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Conference Object Comparative Analysis Of Patch Antennas With Rectangular Slots For Laminate And Wearable Materials At 5g Networks(Institute of Electrical and Electronics Engineers Inc., 2024) Hakanoglu, B.G.; Agaya, E.; Gulmez, G.; Yalinsu, S.In this study, new multi-band patch antenna design models are proposed for use in 5G networks. The purpose of the designs is to open rectangular slots on rectangular shaped patch antennas and bring them to the desired operating conditions with parametric analyzes. The designs were carried out by following the same procedure steps using five different dielectric laminate substrate materials, such as RO3003, RT6006, FR4, RO3203, RO6010, and one denim fabric base material. The antennas were compared in terms of return loss, gain and radiation characteristics. Except for the antenna designed with RO3203 at certain values of rectangular slots, radiation at multiple frequencies was obtained at 5G frequencies. With the proposed method, improvement was observed for return loss and bandwidth characteristics in the RO3203 based antenna. This study will be a resource for antenna researchers by revealing the responses of different substrate materials to the same design method for 5G bands in patch antennas. © 2024 IEEE.Conference Object Need for a Software Development Methodology for Research-Based Software Projects(Institute of Electrical and Electronics Engineers Inc., 2018) Cereci,I.; Karakaya,Z.Software development is mostly carried by a group of individuals. Software development methodologies are heavily utilized to organize these individuals and keep track of the entire software development process. Although previously proposed software development methodologies meet the needs of the industry and the firms, they are not usually suitable for research-based software projects that are carried by universities and individual researchers. In this paper, we aim to show the necessity of a new software development methodology for research-based problems carried by universities. The literature review will show the differences between industry and university software projects from certain aspects. These findings will be supported by the authors own research on the area. This qualitative research involves collecting data through interviews and applying Grounded Theory to better understand the development process. © 2018 IEEE.Conference Object Securing the Internet of Things: Challenges and Complementary Overview of Machine Learning-Based Intrusion Detection(Institute of Electrical and Electronics Engineers Inc., 2024) Isin, L.I.; Dalveren, Y.; Leka, E.; Kara, A.The significant increase in the number of IoT devices has also brought with it various security concerns. The ability of these devices to collect a lot of data, including personal information, is one of the important reasons for these concerns. The integration of machine learning into systems that can detect security vulnerabilities has been presented as an effective solution in the face of these concerns. In this review, it is aimed to examine the machine learning algorithms used in the current studies in the literature for IoT network security. Based on the authors' previous research in physical layer security, this research also aims to investigate the intersecting lines between upper layers of security and physical layer security. To achieve this, the current state of the area is presented. Then, relevant studies are examined to identify the key challenges and research directions as an initial overview within the authors' ongoing project. © 2024 IEEE.Conference Object Hierarchical Cellular Automata Consensus Blockchain(Institute of Electrical and Electronics Engineers Inc., 2025) Çulha, D.Blockchain technology is foundational for decentralized systems, yet current implementations face critical limitations in scalability and communication efficiency, especially within consensus mechanisms like Practical Byzantine Fault Tolerance (PBFT). This paper proposes the Hierarchical Cellular Automata Consensus Mechanism (HCACM), a novel solution leveraging a hierarchical arrangement of cellular automata rings employing Wolfram's Rule 184 and Rule 232. Rule 184 efficiently propagates consensus states across the network, while Rule 232 stabilizes local consensus. HCACM significantly reduces communication overhead by facilitating deterministic consensus through localized interactions, enhancing network scalability and fault tolerance. Simulation results validate that HCACM outperforms traditional consensus algorithms in scalability, communication efficiency, and fault isolation, establishing it as an effective framework for decentralized applications requiring high transaction. © 2025 IEEE.Conference Object Citation - WoS: 1Citation - Scopus: 2Smart Contract Upgradability: a Structured and Natural Approach(Institute of Electrical and Electronics Engineers Inc., 2024) Culha, Davut; Yazici, AliSoftware maintenance is crucial as technology rapidly evolves, requiring software to meet new demands and correct errors. Smart contracts, immutable programs on blockchains like Ethereum, face challenges despite their immutability, often needing updates for errors or new features. Smart contracts are upgraded using different patterns, which are not natural because most of them implement upgrades using low-level operations that deviate from their intended use. In other words, these patterns are not natural because upgrades are done by implementing workarounds. Moreover, smart contracts are also susceptible to security vulnerabilities because they may hold large amounts of money. In this paper, upgradability of smart contracts is considered a necessity. For this purpose, a more structured method is proposed by adding high-level features and combining inheritance properties of object-oriented languages. A key component of this method is the gotoContract variable, which allows for the redirection of function calls to upgraded contracts. The proposed method provides a complete upgrade of data and functions in smart contracts. It aims to minimize the effects of upgrades on end users of the smart contracts. Additionally, this natural way of upgrading will help mitigate security risks in the smart contracts by providing a high-level approach to upgrade.Conference Object A Comparison of Stream Processing Frameworks(Institute of Electrical and Electronics Engineers Inc., 2017) Karakaya,Z.; Yazici,A.; Alayyoub,M.This study compares the performance of Big Data Stream Processing frameworks including Apache Spark, Flink, and Storm. Also, it measures the resource usage and performance scalability of the frameworks against a varying number of cluster sizes. It has been observed that, Flink outperforms both Spark and Storm under equal constraints. However, Spark can be optimized to provide the higher throughput than Flink with the cost of higher latency. © 2017 IEEE.Conference Object Multi-Label Movie Genre Detection From Movie Posters Using Deep Learning Algorithms(Institute of Electrical and Electronics Engineers Inc., 2024) Yılmaz, A.A.In the field of cinema, the concept of genre has emerged as a concept that basically includes films that have common characteristics in terms of subject matter, have adopted a common method, and have a low risk of error because they have been tried before. Identifying the genres of movies is a challenging task because genres are intangible features that are not physically present in any movie scene, so off-the-shelf image detection models may not be easily integrated into this process. In this study, we aim to address the detection of movies according to their genres using deep learning algorithms. Movie poster data of IMDB and MM-IMDB datasets were utilized in our multi-label movie genre detection studies. In our experiments, we utilized four modern pre-trained models follow as DenseNet, VGG-16, ResNet-50, and MobileNet, and evaluated their performance using performance metric values such as accuracy, precision, recall, and F-score. According to the obtained empirical results, the DenseNet architecture achieved the highest accuracy values compared to other deep learning methods in detecting multi-label movie genre detection with an impressive rates of 91.64% and 92.56%. © 2024 IEEE.Conference Object Citation - Scopus: 1A Hybrid Data-Driven and Fuzzy MCDM Approach for Employee Selection(Institute of Electrical and Electronics Engineers Inc., 2025) Sadeghzadeh, K.; Bahreini, P.; Kao, Y.-L.; Yilmaz, I.; Erdebilli, B.; Aghsami, A.; Bahrini, A.Employee selection, a cornerstone of human resource management, critically shapes organizational performance and long-term effectiveness. While traditional approaches primarily rely on expert-based evaluations, this study proposes a novel hybrid framework that integrates Multi-Criteria Decision-Making methods with data mining techniques to reduce the dimensionality of the number of criteria or variables considered. By integrating backward regression with fuzzy Multi-Criteria Decision-Making methods, our framework reduces model complexity and captures criteria interdependencies, while fuzzy logic addresses ambiguity in expert judgment, a gap often overlooked in prior research. The methodology first uses backward regression modeling with the employee attrition rate as the response variable to identify core criteria. Subsequently, the fuzzy Decision-Making Trial and Evaluation Laboratory analyzes interrelationships between criteria, followed by the fuzzy Analytic Network Process for weighting criteria and ranking candidates. We validate our approach using real-world recruitment data - including expert interview scores and historical attrition - from a company specializing in electronic attendance systems. The AI-generated rankings are benchmarked against these expert-based evaluations to assess alignment with human judgment. Initially, 17 criteria were systematically reduced to 11 core factors, resulting in a streamlined yet robust evaluation system. Our findings emphasize that 'Time-of-service,' 'Requested-wage,' 'Teamwork,' and 'Leadership' are the most critical criteria influencing effective IT personnel selection. © 2025 IEEE.Conference Object Reinforcement Learning-Based Multi-Robot Path Planning and Congestion Management in Warehouse Order Picking(Institute of Electrical and Electronics Engineers Inc., 2024) Alam, M.S.; Khan, M.U.; Gunes, A.This paper addresses the multi-robot path planning problem in a warehouse environment using reinforcement learning. The warehouse layout comprises of a grid map with multiple robots for retrieval and delivery of orders, inventory pods for storage, and pick stations for receiving outbound orders. The robots are required to pick and deliver orders from target shelves to their corresponding pick stations by navigating in a complex network of aisles. Q-learning algorithm computes optimal paths for the robots, while avoiding congestion in the aisles. Simulation results demonstrate the efficacy of the proposed method in optimizing both travel time and travel distance, thus enhancing the overall operational efficiency of the warehouse. © 2024 IEEE.
