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Fingerprints are one of the most important scientific proof instruments in solving forensic cases. Identification in fingerprints consists of three levels based on the flow direction of the papillary lines at the first level, the minutiae points at the second level, and the pores at the third level. The inadequacy of existing imaging systems in detecting fingerprints and the lack of pore details at the desired level limit the widespread use of third-level identification. The fact that fingerprints with images based on pores in the unsolved database are not subjected to any evaluation criteria and remain in the database reveals the importance of the study to be carried out. In this study, different from classical fingerprint identification methods, a direct pore-based identification system for fingerprint matching is proposed with the dataset created by using the Docucenter Nirvis device and Projectina Image Acquisition-7000 software as a hyperspectral imaging system where pores were visualized more clearly. Although difficult from an operational perspective, the pores in the 800 fingerprints in the database were manually marked for the accuracy of the results. Next, by using a scoring based on iterative closest point algorithm, latent fingerprints were found. Results suggest that the higher the number of pores examined and the more accurately the pores were marked, the higher the hit score. At the same time, query results showed that the scores of other sequential fingerprints in the database which came after the matching fingerprint were even lower. [ABSTRACT FROM AUTHOR]

In this paper, an online learning framework called adaptive decision fusion (ADF) is employed for short-term wind speed and turbulence intensity forecasting by use of wind speed data for each season for the city ofİzmit, located in the northwest of Turkey. Fixed-weight (FW) linear combination is derived and used for comparison with ADF. Wind speeds and turbulence intensities are predicted from the existing wind speed data and computed turbulence intensities, respectively, using the ADF and FW methods. Simulations are carried out for each season and the results are tested on mean absolute percentage error criterion. It is shown that the proposed model captured the system dynamic behavior and made accurate predictions based on the seasonal wind speed characteristics of the site. The procedure described here can be used to estimate the local velocity and turbulence intensity in a wind power plant during a storm. [ABSTRACT FROM AUTHOR]

This article discusses the role of fuzzy systems in biomedical science and healthcare data analysis. Fuzzy systems are effective in handling the vagueness and uncertainties present in medical big data, providing precise analysis and supporting early prognosis and preventive medicine. The article highlights recent advancements and applications of fuzzy systems in various areas, such as lung cancer detection, melanoma recognition, mental health diagnosis, kidney disease prediction, and gene expression data classification. The special issue aims to stimulate discussions, foster collaborations, and inspire further innovations in leveraging fuzzy systems for more nuanced interpretations of complex biomedical datasets. The authors express their appreciation to the Editor-in-Chief, authors, and reviewers for their contributions to this special issue. [Extracted from the article]

Despite recent advances, object detection in aerial images is still a challenging task. Specific problems in aerial images makes the detection problem harder, such as small objects, densely packed objects, objects in different sizes and with different orientations. To address small object detection problem, we propose a two-stage object detection framework called "Focus-and-Detect". The first stage which consists of an object detector network supervised by a Gaussian Mixture Model, generates clusters of objects constituting the focused regions. The second stage, which is also an object detector network, predicts objects within the focal regions. Incomplete Box Suppression (IBS) method is also proposed to overcome the truncation effect of region search approach. Results indicate that the proposed two-stage framework achieves an AP score of 42.06 on VisDrone validation dataset, surpassing all other state-of-the-art small object detection methods reported in the literature, to the best of authors' knowledge. • An object detection framework is introduced for small objects in aerial images. • IBS approach is proposed to suppress false detections on overlapping regions. • Scale adjustment for bounding boxes is achieved with GMM based clustering technique. • Proposed framework increases small object detection performance in aerial images. [ABSTRACT FROM AUTHOR]

Objective: Myelin is essential for a healthy nervous system. Myelin formed by oligodendrocytes, accelerates impulse propagation and supports neuronal survival. Demyelination leads to neurodegeneration. In multiple sclerosis (MS) immune attack results in demyelination. Our goal is to dissect interactions among oligodendrocytes, neurons, and immune cells to improve our understanding of myelination and the demyelinating diseases. We aim to discover new targets for remyelination therapies. Methods: We are building tools for analyzing protein-protein and cell-to-cell interactions. To identify genes involved in myelination and MS, we developed a bioinformatics-based strategy, interactome analysis, which combines proteome and gene expression methodologies. Identified genes are evaluated in peripheral blood of MS patients and in mouse models. To accelerate drug discovery, 23 machine learning-based methodologies were assessed for myelin identification in fluorescent microscopy images. Results: Interactome analysis identified hundreds of proteinprotein interactions between pairs of oligodendrocytes, neurons, macrophages, microglia, and T cells. Most significant interactions are further analyzed in vivo and in vitro. Our customizedconvolutional neural network and Boosted Tree methods segmented myelin at over 98% accuracy. Identified myelin can be quantified and visualized in 3D. Conclusion: The interactome analysis yielded novel genes that are likely to be linked to MS. Machine learning-based methodologies are very effective in accelerating myelin quantification and thus, drug screens against demyelinating diseases such as MS. Overall, our innovative analysis strategies employing computer assistance produced novel avenues of exploration for myelination and demyelinating diseases. This study was supported TUBITAK (218S495,316S026), Istanbul Medipol University (BAP2018/06), and Turkish Academy of Sciences (GEBIP). [ABSTRACT FROM AUTHOR]