Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - Scopus: 22Transition Towards the Sustainable Development: Unraveling the Effects of Mineral Markets, Belt & Road Initiative, and the Paris Agreement on Green Economic Growth(Elsevier Ltd, 2024-04) Xia, Xiqiang; Chishti, Muhammad Zubair; Dogan, EyupThe Agenda 2030 strongly emphasizes implementing effective and equitable measures to address the urgent challenge of global warming, primarily driven by unsustainable fossil-fuel combustion, and one of its core focuses is Sustainable Development Goal (SDG) – 8, among others. In light of this, the recent article aims to explore the dynamic nexus between minerals (MNR), the Belt and Road Initiative (BRI), the Paris Agreement (PA), green technologies (GT), and green growth, with a specific focus on developing a policy framework for advancing SDG – 8. The study utilizes daily data and advanced econometric tools such as QVAR, Cross-quantileogram, and wavelet-quantile correlation to examine the diverse effects of these factors on green growth across various time horizons. The short-run analysis reveals that MNR, BRI, and GT discourage green growth under most market conditions, except for a few quantiles that exhibit positive or insignificant relationships. In the medium run, impacts are mixed, with both positive and negative effects observed. However, in the long run, MNR, BRI, and GT consistently demonstrate favorable effects on green growth. For PA, short and medium-run effects are mixed, but medium-run results indicate a predominantly positive impact on green growth. In the long run, PA significantly benefits green growth across the majority of market conditions. Overall, the diversified results suggest that minerals, BRI, the Paris Agreement, and green technologies play a crucial role in stimulating green growth to achieve SDG - 8 in the long term. © 2024 Elsevier B.V., All rights reserved.Article Citation - Scopus: 404The Moderating Role of Renewable and Non-Renewable Energy in Environment-Income Nexus for Asean Countries: Evidence From Method of Moments Quantile Regression(Elsevier Ltd, 2021-02) Anwar, Ahsan; Siddique, Muhammad; Dogan, Eyup; Sharif, Arshian AslamA vast body of studies estimates the impact of energy consumption on the environment. A typical empirical study either use aggregate energy consumption or apply conventional econometric techniques in modelling the nexus of energy, income and environment. To correct these gaps, the objective of the study is to use renewable and non-renewable energy consumption in analyzing energy-income-environment nexus, and to apply the novel Method of Moments Quantile Regression for ASEAN countries. The outcomes indicate that non-renewable energy consumption stimulate carbon emissions across all quantiles (10th to 90th), the value of the 10th quantile is 0.257 which rises to 0.501 till 90th quantile. Whereas, the renewable energy consumption leads to a decrease in CO<inf>2</inf> emissions across all the quantiles (10th to 90th) but this association is statistically insignificant at higher quantiles from 60th to 90th. The empirical outcomes also verify the presence of the environmental Kuznets curve relationship, which is statistically significant from the middle (30th) to higher (90th) quantiles. Moreover, the finding of panel estimation approaches (FMOLS, DOLS, FE-OLS) also verify the existence of the EKC hypothesis in ASEAN countries. Their finding also describes that 1% increase in non-renewable energy consumption increase CO<inf>2</inf> emission by 0.29%, 0.26% and 0.30% whereas 1% increase in the usage of renewable energy reduces CO<inf>2</inf> emission by 0.17%, 0.15% and 0.17% in case of FMOLS, DOLS and FE-OLS respectively. The empirical results conclude that the government should encourage and subsidize the sources of green energy to tackle environmental degradation. More policy implications are further discussed in the study. © 2020 Elsevier B.V., All rights reserved.Article Citation - Scopus: 52Solving an Ammunition Distribution Network Design Problem Using Multi-Objective Mathematical Modeling, Combined AHP-TOPSIS, and GIS(Elsevier Ltd, 2019-03) Akgün, Ibrahim; Erdal, HamitWe study a strategic-level ammunution distribution network design problem (ADNDP) where the purpose is to determine the locations and the service assignments of main, regional, and local depots in order to meet the ammunition needs of military units considering several factors, e.g., stock levels at the depots, costs, and risk levels of depot locations. ADNDP is a real-world and large-scale problem for which scientific decision making methods do not exist. We propose a methodology that uses multi-objective mathematical modeling, Analytic Hierarchy Process (AHP), The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Geographic Information System (GIS) to solve the problem. The multi-objective mathematical model determines the locations and the service assignments of depots considering two objectives, namely, to minimize transportation costs and to minimize risk scores of main depot locations. The risk score of a depot location indicates how vulnerable the location is to disruptions and is determined by a combined AHP-TOPSIS analysis where TOPSIS is used to compute the risk scores and AHP is used to compute the weights needed by TOPSIS for the identified risk attributes. The GIS analysis is conducted to determine the potential depot locations using map layers based on spatial criteria. We have applied the proposed methodology in designing and evaluating a real ammunition distribution network under different scenarios in collaboration and cooperation with the area experts. We have employed the weighted-sum method to find non-dominated solutions for each scenario and discussed their tradeoffs with the area experts. The purpose of this paper is to present the proposed methodology, findings, and insights. © 2019 Elsevier B.V., All rights reserved.Article Citation - Scopus: 2Prediction of Colorectal Cancer Based on Taxonomic Levels of Microorganisms and Discovery of Taxonomic Biomarkers Using the Grouping-Scoring (G-S-M) Approach(Elsevier Ltd, 2025-03) Bakir-Güngör, Burcu; Temiz, Mustafa; Canakcimaksutoglu, Beyza; Yousef, MalikColorectal cancer (CRC) is one of the most prevalent forms of cancer globally. The human gut microbiome plays an important role in the development of CRC and serves as a biomarker for early detection and treatment. This research effort focuses on the identification of potential taxonomic biomarkers of CRC using a grouping-based feature selection method. Additionally, this study investigates the effect of incorporating biological domain knowledge into the feature selection process while identifying CRC-associated microorganisms. Conventional feature selection techniques often fail to leverage existing biological knowledge during metagenomic data analysis. To address this gap, we propose taxonomy-based Grouping Scoring Modeling (G-S-M) method that integrates biological domain knowledge into feature grouping and selection. In this study, using metagenomic data related to CRC, classification is performed at three taxonomic levels (genus, family and order). The MetaPhlAn tool is employed to determine the relative abundance values of species in each sample. Comparative performance analyses involve six feature selection methods and four classification algorithms. When experimented on two CRC associated metagenomics datasets, the highest performance metric, yielding an AUC of 0.90, is observed at the genus taxonomic level. At this level, 7 out of top 10 groups (Parvimonas, Peptostreptococcus, Fusobacterium, Gemella, Streptococcus, Porphyromonas and Solobacterium) were commonly identified for both datasets. Moreover, the identified microorganisms at genus, family, and order levels are thoroughly discussed via refering to CRC-related metagenomic literature. This study not only contributes to our understanding of CRC development, but also highlights the applicability of taxonomy-based G-S-M method in tackling various diseases. © 2025 Elsevier B.V., All rights reserved.Article Citation - Scopus: 49EdgeAISim: A Toolkit for Simulation and Modelling of AI Models in Edge Computing Environments(Elsevier Ltd, 2024-02) Nandhakumar, Aadharsh Roshan; Baranwal, Ayush; Choudhary, Priyanshukumar; Golec, Muhammed; Gill, Sukhpal SinghTo meet next-generation Internet of Things (IoT) application demands, edge computing moves processing power and storage closer to the network edge to minimize latency and bandwidth utilization. Edge computing is becoming increasingly popular as a result of these benefits, but it comes with challenges such as managing resources efficiently. Researchers are utilising Artificial Intelligence (AI) models to solve the challenge of resource management in edge computing systems. However, existing simulation tools are only concerned with typical resource management policies, not the adoption and implementation of AI models for resource management, especially. Consequently, researchers continue to face significant challenges, making it hard and time-consuming to use AI models when designing novel resource management policies for edge computing with existing simulation tools. To overcome these issues, we propose a lightweight Python-based toolkit called EdgeAISim for the simulation and modelling of AI models for designing resource management policies in edge computing environments. In EdgeAISim, we extended the basic components of the EdgeSimPy framework and developed new AI-based simulation models for task scheduling, energy management, service migration, network flow scheduling, and mobility support for edge computing environments. In EdgeAISim, we have utilized advanced AI models such as Multi-Armed Bandit with Upper Confidence Bound, Deep Q-Networks, Deep Q-Networks with Graphical Neural Network, and Actor-Critic Network to optimize power usage while efficiently managing task migration within the edge computing environment. The performance of these proposed models of EdgeAISim is compared with the baseline, which uses a worst-fit algorithm-based resource management policy in different settings. Experimental results indicate that EdgeAISim exhibits a substantial reduction in power consumption, highlighting the compelling success of power optimization strategies in EdgeAISim. The development of EdgeAISim represents a promising step towards sustainable edge computing, providing eco-friendly and energy-efficient solutions that facilitate efficient task management in edge environments for different large-scale scenarios. © 2023 Elsevier B.V., All rights reserved.Article Citation - Scopus: 4CCPred: 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, MalikColorectal 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 - Scopus: 8Building a Challenging Medical Dataset for Comparative Evaluation of Classifier Capabilities(Elsevier Ltd, 2024-08) Bozkurt, Berat; Coskun, Kerem; Bakal, GokhanSince the 2000s, digitalization has been a crucial transformation in our lives. Nevertheless, digitalization brings a bulk of unstructured textual data to be processed, including articles, clinical records, web pages, and shared social media posts. As a critical analysis, the classification task classifies the given textual entities into correct categories. Categorizing documents from different domains is straightforward since the instances are unlikely to contain similar contexts. However, document classification in a single domain is more complicated due to sharing the same context. Thus, we aim to classify medical articles about four common cancer types (Leukemia, Non-Hodgkin Lymphoma, Bladder Cancer, and Thyroid Cancer) by constructing machine learning and deep learning models. We used 383,914 medical articles about four common cancer types collected by the PubMed API. To build classification models, we split the dataset into 70% as training, 20% as testing, and 10% as validation. We built widely used machine-learning (Logistic Regression, XGBoost, CatBoost, and Random Forest Classifiers) and modern deep-learning (convolutional neural networks - CNN, long short-term memory - LSTM, and gated recurrent unit - GRU) models. We computed the average classification performances (precision, recall, F-score) to evaluate the models over ten distinct dataset splits. The best-performing deep learning model(s) yielded a superior F1 score of 98%. However, traditional machine learning models also achieved reasonably high F1 scores, 95% for the worst-performing case. Ultimately, we constructed multiple models to classify articles, which compose a hard-to-classify dataset in the medical domain. © 2024 Elsevier B.V., All rights reserved.Article Citation - Scopus: 15An Effective Colorectal Polyp Classification for Histopathological Images Based on Supervised Contrastive Learning(Elsevier Ltd, 2024-04) Yengec-Tasdemir, Sena Busra; Aydin, Zafer; Akay, Ebru; Doǧan, Serkan; Yilmaz, BulentEarly detection of colon adenomatous polyps is pivotal in reducing colon cancer risk. In this context, accurately distinguishing between adenomatous polyp subtypes, especially tubular and tubulovillous, from hyperplastic variants is crucial. This study introduces a cutting-edge computer-aided diagnosis system optimized for this task. Our system employs advanced Supervised Contrastive learning to ensure precise classification of colon histopathology images. Significantly, we have integrated the Big Transfer model, which has gained prominence for its exemplary adaptability to visual tasks in medical imaging. Our novel approach discerns between in-class and out-of-class images, thereby elevating its discriminatory power for polyp subtypes. We validated our system using two datasets: a specially curated one and the publicly accessible UniToPatho dataset. The results reveal that our model markedly surpasses traditional deep convolutional neural networks, registering classification accuracies of 87.1% and 70.3% for the custom and UniToPatho datasets, respectively. Such results emphasize the transformative potential of our model in polyp classification endeavors. © 2024 Elsevier B.V., All rights reserved.Editorial Citation - Scopus: 450 Years of Resources Policy – What Is Next? Key Areas of Future Research(Elsevier Ltd, 2024-08) Fleming-Munoz, David A.; Campbell, Gary A.; Ley, Yalin; Arratia-Solar, Andrea; Aroca, Patricio A.; Atienza-Ubeda, Miguel; Kumral, Mustafa; Weber, Jeremy; Atienza, MiguelIn 2024, Resources Policy reaches its 50th anniversary as a journal. Fifty years leading the field of mineral and fossil fuel policies and economic research worldwide. Considering this special milestone, we provide a forward-looking view in this paper, highlighting seven areas we believe are critical for robust research that Resources Policy should publish in the future. Leveraging our research expertise and knowledge with the journal, these seven areas of future research include implications of post-mining and energy transitions, the dark side of critical minerals, the increasing substitution of local labour by alternative inputs, the role of the resource curse in resilience considerations, the cleaner production role of mining, macroeconomic frameworks, and the future of mining beyond mines (deep-sea and space mining). We believe more research is needed in these seven research areas, which can enhance our understanding of critical aspects, reduce uncertainty, and provide novel ways to address societal, environmental, economic and policy challenges related to the extraction and use of minerals and fossil fuels. © 2024 Elsevier B.V., All rights reserved.Article Citation - Scopus: 7Impact of Sustainable Energy, Fossil Fuels and Green Finance on Ecosystem: Evidence From China(Elsevier Ltd, 2024) Wang, Zuoteng; Zeng, Sheng; Khan, ZohanThe adoption of sustainable energy has increased as a substitute for petroleum derivatives due to growing concerns about environmental degradation caused by pollution and non-renewable energy sources. This study aims to investigate the impact of sustainable energy, green finance, and fossil fuels on the ecology of China. Instead of using traditional intermediaries like CO2 and EF, we employed the ecosystem habitat index to evaluate the conservation of terrestrial ecosystems. This index measures the extent of habitat destruction, deterioration, and fragmentation. The research demonstrated that implementing ecological power and green finance in China has enhanced the country's ability to safeguard and enhance its ecosystem in the short and long term. Furthermore, the findings suggest that using non-renewable energy sources in China has heightened the risk to biodiversity and the ecosystem. The analysis indicates that prioritizing green funding and renewable energy sources is crucial for policymakers, legislators, and investors to safeguard and enhance ecosystem diversity. © 2024 Elsevier B.V., All rights reserved.