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Conference Object Citation - Scopus: 67A Step by Step Guide for Choosing Project Topics and Writing Research Papers in Ict Related Disciplines(Springer Science and Business Media Deutschland GmbH, 2021) Misra,S.ICT is fast-growing and changing field. A lot of researches are being done in various area of ICT, and results are presented in various platforms like conferences, journal and books. This is common observations in the publications from developing countries (especially in sub – Saharan africa) are not being published in reputed and established publishers even their technical/experiments are good. This is due to lack of several factors including professional presentation, the novelty of the topic, quality of literature review etc. This work guides final year bachelor’s students, PG students (masters and PhD) and young researchers, especially working in computing-related disciplines, on how to convert their project works into quality publications. The authors provide details on how these researchers can select suitable project topics, do a proper review, write up the key components of a paper and present their results in an appropriate form (that is, writing style starting from abstract to conclusion). This paper also presents and guides on how to write various types of review papers. © 2020, Springer Nature Switzerland AG.Conference Object Citation - Scopus: 2Reinforcement Learning for Intrusion Detection(Springer Science and Business Media Deutschland GmbH, 2023) Saad,A.M.S.E.; Yildiz,B.Network-based technologies such as cloud computing, web services, and Internet of Things systems are becoming widely used due to their flexibility and preeminence. On the other hand, the exponential proliferation of network-based technologies exacerbated network security concerns. Intrusion takes an important share in the security concerns surrounding network-based technologies. Developing a robust intrusion detection system is crucial to solving the intrusion problem and ensuring the secure delivery of network-based technologies and services. In this paper, we propose a novel approach using deep reinforcement learning to detect intrusions to make network applications more secure, reliable, and efficient. As for the reinforcement learning approach, Deep Q-learning is used alongside a custom-built Gym environment that mimics network attacks and guides the learning process. The NSL-KDD dataset is used to create the reinforcement learning environment to train and evaluate the proposed model. The experimental results show that our proposed reinforcement learning approach outperforms other related solutions in the literature, achieving an accuracy that exceeds 93%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Conference Object Citation - Scopus: 1An Investigation of the Effect of Free-Players on Global Cooperative Behavior in a Spatial Prisoner’s Dilemma Game Environment(Springer Science and Business Media Deutschland GmbH, 2025) Efe, B.; Çerkez, E.; Kılıç, H.In this research, we introduced the concept of Free-Player who rejects to play the dictated rational strategy Defect of the original Prisoner’s Dilemma game setup. Then, we investigated whether Free-Players have any impact on the persistent and stable cooperative behavior of the Players in the context of two dimensional spatial Prisoner’s Dilemma game environment. In simulations, two different Player strategy update setups are considered: State-based Majority and Payoff-driven Stochastic. The results for both setups showed that Free-Players have impact on global cooperative behavior of the system. According to the obtained State-based Majority setup results, the increased number of Free-Players has no direct regulative impact on the control of global cooperative behavior of the proposed system. For the Payoff-driven Stochastic strategy update setup, the increased number of Free-Players has an observable regulative impact on the control of global cooperative behavior of the proposed system. However, the net effect of Free-Players on the cooperativeness of the environment was only in the range 0.007 < Net_Coop(α, β) < 0.036 while the attained cooperation ratio results are mostly not sensitive to the initial cooperation ratios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.Book Part Citation - Scopus: 8Bitcoin Market Price Analysis and an Empirical Comparison With Main Currencies, Commodities, Securities and Altcoins(Springer Science and Business Media Deutschland GmbH, 2019) Pirgaip,B.; Dinçergök,B.; Haşlak,Ş.The purpose of this study is to analyze Bitcoin (BTC) market prices and to answer the question of whether there is a relationship between BTC and other asset prices, where other assets include currencies, commodities, securities and altcoins. In the empirical part, we evaluate the lead-lag relationships among each type of asset. Consequently, we compare BTC with major currencies and stock exchanges of the U.S., the EU, the U.K. and Japan (USD-SPX, EUR-DAX, GBP-FTSE and JPY-NIK), with currencies and stock exchanges of the U.S., the U.K., Russia, Venezuela and China where BTC is actively traded (USD-SPX, GBP-FTSE, RUB-MOEX, VEF-IBVC and YUAN-SSCE), with major commodities (GOLD and OIL) and with major altcoins (ETH, XRP and LTC) on a daily basis for the period spanning from 2010.07 to 2018.12. We employ Johansen co-integration, Granger causality, impulse response functions and forecast error variance decomposition analyses in our study. Our results show that BTC does not have a long-run relationship with any asset type, but that it has a short-run relationship with gold and especially altcoins, which are both significant and bidirectional. While BTC and altcoins are closely interrelated with each other, BTC price variation is mostly borne by its own prices in all cases. © Springer Nature Switzerland AG 2019.Conference Object Contemporary Research Trends in Mobile Learning(Springer Science and Business Media Deutschland GmbH, 2024) Ekin,C.C.; Algabsi,S.E.This study attempts to conduct a bibliometric analysis of the structure and development of mobile learning research. For this, 7829 publications included in the Elsevier SCOPUS database between 1984 and 2021 were examined using bibliometric analysis by identifying key research areas, most influential authors, co-authorship status of countries, and organizations. As a result of this study, most topics related to mobile learning were Computer Science. “Mobile Learning” was the most used keyword followed by “e-learning” and “higher education”. Top performing organizations were in Taiwan. Taiwan was the major contributor in m-learning publications’ co-citation with other co-authorship countries. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Article Differences of Microbial Growth and Biofilm Formation Among Periprosthetic Joint Infection-Causing Species: an Animal Study(Springer Science and Business Media Deutschland GmbH, 2025) Ertan, M.B.; Ayduğan, M.Y.; Evren, E.; İnanç, İ.; Erdemli, E.; Erdemli, B.Purpose: The most frequently used surgical procedures for periprosthetic joint infections (PJIs) are debridement, antibiotics, and implant retention (DAIR), as well as single- or two-stage revision arthroplasty. The choice of surgery is made depending on the full maturation of the biofilm layer. The purpose of this study was to evaluate the biofilm formation and microbial growth using common PJI-causing agents and compare its development on the implant surface. Methods: The in vivo study was performed using 40 Sprague–Dawley rats divided into five groups (n = 8/group): Staphylococcus aureus, Staphylococcus epidermidis, Pseudomonas aeruginosa, Candida albicans, and control. Six standard titanium alloy discs were placed into the subcutaneous air pouches of the interscapular areas of the rats. After the inoculation of microorganisms, disc and soft tissue cultures were collected at 2-week intervals for 6 weeks, and the microbial load and the microscopic appearance of the biofilm were compared. Results: The disc samples from the S. aureus group had the highest infection load at all time points; however, in soft tissue samples, this was only observed at week 4 and 6. Electron microscopic images showed no distinctive differences in the biofilm structures between the groups. Conclusion: S. aureus microbial burden was significantly higher in implant cultures at week 2 compared to other PJI-causing agents examined. These results may explain the higher failure rate seen if the DAIR procedure was performed at < 3–4 weeks after the PJI symptom onset and support the observation that DAIR may not be effective against PJIs caused by S. aureus. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.Book Part Citation - Scopus: 2Novel Covid-19 Recognition Framework Based on Conic Functions Classifier(Springer Science and Business Media Deutschland GmbH, 2022) Karim,A.M.; Mishra,A.The new coronavirus has been declared as a global emergency. The first case was officially declared in Wuhan, China, during the end of 2019. Since then, the virus has spread to nearly every continent, and case numbers continue to rise. The scientists and engineers immediately responded to the virus and presented techniques, devices and treatment approaches to fight back and eliminate the virus. Machine learning is a popular scientific tool and is applied to several medical image recognition problems, involving tumour recognition, cancer detection, organ transplantation and COVID-19 diagnosis. It is proved that machine learning presents robust, fast and accurate results in various medical image recognition problems. Generally, machine learning-based frameworks consist of two stages: feature extraction and classification. In the feature extraction, overwhelmingly unsupervised learning techniques are applied to reduce the input data’s size. This step extracts appropriate features by reducing the computational time and increasing the performance of the classifiers. A classifier is the second step that aims to categorise the input. Within the proposed step, the unsupervised part relies on the feature extraction by using local binary patterns (LBP), followed by feature selection relying on factor analysis technique. The LBP is a kind of visual descriptor, mainly applied for image recognition problem. The aim of using LBP is to analyse the input COVID-19 image and extract salient features. Furthermore, factor analysis is a statistical technique applied to define variability among observed variables in less unnoticed variables named factors. The factor analysis applied to the LBP wavelet aims to select sensitive features from input data (LBP output) and reduce the size input. In the last stage, conic functions classifier is applied to classify two sets of data, categorising the extracted features by using LBP and factor analysis as positive or negative COVID-19 cases. The proposed solution aims to diagnose COVID-19 by using LBP and factor analysis, based on conic functions classifier. The conic functions classifier presents remarkable results compared with these popular classifiers and state-of-the-art studies presented in the literature. © 2022, Springer Nature Switzerland AG.Editorial Preface(Springer Science and Business Media Deutschland GmbH, 2022) Peng,Z.; Hwang,J.-Y.; White,J.F.; Downey,J.P.; Gregurek,D.; Zhao,B.; Mahmoud,M.M.[No abstract available]Conference Object Mean Radiant Temperature Sensing: Comparison of Methods for a Non-Uniform Radiant Floor Heating(Springer Science and Business Media Deutschland GmbH, 2026) Özbey, M.F.; Licina, D.; Meggers, F.; Khovalyg, D.Non-uniform radiant floor heating is increasingly explored for improving localized and personalized thermal comfort. Accurate mean radiant temperature (Tr) measurement is crucial due to its separation from air temperature (Ta) unlike convective systems. This study evaluates the accuracy of Tr obtained using the globe thermometer versus a novel mini.RES cube sensor and examines the differences between Ta and Tr in non-uniform heating environments. Experiments were conducted in a 62 m3 climatic chamber in Fribourg, Switzerland, with four cases: one without heating, two with non-uniform heating to simulate radiant asymmetry, and one with uniform heating for comparison. Additionally, an uncertainty analysis was performed to evaluate measurement precision for the Tr measurements using the globe thermometer. The results indicate that the globe thermometer method can introduce errors of up to 4% in determining the Tr. Moreover, the uncertainty values for the Tr values were found between 1.60 ℃ and 2.31 ℃. In cases with non-uniform heating available, the Tr was found to vary by more than 2 ℃ than the Ta, highlighting the need to consider Tr separately when assessing thermal comfort. These findings emphasize the error of obtaining the Tr with globe thermometer and the difference between Tr and Ta in non-uniform heating scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.Book Part Citation - Scopus: 2A Multi Source Graph-Based Hybrid Recommendation Algorithm(Springer Science and Business Media Deutschland GmbH, 2021) Kurt,Z.; Gerek,Ö.N.; Bilge,A.; Özkan,K.Images that widely exist on e-commerce sites, social networks, and many other applications are one of the most important information resources integrated into the recently deployed image-based recommender systems. In the latest studies, researchers have jointly considered ratings and images to generate recommendations, many of which are still restricted to limited information sources, sources namely, ratings with another input data, or which require the pre-existence of domain knowledge to generate recommendations. In this paper, a new graph-based hybrid framework is introduced to generate recommendations and overcome these challenges. Firstly, a simple overview of the framework is provided and, then, two different information sources (visual images and numerical ratings) are utilized to describe how the proposed framework can be developed in practice. Furthermore, the users’ visual preferences are determined based on which item they have already purchased. Then, each user is represented as a visual feature vector. Finally, the similarity factors between the users or items are evaluated from the user visual-feature or item visual-feature matrices, to be included the proposed algorithm for more efficiency. The proposed hybrid recommendation method depends on a link prediction approach and reveals the significant potential for performance improvement in top-N recommendation tasks. The experimental results demonstrate the superior performance of the proposed appraoch using three quality measurements - hit-ratio, recall, and precision - on the three subsets of the Amazon dataset, as well as its flexibility to incorporate different information sources. Finally, it is concluded that hybrid recommendation algorithms that use the integration of multiple types of input data perform better than previous recommendation algorithms that only utilize one type of input data. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
