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Öğe A Systematic Review of IoT Technology and Applications in Animals(Kafkas Univ, Veteriner Fakultesi Dergisi, 2024) Ozger, Zeynep Banu; Cihan, Pinar; Gokce, ErhanPrecision Livestock Farming (PLF) is a mechanism that manages a production system. This mechanism includes mathematical models and controllable inputs that can predict inputs with processes and results that can be monitored periodically. These parameters of PLF systems can improve resource use efficiency and reduce cultivation costs. Many situations, such as the behaviour of animals on farms, their nutrition, estrus cycles, and epidemics, can be monitored with wearable devices containing various sensors. However, real-time monitoring of the data collected by these devices is possible with Internet of Things (IoT) technology. IoT is a multi-layered network that enables sensors within the system to communicate with each other and implement certain decisions when necessary. Sensors and IoT devices extract information from the raw data they collect from the environment, which is then shared with other objects, devices, or servers via the internet. The real-time data collection, processing, and analysis provided by IoT enables improvements in the management of animal farms. This systematic review addresses IoT concepts and applications in the livestock sector from a systematic perspective for different animal farms.Öğe AI-aided cardiovascular disease diagnosis in cattle from retinal images: Machine learning vs. deep learning models(Elsevier Sci Ltd, 2024) Cihan, Pinar; Saygili, Ahmet; Ermutlu, Celal Sallin; Aydin, Ugur; Aksoy, OzgurCardiovascular diseases (CVD) in animals can severely impact the heart and circulatory systems, like those in humans. Early diagnosis and treatment are crucial for improving animal welfare and lifespan. Traditional diagnostic methods face challenges such as insufficient anamnesis information, high costs of biochemical and hematological tests, and increasing data complexity. This study aims to address these issues by developing AIbased diagnostic systems for fast and accurate CVD diagnosis in cattle using retinal images. A total of 1118 retinal images from 100 cattle were collected, with 52 diagnosed with CVD and 48 as non-CVD. The dataset is publicly available on Kaggle. We evaluated three machine learning methods (Extreme Learning Machine, KNearest Neighbors, and Support Vector Machine) and four deep learning models (DenseNet201, ResNet101, SqueezeNet, and InceptionV3) for their diagnostic capabilities. ResNet101 emerged as the most effective model, achieving an accuracy of 96.1 f 3.15 %, sensitivity of 97.3 f 2.96 %, specificity of 94.9 f 4.07 %, and an F1score of 96.4 f 0.03. This study demonstrates that AI-based systems, particularly deep learning models, can significantly improve the accuracy of CVD diagnosis in animals, marking a critical advancement in veterinary healthcare.Öğe Artificial intelligence methods for modeling gasification of waste biomass: a review(Springer, 2024) Alfarra, Fatma; Ozcan, H. Kurtulus; Cihan, Pinar; Ongen, Atakan; Guvenc, Senem Yazici; Ciner, Mirac NurGasification is a highly promising thermochemical process that shows considerable potential for the efficient conversion of waste biomass into syngas. The assessment of the feasibility and comparative advantages of different biomass and waste gasification schemes is contingent upon a multifaceted combination of interrelated criteria. Conventional analytical approaches employed to facilitate decision-making rely on a multitude of inadequately defined parameters. Consequently, substantial efforts have been directed toward enhancing the efficiency and productivity of thermochemical conversion processes. In recent times, artificial intelligence (AI)-based models and algorithms have gained prominence, serving as indispensable tools for expediting these processes and formulating strategies to address the growing demand for energy. Notably, machine learning (ML) and deep learning (DL) have emerged as cutting-edge AI models, demonstrating exceptional effectiveness and profound relevance in the realm of thermochemical conversion systems. This study provides an overview of the machine learning (ML) and deep learning (DL) approaches utilized during gasification and evaluates their benefits and drawbacks. Many industries and applications related to energy conversion systems use AI algorithms. Predicting the output of conversion systems and subjects linked to optimization are two of this science's critical applications. This review sheds light on the burgeoning utility of AI, particularly ML and DL, which have garnered significant attention due to their applications in productivity prediction, process optimization, real-time process monitoring, and control. Furthermore, the integration of hybrid models has become commonplace, primarily owing to their demonstrated success in modeling and optimization tasks. Importantly, the adoption of these algorithms significantly enhances the model's capability to tackle intricate challenges, as DL methodologies have evolved to offer heightened accuracy and reduced susceptibility to errors. Within the scope of this study, an exhaustive exploration of ML and DL techniques and their applications has been conducted, uncovering existing research knowledge gaps. Based on a comprehensive critical analysis, this review offers recommendations for future research directions, accentuating the pivotal findings and conclusions derived from the study.Öğe Bayesian Hyperparameter Optimization of Machine Learning Models for Predicting Biomass Gasification Gases(Mdpi, 2025) Cihan, PinarPredicting biomass gasification gases is crucial for energy production and environmental monitoring but poses challenges due to complex relationships and variability. Machine learning has emerged as a powerful tool for optimizing and managing these processes. This study uses Bayesian optimization to tune parameters for various machine learning methods, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), Elastic Net, Adaptive Boosting (AdaBoost), Gradient-Boosting Regressor (GBR), K-nearest Neighbors (KNN), and Decision Tree (DT), aiming to identify the best model for predicting the compositions of CO, CO2, H2, and CH4 under different conditions. Performance was evaluated using the correlation coefficient (R), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Relative Absolute Error (RAE), and execution time, with comparisons visualized using a Taylor diagram. Hyperparameter optimization's significance was assessed via t-test effect size and Cohen's d. XGBoost outperformed other models, achieving high R values under optimal conditions (0.951 for CO, 0.954 for CO2, 0.981 for H2, and 0.933 for CH4) and maintaining robust performance under suboptimal conditions (0.889 for CO, 0.858 for CO2, 0.941 for H2, and 0.856 for CH4). In contrast, K-nearest Neighbors (KNN) and Elastic Net showed the poorest performance and stability. This study underscores the importance of hyperparameter optimization in enhancing model performance and demonstrates XGBoost's superior accuracy and robustness, providing a valuable framework for applying machine learning to energy management and environmental monitoring.Öğe CattNIS: Novel identification system of cattle with retinal images based on feature matching method(Elsevier Sci Ltd, 2024) Saygili, Ahmet; Cihan, Pinar; Ermutlu, Celal Sahin; Aydin, Ugur; Aksoy, OzgurSmall and large farm animals are frequently identified using techniques like microchipping or tagging. However, these techniques may have a detrimental impact on animal welfare and may result in theft and fraud. Modern and sophisticated identification procedures using biometrics, including retina, face, and iris recognition, are available and have less of a detrimental effect on animal welfare. Because biometric markers are unchangeable and unaffected, it is feasible to stop situations of theft and fraud. This study uses retina images to identify animals based on each animal ' s different and specific retinal patterns. To the best of our knowledge, there isn ' t a publicly accessible dataset for images of animal retinas. The three main goals of this study are to: 1) produce a novel dataset of Cattle retinal images and make it publicly available; 2) create a successful image processing -based system for retinal identification and recognition; and 3) create a graphical user interface (GUI) that enables querying the database to ascertain whether a specific Cattle retina is present. To accomplish these goals, a dataset comprising 2430 images from the left and right eyes of 300 cattle was produced. After the dataset was created, the animal retinal images were segmented using image processing methods such as scaling, color transformations, image sharpening, contrast enhancements, noise filtering, and histogram equalization. By using the segmented images with the SURF, FAST, BRISK, and HARRIS techniques, distinctive retinal features were found. During the identification phase, the animal whose retinal characteristic matched the sought animal ' s retina the closest was recognized. Within the developed CattNIS system, the SURF approach has the highest accuracy rate of 92.25 percent. The results of this study show that the identification of cattle using retinal vascular patterns is highly successful. The availability of the collected cattle retinal images from this study is expected to significantly contribute to the advancement of future research in this field, both in terms of quantity and quality.Öğe Comparative Performance Analysis of Deep Learning, Classical, and Hybrid Time Series Models in Ecological Footprint Forecasting(Mdpi, 2024) Cihan, PinarIn a globalized world, factors such as increasing population, rising production rates, changing consumption habits, and continuous economic growth contribute significantly to climate change. Therefore, successfully forecasting the Ecological Footprint (EF) effectively indicates global sustainable development. Despite the significant role of the EF as one of the indicators of sustainable development, there is a gap in the literature regarding time series methods and forward-looking predictions. To address this gap, Ecological Footprint (EF) forecasting was performed using deep learning methods such as LSTMs, classical time series methods like ARIMA and Holt-Winters, and the developed hybrid ARIMA-SVR model. In the scope of the study, first, a spreadsheet was created using the total Ecological Footprint (EF) worldwide between 1961 and 2022, obtained from the Global Footprint Network database. Second, the forecasting performances of the ARIMA, Holt-Winters, LSTM, and the hybrid ARIMA-SVR models were compared using MAPE and RMSE metrics. Finally, the forecasting performances of the time series models were statistically validated through Wilcoxon Signed-Rank and Friedman tests. The study findings indicate that the proposed ARIMA (1,1,0) model demonstrated better performance with an average MAPE of 2.12%, compared to Holt-Winters (MAPE of 2.27%), LSTM (MAPE of 3.19%), and ARIMA-SVR (MAPE of 2.68%) methods in the test dataset. Additionally, it was observed that the ARIMA model forecasted the EF, which experienced a sudden decrease due to the COVID-19 lockdown, with a lower error compared to other models. These findings highlight the adaptability of the ARIMA model to variable and uncertain conditions.Öğe Computational analysis of virus-host protein-protein interactions using gene ontology and natural language processing(Springer, 2025) Cihan, Pinar; Ozger, Zeynep Banu; Cakabay, ZeynepThe role of in-silico computational methods in identifying protein-protein interactions (PPIs) between target and host proteins is crucial for developing effective infection treatments. These methods are essential for quickly determining high-quality and accurate PPIs, predicting protein pairs with the highest likelihood of physical interaction from a large pool, and reducing the need for experimental confirmation or prioritizing pairs for experiments. This study proposes using gene ontology and natural language processing (NLP) approaches to extract and quantify features from protein sequences. In the first step, proteins were represented using gene ontology terms, and a set of features was generated. In the second step, NLP techniques treated gene ontology terms as a word dictionary, creating numerical vectors using the bag of words (BoW), count vector, term frequency-inverse document frequency (TF-IDF), and information content methods. In the third step, different machine learning methods, including Decision Tree, Random Forest, Bagging-RepTree, Bagging-RF, BayesNet, Deep Neural Network (DNN), Logistic Regression, Support Vector Machine (SVM), and VotedPerceptron, were employed to predict protein interactions in the datasets. In the fourth step, the Max-Min Parents and Children (MMPC) feature selection algorithm was applied to improve predictions using fewer features. The performance of the developed method was tested on the SARS-CoV-2 protein interaction dataset. The MMPC algorithm reduced the feature count by over 99%, enhancing protein interaction prediction. After feature selection, the DNN method achieved the highest predictive performance, with an AUC of 0.878 and an F-Measure of 0.793. Sequence-based protein encoding methods AAC, APAAC, CKSAAPP, CTriad, DC, and PAAC were applied to proteins in the SARS-CoV-2 interaction dataset and their performance was compared with GO-NLP. The performance of the relevant methods was measured separately and combined. The highest performance was obtained from the combined dataset with an AUC value of 0.888. This study demonstrates that the proposed gene ontology and NLP approach can successfully predict protein-protein interactions for antiviral drug design with significantly fewer features using the MMPC-DNN model.Öğe HAVA KİRLETİCİ PARAMETRELERİNİN HAVA KALİTESİ İNDEKSİNE UZUN ZAMANLI ETKİLERİNİN İNCELENMESİ: ÇERKEZKÖY ORGANİZE SANAYİ BÖLGESİ ÖRNEĞİ(Kırklareli Üniversitesi, 2021) Özel, Hüseyin; Cihan, Pinar; Özcan, H. Kurtuluş; Aydin, Serdar; Hanedar, AsudeHava kirliliği, küresel ölçekte en önemli halk sağlığı etkenlerinden birisidir. Bu nedenle ulusal ve uluslararası değerlendirmeler sonucu hava kalitesini tayin etmek için izlenmesi gereken parametreler belirlenmiştir. Azot dioksit (NO2), kükürt dioksit (SO2), ozon (O3), karbon monoksit (CO) ve partikül madde (PM10) ölçüm sonuçlarına göre hava kirliliği seviyeleri belirlenmektedir. Günümüzde insanlığın büyük bir bölümü yaşamlarını şehirlerde devam ettirmektedir. Yoğun nüfusun olduğu alanlardaki hava kirliliğinin olumsuz etkileri kısa ve uzun vadede çeşitli sağlık sorunlarına neden olmaktadır. İnsani aktiviteler olan ulaşım, ısınma ve sanayi kaynaklı hava kirliliğinin yanı sıra yanardağ, toz taşıma, orman yangınları gibi doğal kaynaklı hava kirliliği oluşmaktadır. Şehir ölçeğinde hava kirliliğinin oluşumunu önleyebilmek için hava kirliliğinin ölçümlerle takip edilmesi ve sonuçlara istinaden gereken önleyici politikaların geliştirilmesi gerekmektedir. Hava kirleticilerinin konsantrasyonlarının takibinde kullanılan Hava kalitesi endeksi (HKİ), hava kalitesinin önemli bir göstergesidir. Bu çalışmada, bir sanayi bölgesindeki hava kirleticilerin hava kalitesi indeksine etkilerinin zamansal değişiminin incelenmesi amaçlanmıştır. Çalışma alanı olarak yoğun endüstriyel faaliyetlerin olduğu Çerkezköy bölgesi seçilmiş ve 4 yıllık hava kalitesi indeksi değişimi incelenmiştir. Ayrıca 2020 yılında COVID-19 pandemisi kaynaklı kısıtlamaların HKİ’ye olan etkileri araştırılmıştır. Elde edilen bulgular ışığında; 2016-2020 yılları arasında HKİ değerlerin ağırlıklı olarak orta ve iyi seviyede olduğu görülmektedir. Ayrıca 2019 ve 2020 yıllarında 2016 ve 2017 yıllarına nazaran HKİ değerlerinin daha iyi seviyede olduğu görülmüştür.Öğe Horse Surgery and Survival Prediction with Artificial Intelligence Models: Performance Comparison of Original, Imputed, Balanced, and Feature-Selected Datasets(Kafkas Univ, Veteriner Fakultesi Dergisi, 2024) Cihan, PinarArtificial intelligence (AI) technology, while less advanced than in human medicine, holds significant potential in the field of veterinary medicine. This technology offers a range of essential benefits, such as disease diagnosis, treatment planning, disease control, and overall animal health improvement. Based on clinical data, this study uses 15 AI models to predict the necessity of surgery and the likelihood of survival in horses displaying symptoms of acute abdominal pain (colic). By comparing surgical and survival predictions across the original, imputed missing values, and balanced datasets, we determine the most effective dataset based on the average accuracy of the 15 AI models. Furthermore, we explore the potential for improved accuracy with a reduced feature set by calculating feature importance scores for surgery and survival predictions. Our results indicate that the balanced dataset achieved the highest average accuracy for predicting surgery and survival, with 80.76% and 77.96%, respectively. The Random Forest (RF) model outperformed others as the most accurate model for both surgery (accuracy = 85.83, Area Under the Curve [AUC] = 0.906) and survival prediction (accuracy = 80.75, AUC = 0.888). It was observed that reducing the number of features in the dataset by 56% led to an increase in surgery prediction accuracy to 86.38%. Similarly, when the number of features was reduced by 24% for survival prediction, the prediction performance increased to 83.75%. This study emphasizes the importance of the precise implementation of artificial intelligence techniques in veterinary medicine, which can significantly enhance model performance.Öğe Identification and Recognition of Animals from Biometric Markers Using Computer Vision Approaches: A Review(Kafkas Univ, Veteriner Fakultesi Dergisi, 2023) Cihan, Pinar; Saygili, Ahmet; Ozmen, Nihat Eren; Akyuzlu, MuhammedAlthough classic methods (such as ear tagging, marking, etc.) are generally used for animal identification and recognition, biometric methods have gained popularity in recent years due to the advantages they offer. Systems utilizing biometric markers have been developed for various purposes in animal management, including more effective and accurate tracking of animals, vaccination, disease management, and prevention of theft and fraud. Animals' irises, retinas, faces, muzzle, and body patterns contain unique biometric markers. The use of these markers in computer vision approaches for animal identification and tracking systems has become a highly effective and promising research area in recent years. This review aims to provide a general overview of the latest developments in image processing approaches for animal identification and recognition applications. In this review, we examined in detail all relevant studies we could access from different electronic databases for each biometric method. Afterward, the opportunities and challenges of classical and biometric methods were compared. We anticipate that this study, which conducts a literature review on animal identification and recognition based on computer vision approaches, will shed light on future research towards developing automated systems with biometric methods.Öğe Performance of machine learning methods for cattle identification and recognition from retinal images(Springer, 2025) Cihan, Pinar; Saygili, Ahmet; Akyuzlu, Muhammed; Ozmen, Nihat Eren; Ermutlu, Celal Sahin; Aydin, Ugur; Yilmaz, AlicanAnimal identification is a critical issue in terms of security, traceability, and animal health, especially in large-scale livestock enterprises. Traditional methods (such as ear tags and branding) both negatively affect animal welfare and may lead to security vulnerabilities. This study aims to develop a biometric system based on retinal vascular patterns for the identification and recognition of cattle. This system aims to provide a safer and animal welfare-friendly alternative by using image processing techniques instead of traditional device-based methods. In the study, preprocessing, segmentation, feature extraction, and performance evaluation steps were applied for the biometric identification and recognition process using retinal images taken from both eyes. Techniques such as green channel extraction, contrast-limited adaptive histogram equalization, morphological operations, noise filtering, and threshold determination were used in the preprocessing stage. Fuzzy C-means, K-means, and Level-set methods were applied for segmentation, and feature extraction was performed using SIFT, SURF, BRISK, FAST, and HARRIS methods. At the end of the study, the highest accuracy rate was obtained as 95.6% for identification and 87.9% for recognition. In addition, the obtained dataset was shared publicly, thus creating a reusable resource that researchers from different disciplines can use. It was concluded that this study made a significant contribution to the field of biometric-based animal identification and recognition and offered a practically usable solution in terms of animal welfare and safety.
