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Article Citation - WoS: 47Citation - Scopus: 67Deep Learning Based Fall Detection Using Smartwatches for Healthcare Applications(Elsevier Sci Ltd, 2022) Sengul, Gokhan; Karakaya, Murat; Misra, Sanjay; Abayomi-Alli, Olusola O.; Damasevicius, RobertasWe implement a smart watch-based system to predict fall detection. We differentiate fall detection from four common daily activities: sitting, squatting, running, and walking. Moreover, we separate falling into falling from a chair and falling from a standing position. We develop a mobile application that collects the acceleration and gyroscope sensor data and transfers them to the cloud. In the cloud, we implement a deep learning algorithm to classify the activity according to the given classes. To increase the number of data samples available for training, we use the Bica cubic Hermite interpolation, which allows us to improve the accuracy of the neural network. The 38 statistical data features were calculated using the rolling update approach and used as input to the classifier. For activity classification, we have adopted the bi-directional long short-term memory (BiLSTM) neural network. The results demonstrate that our system can detect falling with an accuracy of 99.59% (using leave-one-activityout cross-validation) and 97.35% (using leave-one-subject-out cross-validation) considering all activities. When considering only binary classification (falling vs. all other activities), perfect accuracy is achieved.Article Citation - WoS: 78Citation - Scopus: 116Improving Sustainability in the Tourism Industry Through Blockchain Technology: Challenges and Opportunities(Elsevier Sci Ltd, 2022) Erol, Ismail; Neuhofer, Irem Onder; Dogru, Tarik (Dr. True); Oztel, Ahmet; Searcy, Cory; Yorulmaz, Ali C.The tourism industry is extremely important to the world economy; yet, the industry falls short when it comes to economic, social, and environmental issues. Blockchain as an information technology can be utilized to help solve these issues and establish sustainable tourism globally. However, the challenges to blockchain adoption in the tourism industry have not yet been examined systematically. The goal of this study, therefore, is three-fold: we first identify the challenges to blockchain using literature review and expert opinions. Then, we examine them using the proposed rough Interpretive Structural Modeling - Cross-Impact Matrix Multiplication based on expert judgments. Finally, we link these challenges to diffusion of innovation theory. The results suggest that "lack of technical maturity" and "lack of interoperability" are the most important challenges of blockchain in the tourism industry. The findings of the study support macro- and micro-level decision-making in tourism industry's prospective applications of blockchain.Article Innovative 3d Modeling of an Old Oil Field for Sustainable Production: Case Study of Katin-Barbes Oil Field (kbof), Se Anatolia- Turkey(Elsevier Sci Ltd, 2023) Ozer, Zafer; Kamaci, Zuheyr; Aydemir, AttilaThe goal of this study is to establish a workflow for the re-interpretation of almost depleted fields targeting the long-term sustainable oil production; in particular, for the oil fields in Turkey and the neighboring Middle Eastern countries located on the fold and thrust belts of the Zagros Mountains. It also fills some of the gaps in our understanding of the northern part of the Arabian Platform by describing the seismic characteristics of the Cretaceous reservoirs that were deposited during the Aptian to Turonian. The Katin-Barbes oil field (KBOF) in SETurkey was used as a case study. In this area, 3D seismic data were used for structural interpretation and remodeling of Cretaceous carbonates sedimented in the complex tectonic region. The well logs from 55 wells in the field were used to create a compilation of formation tops and were used as reference points for two separate sets of 3-D seismic data, acquired in 1991 and 2017. The quality of seismic data was improved with interpretation filters. The structural model was obtained by using various qualifiers from the seismic cubes and seismic facies changes were identified by analyzing a number of seismic attributes. Therefore, seismic data and velocities from the borehole measurements were combined to form a velocity model in building a structural model. Seismic attributes and well logs were used to create a porosity model. Consequently, top and base of two reservoir units; the Derdere and Sabunsuyu Formations have been clarified and re-defined, and potential new well locations were identified. Depending on the results of this investigation, 9 new wells were drilled in the potential areas in the KBOF, recently. Except the last one drilled on the NE boundary of the northern block, all wells have been completed as the "oil producing wells" and top of the reservoir units were encountered at almost the same depths in our depth model. Therefore, results and proposed methods in this research are confirmed by the real, borehole data. This research will be an examplary study for the re-evaluation of older and/or almost depleted oil fields, either in Turkey or in the other Middle Eastern countries.Article Citation - WoS: 198Citation - Scopus: 297Co-Lstm: Convolutional Lstm Model for Sentiment Analysis in Social Big Data(Elsevier Sci Ltd, 2021) Behera, Ranjan Kumar; Jena, Monalisa; Rath, Santanu Kumar; Misra, SanjayAnalysis of consumer reviews posted on social media is found to be essential for several business applications. Consumer reviews posted in social media are increasing at an exponential rate both in terms of number and relevance, which leads to big data. In this paper, a hybrid approach of two deep learning architectures namely Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) (RNN with memory) is suggested for sentiment classification of reviews posted at diverse domains. Deep convolutional networks have been highly effective in local feature selection, while recurrent networks (LSTM) often yield good results in the sequential analysis of a long text. The proposed Co-LSTM model is mainly aimed at two objectives in sentiment analysis. First, it is highly adaptable in examining big social data, keeping scalability in mind, and secondly, unlike the conventional machine learning approaches, it is free from any particular domain. The experiment has been carried out on four review datasets from diverse domains to train the model which can handle all kinds of dependencies that usually arises in a post. The experimental results show that the proposed ensemble model outperforms other machine learning approaches in terms of accuracy and other parameters.Article Citation - WoS: 3Citation - Scopus: 3Observation of in Situ Enhanced Crystallization, Negative Resistance Effect and Photosensitivity in Tl2ingase4< Crystals(Elsevier Sci Ltd, 2021) Qasrawi, A. F.; Irshaid, Tahani M. A.; Gasanly, N. M.In this work, we report the properties of Tl2InGaSe4 crystals as multifunctional material. Namely, Tl2InGaSe4 crystals are grown by the modified Bridgman method using mixtures of TlInSe2 (50%) and TlGaSe2 (50%) single crystals. The enhanced crystallization and structural stabilities are monitored by the X-ray diffraction technique during the in situ heating and cooling cycles. The structural analyses on the Tl2InGaSe4 crystals revealed domination of both of the monoclinic and tetragonal phases in the crystals. In addition, the produced crystals are used to fabricate Schottky diodes. While the scanning electron microscopy has shown that the crystals are composed of layered nanosheets, the electrical analyses have shown that the crystals exhibit light photosensitivity of 12.7 under tungsten light illumination of 10 kLuxes. The attenuation in the electrical parameters of the Ag/Tl2InGaSe4/C diodes presented by series resistance, barrier height and ideality factor upon light excitations make them promising for applications in optoelectronics as switches and photodetectors. Moreover, the alternating electrical signals analyses on the capacitance spectra displayed resonance -antiresonance oscillations in the frequency domain of 83-100 MHz. The resistance spectra also exhibited negative resistance effect in the range of 55-135 MHz. These features of the device make it suitable for use as microwave resonators and memory devices as well.Article Citation - WoS: 1Citation - Scopus: 2Precision Forecasting for Hybrid Energy Systems Using Five Deep Learning Algorithms for Meteorological Parameter Prediction(Elsevier Sci Ltd, 2025) Ceylan, Ceren; Yumurtaci, ZehraThe intermittent nature of renewable energy sources necessitates accurate power production forecasting to ensure system sustainability and balance between energy supply and demand. Although the deep learning-based meteorological forecasting is significantly studied in literature, most of the current literature applies single-algorithm based on each individual energy source and less multi-algorithm based on comparative studies on multiple architectures as applied to integrated hybrid systems. In addition, most of the research uses the same algorithmic solution to all the meteorological parameters without identifying parameter-specific optimization potential, and recent research is verified on actual future time steps instead of historical train-test split. This study presents a comprehensive comparative analysis of five deep learning algorithms, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and CNN-LSTM hybrid, for forecasting critical meteorological parameters (wind speed, ambient temperature, and solar radiation) that determine energy output in a wind and solar-based hybrid energy system (HES). Using five years of Istanbul meteorological data (2018-2022), optimal algorithms were systematically identified for each parameter through rigorous hyperparameter optimization and cross-validation. Key results demonstrate that GRU achieves superior performance in wind speed prediction (RMSE: 0.049 m/s, R2: 0.8634) and solar radiation forecasting (RMSE: 0.146 W/m2, R2: 0.6643), while CNN-LSTM excels in ambient temperature prediction (RMSE: 0.011 degrees C, R2: 0.9976). The integrated approach predicted annual hybrid system energy production with 89 % accuracy, demonstrating 0.48 % deviation from observed values. Most significantly, our framework successfully forecasted sixth year (2023) energy production with 1.55 % error, validating its real-world applicability. This research contributes to the methodological advancement of renewable energy forecasting by systematically identifying optimal algorithmic approaches for different meteorological parameters in hybrid systems, thereby supporting the integration of intermittent renewable sources into sustainable energy infrastructures.Article Citation - WoS: 36Citation - Scopus: 44An Algorithmic Approach for the Dynamic Reliability Analysis of Non-Repairable Multi-State Weighted k-out-of-n< System(Elsevier Sci Ltd, 2014) Eryilmaz, Serkan; Bozbulut, Ali RizaIn this paper, we study a multi-state weighted k-out-of-n:G system model in a dynamic setup. In particular, we study the random time spent by the system with a minimum performance level of k. Our method is based on ordering the lifetimes of the system's components in different state subsets. Using this ordering along with the Monte-Carlo simulation algorithm, we obtain estimates of the mean and survival function of the time spent by the system in state k or above. We present illustrative computational results when the degradation in the components follows a Markov process. (C) 2014 Elsevier Ltd. All rights reserved.Article Citation - WoS: 45Citation - Scopus: 48Age-Based Preventive Maintenance for Coherent Systems With Applications To Consecutive-k-out-of-n< and Related Systems(Elsevier Sci Ltd, 2020) Eryilmaz, SerkanThis article presents a signature-based representation for the expected cost rate of age-based preventive maintenance policy for a binary coherent system consisting of independent exponential components, and then specializes the method to consecutive k-out-of-n system and its generalizations. According to the age-based preventive maintenance policy, the system is replaced at failure or before failure. For an arbitrary coherent system, the number of failed components at replacement time is a random variable. Thus, the expected cost per unit of time involves the mean number of failed components at replacement time. This mean is represented in terms of signature. Extensive numerical and graphical examples are presented for m-consecutive k-out-of-n:F and consecuthre-k-within-m-out-of-n:F systems.Article Citation - WoS: 113Citation - Scopus: 133Identification of Meat Species by Using Laser-Induced Breakdown Spectroscopy(Elsevier Sci Ltd, 2016) Bilge, Gonca; Velioglu, Hasan Murat; Sezer, Banu; Eseller, Kemal Efe; Boyaci, Ismail HakkiThe aim of the present study is to identify meat speciesby using laser-induced breakdown spectroscopy (LIBS). Elemental composition differences between meat species were used for meat identification. For this purpose, certain amounts of pork, beef and chicken were collected from different sources and prepared as pellet form for LIBS measurements. The obtained LIBS spectra were evaluated with some chemometric methods, and meat species were qualitatively discriminated with principal component analysis (PCA) method with 83.37% ratio. Pork beef and chicken-beef meat mixtures were also analyzed with partial least square (PLS) method quantitatively. Determination coefficient (R-2) and limit of detection (LOD) values were found as 0.994 and 4.4% for pork adulterated beef, and 0.999 and 2.0% for chicken adulterated beef, respectively. In the light of the findings, it was seen that LIBS can be a valuable tool for quality control measurements of meat as a routine method. (C) 2016 Elsevier Ltd. All rights reserved.Article Citation - WoS: 1Citation - Scopus: 1Energy Expenditure and Glucose-Lowering Effect of Different Exercise Modalities in Diabetes Mellitus(Elsevier Sci Ltd, 2022) Bozdemir-Ozel, Cemile; Arikan, Hulya; Calik-Kutukcu, Ebru; Karaduz, Beyza Nur; Inal-Ince, Deniz; Kabakci, Giray; Dagdelen, SelcukObjectives Hypoglycaemia is a serious complication of exercise in patients with type 2 diabetes mellitus (T2DM). The aim of this study was to test energy expenditure and the degree of the glucose-lowering effect of different exercise modalities. Design Cross-sectional study Participants This study included 44 patients {35 women and nine men, mean age 51 [standard deviation (SD) 5] years} with T2DM [mean HbA1c 7% (SD 1%)]. Main outcome measures Standardised exercise tests for walking, running and cycling were performed using the 6-minute walk test (6MWT), incremental shuttle walk test (ISWT), and symptom-limited maximal cycle exercise test, respectively. Energy expenditure was assessed with a multisensory accelerometer. Change in capillary glucose levels ( increment glucose) was measured before and after each exercise modality. Results increment Glucose was lower in the 6MWT {median 14 [interquartile range (IQR) 22] mg/dl} than in the ISWT [median 18 (IQR 23) mg/ dl; median difference 7 mg/dl, 95% confidence interval (CI) of the difference 3-11] and the cycle test [median 18 (IQR 24) mg/dl; median difference 7 mg/dl, 95% CI 0-16]. Energy expenditure was lower during the 6MWT [median 41 (IQR 18) Kcal] compared with the ISWT [median 51 (IQR 23) Kcal; median difference 11 Kcal, 95% CI 6-16] and the cycle test [median 44 (IQR 25) Kcal; median difference 6 Kcal, 95% CI 0-13]. Conclusions Energy expenditure and corresponding glucose-lowering effect during exercise in patients with T2DM can be predicted from the results of an exercise test. The type of exercise is related to the risk of hypoglycaemia. Walking is associated with the lowest energy expenditure and risk of hypoglycaemia, while cycling and running/jogging cause higher energy expenditure and greater reductions in glucose in patients with T2DM. Contribution of the paper center dot Energy expenditure and risk of hypoglycaemia during exercise can be predicted by exercise tests. center dot The intensity and type of exercise are related to the risk of hypoglycaemia. center dot The change in glucose level was greater for running and cycling than for walking. (c) 2022 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.
