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Article Citation - WoS: 13Citation - Scopus: 13Investigating Space Utilization in Skyscrapers Designed with Prismatic Form(Mdpi, 2024) Ilgin, Hueseyin Emre; Aslantamer, Ozlem NurThe enduring appeal of prismatic shapes, historically prevalent in office building designs, persists in contemporary skyscraper architecture, which is attributed particularly to their advantageous aspects concerning cost-efficiency and optimal space utilization. Space efficiency is a crucial factor in prismatic skyscraper design, carrying substantial implications for sustainability. However, the current academic literature lacks a complete exploration of space efficiency in supertall towers with prismatic forms, despite their widespread use. This paper seeks to address this significant gap by conducting a comprehensive analysis of data gathered from a carefully selected set of 35 case studies. The primary discoveries presented in this paper are outlined as follows: (i) average space efficiency stood at approximately 72%, covering a range that extended from 56% to 84%; (ii) average core to gross floor area ratio averaged around 24%, spanning a spectrum that ranged from 12% to 36%; (iii) the majority of prismatic skyscrapers utilized a central core approach, mainly customized for residential use; (iv) the dominant structural system observed in the analyzed cases was the outriggered frame system, with concrete being the commonly utilized material for the structural components; and (v) the impact of diverse structural systems on space efficiency showed no significant deviation, although differences in function led to variations in average space efficiency. The authors expect that these findings will provide valuable guidance, especially for architects, as they strive to enhance the sustainable planning of prismatic towers.Article Citation - WoS: 24Citation - Scopus: 39Network Intrusion Detection With a Hashing Based Apriori Algorithm Using Hadoop Mapreduce(Mdpi, 2019) Azeez, Nureni Ayofe; Ayemobola, Tolulope Jide; Misra, Sanjay; Maskeliunas, Rytis; Damasevicius, RobertasUbiquitous nature of Internet services across the globe has undoubtedly expanded the strategies and operational mode being used by cybercriminals to perpetrate their unlawful activities through intrusion on various networks. Network intrusion has led to many global financial loses and privacy problems for Internet users across the globe. In order to safeguard the network and to prevent Internet users from being the regular victims of cyber-criminal activities, new solutions are needed. This research proposes solution for intrusion detection by using the improved hashing-based Apriori algorithm implemented on Hadoop MapReduce framework; capable of using association rules in mining algorithm for identifying and detecting network intrusions. We used the KDD dataset to evaluate the effectiveness and reliability of the solution. Our results obtained show that this approach provides a reliable and effective means of detecting network intrusion.Article Citation - WoS: 27Citation - Scopus: 43Digital Transformation Strategies, Practices, and Trends: a Large-Scale Retrospective Study Based on Machine Learning(Mdpi, 2023) Gurcan, Fatih; Boztas, Gizem Dilan; Dalveren, Gonca Gokce Menekse; Derawi, MohammadThe purpose of this research is to identify the areas of interest, research topics, and application areas that reflect the research nature of digital transformation (DT), as well as the strategies, practices, and trends of DT. To accomplish this, the Latent Dirichlet allocation algorithm, a probabilistic topic modeling technique, was applied to 5350 peer-reviewed journal articles on DT published in the last ten years, from 2013 to 2022. The analysis resulted in the discovery of 34 topics. These topics were classified, and a systematic taxonomy for DT was presented, including four sub-categories: implementation, technology, process, and human. As a result of time-based trend analysis, "Sustainable Energy", "DT in Health", "E-Government", "DT in Education", and "Supply Chain" emerged as top topics with an increasing trend. Our findings indicate that research interests are focused on specific applications of digital transformation in industrial and public settings. Based on our findings, we anticipate that the next phase of DT research and practice will concentrate on specific DT applications in government, health, education, and economics. "Sustainable Energy" and "Supply Chain" have been identified as the most prominent topics in current DT processes and applications. This study can help researchers and practitioners in the field by providing insights and implications about the evolution and applications of DT. Our findings are intended to serve as a guide for DT in understanding current research gaps and potential future research topics.Article Citation - WoS: 4Citation - Scopus: 5Enhancing Urban Sustainability With Novel Vertical-Axis Wind Turbines: a Study on Residential Buildings in Çeşme(Mdpi, 2025) Saleh, Yousif Abed Saleh; Durak, Murat; Turhan, CihanThis study investigates the integration of three types of vertical-axis wind turbines (VAWTs)-helical, IceWind, and a combined design-on residential buildings in & Ccedil;e & scedil;me, T & uuml;rkiye, a region with an average wind speed of 7 m/s. The research explores the potential of small-scale wind turbines in urban areas, providing sustainable solutions for renewable energy generation and reducing reliance on conventional energy sources. The turbines were designed and analyzed using SolidWorks and ANSYS Fluent, achieving power outputs of 350 W for the helical turbine, 430 W for the IceWind turbine, and 590 W for the combined turbine. A total of 42 turbines were mounted on a five-storey residential building model, and DesignBuilder software was utilized to simulate and evaluate the energy consumption. The baseline energy consumption of 172 kWh/m2 annually was reduced by 18.45%, 22.93%, and 30.88% for the helical, IceWind, and combined turbines, respectively. Furthermore, the economic analysis showed payback periods of 12.89 years for the helical turbine, 10.60 years for the IceWind turbine, and 10.49 years for the combined turbine. These findings emphasize the viability of integrating VAWTs into urban buildings as an effective strategy for reducing energy consumption, lowering costs, and enhancing energy efficiency.Article Citation - WoS: 23Citation - Scopus: 26Reconstruction of 3d Object Shape Using Hybrid Modular Neural Network Architecture Trained on 3d Models From Shapenetcore Dataset(Mdpi, 2019) Kulikajevas, Audrius; Maskeliunas, Rytis; Damasevicius, Robertas; Misra, SanjayDepth-based reconstruction of three-dimensional (3D) shape of objects is one of core problems in computer vision with a lot of commercial applications. However, the 3D scanning for point cloud-based video streaming is expensive and is generally unattainable to an average user due to required setup of multiple depth sensors. We propose a novel hybrid modular artificial neural network (ANN) architecture, which can reconstruct smooth polygonal meshes from a single depth frame, using a priori knowledge. The architecture of neural network consists of separate nodes for recognition of object type and reconstruction thus allowing for easy retraining and extension for new object types. We performed recognition of nine real-world objects using the neural network trained on the ShapeNetCore model dataset. The results evaluated quantitatively using the Intersection-over-Union (IoU), Completeness, Correctness and Quality metrics, and qualitative evaluation by visual inspection demonstrate the robustness of the proposed architecture with respect to different viewing angles and illumination conditions.Article Citation - WoS: 25Citation - Scopus: 37A Rule-Based Approach To Embedding Techniques for Text Document Classification(Mdpi, 2020) Aubaid, Asmaa M.; Mishra, AlokWith the growth of online information and sudden expansion in the number of electronic documents provided on websites and in electronic libraries, there is difficulty in categorizing text documents. Therefore, a rule-based approach is a solution to this problem; the purpose of this study is to classify documents by using a rule-based. This paper deals with the rule-based approach with the embedding technique for a document to vector (doc2vec) files. An experiment was performed on two data sets Reuters-21578 and the 20 Newsgroups to classify the top ten categories of these data sets by using a document to vector rule-based (D2vecRule). Finally, this method provided us a good classification result according to the F-measures and implementation time metrics. In conclusion, it was observed that our algorithm document to vector rule-based (D2vecRule) was good when compared with other algorithms such as JRip, One R, and ZeroR applied to the same Reuters-21578 dataset.Review Citation - WoS: 4Citation - Scopus: 4Comparative Analysis of Space Efficiency in Skyscrapers With Prismatic, Tapered, and Free Forms(Mdpi, 2024) Ilgin, Huseyin Emre; Aslantamer, Ozlem NurThis study offers a thorough comparative analysis of space efficiency in skyscrapers across three distinct forms: prismatic, tapered, and free. By examining case studies from each form category, this research investigates how architectural and structural design features impact space utilization in supertall towers. The findings reveal form-based differences in space efficiency and design element usage. In prismatic skyscrapers, which are primarily residential and utilize concrete outrigger frames, the average space efficiency was around 72%, with the core occupying 24% of the gross floor area (GFA). Tapered skyscrapers, commonly mixed-use with composite outrigger frames, showed an average space efficiency of over 70%, with a core-to-GFA ratio of 26%. Freeform towers, often mixed-use and using composite outrigger frames, demonstrated a space efficiency of 71%, with an average core-to-GFA ratio of 26%. Despite these variations, a consistent trend emerged: as the height of a building increases, there is a general decline in space efficiency, highlighting the challenges in optimizing space in taller structures. This analysis adds to the understanding of skyscraper design and space utilization, providing important insights for architects and urban planners aiming to improve the efficiency of future high-rise developments.Article Citation - WoS: 9Citation - Scopus: 12On the Performance of Energy Criterion Method in Wi-Fi Transient Signal Detection(Mdpi, 2022) Mohamed, Ismail; Dalveren, Yaser; Catak, Ferhat Ozgur; Kara, AliIn the development of radiofrequency fingerprinting (RFF), one of the major challenges is to extract subtle and robust features from transmitted signals of wireless devices to be used in accurate identification of possible threats to the wireless network. To overcome this challenge, the use of the transient region of the transmitted signals could be one of the best options. For an efficient transient-based RFF, it is also necessary to accurately and precisely estimate the transient region of the signal. Here, the most important difficulty can be attributed to the detection of the transient starting point. Thus, several methods have been developed to detect transient start in the literature. Among them, the energy criterion method based on the instantaneous amplitude characteristics (EC-a) was shown to be superior in a recent study. The study reported the performance of the EC- a method for a set of Wi-Fi signals captured from a particular Wi-Fi device brand. However, since the transient pattern varies according to the type of wireless device, the device diversity needs to be increased to achieve more reliable results. Therefore, this study is aimed at assessing the efficiency of the EC-a method across a large set ofWi-Fi signals captured from variousWi-Fi devices for the first time. To this end, Wi-Fi signals are first captured from smartphones of five brands, for a wide range of signalto-noise ratio (SNR) values defined as low (3 to 5 dB), medium (5 to 15 dB), and high (15 to 30 dB). Then, the performance of the EC-a method and well-known methods was comparatively assessed, and the efficiency of the EC-a method was verified in terms of detection accuracy.Article Citation - WoS: 4Citation - Scopus: 4Further Development of Polyepichlorohydrin Based Anion Exchange Membranes for Reverse Electrodialysis by Tuning Cast Solution Properties(Mdpi, 2022) Eti, Mine; Cihanoglu, Aydin; Guler, Enver; Gomez-Coma, Lucia; Altiok, Esra; Arda, Muserref; Kabay, NalanRecently, there have been several studies done regarding anion exchange membranes (AEMs) based on polyepichlorohydrin (PECH), an attractive polymer enabling safe membrane fabrication due to its inherent chloromethyl groups. However, there are still undiscovered properties of these membranes emerging from different compositions of cast solutions. Thus, it is vital to explore new membrane properties for sustainable energy generation by reverse electrodialysis (RED). In this study, the cast solution composition was easily tuned by varying the ratio of active polymer (i.e., blend ratio) and quaternary agent (i.e., excess diamine ratio) in the range of 1.07-2.00, and 1.00-4.00, respectively. The membrane synthesized with excess diamine ratio of 4.00 and blend ratio of 1.07 provided the best results in terms of ion exchange capacity, 3.47 mmol/g, with satisfactory conductive properties (area resistance: 2.4 omega center dot cm(2), electrical conductivity: 6.44 mS/cm) and high hydrophilicity. RED tests were performed by AEMs coupled with the commercially available Neosepta CMX cation exchange membrane (CEMs).Article Citation - WoS: 18Citation - Scopus: 35Distributed Centrality Analysis of Social Network Data Using Mapreduce(Mdpi, 2019) Behera, Ranjan Kumar; Rath, Santanu Kumar; Misra, Sanjay; Damasevicius, Robertas; Maskeliunas, RytisAnalyzing the structure of a social network helps in gaining insights into interactions and relationships among users while revealing the patterns of their online behavior. Network centrality is a metric of importance of a network node in a network, which allows revealing the structural patterns and morphology of networks. We propose a distributed computing approach for the calculation of network centrality value for each user using the MapReduce approach in the Hadoop platform, which allows faster and more efficient computation as compared to the conventional implementation. A distributed approach is scalable and helps in efficient computations of large-scale datasets, such as social network data. The proposed approach improves the calculation performance of degree centrality by 39.8%, closeness centrality by 40.7% and eigenvalue centrality by 41.1% using a Twitter dataset.
