Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this communityhttps://hdl.handle.net/11413/11

Browse

Now showing 1 - 20 of 153

Open Access
A Bayesian Deep Neural Network Approach to Seven-Point Thermal Sensation Perception
(IEEE-Inst Electrical Electronics Engineers Inc., 2022) ÇAKIR, MUSTAFA; AKBULUT, AKHAN
To create and maintain comfortable indoor environments, predicting occupant thermal sensation is an important goal for architects, engineers, and facility managers. The link between thermal comfort, productivity, and health is common knowledge, and researchers have developed many state-of-the-art thermal-sensation models from dozens of research projects over the last 50 years. In addition to these, the use of intelligent data-analysis techniques, such as black-box artificial neural networks (ANNs), is receiving research attention with the aim of designing building thermal-behavior models from collected data. With the convergence of the internet of things (IoT), cloud computing, and artificial intelligence (AI), smart buildings now protect us and keep us comfortable while saving energy and cutting emissions. These types of smart buildings play a vital role in building smart cities of the future. The aim of this study is to help facility managers predict the thermal sensation of the occupants under the given circumstances. To achieve this, we applied a data-driven approach to predict the thermal sensation of occupants of an indoor environment using previously collected data. Our main contribution is to design and evaluate a deep neural network (DNN) for predicting thermal sensations with a high degree of accuracy regardless of building type, climate zone, or a building's heating and/or ventilation methods. We used the second version of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Global Thermal Comfort Database to train our model. The hyperparameter-tuning process of the proposed model is optimized using the Bayesian strategy and predicts the thermal sensation of occupants with 78% accuracy, which is much higher than the traditional predicted mean vote (PMV) model and the other shallow and deep networks compared.
Restricted
Adaptation of Gamification as a Man-Machine Interface to Franchise Management System
(Institute of Electrical and Electronics Engineers Inc., 2021) ALTUNEL, YUSUF; GÜNAYDIN, BURAK; KALABALIKOĞLU, FURKAN
Current man-machine interfaces are far from fitting human expectations in understanding and transmission of noteworthy signs and hints that are natural ingredients of human communication. There are certain indicators and interaction possibilities that can be passed between the two sides but as a result of complex behavior and dynamic changing conditions of environments they can be lost, or at least might require long and complex processing. Gamification is used to enhance the interaction possibilities providing the visual representation of environmental conditions and critical indicators, as well as providing the ability to send and receive requests using suitable techniques for human such as touches, and finger moves on Unity environment. Franchise Management System is selected as a case study and adapted to maintain the interactions between franchiser and franchisee. © 2021 IEEE.
Restricted
Adaptive Direction-Guided Structure Tensor Total Variation
(Elsevier, 2021) Kamasak, Mustafa E.; TÜREYEN, EZGİ DEMİRCAN
Direction-guided structure tensor total variation (DSTV) is a recently proposed regularization term that aims at increasing the sensitivity of the structure tensor total variation (STV) to the changes towards a predetermined direction. Despite of the plausible results obtained on the uni-directional images, the DSTV model is not applicable to the arbitrary (multi-directional and/or partly nondirectional) images. In this study, we build a two-stage denoising framework that brings adaptivity to the DSTV based denoising. We design a DSTV-like alternative to STV, which encodes the first-order information within a local neighborhood under the guidance of spatially varying directional descriptors (i.e., orientation and the dose of anisotropy). In order to estimate those descriptors, we propose an efficient preprocessor that captures the local geometry based on the structure tensor. Through the extensive experiments, we demonstrate how beneficial the involvement of the directional information in STV is, by comparing the proposed method with the state-of-the-art analysis-based denoising models, both in terms of quality and computational efficiency.
Open Access
An Efficient Generation and Security Analysis of Substitution Box Using Fingerprint Patterns
(IEEE, 2020) ŞENGEL, ÖZNUR; Aydın, Muhammed Ali; Sertbaş, Ahmet
Information and its security have attracted the research community in recent years with increasing usage of mobile applications. Mobile devices have different security options in data transmission such as reading some biometric values. The keystone of the modern block and stream ciphers is the use of a substitution box (s-box) that obscures correlation between plaintext and ciphertext. In this study, we proposed a novel s-box generation algorithm by using the fingerprint pattern of the person who transfers information to the target. We generated several s-boxes by using bifurcation and ridge ending features of the fingerprint. Proposed s-boxes are compared with several known s-boxes over nonlinearity, bijectiveness, strict avalanche criterion, bit independence criterion, linear probability, and differential probability. Along with these properties, we analyzed confidence interval and randomness properties of new s-boxes as well. Also, the execution time of the proposed s-box generation algorithm is calculated and examined. The results of the cryptographic properties have shown that the proposed s-boxes by using ridge ending of the fingerprint performs better. The performances analysis show that the proposed s-box has satisfactory results according to the results of chaotic-based s-boxes. On the other hand, the fingerprint s-boxes are much better than the existing biometric s-boxes according to the s-box security metrics. The results have shown that the execution time of the proposed s-box generation algorithm is more minimum than the existing biometric s-box generation algorithms. Resulting from applying fingerprint biometric data to generate an s-box, such a successful algorithm is promising to be used in mobile devices.
Open Access
Analysis of a Prefabricated Vertical Drain (PVD) Soil Improvement Project
(Turkish Chamber Civil Engineers, 2022) MERT, AHMET CAN; AREL, ERSİN; ÖNALP, AKIN
A settlement analysis has been carried out for several sectors of a rail station yard improved with prefabricated vertical drains (PVD) in Istanbul, that exhibited prolonged consolidation beyond the predicted values in certain sectors of the treated zone. Final settlement and End of Primary (EOP) settlement times have been estimated theoretically as well as using the Asaoka graphical procedure. The compliance of settlement-time curves with in-situ measurements and Asaoka solution has been investigated. A geotechnical model was developed for finite element and three-dimensional consolidation analyses. The settlement curves obtained by varying horizontal-vertical permeability coefficient ratio (k(h)/k(v)) and in-situ measurements have been compared, and k(h)/k(v) values corresponding to 90% degree of consolidation has been computed for all sectors. The effect of drain spacing (S-drain) as well as drain length (L-drain) on the rate of consolidation have been evaluated for each sector, keeping the specified ratios constant. The times corresponding to 95% degree of consolidation (t(95)) have been calculated using the theoretical solution and compared to in-situ measurements. Calculated t(95)'s has also been compared to their estimated values by varying the spacing (S-drain) and the length (L-drain). Additionally, the required intervals of S-drain and L-drain have been obtained corresponding to the calculated t(95) times. The analyses suggest that the main reason for prolonged consolidation was the horizontal to vertical permeability coefficient ratio. According to the analysis results, PVD implementation was not efficient in clays having k(h)/k(v) of approximately unity. The main conclusion of this study was to discover the necessity for optimizing the variables in any such project. The efficacy of the works can be significantly enhanced if simultaneous evaluation of the parameters S-drain and L-drain and the permeability ratio k(h)/k(v) is carried out prior to field work. Otherwise, "accidents" may emerge as found out in this project.
Open Access
Analysis of Facial Emotion Expression in Eating Occasions Using Deep Learning
(Springer, 2023) ELİF, YILDIRIM; AKBULUT, FATMA PATLAR; Çatal, Çağatay
Eating is experienced as an emotional social activity in any culture. There are factors that influence the emotions felt during food consumption. The emotion felt while eating has a significant impact on our lives and affects different health conditions such as obesity. In addition, investigating the emotion during food consumption is considered a multidisciplinary problem ranging from neuroscience to anatomy. In this study, we focus on evaluating the emotional experience of different participants during eating activities and aim to analyze them automatically using deep learning models. We propose a facial expression-based prediction model to eliminate user bias in questionnaire-based assessment systems and to minimize false entries to the system. We measured the neural, behavioral, and physical manifestations of emotions with a mobile app and recognize emotional experiences from facial expressions. In this research, we used three different situations to test whether there could be any factor other than the food that could affect a person’s mood. We asked users to watch videos, listen to music or do nothing while eating. This way we found out that not only food but also external factors play a role in emotional change. We employed three Convolutional Neural Network (CNN) architectures, fine-tuned VGG16, and Deepface to recognize emotional responses during eating. The experimental results demonstrated that the fine-tuned VGG16 provides remarkable results with an overall accuracy of 77.68% for recognizing the four emotions. This system is an alternative to today’s survey-based restaurant and food evaluation systems.
Metadata only
Analysis of the Lingering Effects of COVID-19 on Distance Education
(Springer Science and Business Media Deutschland GmbH, 2023) KOCAÇINAR, BÜŞRA; QARIZADA, NASIBULLAH; DİKKAYA, CİHAN; AZGUN, EMİRHAN; ELİF, YILDIRIM; AKBULUT, FATMA PATLAR
Education has been severely impacted by the spread of the COVID-19 virus. In order to prevent the spread of the COVID-19 virus and maintain education in the current climate, governments have compelled the public to adopt online platforms. Consequently, this decision has affected numerous lives in various ways. To investigate the impact of COVID-19 on students’ education, we amassed a dataset consisting of 10,000 tweets. The motivations of the study are; (i) to analyze the positive, negative, and neutral effects of COVID-19 on education; (ii) to analyze the opinions of stakeholders in their tweets about the transition from formal education to e-learning; (iii) to analyze people’s feelings and reactions to these changes; and (iv) to analyze the effects of different training methods on different groups. We constructed emotion recognition models utilizing shallow and deep techniques, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long-short Term Memory (LSTM), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), and Logical Regression (LR). RF algorithms with a bag-of-words model outperformed with over 80% accuracy in recognizing emotions.
Metadata only
An Analytical Approach to Machine Layout Design at a High-Pressure Die Casting Manufacturer
(Springer-Verlag Singapore Pte Ltd., 2022) EMİR, OĞUZ; AKTİN, AYŞE TÜLİN
Sahin Metal was founded in 1975 on a 7500 m(2) area in.Istanbul to supply high-pressure aluminum casting parts to a variety of industries, especially automotive manufacturers. 64 different products are produced with various routings through 14 workstations in the plant. Being a Tier 2 company, these products are then sent to Tier 1 firms and finally reach the leading vehicle manufacturers. Nowadays, increasing competition, changing customer demands, and quality targets bring the necessity of restructuring internal processes. In this regard, Sahin Metal plans to rearrange the existing machine layout to minimize the distance traveled between departments by taking into account the material flow. This study aims to determine an efficient machine layout design by implementing analytical approaches. The study is started by visiting the production facility and meeting with the company's engineers to determine the project roadmap. Following that, the data collection process is initiated, and ABC analysis is performed to define product classes. After this identification, two approaches are utilized simultaneously. The Hollier method is employed to find a logical machine arrangement. In addition, a mathematical model based on the Quadratic Assignment Problem (QAP) is developed to obtain the optimum machine layout. The developed integer nonlinear model is solved by CONOPT using GAMS software under various scenarios. Finally, these results are compared with the existing system, and a convenient layout design is proposed to the company.
Metadata only
Analyzing the Operations at a Textile Manufacturer’s Logistics Center Using Lean Tools
(Springer Science and Business Media Deutschland GmbH, 2024) GÜNAY, AHMET CAN; ÖZBEK ,ONUR; MUTLU, FİLİZ; AKTİN, AYŞE TÜLİN
Compliance with delivery times is crucial for businesses in the logistics sector. Numerous research has been conducted to improve distribution performance. Many of these studies touch on lean production as well. The strategies used in lean manufacturing are often employed by businesses and have a positive impact on performance. This study focuses on the overseas shipping department of a textile company’s logistics center. Workflow starts with product acceptance from manufacturers and ends with shipment to customers abroad. After a thorough examination, some bottlenecks that increase delivery times are observed. Value Stream Mapping (VSM), which is a lean manufacturing technique, is chosen as the main method to be used. It aims to determine value added and non-value-added activities, resulting in minimizing or eliminating the non-value-added ones. Initially, necessary data are gathered through workshops and interviews, and observations on Current State VSM are made. During these workshops, various improvements are proposed and evaluated together with the company’s engineers. After takt time and cycle time calculations, label change station is identified as the bottleneck. In the next step, Kaizens are suggested for the stations, and some lean techniques are employed to solve different workflow problems. Finally, short-term applicability of proposed improvements is discussed, and Future State VSM is drawn. It can be concluded that significant improvements are achieved especially in lead time, changeover time, productivity rate and production speed. By reducing or eliminating non-value-added activities and identifying deficiencies that slow process flow, a standard, sustainable and developable process is proposed to the company. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Restricted
Analyzing the Wastewater Treatment Facility Location/Network Design Problem via System Dynamics: Antalya, Turkey Case
(Academic Press Ltd. - Elsevier Science Ltd., 2022) DEMİREL, DUYGUN FATİH; Gönül-Sezer, Eylül Damla; Pehlivan, Seyda Alperen
Wastewater treatment facility location selection and network design issues have become attractive topics in the field of wastewater management due to increasing human population, resource scarcity, environmental concerns, and rise of necessity for sustainable solutions for future policy designs. Especially in areas where the demand for wastewater treatment increases dramatically over the years because of reasons such as high migration levels, rapid industrialization, and tourism activities, the problem turns out to be more critical and dynamic. The existing studies try to deal with the issue through mathematical modeling approaches based on optimization perspectives, which require significant computational effort. In this study, an alternative approach based on system dynamics (SD) method is proposed to examine the complex dynamic and nonlinear structure of waste-water treatment facility location selection and network design problems. The proposed SD simulation model is designed for a densely populated industrial and tourism spot, the city of Antalya, located on the Mediterranean coast of Turkey. The model is capable of determining where and when to build a new wastewater treatment facility as well as generating the generic wastewater network structure to be built for the five districts situated in the city center based on cost issues for 2015-2040 period. In addition, the impacts of demand level changes for wastewater treatment due to population variations are analyzed via several scenarios to help decision makers to develop sustainable and cost-efficient management policies. Although SD is a frequently utilized approach in the water/wastewater management arena, to the best of our knowledge, this study is the first attempt to examine the complex and dynamic nature of wastewater treatment facility location selection and network design problems through SD approach.
Metadata only
ANNs-Based Prediction Models for Consistency and Compaction Characteristics of Bentonite–Sand Mixtures
(Springer Nature, 2024) Yücel, Melda; Akbay Arama, Zülal; GENÇDAL, HAZAL BERRAK; BAŞBUĞ, BEGÜM; SEÇKİN, EDİP
This study is fictionalized with the use of ANNs logic to estimate the compaction parameters of bentonite–sand mixtures. Totally 230 sets of tests were digitized from the nine well-accepted literature sources to specify the grain size, consistency, and compaction parameters of the bentonite–sand mixtures. Matlab R2018a software is used to perform the estimation process of the compaction parameters, and representative expressions were derived to ease the determination process of mixtures. Consequently, the applicability of the suggested expressions has been checked by the determination and comparison of well-known international metric measurements. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Metadata only
The Applicability of Regression Analysis and Artificial Neural Networks to the Prediction Process of Consistency and Compaction Properties of High Plastic Clays
(Springer Science and Business Media Deutschland GmbH, 2021) Akbay Arama, Zülal; GENÇDAL, HAZAL BERRAK; Nuray, Said Enes; Yücel, Melda
In all kinds of site investigation reports prepared to acquire the current situation of the project site, it is a common fact to perform the consistency tests which are specialized as Atterberg limit tests. Consistency can be defined as an important term, especially for fine-grained soils, to appoint the current state of the water content of soil formation in the field. Based on the ease and cost-effectiveness of the Atterberg tests, it has become a traditional solution to determine the fundamental design properties such as the rigidity and strength of the soil formation with the use of empirical approaches that are developed according to them. In this context, “compaction” can be an interesting term to investigate the appropriateness of determination of special characteristics of the phenomenon such as the optimum water content and the maximum dry unit weight with the development of a new perspective based on a simplest experimental process formed with only the evaluation of water content. Because it is a complicated and time-consuming process to apply the compaction test beginning of the sample preparation step to the ultimate evaluation step. Hence, in this paper, an integrated study is performed for highly plastic clays to acquire the consistency and the compaction properties together with a direct relationship. A huge database was prepared according to the data’s given in the well-accepted literature sources by the transmission of liquid limit and plastic limit test results conducted for only the high plastic clays. Besides, simple equations are tried to be obtained to calculate the plasticity index and approximations are proposed to find the maximum dry unit weight and the optimum water content of the soil, respectively. As a result, the applicability of both the regression analysis and the artificial neural network studies to the attainment process of both consistency characteristics and compaction problem were compared with each other to procure a reliable determination process. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Restricted
The Art of Machine Learning as Fashion Stylish for Designing Clothes
(Institute of Electrical and Electronics Engineers Inc., 2022) KEYDAL, DUYGU; OYMAK, ERENCAN; DEMİR, KADİR BATUHAN; Yılmaz, Güray; ŞAHİNGÖZ, ÖZGÜR KORAY
Over the years, designers have come to the fore with their originality and personal styles and have shaped the fashion industry with their designs. However, due to the progress of time, designers have become unable to meet the demands of all consumers. Since it takes a lot of time to produce an original design, the production process progresses slowly, and customers are uncomfortable with this situation. As in many other industries, designers are trying to solve this problem with the help of artificial intelligence, which is indispensable in the fields of commerce, art, and security. It first entered the fashion sector with drawing programs in the 1950s and has started to change the fashion sector since the 2000s. In the 1950s, artificial intelligence was used only to create a virtual drawing environment When the designer makes a mistake, he can simply erase the mistake and continue working on the design without having to start the whole design from scratch. These programs have greatly facilitated the work of designers. Designers are now able to draw their designs in a much shorter time. But even this shortened period is not enough for the whole fashion industry. Designers could still not keep up with the demands of all customers. Thanks to researchers who added different perspectives to artificial intelligence in the early 2000s with its usage not only for drawing but also for designing, Therefore, in this paper, it is aimed at producing some original designs by preserving the designer's style with the use of Aí techniques. With the proposed model, it has become able to produce ready-made designs by using features such as object detection and visual processing. The experimental results showed that Aí techniques are very successful for combining different patterns for producing an original fashion style. © 2022 IEEE.
Metadata only
Assessment of Seismic Demand and Damping of a Reinforced Concrete Building After CFRP Jacketing of Columns
(Techno-Press, 2022) KOÇAK, PINAR İNCİ; Göksu, Çağier; Töre, Erkan; Binbir, Ergun; Ateş, Ali Osman; İlki, Alper
While the lateral confinement provided by an FRP jacket to a concrete column is passive in nature, confinement is activated when the concrete expands due to additional compression stresses or significant shear deformations. This characteristic of FRP jacketing theoretically leads to similar initial stiffness properties of FRP retrofitted buildings as the buildings without retrofit. In the current study, to validate this theoretical assumption, the initial stiffness characteristics, and thus, the potential seismic demands were investigated through forced vibration tests on two identical full-scale substandard reinforced concrete buildings with or without FRP retrofit. Power spectral density functions obtained using the acceleration response data captured through forced vibration tests were used to estimate the modal characteristics of these buildings. The test results clearly showed that the natural frequencies and the mode shapes of the buildings are quite similar. Since the seismic demand is controlled by the fundamental vibration modes, it is confirmed using vibration-based full-scale tests that the seismic demands of RC buildings remain unchanged after CFRP jacketing of columns. Furthermore, the damping characteristics were also found similar for both structures.
Open Access
AT-ODTSA: A Dataset of Arabic Tweets for Open Domain Targeted Sentiment Analysis
(University of Bahrain, 2022) Sahmoud, Shaaban; Abudalfa, Shadi; ELMASRY, WİSAM
In the field of sentiment analysis, most of research has conducted experiments on datasets collected from Twitter for manipulating a specific language. Little number of datasets has been collected for detecting sentiments expressed in Arabic tweets. Moreover, very limited number of such datasets is suitable for conducting recent research directions such as target dependent sentiment analysis and open-domain targeted sentiment analysis. Thereby, there is a dire need for reliable datasets that are specifically acquired for open-domain targeted sentiment analysis with Arabic language. Therefore, in this paper, we introduce AT-ODTSA, a dataset of Arabic Tweets for Open-Domain Targeted Sentiment Analysis, which includes Arabic tweets along with labels that specify targets (topics) and sentiments (opinions) expressed in the collected tweets. To the best of our knowledge, our work presents the first dataset that manually annotated for applying Arabic open-domain targeted sentiment analysis. We also present a detailed statistical analysis of the dataset. The AT-ODTSA dataset is suitable for train numerous machine learning models such as a deep learning-based model. © 2022 University of Bahrain. All rights reserved.
Restricted
Biosignal Based Emotion-Oriented Video Summarization
(Springer, 2023) DERDİYOK, ŞEYMA; AKBULUT, FATMA PATLAR
Digital video is a crucial component of multimedia that enhances presentations with accurate, engaging visual and aural data that affects several industries. The transition of video storage from analog to digital is being fueled by a variety of causes. Improved compression methods, cheaper technology, and more network needs are some of these drivers. This paper presents a novel video summarization based on physiological signals provided by emotional stimuli. Through these stimuli, 15 emotions are analyzed using physiological signals. The dataset was gathered from 15 participants who watched 61 episodes of 14 television series while wearing a wristband. We built several deep-learning models for the main purpose of recognizing emotions to summarize video. Among the established networks, the best performance has been obtained with the 1D-CNN, with 92.87% accuracy. This work has been done through a series of empirical experiments; since the frequency of the physiological signals is different, we used models with original and resampled configurations in each experiment. The comprehensive comparison result indicates that the oversampling approach gives the highest accuracy as well as the lowest computational complexity. The performance of the proposed video summarization approach was evaluated by a survey of participants, and the results showed that the summaries contained the critical moments of the video. The proposed approach may be useful and effective in physiological signal-based applications requiring emotion recognition, such as emotion-based video summarization or film genre detection. Additionally, reading such summaries facilitates comprehension of the significance of making rapid judgments regarding likes, ratings, comments, etc.
Open Access
Biosignals, Facial Expressions, and Speech as Measures of Workplace Stress: Workstress3d Dataset
(Elsevier, 2024) Doğan, Gülin; AKBULUT, FATMA PATLAR; Çatal, Çağatay
WorkStress3D is a comprehensive collection of multimodal data for the research of stress in the workplace. This dataset contains biosignals, facial expressions, and speech signals, making it an invaluable resource for stress analysis and related studies. The ecological validity of the dataset was ensured by the fact that the data were collected in actual workplace environments. The biosignal data contains measurements of electrodermal activity, blood volume pressure, and cutaneous temperature, among others. High-resolution video recordings were used to capture facial expressions, allowing for a comprehensive analysis of facial cues associated with tension. In order to capture vocal characteristics indicative of tension, speech signals were recorded. The dataset contains samples from both stress-free and stressful work situations, providing a proportionate representation of various stress levels. The dataset is accompanied by extensive metadata and annotations, which facilitate in-depth analysis and interpretation. WorkStress3D is a valuable resource for developing and evaluating stress detection models, examining the impact of work environments on stress levels, and exploring the potential of multimodal data fusion for stress analysis.
Open Access
Blockchain-Based KYC Model for Credit Allocation in Banking
(IEEE-Inst Electrical Electronics Engineers Inc., 2024) Karadağ, Bulut; Zaim, A. Halim; AKBULUT, AKHAN
The implementation of the Know Your Customer (KYC) strategy by banks within the financial sector enhances the operational efficiency of such establishments. The data gathered from the client during the KYC procedure may be applied to deter possible fraudulent activities, money laundering, and other criminal undertakings. The majority of financial institutions implement their own KYC procedures. Furthermore, a centralized system permits collaboration and operation execution by multiple financial institutions. Aside from these two scenarios, KYC processes can also be executed via a blockchain-based system. The blockchain's decentralized network would be highly transparent, facilitating the validation and verification of customer data in real-time for all relevant stakeholders. In addition, the immutability and cryptography of the blockchain ensure that client information is secure and immutable, thereby eradicating the risk of data breaches. Blockchain-based KYC can further improve the client experience by eliminating the requirement for redundant paperwork and document submissions. After banks grant consumers loans, a blockchain-based KYC system is proposed in this study to collect limit, risk, and collateral information from them. The approach built upon Ethereum grants financial institutions the ability to read and write financial data on the blockchain network. This KYC method establishes a transparent, dynamic, and expeditious framework among financial institutions. In addition, solutions are discussed for the Sybil attack, one of the most severe problems in such networks.
Open Access
Boosting the Visibility of Services in Microservice Architecture
(Springer, 2023) TOKMAK, AHMET VEDAT; AKBULUT, AKHAN; Çatal, Çağatay
Monolithic software architectures are no longer sufficient for the highly complex software-intensive systems, which modern society depends on. Service Oriented Architecture (SOA) surpassed monolithic architecture due to its reusability, platform independency, ease of maintenance, and scalability. Recent SOA implementations made use of cloud-native architectural approaches such as microservice architecture, which has resulted in a new challenge: the discovery difficulties of services. One way to dynamically discover and route traffic to service instances is to use a service discovery tool to locate the Internet Protocol (IP) address and port number of a microservice. In the event that replicated microservice instances are found to provide the same function, it is crucial to select the right microservice that provides the best overall experience for the end-user. Parameters including success rate, efficiency, delay time, and response time play a vital role in establishing a microservice's Quality of Service (QoS). These assessments can be performed by means of a live health-check service, or, alternatively, by making a prediction of the current state of affairs with the application of machine learning-based approaches. In this research, we evaluate the performance of several classification algorithms for estimating the quality of microservices using the QWS dataset containing traffic data of 2505 microservices. Our research also analyzed the boosting algorithms, namely Gradient Boost, XGBoost, LightGBM, and CatBoost to improve the overall performance. We utilized parameter optimization techniques, namely Grid Search, Random Search, Bayes Search, Halvin Grid Search, and Halvin Random Search to fine-tune the hyperparameters of our classifier models. Experimental results demonstrated that the CatBoost algorithm achieved the highest level of accuracy (90.42%) in predicting microservice quality.
Open Access
A Broadband, Polarization Insensitive, Wide Incidence-Angle-Slotted Ring/Lumped Resistor-Based Metamaterial Absorber for K-u-Band Applications
(Istanbul University - Cerrahpaşa, 2021) AKŞİMŞEK, HÜSEYİN SİNAN
A broadband-slotted ring/lumped resistor-based metamaterial absorber (MA) is presented in this study for K-u-band microwave applications. Numerical results of the MA indicate that it can achieve a broadband absorption ratio of more than 85% in the frequency range of 12.4-17.6 GHz and has active polarization insensitivity and wide incidence-angle response over the entire operation band between 12.4-17.6 GHz. The designed MA is ultrathin around lambda/14.7 in terms of wavelength at its lowest operation frequency, corresponding to 1.7 mm. The proposed unit-cell structure of the MA is novel, consisting of a slotted ring with eight symmetrically-located lumped resistors, FR-4 material, and a metallic ground, which is compatible with low-cost PCB fabrication; therefore, the MA is suitable for practical microwave applications in the K-u-band.

Browse

Browsing Mühendislik Fakültesi / Faculty of Engineering by Language "en"