Ramazan Aygun

Kennesaw State University | KSU · Department of Computer Science and Information Systems

With advances in machine learning and artificial intelligence, learning models have been used in many decision-making and classification applications. The nature of critical applications, which require a high level of trust in the prediction results, has motivated researchers to study classification algorithms that would minimize misclassification...

With advances in machine learning and artificial intelligence, learning models have been used in many decision-making and classification applications. The nature of critical applications, which require a high level of trust in the prediction results, has motivated researchers to study classification algorithms that would minimize misclassification...

Convolutional Neural Networks (CNNs) are an emerging research area for detection of Diabetic Retinopathy (DR) development in fundus images with highly reliable results. However, its accuracy depends on the availability of big datasets to train such a deep network. Due to the privacy concerns, the strict rules on medical data limit accessibility of...

Currently, Natural Language Processing (NLP) applications like chatbots are very close to mimick human responses. This has been achieved via powerful and sophisticated models like Bidirectional Encoder Representations from Transformers (BERT). Although, the capabilities that such models offer are superior to the technologies that preceded it, these...

A method is proposed for real-time vision-based localization in the 360° area around a three-dimensional (3D) reference object with a single camera. The problem is split into three subproblems. First, 360° 3D object recognition is proposed, in which a computer vision solution can recognize a reference object from all possible 360° locations. Second...

Misclassification is a critical problem in many machine learning applications. Since even the classifier models with high accuracy (e.g., > 95%) still introduce some misclassification error, it may not be possible to rely on the output of a classifier. In this paper, we introduce trustable learning, which prompts the learning model to yield only th...

A camera footage which is essential for forensic investigations can easily be modified with advanced video tampering techniques. This makes it necessary to employ novel methods to retain and prove the integrity of captured scene in criminal investigations. In this vein, blockchain technology has received a substantial interest in the last decade as...

In high-throughput systems, the crystallization experiments require the inspection and analysis of a large number of trial images. The visualization and analysis tools are needed to view and analyze the experimental results, and recommend novel crystalline conditions by analyzing prior results. It is essential to integrate all these components into...

Unmanned Aerial Vehicles (UAVs) also known as drones are being used in many applications where they can record or stream videos. One interesting application is the Intelligent Transportation Systems (ITS) and public safety applications where drones record videos and send them to a control center for further analysis. These videos are shared by vari...

The crystallization of biological macromolecules like proteins is an important process to study their molecular structures. The quality of crystals is critical to be able to determine their structures using methods such as X-ray crystallography. Therefore, many wet-lab experiments are conducted using numerous screening plates to obtain successful c...

The accuracy of detecting protein crystals for fluorescence microscopy images is very critical for high throughput and automated systems. Although the trace fluorescent labeling method could highlight protein crystals, reflection and emission from the fluorescence dye is not always due to crystal regions. Therefore, the analysis of the peak wavelen...

Protein crystallization screening helps determine factors (e.g., salts, pH of buffers, ionic strengths, temperature, and type of precipitants) that are favorable for the formation of a large protein crystal suitable for X-ray crystallography. While existing commercial screens may not generate crystalline outcomes for difficult proteins, their outco...

The data representation as well as naming conventions used in commercial screen files by different companies make the automated analysis of crystallization experiments difficult and time-consuming. In order to reduce the human effort required to deal with this problem, we present an approach for computationally matching elements of two schemas usin...

Protein crystallization well plate is a rectangular platform that contains wells usually organized as a grid structure. The crystallization conditions are studied through a screening process by setting up the trial conditions in the well plate. In the past, the expert evaluates the trial wells for the growth of crystals by manually viewing the plat...

The supervisory control and data acquisition (SCADA) systems monitor and control industrial control systems in many industrial and economic sectors such as water treatment, power plants, railroads, and gas pipelines. The integration of SCADA systems with the internet and corporate enterprise networks for various economical reasons exposes SCADA sys...

Recently, mirage pose estimation method was proposed for multi-camera systems. Multi-camera mirage analytically solves a system of linear equations for six pose parameters in O(n) time. Mirage promises to execute in real time with high accuracy and shows lower rotational and translational errors compared to eight other well-known perspective-n-poin...

In this paper, we evaluate maximum subarrays for approximate string matching and alignment. The global alignment score as well as local sub-alignments are indicators of good alignment. After showing how maximum sub-arrays could be used for string matching, we provide several ways of using maximum subarrays: long, short, loose, strict, and top-k. Wh...

Artificial Neural Networks are a widely used computing system implemented for a wide variety of tasks and problems. A common application of such networks is classification problems. However, a significant amount of this research focuses on one and two-dimensional information, such as vectorized data and images. There is limited research performed o...

Protein crystallization is a complex phenomenon requiring thousands of experiments corresponding to different crystallization conditions for successful crystallization. In recent years, high-throughput robotic setups have been developed to automate the protein crystallization experiments, and imaging techniques are used to monitor the crystallizati...

In general, a single thresholding technique is developed or enhanced to separate foreground objects from the background for a domain of images. This idea may not generate satisfactory results for all images in a dataset, since different images may require different types of thresholding methods for proper binarization or segmentation. To overcome t...

This book provides the lifecycle of data analytics for protein crystallization. A wide range of topics starting from setting up screens to identifying macromolecular structure has been covered. In earlier chapters, the status-of-art and effective low-cost and real-time techniques for protein crystallization analysis have been provided. This chapter...

This chapter reviews the basics of the protein crystallization process. As amply proven by the protein structure initiative, protein crystallization can be carried out without any basic knowledge about the specific protein or how it behaves in solution. However, when the goal is not just processing as many proteins as can be produced, but is direct...

As high throughput, crystallization screening and analysis systems automate the processes starting from setting up plates to scoring, this enables conducting thousands of experiments in a short time. Analysis of crystallization trial experiments in the past has been cumbersome due to the physical environment where an expert needs to look crystalliz...

The goal of protein crystallization screening is to determine the main factors of importance to crystallize a protein under investigation. The protein crystallization screening is often expanded to many hundreds or thousands of conditions to maximize combinatorial chemical space coverage for maximizing the chances of a successful (crystalline) outc...

Large number of features are extracted from protein crystallization trial images to improve the accuracy of classifiers for predicting the presence of crystals or phases of the crystallization process. The excessive number of features and computationally intensive image processing methods to extract these features make utilization of automated clas...

Automated image analysis of protein crystallization images is one of the important research areas. For proper analysis of the microscopic images, it is necessary to have all objects in good focus. If objects in a scene (or specimen) appear at different depths with respect to the camera’s focal point, objects outside the depth of field usually appea...

The practice of scoring of protein crystallization screening results is more honored in the breach than in the observance. However, as we hope to show in the balance of this treatise, it can lead to a means for extracting more information than immediately apparent from a crystallization experiment. Scoring has advantages beyond simple good scientif...

In recent years, high-throughput robotic setups have been developed to automate the protein crystallization experiments, and imaging techniques are used to monitor the crystallization progress. Images are collected multiple times during the course of an experiment. Huge number of collected images make manual review of images tedious and discouragin...

There are more ways of gaining insight into macromolecular structure than X-ray diffraction. Like X-ray diffraction, some of these are based on the generation of ordered arrays of the molecule to be studied. For many reasons, based on either the protein or its function, this is not always possible. Others, some of which are currently enjoying a mar...

This paper presents a new method of segmenting and classifying protein crystallization trial images that were collected using trace fluorescent labeling. Trace fluorescent labeling typically involves fluorescence dye that can re-emit the illumination light at other wavelengths around the principal wavelength. The captured image has a primary color...

Tropical cyclone intensity estimation is a challenging task as it required domain knowledge while extracting features, significant pre-processing, various sets of parameters obtained from satellites, and human intervention for analysis. The inconsistency of results, significant pre-processing of data, complexity of the problem domain, and problems...

Background: Large number of features are extracted from protein crystallization trial images to improve the accuracy of classifiers for predicting the presence of crystals or phases of the crystallization process. The excessive number of features and computationally intensive image processing methods to extract these features make utilization of a...

Mirage is a camera pose estimation method that analytically solves pose parameters in linear time for multi-camera systems. It utilizes a reference camera pose to calculate the pose by minimizing the 2D projection error between reference and actual pixel coordinates. Previously, Mirage has been successfully applied to trajectory tracking (visual se...

This unique text/reference presents an overview of the computational aspects of protein crystallization, describing how to build robotic high-throughput and crystallization analysis systems. The coverage encompasses the complete data analysis cycle, including the set-up of screens by analyzing prior crystallization trials, the classification of cry...

Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed fo...

Camera calibration has many applications in various computer vision fields such as pose estimation, robot navigation, trajectory tracking, and object recognition. Camera calibration involves (mostly) determining the intrinsic parameters of a camera so that problems or distortions caused by the camera's optics or manufacturing could be estimated for...

Online social network analysis has attracted great attention with a vast number of users sharing information and availability of APIs that help to crawl online social network data. In this paper, we study the research studies that are helpful for user characterization as online users may not always reveal their true identity or attributes. We espec...

Identifying the speakers in TV news would help listeners analyze and understand news content, but doing so in news videos is challenging because new faces often appear. Previous research has identified speakers on pretrained faces for TV shows and movies. Using an unsupervised method, this article proposes labeling speakers using just the available...

In general, a single thresholding technique is developed or enhanced to separate foreground objects from background for a domain of images. This idea may not generate satisfactory results for all images in a dataset, since different images may require different types of thresholding methods for proper binarization or segmentation. To overcome this...

Unmanned vehicles are autonomous robotic systems that are fully or partially controlled by an operator remotely from a station. In the last two decades, massive amount of advancements have been observed regarding unmanned vehicles for both military and civilian purposes. Today majority of these vehicles require human guidance even for basic mission...

The goal of protein crystallization screening is the determination of the main factors of importance to crystallizing the protein under investigation. One of the major issues about determining these factors is that screening is often expanded to many hundreds or thousands of conditions to maximize combinatorial chemical space coverage for maximizin...

This work introduces our method for automatic classification of crystallization trial images according to the types of protein crystals present in the images. The images are classified into four categories: needles, small crystals, large crystals, and other crystals. Because protein crystals are characterized by some geometric shapes, we focus on e...

Sequence similarity search and sequence alignment methods are fundamental steps in comparative genomics and have a wide spectrum of application in the field of medicine, agriculture, and environment. The dynamic programming sequence alignment methods produce optimal alignments but are impractical for a similarity search due to their large running t...

Thousands of experiments corresponding to different combinations of conditions are set up to determine the relevant conditions for successful protein crystallization. In recent years, high-throughput robotic setups have been developed to automate the protein crystallization experiments, and imaging techniques are used to monitor the crystallization...

Protein crystallization remains a highly empirical process. The purpose of protein crystallization screening is the determination of the main factors of importance leading to protein crystallization. One of the major problems about determining these factors is that screening is often expanded to many hundreds or thousands of conditions to maximize...

Spatio-temporal querying and retrieval is a challenging task due to the lack of simple user interfaces for building queries despite the availability of powerful indexing structures and querying languages. In this paper, we propose Query-by-Gaming scheme for spatio-temporal querying that can benefit from gaming controller for building queries. By us...

With the proliferation of Internet of Things (IoT) devices such as smartphones, sensors, cameras, and RFIDs, it is possible to collect massive amount of data for localization and tracking of people within commercial buildings. Enabled by such occupancy monitoring capabilities, there are extensive opportunities for improving the energy consumption o...

In image thresholding problems, there are some cases that single thresholding technique may not generate good binary images for all samples. Using multiple methods may help to overcome this limitation, but this idea brings another problem. It is not a trivial task to select proper thresholding method for each image in the dataset. In this study, we...

Automated image analysis of microscopic images such as protein crystallization images and cellular images is one of the important research areas. If objects in a scene appear at different depths with respect to the camera's focal point, objects outside the depth of field usually appear blurred. Therefore, scientists capture a collection of images w...

Multi-class classification is an important and challenging problem for biological data classification. Typical methods for dealing with multi-class classification use a powerful single classifier such as neural networks to classify the data into one of many classes. Alternatively, the binary classifiers are used in one-versus-one (OVO) and one-vers...

There had been significant research on background mosaic or sprite generation in the past. In some application domains such as surveillance, the complete view of an object might be of interest rather than the background scene. In this paper, we present object mosaicking for reconstruction of a moving object from its partial views due to limited vie...

In this paper, we investigate the performance of two wrapper methods for semi-supervised learning algorithms for classification of protein crystallization images with limited labeled images. Firstly, we evaluate the performance of semi-supervised approach using self-training with naïve Bayesian (NB) and sequential minimum optimization (SMO) as the...

Today some camera systems provide various opportunities to the scientists in computer vision since they capture color and depth images of a scene simultaneously. This paper presents a new 3D model construction and surface texture mapping technique for real object images captured by Microsoft (MS) Kinect camera system. Our ultimate goal is to constr...

One of the difficulties for proper imaging in microscopic image analysis is defocusing. Microscopic images such as cellular images, protein images, etc. need properly focused image for image analysis. A small difference in focal depth affects the details of an object significantly. In this paper, we introduce a novel auto-focusing approach based on...

In this paper, we investigate the performance of classification of protein crystallization images captured during protein crystal growth process. We group protein crystallization images into 3 categories: noncrystals, likely leads (conditions that may yield formation of crystals) andcrystals. In this research, we only consider the subcategories of...

A single thresholding technique may not provide the best binarization for all images of datasets such as protein crystallization images. To overcome this limitation, multiple thresholding methods are used to binarize images. Whenever multiple thresholding techniques are used, it is important to know which one provides the best result auto- maticall...

Vision-based target following using a camera system mounted on a mobile platform has been a challenging problem. A scenario is assumed in which the platform/camera system is required to have a desired trajectory for relative position and orientation (pose) with respect to a target object. It is assumed that the actual pose of mobile platform with r...

Measuring the performance of a classifier properly is important to determine which classifier to use for an application domain. The comparison is not straightforward since different experiments may use different datasets, different class categories, and different data distribution, thus biasing the results. Many performance (correctness) measures h...

Since hyperspectral imagery (HSI) (or remotely sensed data) provides more information (or additional bands) than traditional gray level and color images, it can be used to improve the performance of image classification applications. A hyperspectral image presents spectral features (also called spectral signature) of regions in the image as well as...

In this paper, we describe the design and implementation of a stand-alone real-time system for protein crystallization image acquisition and classification with a goal to assist crystallographers in scoring crystallization trials. In-house assembled fluorescence microscopy system is built for image acquisition. The images are classified into three...

Editing or browsing video based on spatial content without distortion or cropping has been challenging because of providing unavailable information (view) in a single frame. Virtual camera control enables the user to view the video from her perspective by using camera functions such as pan, tilt, and zoom on a recorded video. In this paper, we prop...

Multi-class classification problem has become a challenging problem in bioinformatics research. The problem becomes more difficult as the number of classes increases. Decomposing the problem into a set of binary problems can be a good solution in some cases. One of the popular approaches is to build a hierarchical tree structure where a binary clas...