in an adaptive way that will enable us to monitor and control the workload, which ultimately leads to performance tuning of the DBMS. This study proposes a predictive and adaptive framework named as the Autonomic Workload Performance Prediction (AWPP) framework. The proposed AWPP framework predicts and adapts the DBMS workload performance on the basis of information available in advance before executing the workload. The Case-Based Reasoning (CBR) approach is used to solve the workload management problem. The proposed CBR approach is compared with other machine learning techniques. To validate the AWPP framework, a number of benchmark workloads of the Decision Support System (DSS) and the Online Transaction Processing (OLTP) are executed on the MySQL DBMS. For preparation of training and testing data, we executed more than 1000 TPC-H and TPC-C like workloads on a standard data set. The results show that our proposed AWPP framework through CBR modeling performs better in predicting and adapting the DBMS workload. DBMSs algorithms can be optimized for this prediction and workload can be controlled and managed in a better way. In the end, the results are validated by performing post-hoc tests. (C) 2018 Elsevier Ltd. All rights reserved.
  • Article
    Citation - Scopus: 4
    CCPred: Global and Population-Specific Colorectal Cancer Prediction and Metagenomic Biomarker Identification at Different Molecular Levels Using Machine Learning Techniques
    (Elsevier Ltd, 2024-11) Bakir-Güngör, Burcu; Temiz, Mustafa; Inal, Yasin; Cicekyurt, Emre; Yousef, Malik
    Colorectal cancer (CRC) ranks as the third most common cancer globally and the second leading cause of cancer-related deaths. Recent research highlights the pivotal role of the gut microbiota in CRC development and progression. Understanding the complex interplay between disease development and metagenomic data is essential for CRC diagnosis and treatment. Current computational models employ machine learning to identify metagenomic biomarkers associated with CRC, yet there is a need to improve their accuracy through a holistic biological knowledge perspective. This study aims to evaluate CRC-associated metagenomic data at species, enzymes, and pathway levels via conducting global and population-specific analyses. These analyses utilize relative abundance values from human gut microbiome sequencing data and robust classification models are built for disease prediction and biomarker identification. For global CRC prediction and biomarker identification, the features that are identified by SelectKBest (SKB), Information Gain (IG), and Extreme Gradient Boosting (XGBoost) methods are combined. Population-based analysis includes within-population, leave-one-dataset-out (LODO) and cross-population approaches. Four classification algorithms are employed for CRC classification. Random Forest achieved an AUC of 0.83 for species data, 0.78 for enzyme data and 0.76 for pathway data globally. On the global scale, potential taxonomic biomarkers include ruthenibacterium lactatiformanas; enzyme biomarkers include RNA 2′ 3′ cyclic 3′ phosphodiesterase; and pathway biomarkers include pyruvate fermentation to acetone pathway. This study underscores the potential of machine learning models trained on metagenomic data for improved disease prediction and biomarker discovery. The proposed model and associated files are available at https://github.com/TemizMus/CCPRED. © 2024 Elsevier B.V., All rights reserved.
  • Article
    Citation - WoS: 7
    Citation - Scopus: 8
    Autonomic Workload Performance Tuning in Large-Scale Data Repositories
    (Springer London Ltd, 2018-09-04) Raza, Basit; Sher, Asma; Afzal, Sana; Malik, Ahmad Kamran; Anjum, Adeel; Kumar, Yogan Jaya; Faheem, Muhammad
    The workload in large-scale data repositories involves concurrent users and contains homogenous and heterogeneous data. The large volume of data, dynamic behavior and versatility of large-scale data repositories is not easy to be managed by humans. This requires computational power for managing the load of current servers. Autonomic technology can support predicting the workload type; decision support system or online transaction processing can help servers to autonomously adapt to the workloads. The intelligent system could be designed by knowing the type of workload in advance and predict the performance of workload that could autonomically adapt the changing behavior of workload. Workload management involves effectively monitoring and controlling the workflow of queries in large-scale data repositories. This work presents a taxonomy through systematic analysis of workload management in large-scale data repositories with respect to autonomic computing (AC) including database management systems and data warehouses. The state-of-the-art practices in large-scale data repositories are reviewed with respect to AC for characterization, performance prediction and adaptation of workload. Current issues are highlighted at the end with future directions.
  • Article
    Citation - Scopus: 98
    An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo-Tompa and Stacked Genetic Algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2020) Ali, Syed Arslan; Raza, Basit; Malik, Ahmad Kamran Kamran; Shahid, Ahmad Raza; Faheem, Muhammed Yasir; Alquhayz, Hani Ali; Kumar, Y. J.
    A rapid increase in heart disease has occurred in recent years, which might be the result of unhealthy food, mental stress, genetic issues, and a sedentary lifestyle. There are many advanced automated diagnosis systems for heart disease prediction proposed in recent studies, but most of them focus only on feature preprocessing, some focus on feature selection, and some only on improving the predictive accuracy. In this study, we focus on every aspect that may have an influence on the final performance of the system, i.e., to avoid overfitting and underfitting problems or to solve network configuration issues and optimization problems. We introduce an optimally configured and improved deep belief network named OCI-DBN to solve these problems and improve the performance of the system. We used the Ruzzo-Tompa approach to remove those features that are not contributing enough to improve system performance. To find an optimal network configuration, we proposed a stacked genetic algorithm that stacks two genetic algorithms to give an optimally configured DBN. An analysis of a RBM and DBN trained is performed to give an insight how the system works. Six metrics were used to evaluate the proposed method, including accuracy, sensitivity, specificity, precision, F1 score, and Matthew's correlation coefficient. The experimental results are compared with other state-of-the-art methods, and OCI-DBN shows a better performance. The validation results assure that the proposed method can provide reliable recommendations to heart disease patients by improving the accuracy of heart disease predictions by up to 94.61%. © 2020 Elsevier B.V., All rights reserved.