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Book ; Online: Hot Topics in Burn Injuries

Pelin Kartal, Selda / Bayramgürler, Dilek

2018

Keywords	Accident & emergency medicine ; burn injury, inflammation, surgery, wound healing, fibrosis, phytochemical
Language	English
Size	1 electronic resource (128 pages)
Publisher	IntechOpen
Document type	Book ; Online
Note	English
HBZ-ID	HT030647760
ISBN	9781838813802 ; 1838813802
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Book ; Online: Acne and Acneiform Eruptions

Pelin Kartal, Selda / Gonul, Muzeyyen

2017

Keywords	Epidemiology & medical statistics ; pregnancy, laser, algorithm, behçet's disease, scar, dry eye
Language	English
Size	1 electronic resource (222 pages)
Publisher	IntechOpen
Document type	Book ; Online
Note	English
HBZ-ID	HT030646566
ISBN	9789535173502 ; 9535173502
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Book ; Online: Urticaria and Angioedema

Pelin Kartal, Selda / Kutlubay, Zekayi

A Comprehensive Review of

2017

Keywords	Dermatology ; complement system, bradykinin, hereditary angioedema, metabolic syndrome, autoimmunity, infectious diseases
Language	English
Size	1 electronic resource (250 pages)
Publisher	IntechOpen
Document type	Book ; Online
Note	English
HBZ-ID	HT030645957
ISBN	9789535148180 ; 9535148184
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article ; Online: Next-level vegetation health index forecasting: A ConvLSTM study using MODIS Time Series.

Kartal, Serkan / Iban, Muzaffer Can / Sekertekin, Aliihsan

Environmental science and pollution research international

2024 Volume 31, Issue 12, Page(s) 18932–18948

Abstract: The Vegetation Health Index (VHI) is a metric used to assess the health and condition of vegetation, based on satellite-derived data. It offers a comprehensive indicator of stress or vigor, commonly used in agriculture, ecology, and environmental ... ...

Abstract	The Vegetation Health Index (VHI) is a metric used to assess the health and condition of vegetation, based on satellite-derived data. It offers a comprehensive indicator of stress or vigor, commonly used in agriculture, ecology, and environmental monitoring for forecasting changes in vegetation health. Despite its advantages, there are few studies on forecasting VHI as a future projection, particularly using up-to-date and effective machine learning methods. Hence, the primary objective of this study is to forecast VHI values by utilizing remotely sensed images. To achieve this objective, the study proposes employing a combined Convolutional Neural Network (CNN) and a specific type of Recurrent Neural Network (RNN) called Long Short-Term Memory (LSTM), known as ConvLSTM. The VHI time series images are calculated based on the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. In addition to the traditional image-based calculation, the study suggests using global minimum and global maximum values (global scale) of NDVI and LST time series for calculating the VHI. The results of the study showed that the ConvLSTM with a 1-layer structure generally provided better forecasts than 2-layer and 3-layer structures. The average Root Mean Square Error (RMSE) values for the 1-step, 2-step, and 3-step ahead VHI forecasts were 0.025, 0.026, and 0.026, respectively, with each step representing an 8-day forecast horizon. Moreover, the proposed global scale model using the applied ConvLSTM structures outperformed the traditional VHI calculation method.
MeSH term(s)	Satellite Imagery ; Time Factors ; Temperature ; Ecology ; Neural Networks, Computer ; Environmental Monitoring/methods
Language	English
Publishing date	2024-02-14
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1178791-0
ISSN	1614-7499 ; 0944-1344
ISSN (online)	1614-7499
ISSN	0944-1344
DOI	10.1007/s11356-024-32430-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Comparison of semantic segmentation algorithms for the estimation of botanical composition of clover-grass pastures from RGB images

Kartal, Serkan

Ecological informatics. 2021 Dec., v. 66

2021

Abstract: In dairy industry, estimation of the in-field clover-grass ratio is an important factor in composing feed ratios for cows. Accurate estimation of the grass and clover ratios enables smart decisions to optimize seeding density and fertilization, resulting ...

Abstract	In dairy industry, estimation of the in-field clover-grass ratio is an important factor in composing feed ratios for cows. Accurate estimation of the grass and clover ratios enables smart decisions to optimize seeding density and fertilization, resulting in increased yield and reduced amount of chemicals used. In practice, this process is still primarily performed by human-eye, which is labor-intensive, subjective, and error-prone. Therefore, plant species ratio estimation using traditional methods is hardly possible and misleading. Modern semantic segmentation models on digital images offer a promising alternative to overcome these drawbacks. In this paper, an extensive comparison of Deep Learning (DL) models for estimating the ratio of clover, grass, and weeds in red, green, and blue (RGB) images is presented. Three DL architectures (Unet, Linknet, FPN) are combined with ten randomly initialized encoders (variations of VGG, DenseNet, ResNet, Inception and EfficientNet) to construct thirty different segmentation models. Evaluation of models was performed on a publicly available dataset provided by the Biomass Prediction Challenge. The best segmentation accuracy was reached by the FPN-Inceptionresnetv2 model by 76.7%. This result indicates the great potential in deep convolutional neural networks for the segmentation of plant species in RGB images. Furthermore, this study lays the foundation for our next set of experiments with DL to improve the benchmarks and will further the quest to identify phenotype characteristics from agricultural imagery collected from the field.
Keywords	biomass ; botanical composition ; dairy industry ; data collection ; grasses ; prediction
Language	English
Dates of publication	2021-12
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	2212016-6
ISSN	1878-0512 ; 1574-9541
ISSN (online)	1878-0512
ISSN	1574-9541
DOI	10.1016/j.ecoinf.2021.101467
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Prediction of MODIS land surface temperature using new hybrid models based on spatial interpolation techniques and deep learning models.

Kartal, Serkan / Sekertekin, Aliihsan

Environmental science and pollution research international

2022 Volume 29, Issue 44, Page(s) 67115–67134

Abstract: Land surface temperature (LST) prediction is of great importance for climate change, ecology, environmental and industrial studies. These studies require accurate LST map predictions considering both spatial and temporal dynamics. In this study, ... ...

Abstract	Land surface temperature (LST) prediction is of great importance for climate change, ecology, environmental and industrial studies. These studies require accurate LST map predictions considering both spatial and temporal dynamics. In this study, multilayer perceptron (MLP), long short-term memory (LSTM) and an integrated machine learning model, namely Convolutional LSTM (ConvLSTM), were utilized for one step ahead LST prediction. Data were gathered from 1-day (MYD11A1) and 8-day composite (MYD11A2) Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, which have 1-km × 1-km spatial resolution. Considering the inability of MODIS sensors to provide LST data under cloudy conditions, Inverse DISTANCE WEIGHTING (IDW), natural neighbor (NN), and cubic spline (C) methods were used to overcome the missing pixel problem. The proposed methods were tested over the Northern part of Adana province, Turkey, and the performances of the models were quantitatively evaluated through performance measures, namely, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The selected datasets range from 01 January 2017 to 01 November 2020 and from 01 January 2015 to 01 November 2020 for daily LST and 8-day composite LST, respectively. While 60% of the datasets were used as training set, the remaining 40% were used as validation (20%) and test (20%) sets. RMSE maps were generated to evaluate the pixelwise performance of the proposed method. On the other hand, the best average RMSE and MAE for the daily test set were obtained from the combination of ConvLSTM and NN (NN-ConvLSTM) as 3.62 °C and 2.85 °C, respectively, while they were acquired 3.57 °C and 2.69
MeSH term(s)	Deep Learning ; Neural Networks, Computer ; Satellite Imagery ; Spatial Analysis ; Temperature
Language	English
Publishing date	2022-05-06
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1178791-0
ISSN	1614-7499 ; 0944-1344
ISSN (online)	1614-7499
ISSN	0944-1344
DOI	10.1007/s11356-022-20572-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: An evaluation of the knowledge, attitudes, and behaviors of parents regarding epilepsy.

Akbas, Yılmaz / Kartal, Servet

Epilepsy & behavior : E&B

2022 Volume 129, Page(s) 108621

Abstract: Background and aims: Knowledge about epilepsy and attitudes toward people with epilepsy can influence measures taken to manage epilepsy and seizures. The support and understanding of parents of children with epilepsy are invaluable in helping develop ... ...

Abstract	Background and aims: Knowledge about epilepsy and attitudes toward people with epilepsy can influence measures taken to manage epilepsy and seizures. The support and understanding of parents of children with epilepsy are invaluable in helping develop ordinary living skills. To determine the educational needs of parents of children with epilepsy, their knowledge, attitudes, and behaviors should be evaluated. Therefore, we interviewed parents who had a child with epilepsy who was treated at the pediatric neurology clinic of a university hospital. We aimed to evaluate parents' knowledge, attitudes, and behaviors toward children with epilepsy to determine their educational needs. Methods: This descriptive study included 221 parents of children with epilepsy who applied to Hatay Mustafa Kemal University Medical Faculty Hospital. A questionnaire was used to collect data. Pearson and exact chi-square tests were used for analysis. Results: In the present study, 221 parents were interviewed. A total of 66.5% of the participants were at the primary education level. The rate of participants who preferred healthcare professionals as a source of information about epilepsy was 78.9%. Forty-eight percent of the parents did not see consanguineous marriage as a reason. While some parents stated that epilepsy was supernatural, others had no idea whether it was contagious or not, and 46.2% of the participants stated that epilepsy is not a hereditary disease. The rate of those who tried nonphysician treatment was 16.3%. The rate of participants who thought that drinking alcohol would not trigger seizures was 86.9%. It was determined that 30.8% of the participants pulled the patient's tongue out during seizures. 16.7% of participants thought that patients with epilepsy were disabled. 50.7% of the participants stated that their children with epilepsy could do any profession. Conclusions: Our study documented parents' lack of knowledge about epilepsy. Many parents have significant misunderstandings, negative attitudes, and parenting practices, and their knowledge and understanding of epilepsy needs to be improved.
MeSH term(s)	Child ; Epilepsy/therapy ; Health Knowledge, Attitudes, Practice ; Humans ; Parents ; Seizures ; Surveys and Questionnaires
Language	English
Publishing date	2022-02-23
Publishing country	United States
Document type	Journal Article
ZDB-ID	2010587-3
ISSN	1525-5069 ; 1525-5050
ISSN (online)	1525-5069
ISSN	1525-5050
DOI	10.1016/j.yebeh.2022.108621
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Article ; Online: Prediction of MODIS land surface temperature using new hybrid models based on spatial interpolation techniques and deep learning models

Kartal, Serkan / Sekertekin, Aliihsan

Environ Sci Pollut Res. 2022 Sept., v. 29, no. 44 p.67115-67134

2022

Abstract	Land surface temperature (LST) prediction is of great importance for climate change, ecology, environmental and industrial studies. These studies require accurate LST map predictions considering both spatial and temporal dynamics. In this study, multilayer perceptron (MLP), long short-term memory (LSTM) and an integrated machine learning model, namely Convolutional LSTM (ConvLSTM), were utilized for one step ahead LST prediction. Data were gathered from 1-day (MYD11A1) and 8-day composite (MYD11A2) Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, which have 1-km × 1-km spatial resolution. Considering the inability of MODIS sensors to provide LST data under cloudy conditions, Inverse DISTANCE WEIGHTING (IDW), natural neighbor (NN), and cubic spline (C) methods were used to overcome the missing pixel problem. The proposed methods were tested over the Northern part of Adana province, Turkey, and the performances of the models were quantitatively evaluated through performance measures, namely, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The selected datasets range from 01 January 2017 to 01 November 2020 and from 01 January 2015 to 01 November 2020 for daily LST and 8-day composite LST, respectively. While 60% of the datasets were used as training set, the remaining 40% were used as validation (20%) and test (20%) sets. RMSE maps were generated to evaluate the pixelwise performance of the proposed method. On the other hand, the best average RMSE and MAE for the daily test set were obtained from the combination of ConvLSTM and NN (NN-ConvLSTM) as 3.62 °C and 2.85 °C, respectively, while they were acquired 3.57 °C and 2.69 ᵒC from the combination of MLP and NN (NN-MLP) for the 8-day composite LST test set. The results revealed that the proposed hybrid models could be used for one step ahead spatiotemporal prediction of LST data.
Keywords	climate change ; data collection ; ecology ; neural networks ; prediction ; spectroradiometers ; surface temperature ; temporal variation
Language	English
Dates of publication	2022-09
Size	p. 67115-67134.
Publishing place	Springer Berlin Heidelberg
Document type	Article ; Online
ZDB-ID	1178791-0
ISSN	1614-7499 ; 0944-1344
ISSN (online)	1614-7499
ISSN	0944-1344
DOI	10.1007/s11356-022-20572-9
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Cold plasma application to fresh green leafy vegetables: Impact on microbiology and product quality.

Özdemir, Emel / Başaran, Pervin / Kartal, Sehban / Akan, Tamer

Comprehensive reviews in food science and food safety

2023 Volume 22, Issue 6, Page(s) 4484–4515

Abstract: Fresh green leafy vegetables (FGLVs) are consumed either garden-fresh or by going through very few simple processing steps. For this reason, foodborne diseases that come with the consumption of fresh products in many countries have prioritized the ... ...

Abstract	Fresh green leafy vegetables (FGLVs) are consumed either garden-fresh or by going through very few simple processing steps. For this reason, foodborne diseases that come with the consumption of fresh products in many countries have prioritized the development of new and reliable technologies to reduce food-related epidemics. Cold plasma (CP) is considered one of the sustainable and green processing approaches that inactivate target microorganisms without causing a significant temperature increase during processing. This review presents an overview of recent developments regarding the commercialization potential of CP-treated FGLVs, focusing on specific areas such as microbial inactivation and the influence of CP on product quality. The effect of CP differs according to the power of the plasma, frequency, gas flow rate, application time, ionizing gases composition, the distance between the electrodes and pressure, as well as the characteristics of the product. As well as microbial decontamination, CP offers significant potential for increasing the shelf life of perishable and short-shelf-life products. In addition, organizations actively involved in CP research and development and patent applications (2016-2022) have also been analyzed.
MeSH term(s)	Vegetables ; Plasma Gases ; Food Microbiology ; Microbial Viability ; Foodborne Diseases/microbiology
Chemical Substances	Plasma Gases
Language	English
Publishing date	2023-09-04
Publishing country	United States
Document type	Review ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2185829-9
ISSN	1541-4337 ; 1541-4337
ISSN (online)	1541-4337
ISSN	1541-4337
DOI	10.1111/1541-4337.13231
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Association between work-related musculoskeletal symptoms and quality of life among dental students: a cross-sectional study.

Sezer, Berkant / Kartal, Sinan / Sıddıkoğlu, Duygu / Kargül, Betül

BMC musculoskeletal disorders

2022 Volume 23, Issue 1, Page(s) 41

Abstract: Background: Dental students are frequently affected by work-related musculoskeletal symptoms (WMSs) due to reasons such as working conditions, difficult education process and long work periods. The aim of the study was to investigate the frequency and ... ...

Abstract	Background: Dental students are frequently affected by work-related musculoskeletal symptoms (WMSs) due to reasons such as working conditions, difficult education process and long work periods. The aim of the study was to investigate the frequency and anatomical distribution of WMSs, and its effect on the quality of life (QoL) in dental students. Methods: Sociodemographic and health-related characteristics of one-hundred and five dental students were recorded. WMSs were scored by the participants with the Nordic Musculoskeletal Questionnaire. Then, participants were asked to evaluate their QoL by scoring the World Health Organization Quality of Life-Brief Form. Differences between independent groups for continuous variables were evaluated by Student's t-test and ANOVA as appropriate. Linear regression analysis was performed to determine the effect of demographic and health-related parameters in predicting the QoL subscales. Results: The most common painful region in the last 12 months was the neck (66.7%). The body region with the most WMSs in the last 7 days was the upper back (43.8%). Physical health-related QoL of those with diagnosed musculoskeletal symptoms, and general health-related QoL of those using medicine due to any musculoskeletal symptoms were found to be statistically significantly lower (p = 0.018, p = 0.041, respectively). It was observed that the general and physical health, psychological well-being, and social relationship of the participants who reported the presence of neck pain in the last 7 days were statistically significantly lower (p = 0.003, p < 0.001, p = 0.004, p = 0.012; respectively). According to multiple regression analyses, pain occurrence in the body in the last 12 months and/or in the last 7 days had a negative impact on the participants' general and physical health, psychological well-being, social relationship, and environmental status and related QoL (p = 0.026, p = 0.047, p = 0.021, p = 0.001, p = 0.027, respectively). Conclusions: The results of this study show that dental students' body regions, especially the neck and the back, are affected by WMSs. These negative changes observed in the body had a negative effect on the QoL of the dental students.
MeSH term(s)	Cross-Sectional Studies ; Humans ; Musculoskeletal Diseases ; Occupational Diseases ; Quality of Life ; Students, Dental ; Surveys and Questionnaires
Language	English
Publishing date	2022-01-10
Publishing country	England
Document type	Journal Article
ISSN	1471-2474
ISSN (online)	1471-2474
DOI	10.1186/s12891-022-04998-3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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