Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14365/3

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Citation - Scopus: 318
1-D Convolutional Neural Networks for Signal Processing Applications
(Institute of Electrical and Electronics Engineers Inc., 2019) Kiranyaz, Serkan; İnce, Türker; Abdeljaber, O.; Avci, O.; Gabbouj, M.
1D Convolutional Neural Networks (CNNs) have recently become the state-of-the-art technique for crucial signal processing applications such as patient-specific ECG classification, structural health monitoring, anomaly detection in power electronics circuitry and motor-fault detection. This is an expected outcome as there are numerous advantages of using an adaptive and compact 1D CNN instead of a conventional (2D) deep counterparts. First of all, compact 1D CNNs can be efficiently trained with a limited dataset of 1D signals while the 2D deep CNNs, besides requiring 1D to 2D data transformation, usually need datasets with massive size, e.g., in the »Big Data» scale in order to prevent the well-known »overfitting» problem. 1D CNNs can directly be applied to the raw signal (e.g., current, voltage, vibration, etc.) without requiring any pre- or post-processing such as feature extraction, selection, dimension reduction, denoising, etc. Furthermore, due to the simple and compact configuration of such adaptive 1D CNNs that perform only linear 1D convolutions (scalar multiplications and additions), a real-time and low-cost hardware implementation is feasible. This paper reviews the major signal processing applications of compact 1D CNNs with a brief theoretical background. We will present their state-of-the-art performances and conclude with focusing on some major properties. Keywords - 1-D CNNs, Biomedical Signal Processing, SHM. © 2019 IEEE.
Citation - WoS: 1893
Citation - Scopus: 2230
1d Convolutional Neural Networks and Applications: a Survey
(Academic Press Ltd- Elsevier Science Ltd, 2021) Kiranyaz, Serkan; Avcı, Onur; Abdeljaber, Osama; İnce, Türker; Gabbouj, Moncef; Inman, Daniel J.
During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and subsampling layers. Deep 2D CNNs with many hidden layers and millions of parameters have the ability to learn complex objects and patterns providing that they can be trained on a massive size visual database with ground-truth labels. With a proper training, this unique ability makes them the primary tool for various engineering applications for 2D signals such as images and video frames. Yet, this may not be a viable option in numerous applications over 1D signals especially when the training data is scarce or application specific. To address this issue, 1D CNNs have recently been proposed and immediately achieved the state-of-the-art performance levels in several applications such as personalized biomedical data classification and early diagnosis, structural health monitoring, anomaly detection and identification in power electronics and electrical motor fault detection. Another major advantage is that a real-time and low-cost hardware implementation is feasible due to the simple and compact configuration of 1D CNNs that perform only 1D convolutions (scalar multiplications and additions). This paper presents a comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field. Their state-of-the-art performance is highlighted concluding with their unique properties. The benchmark datasets and the principal 1D CNN software used in those applications are also publicly shared in a dedicated website. While there has not been a paper on the review of 1D CNNs and its applications in the literature, this paper fulfills this gap. (C) 2020 The Author(s). Published by Elsevier Ltd.
A 2-Hop Coloring-Based Collision Free Infrastructure Design for Wireless Sensor Networks
(Institute of Electrical and Electronics Engineers Inc., 2016) Korkmaz I.; Dagdeviren O.; Dalkilic M.E.
This paper mainly proposes a design for a communication infrastructure for Wireless Sensor Networks. The proposed design prevents message collisions by arranging the time schedules to send, receive, forward and overhear packets of the nodes considering 2-hop graph coloring mechanism. The system aims to exclude the compromised nodes in the network using the overhearing mechanism, and copes with recovering the paths of the legitimate nodes using multipath redundancy. The proposed scheduling-based and overhearing supported infrastructure brings the advantage of providing the Sensor Networks with both reliable communication using backup paths and energy conservation by preventing the collisions. © 2016 IEEE.
Citation - WoS: 1
Citation - Scopus: 1
25-Hydroxyvitamin Levels in Sjögren’s Syndrome: Is It the Right Time to Dismiss the Case or Not
(Walter de Gruyter GmbH, 2024) Sımsır, Ilgın Yıldırım; Tanigor, Goksel; Karabulut, Gonca; Barutcuoglu, Burcu; Yılmaz, Zevcet
Objectives: This study aimed to investigate whether patients with primary Sjögren syndrome (SjS) have different levels of 25 OH-D3 (vitamin D) when compared to healthy populations and whether differences in 25 OH-D3 correlated with disease activity or markers. Methods: Eighty-eight female patients with SjS and 3,338 age-matched healthy female controls were included in this study. 25 OH-D3 levels were compared with healthy controls. Then the patients were stratified according to their 25 OH-D3 levels, either insufficient/deficient or normal (<50 nmol/L or ≥50 nmol/L). The disease activity was evaluated using The EULAR SjS disease activity index (ESSDAI) and its components. Correlation analyses were also performed for a possible correlation with disease characteristics and markers of activity. Results: No differences in 25 OH-D3 levels were found between SjS and healthy populations (p>0.05). No correla- tions were found between patient characteristics or labo- ratory values (p>0.05). Conclusions: This study did not find a link between disease characteristics and disease activity and 25 OH-D3 levels. Prospective studies with more patients should be conducted to reach a conclusion.
Citation - WoS: 1
Citation - Scopus: 2
2d Model of a Biomass Single Particle Pyrolysis-Analysis of the Influence of Fiber Orientation on the Thermal Decomposition Process
(Mdpi, 2025) Hercel, Paulina; Orhon, Atahan; Jozwik, Michal; Kardas, Dariusz
Understanding the influence of heat transfer on the pyrolysis process is crucial for optimizing industrial biofuel production processes. While numerous scientific studies focus on experimental investigations of pyrolysis using laboratory-scale devices, many neglect the essential role of thermal energy in initiating and controlling thermal decomposition processes. This study presents a transient two-dimensional numerical model of biomass single-particle pyrolysis, which includes the energy balance, mass conservation equations and pyrolysis gas pressure and velocity equations. The model employs explicit numerical methods to manage the high computational demands of 2D transient simulations, but is successfully validated with the use of experimental data found in the literature. The model reflects the heterogeneous structure of wood by using different thermal conductivity coefficients depending on the wooden fibers' orientation. The results demonstrate the impact of fiber orientation on the heat transfer and thermal decomposition processes. The anisotropic properties of wood led to varied temperature fields and pyrolysis decomposition stages, aligning well with experimental data, thus validating the model's accuracy. The proposed approach can provide a better understanding and lead to improvement in biofuel production processes, enabling more efficient and controlled conversion of biomass into fuel. By optimizing the pyrolysis process, it contributes to the development of sustainable energy preservation and regeneration methods, supporting a shift towards more sustainable fuel production patterns using renewable biomass resources like wood.
Citation - WoS: 6
Citation - Scopus: 7
2db: a Proteomics Database for Storage, Analysis, Presentation, and Retrieval of Information From Mass Spectrometric Experiments
(Biomed Central Ltd, 2008) Allmer, Jens; Kuhlgert, Sebastian; Hippler, Michael
Background: The amount of information stemming from proteomics experiments involving (multi dimensional) separation techniques, mass spectrometric analysis, and computational analysis is ever-increasing. Data from such an experimental workflow needs to be captured, related and analyzed. Biological experiments within this scope produce heterogenic data ranging from pictures of one or two-dimensional protein maps and spectra recorded by tandem mass spectrometry to text-based identifications made by algorithms which analyze these spectra. Additionally, peptide and corresponding protein information needs to be displayed. Results: In order to handle the large amount of data from computational processing of mass spectrometric experiments, automatic import scripts are available and the necessity for manual input to the database has been minimized. Information is in a generic format which abstracts from specific software tools typically used in such an experimental workflow. The software is therefore capable of storing and cross analysing results from many algorithms. A novel feature and a focus of this database is to facilitate protein identification by using peptides identified from mass spectrometry and link this information directly to respective protein maps. Additionally, our application employs spectral counting for quantitative presentation of the data. All information can be linked to hot spots on images to place the results into an experimental context. A summary of identified proteins, containing all relevant information per hot spot, is automatically generated, usually upon either a change in the underlying protein models or due to newly imported identifications. The supporting information for this report can be accessed in multiple ways using the user interface provided by the application. Conclusion: We present a proteomics database which aims to greatly reduce evaluation time of results from mass spectrometric experiments and enhance result quality by allowing consistent data handling. Import functionality, automatic protein detection, and summary creation act together to facilitate data analysis. In addition, supporting information for these findings is readily accessible via the graphical user interface provided. The database schema and the implementation, which can easily be installed on virtually any server, can be downloaded in the form of a compressed file from our project webpage.
3D dendritic spine segmentation using nonparametric shape priors
(Institute of Electrical and Electronics Engineers Inc., 2017) Bocugoz E.; Erdil E.; Argunsah A.O.; Unay D.; Cetin M.
Analyzing morphological and structural changes of dendritic spines in 2-photon microscopy images in time is important for neuroscience researchers. Correct segmentation of dendritic spines is an important step of developing robust and reliable automatic tools for such analysis. In this paper, we propose an approach for segmentation of 3D dendritic spines using nonparametric shape priors. The proposed method learns the prior distribution of shapes through Parzen density estimation on the training set of shapes. Then, the posterior distribution of shapes is obtained by combining the learned prior distribution with a data term in a Bayesian framework. Finally, the segmentation result that maximizes the posterior is found using active contours. Experimental results demonstrate that using nonparametric shape priors leads to better 3D dendritic spine segmentation results. © 2017 IEEE.
Citation - WoS: 4
Citation - Scopus: 5
3d Helmholtz Coil System Setup for Thermal Conductivity Measurements of Magnetic Nanofluids
(Pergamon-Elsevier Science Ltd, 2023) Alsangur, Rahime; Dog, Serkan; Ateş, Ismet; Turgut, Alpaslan; Çetin, Levent
This study aims to design a mechatronic system that involves a 3D Helmholtz coil system implemented with the 3 omega; method to measure the thermal conductivity of magnetic nanofluids under uniform and rotating magnetic fields. For this purpose, a 3D Helmholtz coil system was designed and manufactured to generate a uniform and rotating magnetic field up to 400 G. First, the uniformity and rotation abilities of the magnetic field generated by the system were investigated numerically and experimentally. The investigations pointed out that the 3D Helmholtz coil system can generate a uniform magnetic field in 1D, 2D, and 3D with a maximum non-uniformity factor of 0.0016. After that, the thermal conductivity of Fe3O4 - water magnetic nanofluid samples with 1, 2, 3, 4, and 4.8 vol.% were measured under 1D, 2D, and 3D uniform magnetic field application. The magnetic field was applied at different direction angles between X, Y, and Z axes in the Cartesian coordinate system. The results pointed out that the thermal conductivity of the samples increases as the magnetic field and particle concentration increase. The maximum thermal conductivity enhancement was observed as similar to 9.1% and the minimum thermal conductivity was observed as similar to 1.9% when the magnetic field is applied in parallel and perpendicular directions, respectively. The measurement results also pointed out that under the external uniform magnetic field application at 2D and 3D, thermal conductivity enhancement is less affected by the particle concentration increment.
Citation - Scopus: 3
3d Printing With Bacterial Cellulose-Based Bioactive Composites for Design Applications
(Education and research in Computer Aided Architectural Design in Europe, 2022) Turhan G.D.; Afsar S.; Ozel B.; Doyuran A.; Varinlioglu G.; Bengisu, Murat
The bacterial cellulose (BC) biofilms are explored in design applications as replacements to petroleum-based materials in order to overcome the irreversible effects of the Anthropocene. Unlike biomaterials, designers as mediators could collaborate with bioactive polymers as a form of wetware to manufacture living design products with the aid of novel developments in biology and engineering. Past and ongoing experiments in the literature show that BC has a strong nanofibril structure that provides adhesion for attachment to plant cellulose-based networks and it could grow on the surfaces of the desired geometry thanks to its inherited, yet, controllable bio-intelligence. This research explores BC-based bioactive composites as wetware within the context of digital fabrication in which the methodology involves distinct, yet integrated, three main stages: Digital design and G-code generation (software stage); BC cultivation and printable bioactive composite formulation (wetware stage); digital fabrication with a customized 3D printer (hardware stage). The results have shown that the interaction of BC and plant-based cellulose fibers ofjute yarns has enhanced the structural load-bearing capacity of the form against compressive forces, while pure BC is known only by its tensile strength. Since the outcomes were fabricated with the use of a bioactive material, the degradation process also adds a fourth dimension: Time, by which the research findings could further establish a bio-upcycling process of wastes towards biosynthesis of valuable products. Moreover, developing a BC-based bioactive filament indicates potentially a feasible next step in the evolution of multiscale perspectives on the growth of habitable living structures that could reinforce the interaction between nature and architecture through collaboration with software, hardware, and wetware in innovative and sustainable ways. © 2022, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.
An Abbreviated History of Liver Transplantation
(Wolters Kluwer Medknow Publications, 2024) Schilsky, M.L.; Emre, S.H.
[No abstract available]
Citation - Scopus: 1
Ablation of Atrioventricular Nodal Reentrant Tachycardia With Focal Cryoablation, Compared With Radiofrequency Ablation: Single-Center Experience
(2024) Topaloğlu, C.; Fici, F.; Borne, P.V.; Taşkin, U.; Dogdus, M.; Saygi, S.; Tengiz, I.
BACKGROUND: The ablation of atrioventricular nodal reentrant tachycardia (AVNRT) with cryoablation is an alternative to radiofrequency (RF) ablation in patients due to the low risk of total atrioventricular block. An increase in early-late recurrences after cryoablation is reported as an important disadvantage. OBJECTIVES: In this study, we aimed to compare the acute procedural success and the long-term recurrence rates of patients, with AVNRT who underwent methods. METHODS: 73 patients with AVNRT were included in the study: 32 with cryoablation and 41 with RF ablation. There was no statistically significant difference between acute procedural success in methods. The ablation procedure was performed by an operator experienced in arrhythmology. The choice of RF or cryoablation was made in the electrophysiology laboratory based on the material already available during the procedure. After the procedure, the patients were evaluated every 3 months for 2 years in polyclinic control. The significance level adopted in the statistical analysis was 5%. RESULTS: The 2 groups of patients were homogeneous.
Citation - WoS: 9
Citation - Scopus: 8
Abnormal Cross Frequency Coupling of Brain Electroencephalographic Oscillations Related To Visual Oddball Task in Parkinson's Disease With Mild Cognitive Impairment
(Sage Publications Inc, 2023) Bayraktaroglu, Zubeyir; Akturk, Tuba; Yener, Görsev; de Graaf, Tom A.; Hanoglu, Lutfu; Yildirim, Ebru; Gunduz, Duygu Hunerli
Parkinson's disease (PD) is a movement disorder caused by degeneration in dopaminergic neurons. During the disease course, most of PD patients develop mild cognitive impairment (PDMCI) and dementia, especially affecting frontal executive functions. In this study, we tested the hypothesis that PDMCI patients may be characterized by abnormal neurophysiological oscillatory mechanisms coupling frontal and posterior cortical areas during cognitive information processing. To test this hypothesis, event-related EEG oscillations (EROs) during counting visual target (rare) stimuli in an oddball task were recorded in healthy controls (HC; N = 51), cognitively unimpaired PD patients (N = 48), and PDMCI patients (N = 53). Hilbert transform served to estimate instantaneous phase and amplitude of EROs from delta to gamma frequency bands, while modulation index computed ERO phase-amplitude coupling (PAC) at electrode pairs. As compared to the HC and PD groups, the PDMCI group was characterized by (1) more posterior topography of the delta-theta PAC and (2) reversed delta-low frequency alpha PAC direction, ie, posterior-to-anterior rather than anterior-to-posterior. These results suggest that during cognitive demands, PDMCI patients are characterized by abnormal neurophysiological oscillatory mechanisms mainly led by delta frequencies underpinning functional connectivity from frontal to parietal cortical areas.
Citation - WoS: 9
Citation - Scopus: 17
Abnormal Ecg Beat Detection Based on Convolutional Neural Networks
(Institute of Electrical and Electronics Engineers Inc., 2020) Ozdemir M.A.; Guren O.; Cura O.K.; Akan A.; Onan A.
The heart is the most critical organ for the sustainability of life. Arrhythmia is any irregularity of heart rate that causes an abnormality in your heart rhythm. Clinical analysis of Electrocardiogram (ECG) signals is not enough to quickly identify abnormalities in the heart rhythm. This paper proposes a deep learning method for the accurate detection of abnormal and normal heartbeats based on 2-D Convolutional Neural Network (CNN) architecture. Two channels of ECG signals were obtained from the MIT-BIH arrhythmia dataset. Each ECG signal is segmented into heartbeats, and each heartbeat is transformed into a 2-D grayscale heartbeat image as an input for CNN structure. Due to the success of image recognition, CNN architecture is utilized for binary classification of the 2-D image matrix. In this study, the effect of different CNN architectures is compared based on the classification rate. The accuracies of training and test data are found as 100.00% and 99.10%, respectively for the best CNN model. Experimental results demonstrate that CNN with ECG image representation yields the highest success rate for the binary classification of ECG beats compared to the traditional machine learning methods, and one-dimensional deep learning classifiers. © 2020 IEEE.
Citation - WoS: 1
Citation - Scopus: 1
About The Article Titled “a Different Scintigraphic Perspective On The Systolic Function Of The Left Ventricle-1” [“sol Ventrikül Sistolik Fonksiyonuna Sintigrafik Olarak Farklı Bir Bakış Açısı-1” Başlıklı Makale Hakkında]
(Galenos Publishing House, 2024) Taşçı, Cengiz
[No abstract available]
Citation - WoS: 1
Citation - Scopus: 1
Abstinence-Related Motivational Engagement Scale: Validity and Reliability in Turkish People
(Bilimsel Tip Publishing House, 2018) Yavan, Tulay; Gulesen, Asli; Bebis, Hatice
OBJECTIVES: This research aimed to conduct a validity and reliability study of the Turkish version of the abstinence-related motivational engagement (ARME) scale. MATERIALS AND METHODS: This study included 122 people and was administered in a smoking cessation clinic. The sociodemographic-smoking status characteristics questionnaire and the ARME scale were used for data collection. A psycholinguistic language adaptation was performed. In the validity, analyses, content, construct, and criterion-related validities were used. For content validity, expert evaluation was performed. For construct validity, principal component analyses (exploratory factor analyses) were performed. Orthogonal (Varimax) rotation was used to explore multiple factors. The Kaiser-Meyer-Olkin test was used to assess the adequacy of the sample size. For criterion-related validity, we compared the ARME scale points of people who were abstinent and had relapse for smoking at the end of the sixth month. In the reliability analysis, standard deviation (SD) and item analysis, internal consistency, and test-retest methods were used. RESULTS: The four factors explain 58% of the total variance. Items have factor loading between 0.409 and 0.805. When the factor structure of the scale was assessed, the items in each factor group have a factor load of at least 0.40. Due to one-dimensional use of the original scale, it has been decided to maintain this scale in its original form. The ARME scale points of people who quit smoking were statistically higher than the points of people who had relapse at the end of the sixth month. Cronbach's alpha coefficients were between 0.846 and 0.763. Significant and positive correlation was found between the test-retest scale scores. CONCLUSION: The Turkish adaptation of the ARME scale, which was developed for adults who quitted smoking, is an adequately valid and reliable measurement instrument. It is considered that the scale might be used reliably in different cultures as well.
Citation - Scopus: 1
Academic Performance Management Policy for Changing Roles of Universities in Innovation Systems
(Institute of Electrical and Electronics Engineers Inc., 2017) Ozcan S.; Ozyazici M.S.; Ozerdem M.B.
The purpose of this study is to establish a new performance measurement method for academic actors for their changing roles in innovation systems. The widely accepted triple helix and systems of innovation models show changing and overlapping roles of academic, industrial and governmental actors. In previous innovation systems, universities were not focused on applied research and technology transfer as much as they are now. Current literature shows a changing role of universities and importance of their involvement in innovation systems. Although academic organizations' roles have changed in innovation systems, academic performance measurement systems (APMS) are not adapted to examine innovation related performance factors. Many APMS focus on key performance indicators (KPIs) such as; publications, research projects and patents. However, the new APMS needs to assess the activities and processes that are related to innovation, such as; technology transfer processes, collaborative innovation activities, consultancies and academic spin-offs. For this study a new APMS is applied according to the needs of universities by using a synthetic data based on an engineering department's KPIs. APMS scores are calculated based on the cumulative metric of all research and innovation activities and, weighted according to the needs and considerations of the university. The results of this study show that many of those academicians who have great performance in publications and academic research do not necessarily have high-level performance in innovative activities. In fact the results show that those who had high points in some measurements have very low performance in others. For the management point of view, it may be more effective to position academicians for different roles and assess their performance accordingly as innovation-targeted, teaching-targeted and research-targeted academicians. © 2016 Portland International Conference on Management of Engineering and Technology, Inc.
Accounting for digital products
(IGI Global, 2010) Karaibrahimo?lu Y.Z.
Digital products are "content" goods such as software, books, music, or movies which can be digitized and traded on a digital market place. With the increase in trade and ownership of digital products, several important management issues have arisen. Accounting treatment for digital products is one important management issue. It is argued that digital products require regulations in terms of recognition, measurement, valuation, reporting and taxation. Therefore, the purpose of this chapter is to discuss the accounting problems that arise as a result of the growing importance of digital products in the business environment and to propose suggestions based on the accounting concepts and standards. For this purpose, first, the increasing importance of digital products is briefly explained. Then, the challenge created as a result of expanding trading volume of digital products are discussed in terms of accounting with suggestions for the appropriate accounting for digital products. © 2011, IGI Global.
Accurate Dictionary Matching for Mr Fingerprinting Using Neural Networks and Feature Extraction
(Institute of Electrical and Electronics Engineers Inc., 2020) Soyak R.; Ersoy E.O.; Navruz E.; Fakultesi M.; Unay D.; Oksuz I.
Magnetic Resonance Fingerprinting is a recent technique which aims at providing simultaneous measurements of multiple parameters. MRF works by varying acquisition parameters in a pseudorandom manner so as to get unique, uncorrelated signal evolutions from each tissue. MRF is a dictionary based approach, and thus requires a database. This database can be created by simulating the signal evolutions from first principles using different physical models for a wide variety of tissue parameter combinations. Having this dictionary, a pattern recognition algorithm is used to match the acquired signal evolutions from each voxel with each signal evolution in the dictionary. In this paper, we compare the efficiency of deep learning based feature extraction method and neural network architectures in order to achieve state-of-the-art accuracy in dictionary matching for MRF. Our results showcase successful dictionary matching with high accuracy both quantitatively and qualitatively. © 2020 IEEE.
Achieving Sustainable Learning Through Erp Based Supply Chain in Vitro Laboratory
(Elsevier Science Bv, 2011) Gocer, Aysu; Saatcioglu, Omur Yasar; Demir, Muhittin H.; Tuna, Okan; Baltacioglu, Tuncdan; Adali, Erman
In order to enable sustainable learning, practical motivation behind every theory in consideration needs to be experienced extensively. The purpose of this study is to enhance sustainable learning on logistics and supply chain management through an in vitro laboratory environment in which real life supply chain structure is simulated over actual physical flows, and also through enterprise resource planning (ERP) systems, and then to measure the level of sustainability achieved. Research is conducted by including a group of students to a hands-on implementation through both physical and computerized applications in this representative business environment. To support the findings, surveys and focus groups are conducted. (C) 2011 Published by Elsevier Ltd.
Citation - WoS: 5
Citation - Scopus: 6
Achieving Turkey's Indc Target: Assessments of Nccap and Indc Documents and Proposing Conceivable Policies
(Mdpi, 2018) Alkan, Ayla; Oğuş Binatlı, Ayla; Deger, Cagacan
In 2015, Turkey submitted its Intended Nationally Determined Contribution (INDC) to the United Nations Framework on Climate Change Convention (UNFCCC) before the Paris Conference of the Parties (COP 21), expressing its intention to decrease emissions level at a rate of 21% from business as usual. This emissions reduction target is important as it is the first one for Turkey. However, Turkey faces unemployment problems and needs to sustain its growth. In this study, an Environmentally Extended Social Accounting Matrix (SAM), based on 2012 Input-Output data, was created, emissions reduction potentials of the National Climate Change Action Plan (NCCAP) together with the INDC were calculated, and alternative policies to reduce emissions to the target level and to boost the economy were proposed separately. The study finds that both the preparation and implementation of the previous national documents are problematic, and that Turkey was not meticulous about implementation of the climate mitigation policies in the previous national documents. The study also finds that reaching the emissions target with the INDC policies seems impossible and more conceivable policies are needed, and recommends that the INDC target and document itself should be revised substantially.

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