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Öğe A novel approach to obtain trajectories of targets from laser scanned datasets(Ieee, 2015) Galip, Feyza; Çaputcu, Mehmet; İnan, Rüveyda Hilal; Sharif, Haidar; Karabayır, Aykut; Kaplan, Sezin; Uyaver, ŞahinLaser scanner has several bons when compared with video camera. It does not record real world videos except scanned points. As a result, processing of data becomes faster and easier. Over and above, it takes away the problem of private life conservation. This paper proposes a new and competent computer vision based approach for detecting and tracking targets (e.g., pedestrians and vehicles) from laser scanned datasets. Laser scanned data points from each scan have been deemed as a video frame. Blobs are extracted and then computer vision techniques (e.g., Kalman filter, Hungarian algorithms, and etc.) are applied to recognize and track the kind of targets. Scanned datasets, collected from two kinds of laser scanners, were used to conduct experiments. Full trajectories of pedestrians, vehicles, and noises were resulted in three dimensional spaces. Experimental results give evidence of the efficacy of our proposed framework.Öğe A proof of concept for home automation system with implementation of the Internet of Things standards(International University of Sarajevo, 2018) Sharif, Haidar; Despot, Ivan; Uyaver, ŞahinWho does not want a home having comfortability, security, safety, and reliability? A system of interconnected devices and sensors with internet of things standards can communicate independently with less or no human interaction. With such a system, everyday tasks (e.g., control of light, heat, humidity, air flow, and etc.) in and around our living units can be simplified. It also adds a list of desirable states, e.g., economy, peace of mind, comfortability, convenience, logistics, security, safety, and reliability. In this paper, an approach for home automation system that brings miscellaneous tasks in our living units into one centralized action point and functions with remotely controlled devices (e.g., smartphone, iPad, laptop, and etc.) has been implemented. The necessity to visit individual device involved in corresponding task has been perished. The proposed model is free of construction impediments as it deems to be developing a system alongside the architecture of the household. It is a proof of concept. So its potential serviceability for many real world applications is extremely high. © 2018, International University of Sarajevo.Öğe A simple approach to count and track underwater fishes from videos(Ieee, 2015) Sharif, Haidar; Galip, Feyza; Güler, Adil; Uyaver, ŞahinFishes are of great importance to the ecosystem. Behavior of fishes is interesting. Counting and tracking of fishes can provide good knowledge about the behavior of fishes. Counting and behavior quantifying of fishes within a turbulence or trawl environment are challenging tasks. The traditional methods are not only inefficient but also expensive. Thus counting and tracking under water fishes from videos are emerging topic for ichthyologists. This paper addresses a simple method to count and track underwater fishes from videos. It is a hybrid of background subtraction, Hungarian algorithm, and Kalman filter. It enables tracking of fishes whose number can vary over time. Theoretical runtime of the tracking algorithm is O(n(3)) with problem size n. Experimental results demonstrate its effectiveness.Öğe An Ab Initio and DFT study of structure and conformers of glycerol(American Institute of Physics, 2020) Navini, Nasim Yousefpour; Shojaei, S. H. Reza; Uyaver, ŞahinIn this paper, the effect of the simultaneous rotation of two different groups, hydroxyl (OH) and hydroxymethyl (CH2OH) groups, on the basic properties of Glycerol are comprehensively studied. Relative energies are reported at the HF/aug-cc-pVDZ, b3lyp/ aug-cc-pVDZ levels with corrections for zero-point vibrational energies. Structural parameters, Electric Dipole Moment and HOMO-LUMO energy gap of the identified conformers are also tabled. An inverse correlation between the relative energy and HOMO-LUMO energy gap is seen.Öğe Classification and detection of various geographical features from satellite imagery(International University of Sarajevo, 2018) Sharif, Haidar; Uyaver, Şahin; Zerdo, ZaidIt is a challenging task to classify and detect various geographical features from the satellite imagery of the Earth as well as the celestial bodies. This paper puts forward several pixel based classification algorithms to classify geographical features from the satellite images of the Earth. The recorded experimental results, from a total of 606 satellite images to classify miscellaneous geographical features, demonstrate that the maximum algorithmic performances can approximate to 87%. This paper also addresses a simple algorithm based on edge approximation and circular Hough transformation to detect craters from the satellite imagery of celestial bodies. An online available dataset to detect craters evaluates the performance of the algorithm. In general, all the proposed algorithms are straightforward but in many ways effective. © 2018, International University of Sarajevo.Öğe Classification of breast cancer histopathological images using DenseNet and transfer learning(Hindawi Publishing Corporation, 2022) Wakili, Musa Adamu; Shehu, Harisu Abdullahi; Sharif, Md. Haidar; Sharif, Md. Haris Uddin; Umar, Abubakar; Kusetoğulları, Hüseyin; İnce, İbrahim Furkan; Uyaver, ŞahinBreast cancer is one of the most common invading cancers in women. Analyzing breast cancer is nontrivial and may lead to disagreements among experts. Although deep learning methods achieved an excellent performance in classification tasks including breast cancer histopathological images, the existing state-of-the-art methods are computationally expensive and may overfit due to extracting features from in-distribution images. In this paper, our contribution is mainly twofold. First, we perform a short survey on deep-learning-based models for classifying histopathological images to investigate the most popular and optimized training-testing ratios. Our findings reveal that the most popular training-testing ratio for histopathological image classification is 70%: 30%, whereas the best performance (e.g., accuracy) is achieved by using the training-testing ratio of 80%: 20% on an identical dataset. Second, we propose a method named DenTnet to classify breast cancer histopathological images chiefly. DenTnet utilizes the principle of transfer learning to solve the problem of extracting features from the same distribution using DenseNet as a backbone model. The proposed DenTnet method is shown to be superior in comparison to a number of leading deep learning methods in terms of detection accuracy (up to 99.28% on BreaKHis dataset deeming training-testing ratio of 80%: 20%) with good generalization ability and computational speed. The limitation of existing methods including the requirement of high computation and utilization of the same feature distribution is mitigated by dint of the DenTnet.Öğe Classification of geographical features from satellite imagery(Springer Verlag, 2019) Sharif, Haidar; Uyaver, Şahin; Sharif, Haris Uddin; İnce, İbrahim Furkan; Zerdo, ZaidIt is a challenging task to classify heterogeneous geographical features from satellite imagery. This paper addresses 31 straightforward classification algorithms based on predominantly pixels to classify miscellaneous geographical features from satellite imagery. The addressed algorithms can extract and process the features of a large dataset with high-resolution images expeditiously. A total of 606 red-green-blue satellite images of the Bosnian city of Banja Luka are exercised to comprehend their performances for classifying cemeteries, fields, houses, industries, rivers, and trees. The recorded experimental results demonstrate that the best average performance can come into possession of 87%. © Springer Nature Singapore Pte Ltd. 2019.Öğe Concentration effects on the self-assembly of tyrosine molecules(The Royal Society of Chemistry, 2021) Ahmadabadi, Hajar Nili; Masoudi, Amir Ali; Uyaver, ŞahinMolecular self-assembly is a ubiquitous phenomenon in which individual atoms or molecules set up an ordered structure. It is of high interest for understanding the biology and a variety of diseases at the molecular level. In this work, we studied the self-assembly of tyrosine molecules via extensive molecular dynamics simulations. The formation of structures by self-assembly was systematically studied at various concentrations, from very low to very high. The temperature was kept constant, at which, in our former studies, we have already observed well-formed self-assembled structures. Depending on the concentration, the system displays a wide range of different structures, ranging from freely scattered monomers to very well formed four-fold structures. Different regimes of concentration dependence are observed. The results are proved by calculating the moments of inertia of the structures and the number of hydrogen bonds formed. Free energy landscapes calculated for the number of hydrogen bonds versus the number of contacts within a criterion provide insights into the structures observed.Öğe Deep sentiment analysis: a case study on stemmed Turkish Twitter data(IEEE, 2021) Shehu, Harisu Abdullahi; Sharif, Md. Haidar; Sharif, Md. Haris Uddin; Datta, Ripon; Tokat, Sezai; Uyaver, Şahin; Kusetoğulları, Hüseyin; Ramadan, Rabie A.Sentiment analysis using stemmed Twitter data from various languages is an emerging research topic. In this paper, we address three data augmentation techniques namely Shift, Shuffle, and Hybrid to increase the size of the training data; and then we use three key types of deep learning (DL) models namely recurrent neural network (RNN), convolution neural network (CNN), and hierarchical attention network (HAN) to classify the stemmed Turkish Twitter data for sentiment analysis. The performance of these DL models has been compared with the existing traditional machine learning (TML) models. The performance of TML models has been affected negatively by the stemmed data, but the performance of DL models has been improved greatly with the utilization of the augmentation techniques. Based on the simulation, experimental, and statistical results analysis deeming identical datasets, it has been concluded that the TML models outperform the DL models with respect to both training-time (TTM) and runtime (RTM) complexities of the algorithms; but the DL models outperform the TML models with respect to the most important performance factors as well as the average performance rankings.Öğe Facial expression recognition using deep learning(American Institute of Physics, 2021) Shehu, Harisu Abdullahi; Sharif, Md Haidar; Uyaver, ŞahinFacial expression recognition has become an increasingly important area of research in recent years. Neural network-based methods have made amazing progress in performing recognition-based tasks, winning competitions set up by various data science communities, and achieving high performance on many datasets. Miscellaneous regularization methods have been utilized by various researchers to help combat over-fitting, to reduce training time, and to generalize their models. In this paper, by applying the Haar Cascade classifier to crop faces and focus on the region of interest, we hypothesize that we would attain a fast convergence without using the whole image to analyze facial expressions. We also apply label smoothing and analyze its effect on the databases of CK+, KDEF, and RAF. The ResNet model has been employed as an example of a neural network model. Label smoothing has demonstrated an improvement of the recognition accuracy up to 0.5% considering CK+ and the KDEF databases. While the application of Haar Cascade has shown to decrease the achieved accuracy on KDEF and RAF databases with a small margin, fast convergence of the model has been observed.Öğe Preface to "computer science and technology"(American Institute of Physics Inc., 2019) Uyaver, Şahin[No abstract available]Öğe Recognition of objects from laser scanned data points using SVM(Ieee Computer Soc, 2016) Galip, Feyza; Sharif, Haidar; Çaputcu, Mehmet; Uyaver, ŞahinNowadays, laser scanners are operated for data collection instead of video cameras. Laser scanners do not record real world videos except scanned points. Thus it takes away problems of private life conservation. Plus data processing gets very fast and easy. But from laser scanned data points, recognition of objects is a challenging task. This paper points to the usability of SVM to recognize pedestrians and vehicles from laser scanned data points. Data points from each scan are esteemed as a video frame. Moving blobs are extracted and then SVM is used to recognize each blob as either a pedestrian or a vehicle. Experimental results show that SVM can be actually and robustly used to recognize objects from laser scanned data points.Öğe Self-assembly of aromatic amino acids: a molecular dynamics study(Royal Soc Chemistry, 2018) Uyaver, Şahin; Hernandez, Helen W.; Habiboğlu, Mehmet GökhanThe self assembly processes of aromatic amino acids, phenylalanine, tyrosine, and tryptophan have been simulated and were observed to form fibril-like aggregates linked to certain rare diseases and instances of biological membrane disruption. Pure systems and their mixtures were studied systematically at constant temperatures and free energy landscapes were produced describing the height and the number of assembled monomers associated with lower energy structures. Consistent with some previous work, aromatic amino acid monomers display a tendency to arrange with a four-fold symmetry. The occurrence of this and other ordered structures increases at higher temperatures. At lower temperatures our binary mixture simulations indicate that increasing tryptophan content drives the assembly process away from the formation of distinct nanostructures and toward disordered aggregates which is in line with experimental observations of pure tryptophan solutions. This work provides molecular level insight to a variety of different physical phenomena relevant to fields including human disease.Öğe Sentiment analysis of turkish twitter data using polarity lexicon and artificial intelligence(Springer Science and Business Media Deutschland GmbH, 2020) Shehu, Harisu Abdullahi; Haidar, Sharif; Uyaver, Şahin; Tokat, Sezai; Ramadan, Rabie A.Sentiment analysis is a process of computationally detecting and classifying opinions written in a piece of writer’s text. It determines the writer’s impression as achromatic or negative or positive. Sentiment analysis became unsophisticated due to the invention of Internet-based societal media. At present, usually people express their opinions by dint of Twitter. Henceforth, Twitter is a fascinating medium for researchers to perform data analysis. In this paper, we address a handful of methods to prognosticate the sentiment on Turkish tweets by taking up polarity lexicon as well as artificial intelligence. The polarity lexicon method uses a dictionary of words and accords with the words among the harvested tweets. The tweets are then grouped into either positive tweets or negative tweets or neutral tweets. The methods of artificial intelligence use either individually or combined classifiers e.g., support vector machine (SVM), random forest (RF), maximum entropy (ME), and decision tree (DT) for categorizing positive tweets, negative tweets, and neutral tweets. To analyze sentiment, a total of 13000 Turkish tweets are collected from Twitter with the help of Twitter’s application programming interface (API). Experimental results show that the mean performance of our proposed methods is greater than 72%. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020.Öğe Shape investigations of structures formed by the self-assembly of aromatic amino acids using the density-based spatial clustering of applications with noise algorithm(Scientific & Technical Research Council Turkey-TUBITAK, 2022) Habiboğlu, Mehmet Gökhan; Hernandez, Helen W.; Uyaver, ŞahinTyrosine, tryptophan, and phenylalanine are important aromatic amino acids for human health. If they are not properly metabolized, severe rare mental or metabolic diseases can emerge, many of which are not researched enough due to economic priorities. In our previous simulations, all three of these amino acids are discovered to be self-organizing and to have complex aggregations at different temperatures. Two of these essential stable formations are observed during our simulations: tubular-like and spherical-like structures. In this study, we develop and implement a clustering analyzing algorithm using density-based spatial clustering of applications with noise (DBSCAN) to measure the shapes of the formed structures by the self-assembly processes of these amino acids. We present the results in quantitative and qualitative ways. To the best of our knowledge, the geometric shapes of the formed structures by the self-assembly processes of these amino acids are not measured quantitatively in the literature. Analytical measurements and comparisons of these aggregations might help us to identify the self-aggregations quickly at early stages in our simulations and hence provide us with more opportunity to experiment with different parameters of the molecular simulations (like temperature, mixture rates, and density). We first implement the DBSCAN method to identify the main self-aggregation cluster and then we develop and implement two algorithms to measure the shapes of the formed structures by the self-assembly processes of these amino acids. The measurements, which are completely in line with our simulation results, are presented in quantitative and qualitative ways.Öğe Shape measurement for cylindirical structures formed by tyrosine molecules(Amer Inst Physics, 2019) Habiboğlu, Mehmet Gökhan; Uyaver, ŞahinTyrosine is one of the important aromatic amino acids, the wrong metabolization of which usually results in severe mental diseases. Based on the simulation results it was observed that tyrosine molecules form fibril-like structures at high temperatures. In order to have a better understanding of tyrosine self-assembly, we developed a quantitative measure to analyze fibril-like shapes formed by tyrosine molecules. This was applied to 4 different temperatures and then was compared with the analysis from simulation data. As expected, tyrosine molecules indeed exhibit fibril-like structures at 350 K at a fast rate, which is perfectly in agreement with the analysis in literature.Öğe Theta Point Calculation of a Polymer Chain with Electric Dipole Moments: Monte Carlo Simulation(2020) Uyaver, ŞahinMonte Carlo simulations are used to simulate a single polymer chain in a more generalized model. The more generalized model differs from the simpler models by including dipole-dipole interactions. The polymer chain is modeled as a freely rotating chain where the neighboring beads are connected by harmonic spring. Excluded volume effects are included employing modified Lennard-Jones potential. As the extension in this work, each monomer unit carries permanently a freely-rotating electric dipole moment. After getting the system equilibrated the average values are measured and ?-temperature of the system is determined. The effects of the presence of the dipole moments to the ?-temperature of the system are investigated. The results are analyzed in comparison with a bare model.Keywords: Polymer Chain, Theta Temperature, Monte Carlo, Dipole-dipole interactionÖğe Tyrosine, phenylalanine, and tryptophan undergo self-aggregation in similar and different manners(MDPI-Multidisciplinary Digital Publishing Institute, 2022) Uyaver, ŞahinPhenylalanine, tyrosine, and tryptophan are aromatic amino acids, and they are of high interest in both health science and biotechnology. These amino acids form organized structures, like fibrils and nanotubes. Although these amino acids belong to the same family, they still differ from each other with respect to polarity, hydrophobicity as well as internal structures. In this work, we performed extensive molecular dynamics simulations to investigate the dynamics of the self-aggregations of these amino acids and studied the details of the formed structures. The amino acid monomers placed in water were simulated at a constant temperature. It has been observed that they compose nanostructures with similarities and differences.
