Wos İndeksli Açık & Kapalı Erişimli Yayınlar

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Acoustic Scene Classification Using Spatial Pyramid Pooling With Convolutional Neural Networks
(2019) Basbug, Ahmet Melih; Sert, Mustafa; 0000-0002-7056-4245; AAB-8673-2019
Automatic understanding of audio events and acoustic scenes has been an active research topic for researchers from signal processing and machine learning communities. Recognition of acoustic scenes in the real life scenarios is a challenging task due to the diversity of environmental sounds and uncontrolled environments. Efficient methods and feature representations are needed to cope with these challenges. In this study, we address the acoustic scene classification of raw audio signal and propose a cascaded CNN architecture that uses spatial pyramid pooling (SPP, also referred to as spatial pyramid matching) method to aggregate local features coming from convolutional layers of the CNN. We use three well known audio features, namely MFCC, Mel Energy, and spectrogram to represent audio content and evaluate the effectiveness of our proposed CNN-SPP architecture on the DCASE 2018 acoustic scene performance dataset. Our results show that, the proposed CNN-SPP architecture with the spectrogram feature improves the classification accuracy.
Anomaly Detection in Smart Home Environments using Convolutional Neural Network
(2021) Ercan, Naci Mert; Sert, Mustafa
The use of smart devices in home environments has been increasing in recent years. The wireless connection of these devices to the internet enables smart homes to be built with less cost and hence, recognition of activities in home environments and the detection of possible anomalies in activities is important for several applications. In this study, we propose a new method based on the changepoint representation of sensor data and variable-length windowing for the recognition of abnormal activities. We present comparative analyses with different representations to demonstrate the efficacy of the proposed scheme. Our results on the WSU performance dataset show that, the use of variable-length windowing improves the anomaly detection performance in comparison to fixed-length windowing.
Applications of Deep Learning Techniques to Wood Anomaly Detection
(2022) Celik, Yaren; Guney, Selda; Dengiz, Berna; Xu, J; Altiparmak, F.; Hassan, MHA; Marquez, FPG
Wood products and structures have an important place in today's industry. They are widely used in many fields. However, there are various difficulties in production systems where wood raw material is under many processes. Some difficulty and complexity of production processes result in high variability of raw materials such as a wide range of visible structural defects that must be checked by specialists on line or of line. These issues are not only difficult and biased in manual processes, but also less effective and misleading. To overcome the drawbacks of the manual quality control processes, machine vision-based inspection systems are in great of interest recently for quality control applications. In this study, the wood anomaly has been detected by using deep learning. As it will be a distinction-based method on image processing, the Convolution Neural Network (CNN), which is one of the most suitable methods, has been used for anomaly detection. In addition, it will be tried to obtain the most suitable one among different CNN architectures such as ShuffleNet, AlexNet, GoogleNet for the problem. MobileNet, SqueezeNet, GoogleNet, ShuffleNet among considered methods show promising results in classifying normal and abnormal wood products.
Benefiting from the popular films integrated into the curriculum in boosting the efficacy of the social work education
(2014) Nadir, Ural
Benefiting from visuals both in every facet of social sciences in broad terms and in social work education in narrow terms improves students learning significantly. It is a known fact that learning a material by heating, watching and discussing altogether can increase the efficiency of learning up to ninety per cent. Besides, it is aimed that especially the social work students whose curriculum is designed in a way that they can work with people from all aspects of life and with all kinds of problems are provided with different methods together with lectures and practical training. Thus, they graduate as people who are ready to work with different client and problem groups after a four-year-education. From this aspect, the effectiveness of adding both the materials which are specifically prepared for the lessons and the popular films for the general public to the syllabus is indisputable. It is stated in literature that integrating films into curriculum in various fields and providing students with audio and visual materials contributes to the effectiveness of learning outcomes. It is also possible to conic across various resources concerning making use of the popular films especially in the fields such as social problems, counseling skills, cultural studies, developmental psychology, clinical psychology and psychiatry. This paper specifically argues how integrating the popular films into the curriculum in the various fields of the social work education would affect the learning outcomes of the prospective social workers positively and supports the thesis with some sample cases and films. (C) 2014 The Authors. Published by Elsevier Ltd.
A Component Based Model Developed for Machine Tool Selection Decisions
(2020) Ic, Yusuf Tansel; Yurdakul, Mustafa; AAI-1081-2020
Machine tools are widely used in manufacturing sectors; such as automotive industry, metal cutting industry, aerospace industry etc. Purchase of a machine tool is a long-term capital investment decision and requires a high initial investment cost. Machine tool producers offer a wide-ranging types and models of machine tools. On the other hand, expectations and requirements of the manufacturing companies differ depending on the parts produced and their strategic objectives. High stiffness, rigidity, metal cutting performance, surface finish and low tolerance range are common expectations from machine tools. This paper aims to develop a technical evaluation model to help manufacturing companies in their machine tool purchasing decisions. In the proposed model, first components used in machine tools are analyzed and based on this analysis a technical evaluation model is developed. The application of the developed model is illustrated by making a selection among nine different machine tool alternatives.
Daphnet Freezing Recognition with Gait Data by Using Machine Learning Algorithms
(2020) Guney, Selda; Boluk, Busra
The aim of this study was to test the success of the data set obtained by a wearable health assistant developed for the symptom of freezing (FOG) in gait of Parkinson's patients and to increase the success of the system. The system was tested with different machine learning methods to measure the success of the wearable health assistant system. For all patients (ten patients), the highest success value was obtained and the mean sensitivity and specificity values of the system were calculated and compared with the results obtained in the literature review. In the literature, mean sensitivity and specificity were 73.1% and 81.6%, respectively; In this study, mean sensitivity and specificity were 91.9% and 71.14%, respectively. In order to better analyze the success of the system, two patients with successful and unsuccessful results were selected for the data set in line with the results obtained in the literature review. The success of the system was tested by using different machine learning methods on the data sets of two patients. Finally, the successes obtained by feature extraction methods were tried to be increased. Among the different machine learning methods on the data sets used for patient 8 and patient 3, the most successful method was obtained by combining the models (ensemble). The highest achievement value obtained by attribute extraction methods was obtained when PCA was applied. However, the success value obtained with raw data could not be increased. All results are tabulated and presented.
A Deep LSTM Approach for Activity Recognition
(2019) Guney, Selda; Erdas, Cagatay Berke; 0000-0002-0573-1326; 0000-0003-3467-9923; AAC-7404-2020
Since 1990s, activity recognition effectual field in machine learning literature. Most of studies that relevant activity recognition, use feature extraction method to achieve higher classification performance. Moreover, these studies mostly use traditional machine learning algorithms for classification. In this paper, we focus on a deep (Long Short Term Memory) LSTM neural network for feature free classification of seven daily activities by using raw data that collected from three-dimensional accelerometer. Based on the results, the proposed deep LSTM approach can classify raw data with high performance. The results show that the proposed deep LSTM approach achieved 91.34, 96.91, 88.78, 87.58 as percent classification performance in terms of accuracy, sensitivity, specificity, F-measure respectively.
Detecting COVID-19 from Respiratory Sound Recordings with Transformers
(2022) Aytekin, Idil; Dalmaz, Onat; Ankishan, Haydar; Saritas, Emine U.; Bagci, Ulas; Cukur, Tolga; Celik, Haydar; Drukker, K; Iftekharuddin, KM
Auscultation is an established technique in clinical assessment of symptoms for respiratory disorders. Auscultation is safe and inexpensive, but requires expertise to diagnose a disease using a stethoscope during hospital or office visits. However, some clinical scenarios require continuous monitoring and automated analysis of respiratory sounds to pre-screen and monitor diseases, such as the rapidly spreading COVID-19. Recent studies suggest that audio recordings of bodily sounds captured by mobile devices might carry features helpful to distinguish patients with COVID-19 from healthy controls. Here, we propose a novel deep learning technique to automatically detect COVID-19 patients based on brief audio recordings of their cough and breathing sounds. The proposed technique first extracts spectrogram features of respiratory recordings, and then classifies disease state via a hierarchical vision transformer architecture. Demonstrations are provided on a crowdsourced database of respiratory sounds from COVID-19 patients and healthy controls. The proposed transformer model is compared against alternative methods based on state-of-the-art convolutional and transformer architectures, as well as traditional machine-learning classifiers. Our results indicate that the proposed model achieves on par or superior performance to competing methods. In particular, the proposed technique can distinguish COVID-19 patients from healthy subjects with over 94% AUC.
Distributed Database Design: A Case Study
(2014) Tosun, Umut
Data Allocation is an important problem in Distributed Database Design. Generally, evolutionary algorithms are used to determine the assignments of fragments to sites. Data Allocation Algorithms should handle replication, query frequencies, quality of service (QoS), cite capacities, table update costs, selection and projection costs. Most of the algorithms in the literature attack one or few components of the problem. In this paper, we present a case study considering all of these features. The proposed model uses Integer Linear Programming for the formulation of the problem. (C) 2014 The Authors. Published by Elsevier B.V.
Explaining intrapreneurial behaviors of employees with perceived organizational climate and testing the mediating role of organizational identification: A research study among employees of Turkish innovative firms
(2014) Tastan, Secil Bal; Gucel, Cem
This study examines perceived organizational climate and organizational identification as potential antecedents of employees' intrepreneurial behaviors. In particular, the study suggests positive relationships between perceived organizational climate components-structural support and recognition-and intrepreneurial behaviors construct. In addition, employees' organizational identification is suggested to have a mediating role on the relationship between organizational climate and intrepreneurial behaviors. The survey of this study is performed among employees working in high performing and innovative firms operating in White Good Manufacturing, Food and Drink, Telecommunication, and Textile industries in Turkey. The obtained data from the questionnaires are analyzed through the SPSS statistical packaged software. Analyses results revealed that both dimensions of organizational climate (structural support and organizational recognition) significantly and positively related to intrepreneurial behaviors and perceived organizational identification mediate the effects of the organizational climate on the intrepreneurial behaviors construct. (C) 2014 The Authors. Published by Elsevier Ltd.
How Elementary School Principals' Change Tendencies Are Related With Their Opinion About Curriculum Change
(2014) Altun, Sadegul Akbaba; Buyukkurt, Sener
Leadership is important in change process and change management. Turkish educational system is undergoing a constant change. One of those changes was the change of the elementary school curricula, which had been accepted in the 2004-2005 academic year. Therefore, it is important to understand how school principals, as the persons who are in charge of this change, handle this process. In this respect, this study was aimed to determine school principals' tendencies toward change. Furthermore, it will also be explored whether those tendencies show any significant differences on some variables. Finally, having incorporated school principals' views on curriculum, school principals' change tendencies will be interpreted within this change phenomenon. This study was designed with a mixed methodology. In order to understand school principals' tendencies toward change, a change tendency scale, developed by Akbaba-Altun and Buyukozturk (2011), was utilized. In addition, school principals were asked to narrate their opinions regarding the changed curricula and its reflections on practice. The quantitative data were analysed through descriptive and interpretive analysis whereas the qualitative data were analysed through content analysis. A total of 179 elementary school principals joined the quantitative part of this study, whereas 154 of them participated in the qualitative one. It was observed that school principals were generally homogenous in their change tendencies. Within this context, this finding was supported by the qualitative data, as well. (C) 2014 The Authors. Published by Elsevier Ltd.
IS THERE A WAY OUT FOR THE TURKISH ECONOMY?
(2019) Can, Ziya; 0000-0001-5919-4821; AAC-5504-2020
International trade is one of the most important issues of macroeconomics. Almost all international trade theories have tried to determine which country produces what, for which price it sells its products to whom. In a number of theories, these inequalities are based on the differences between factor endowment, while in some others they result from technological development or capital accumulation. However, very few theories have revealed that these differences bear the traces of colonial period. Financial liberalization and the spread of international capital movements by the end of colonialism were major developments in the second half of the 20th century. It is no coincidence that these developments occurred simultaneously. The financial revolution has led to a new kind of relationship between capital owner countries and the others. This is a sort of centre-periphery relationship. Those peripheral countries are the ones that have been affected by the hitches of last slowdown of the world economy. Just like Argentina, Turkey has encountered problems such as the higher inflation rate and the inevitable rise in interest rates, following the melting down in currencies in 2018. Again, in the same period, in contrast with the conventional economic views, current account deficit shrank depending not on the increase in exports, but the dramatic decline in imports instead. As a result of all these occurrences, there was a great loss of prosperity in the country. This study investigates whether it is possible for countries depending on the foreign capital like Turkey and Argentina to follow an independent policy from the fluctuations in the economic conjuncture or not. Is it possible to develop permanent policies that will eliminate dependency on foreign capital, instead of familiar ones such as targeting in inflation, exchange rate or current account deficit? Which has a higher cost? To develop and implement these policies, or to sway in every wind?
Ka Band Far Field Radio Link System Based on OAM Multiplexed Vortex Beams Collimated by a Paraboloidal Reflector
(2021) Hizal, Altunkan; Yildiz, Hayrullah
Electromagnetic vortex-waves (VW) have linear azimuthal-phase, orthogonality in azimuth and orbital-angular-momentum (OAM). The VW-pattern has a null along the vorticity-axis and the cone-half-angle (CHA) and the beam-width (BW) expands with the OAM mode number p. Here, we collimated all p-VW-beams into a radiation cone (Rcon) with a small CHA and BW using a paraboloidal reflector (PaR) fed by a VW uniform-circular-array (UCA). We multiplexed all the transmitted (TX) +/- p-modes, each modulated by a 16QAM symbol-vector. We receive (RX) the TX-signal by p(max) number of nonvortex PaR antennas placed on a small arc of the Rcon. The RX-signal is cast into the standard discrete-Fourier-transform (DFT) format, using the beam-collimation, the azimuthal-orthogonality and zero-padding. The demultiplexing is performed by IDFT. The UCA is designed at Ka-band using circular microstrip-patch-antennas (msPA). The +/- p-modes are TX'ed by orthogonally-polarized separate msPA's. The effects of coupling of +/- p-modes, the calibration inaccuracies and signal-to-noise-ratio (SNR) are simulated by the Monte-Carlo method. It was found that the SNR is very high and the far-field radio-link is feasible. The bit rate of the present OAM-16QAM radio-link is increased by a factor of 2 p(max).
Multiple Service Home Health Care Routing and Scheduling Problem: A Mathematical Model
(2020) Dengiz, Asiye Ozge; Atalay, Kumru Didem; Altiparmak, Fulya
The home health care routing and scheduling problem (HHCRSP) is an extension of the vehicle routing problem (VRP) that are scheduled and routed to perform a wide range of health care services. Nurses, doctors and/or caregivers provide these services at patients' home. In this study, a mathematical model for HHCRSP is presented. The model is extended to take into account additional characteristics and/or constraints based on specific services, patient needs. In the home health care (HHC) problems, services that must be performed simultaneously or within a convinced time are undoubtedly very important. Thus, we consider several numbers of services, skill requirements for the care workers and time windows. Generally, the main aim of the HHC problems is minimizing the travelling distance as well as maximizing the patients' satisfaction. Thus, the model in this study contains both of these objectives taking into account several measurements.
A New Modeling Approach for Stability of Micro/Nano Bubbles
(2021) Dogan, Mustafa; Bunyatova, Ulviya; Ferhanoglu, Onur
Microbubbles and nanobubbles have several characteristics that are comparable with millimeter- and centimeter-sized bubbles. These characteristics are their small size, which results in large surface area and high bioactivity, low rising velocity, decreased friction drag, high internal pressure, large gas dissolution capacity, negatively charged surface, and ability to be crushed and form free radicals. Controlling and modeling fundamental properties such as nucleation and of the dynamics of these bubbles is key to successfully exploiting their potential in the growing number of applications such as biomedical diagnosis and therapy, antimicrobial in aquaculture, environment, engineering, stock raising and marine industry. Laser-generated bubble dimensions can be characterized with an optical setup employing a high power continuous wave green laser for bubble generation. In this work, non-resonant, self-excited due to structurally nonlinear properties of the hydrogel, bubble formation was modeled as functions of well-controlled parameters of the colloidal media that is multi-layered and anisotropic, engineered uniquely. Copyright (C) 2021 The Authors.
A New Recombination Operator for the Genetic Algorithm Solution of the Quadratic Assignment Problem
(2014) Tosun, Umut
The Quadratic Assignment Problem (QAP) is a well known combinatorial optimization problem with a diverse set of applications. It can be transformed into many problems such as the travelling salesman, weapon target assignment, and query optimization in distributed databases. Exhaustive search methods are inadequate to solve large data sets. Genetic algorithms and tabu search meta-heuristics may provide near optimal solutions for large QAP instances taking a reasonable time to complete. In this paper, we present a new recombination operator based on Order-1 crossover algorithm. The suggested approach runs quick sort partitioning algorithm to generate different chromosomes from partitions. The minimum cost partition produces offsprings with the other chromosome. The proposed approach shows outstanding performance especially for instance sizes smaller than 50 with respect to the optimal results proposed in QAPLIB. (C) 2014 Published by Elsevier B.V.
Objective Pain Assessment Using Vital Signs
(2020) Erdogan, Burak; Ogul, Hasan
Pain is considered as an emotional experience and unrestful feeling associated with tissue damage. The feeling of pain occurs when the interpretation starts in brain; as a signal is sent through nerve fiber to the brain. Pain allows the body to prevent further tissue damage. Since there are different ways of expressing and feeling pain, the experience of pain is unique for everybody. In this respect, objective pain assessment is a key step and a major challenge in proper management of pain in different individuals. In this study, we offer a computational solution for objective assessment of pain using vital signs. To this end, we have reported the prediction for existence of pain by calculating the performances of several computational methods that take the sequence of vital signs acquired until pain observation as input. We claim that the use of computational intelligence methods can encourage computer-aided monitoring of pain in a hospitalized environment to a certain degree. (C) 2020 The Authors. Published by Elsevier B.V.
On computer-aided prognosis of septic shock from vital signs
(2019) Ogul, Hasan; Baldominos, Alejandro; Asuroglu, Tunc; Colomo-Palacios, Ricardo; AAC-7834-2020
Sepsis is a life-threatening condition due to the reaction to an infection. With certain changes in circulatory system, sepsis may progress to septic shock if it is left untreated. Therefore, early prognosis of septic shock may facilitate implementing correct treatment and prevent more serious complications. In this study, we assess the feasibility of applying a computer-aided prognosis system for septic shock. The system is envisaged as a tool to predict septic shock at the time of sepsis onset using only vital signs which are collected routinely in intensive care units (ICUs). To this end, we evaluate the performances of computational methods that take the sequence of vital signs acquired until sepsis onset as input and report the possibility of progressing to a septic shock before any further clinical analysis is performed. Results show that an adaptation of multivariate dynamic time warping can reveal higher accuracy than other known time-series classification methods on a new dataset built from a public ICU database. We argue that the use of computational intelligence methods can promote computer-aided prognosis of septic shock in hospitalized environment to a certain degree.
Order Acceptance and Scheduling Problem: A Proposed Formulation and the Comparison with the Literature
(2020) Bicakci, Papatya S.; Kara, Imdat
In classical scheduling problem, it is assumed that all orders must be processed. In the order acceptance and scheduling (OAS) problem, some orders are rejected due to limited capacity. In make-to-order production environment, in which the OAS problem occurs, accepting all orders may cause overloads, delay in deliveries and unsatisfied customers. Oguz et al. (2010) introduced the OAS problem with sequence-dependent setup times and release dates. In this paper, we propose a new mixed integer programming formulation with O(n(2)) decision variables and O(n2) constraints for the same problem. We conduct a computational analysis comparing the performance of our formulation with Oguz et al. (2010) formulation. We use the benchmark instances, which are available in the literature. We observe that our formulation can solve all the instances up to 50 orders in a reasonable time, while Oguz et al. (2010) formulation can solve only the instances with 10 orders in the same time limit.
Policy Misuse Detection in Communication Networks with Hidden Markov Models
(2014) Tosun, Umut
With the recent advances in computer networking applications, Intrusion Detection Systems (IDS) are widely used to detect the malicious connections in computer networks. IDS provide a high level security between organizations while preventing misuses and intrusions in data communication through internet or any other network. Adherence to network usage policies is crucial since a system or network administrator needs to be informed whether the information is compromised, if the resources are appropriately used or if an attacker exploits a comprised service. Server flow authentication via protocol detection analyzes penetrations to a communication network. Generally, port numbers in the packet headers are used to detect the protocols. However, it is easy to re-map port numbers via proxies and changing the port number via compromised host services. Using port numbers may be misleading for a system administrator to understand the natural flow of communications through network. It is also difficult to understand the user behavior when the traffic is encrypted since there is only packet level information to be considered. In this paper, we present a novel approach via Hidden Markov Models to detect user behavior in network traffic. We perform the detection process on timing measures of packets. The results are promising and we obtained classification accuracies between %70 and %100. (C) 2014 Published by Elsevier B.V.