Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 2Citation - Scopus: 2Temporal Electroencephalography Features Unveiled Via Olfactory Stimulus as Biomarkers for Mild Alzheimer's Disease(Elsevier Sci Ltd, 2025) Olcay, Bilal Orkan; Pehlivan, Murat; Karacali, BilgeAim: Our primary aim is to capture and use the timings of the characteristic brain responses to olfactory stimulation for mild Alzheimer's disease diagnosis purposes. Proposed method: Our method identifies the timings of short-lived signal segments where characteristic distances between pre- and post-stimulus relative spectral energies are attained for each EEG channel and frequency band. These timings and timing-derived features were subsequently used in a leave-one-subject-out cross-validation scenario to assess the diagnostic performance of our framework. We evaluated seven distinct statistical distance measures to determine the most effective one for characterizing the neurological conditions of the subjects. Results: The average cross-validation performance shows that our framework achieved 87.50% diagnosis performance. The frequently used features were mainly derived from the delta and alpha activity of the prefrontal region (Fp1) and the beta activity of the parietal region (Pz), which agree with the current findings of olfaction biophysics. Comparison with existing methods: We compared the performance of our method with that of four existing methods in the literature. Our method outperformed these four methods. Moreover, our method elicited the highest accuracy when the clinical olfactory score (UPSIT) was included as a feature. Conclusions: Our analysis framework reveals a significant alteration of the timing organization of the brain that emerged upon olfactory stimulation in Alzheimer's patients. The timings of characteristic response and the features calculated via these timings contribute to Alzheimer's disease diagnosis performance remarkably. The perspective proposed here may facilitate early diagnosis, thereby facilitating the exploration of novel therapeutic and treatment strategies.Article Citation - WoS: 6Citation - Scopus: 10Using Chemosensory-Induced Eeg Signals To Identify Patients With <i>de Novo</I> Parkinson's Disease(Elsevier Sci Ltd, 2024) Olcay, Orkan; Onay, Fatih; Ozturk, Guliz Akin; Oniz, Adile; Ozgoren, Murat; Hummel, Thomas; Guducu, CagdasObjective: Parkinson's disease (PD) patients generally exhibit an olfactory loss. Hence, psychophysical or electrophysiological tests are used for diagnosis. However, these tests are susceptible to the subjects' behavioral response bias and require advanced techniques for an accurate analysis. Proposed Approach: Using well-known feature extraction methods, we characterized chemosensory-induced EEG responses of the participants to classify whether they have PD. The classification was performed for different time intervals after chemosensory stimulation to see which temporal segment better separates healthy controls and subjects with de novo PD. Results: The performances show that entropy and connectivity features discriminate effectively PD and HC participants when olfactory-induced EEG signals were used. For these methods, discrimination is over 80% for segments 100-700 and 200-800 milliseconds after stimulus onset. Comparison with Existing Methods: We compared the performance of our framework with linear predictive coding, bispectrum, wavelet entropy-based methods, and TDI score-based classification. While the entropy- and connectivity-based methods elicited the highest classification performances for olfactory stimuli, the linear predictive coding-based method elicited slightly higher performance than our framework when the trigeminal stimuli were used. Conclusion: This is one of the first studies that use chemosensory-induced EEG signals along with different feature extraction methods to classify healthy subjects and subjects with de novo PD. Our results show that entropy and functional connectivity methods unravel the chemosensory-induced neural dynamics encapsulating critical information about the subjects' olfactory performance. Furthermore, time- and frequency-resolved feature analysis is beneficial for capturing disease-affected neural patterns.Article Citation - WoS: 13Citation - Scopus: 13Multi-Scale Benchtop 1h Nmr Spectroscopy for Milk Analysis(Academic Press, 2021) Söyler, Alper; Çıkrıkçı, Sevil; Çavdaroğlu, Çağrı; Bouillaud, Dylan; Farjon, Jonathan; Giraudeau, Patrick; Öztop, Mecit H.Benchtop NMR systems offers various advantages such as being easy to use, not requiring constant maintenance and being available at affordable prices. In this study, multiple aspects of benchtop NMR spectroscopy were explored to analyze milk in an industrial context, either regarding the quality of production or regarding the differentiation of the final product. The first part focuses on the production conditions of lactose hydrolysis in milk and quantitative online NMR spectroscopy was adapted to follow lactose hydrolysis in milk in continuous flow mode. The second part focuses on differentiating milk samples having different properties. 36 milk samples from France and Turkey were analysed and glycerol, fat and sugar contents were measured from the NMR spectra. Combination of spectroscopic data with a proposed Artificial Neural Network model enabled to classify milk of different origins and different properties. This study shows that benchtop NMR spectroscopy is a versatile non-destructive control method that can help controlling milk quality both during and after production. © 2020 Elsevier LtdArticle Citation - WoS: 42Citation - Scopus: 47Effects of Malaxation Temperature and Harvest Time on the Chemical Characteristics of Olive Oils(Elsevier Ltd., 2016) Jolayemi, Olusola Samuel; Tokatlı, Figen; Özen, BanuThe aim of the study was to determine the effects of harvest time and malaxation temperature on chemical composition of olive oils produced from economically important olive varieties with a full factorial experimental design. The oils of Ayvalik and Memecik olives were extracted in an industrial two-phase continuous system. The quality parameters, phenolic and fatty acid profiles were determined. Harvest time, olive variety and their interaction were the most significant factors. Malaxation temperature was significant for hydroxytyrosol, tyrosol, p-coumaric acid, pinoresinol and peroxide value. Early and mid-harvest oils had high hydroxytyrosol and tyrosol (maximum 20.7 mg/kg) and pigment concentrations (maximum chlorophyll and carotenoids as 4.6 mg/kg and 2.86 mg/kg, respectively). Late harvest oils were characterized with high peroxide values (9.2-25 meq O2/kg), stearic (2.4-3.1%) and linoleic acids (9.3-10.4%). Multivariate regression analysis showed that oxidative stability was affected positively by hydroxytyrosol, tyrosol and oleic acid and negatively by polyunsaturated fatty acids.Article Citation - WoS: 6Citation - Scopus: 7Classification of Manipulators of the Same Origin by Virtue of Compactness and Complexity(Elsevier Ltd., 2011) Gezgin, Erkin; Özdemir, SerhanThis work deals with a classification method that employs concepts such as complexity and compactness. The idea is to classify manipulators, or any other mechanism for that matter, of the same origin, based on the geometry of the joints, the tasks performed by the joints, the efficiency and the manufacturing cost to generate the specified efficiency. It is known that successive units on a single branch create individual uncertainties that affect the eventual quality of the performed operation [1]. An entropic expression quantifies this uncertainty in terms of the number of links and the unit effectiveness. The concepts of compactness and complexity have been formulated, and these concepts are explained through serial and parallel manipulators with varying parameters. Eventually, a cost function is created which is a function of complexity, uncertainty and the manufacturing cost. A worked example on M = 6 Stewart-Gough platform is given how this cost function could be taken advantage of when deciding an initial manipulator. A genetic algorithm is used for the optimization of the cost function, where the results are tabulated.Article Citation - WoS: 24Citation - Scopus: 31Dynamic Replication Strategies in Data Grid Systems: A Survey(Springer Verlag, 2015) Tos, Uras; Mokadem, Riad; Hameurlain, Abdelkader; Ayav, Tolga; Bora, ŞebnemIn data grid systems, data replication aims to increase availability, fault tolerance, load balancing and scalability while reducing bandwidth consumption, and job execution time. Several classification schemes for data replication were proposed in the literature, (i) static vs. dynamic, (ii) centralized vs. decentralized, (iii) push vs. pull, and (iv) objective function based. Dynamic data replication is a form of data replication that is performed with respect to the changing conditions of the grid environment. In this paper, we present a survey of recent dynamic data replication strategies. We study and classify these strategies by taking the target data grid architecture as the sole classifier. We discuss the key points of the studied strategies and provide feature comparison of them according to important metrics. Furthermore, the impact of data grid architecture on dynamic replication performance is investigated in a simulation study. Finally, some important issues and open research problems in the area are pointed out.