WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/6
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Review Citation - WoS: 5Citation - Scopus: 4A Systematic Review and Meta-Analysis for the Efficacy of Transcranial Direct Current Stimulation (tdcs) in Ocd Treatment: A Non-Pharmacological Approach To Clinical Interventions(Pergamon-elsevier Science Ltd, 2024) Ibrahim, Ismail A.; Nada, Ahmed Hosney; Asar, Nada Khalid; Ibrahim, Rand; Farouk, Rawan Ahmed; Al-Qiami, Almonzer; Noorbakhsh, Seyed AliObsessive-compulsive disorder (OCD) is a prevalent mental condition characterized by recurrent, unwanted thoughts (obsessions) and repetitive behaviors (compulsions), significantly disrupting daily functioning and social interactions. Transcranial direct current stimulation (tDCS) presents a promising non-invasive treatment modality aimed at alleviating symptoms. However, the evidence regarding its effectiveness remains inconclusive. This study seeks to address this gap by conducting a systematic review and meta-analysis of clinical trials, offering improved guidance for clinical intervention. A comprehensive search strategy was implemented across multiple databases, including PubMed, Cochrane CENTRAL, Embase, Scopus, and Web of Science. This search focused strictly on randomized controlled trials (RCTs) involving 147 patients. These trials evaluated the efficacy of tDCS in OCD patients. Subsequent data extraction, risk of bias assessment, and statistical analysis using Review Manager software revealed the potential efficacy of tDCS in reducing OCD symptoms. The meta-analysis not only fails to demonstrate significant superiority of active tDCS over sham tDCS but also suggests that sham tDCS may be more effective than active tDCS in reducing OCD symptoms. This finding diminishes the promise of tDCS as an effective treatment for OCD. Larger trials are warranted to further elucidate these findings.Article Citation - WoS: 16Citation - Scopus: 20A Fast Intrusion Detection System Based on Swift Wrapper Feature Selection and Speedy Ensemble Classifier(Pergamon-elsevier Science Ltd, 2024) Zorarpaci, EzgiDue to the widespread use of the internet, computer network systems may be exposed to different types of attacks. For this reason, the intrusion detection systems (IDSs) are often used to protect the network systems. Network traffic data (i.e., network packets) includes many features. However, most of them are irrelevant and can lead to a decrease in the runtime and/or the detection performance of the IDS. Although various data mining methods have been applied to improve the effectiveness of IDS, research regarding IDSs having high detection rates and better runtime performance (i.e., lower computational cost) is ongoing. On the other hand, the dimensionality reduction techniques help to eliminate unnecessary features and reduce the computation time of a classification algorithm. In the literature, the feature selection methods (i.e., filter and wrapper) have been widely used for the dimensionality reduction in IDSs. Although the wrapper feature selection techniques outperform the filters, they are time-consuming. Again, the ensemble classifiers can achieve higher detection rates for IDSs compared to the stand-alone classifiers, but they require more computation time to build the model. In order to improve the runtime performance and the detection rate of IDS, a swift wrapper feature selection and a speedy ensemble classifier are proposed in this study. For the dimensionality reduction, the swift wrapper feature selection (i.e., DBDE-QDA) is used, which consists of dichotomous binary differential evolution (DBDE) and quadratic discriminant analysis (QDA). For attack detection, the speedy ensemble classifier is used, which combines Holte's 1R, random tree, and reduced error pruning tree. In the experiments, the NSL-KDD, UNSW-NB15, and CICDDoS2019 datasets are used. According to the experimental results, the proposed IDS reaches 95%-97.4%, 82.7%, and 99.5%-99.9% detection rates for the NSL-KDD, UNSW-NB15, and CICDDoS2019 datasets. In this way, the proposed IDS competes with the state-of-the-art methods in terms of detection rate and false alarm rate. In addition, the proposed IDS has a lower computational cost than the state-of-the-art methods. Moreover, DBDE-QDA reduces the dimension by 60.97%-82.92%, 73.46%, and 96.55%-98.85% for the NSL-KDD, UNSW-NB15, and CICDoS2019 datasets.Article Citation - WoS: 17Citation - Scopus: 24A Novel Neutrosophic Set Based Hierarchical Challenge Analysis Approach for Servicizing Business Models: A Case Study of Car Share Service Network(Pergamon-elsevier Science Ltd, 2022) Karadayi-Usta, SalihaServicization as a part of circular economy has a prominent role in operations management research by emphasizing the functionality of the products. Servicizing business model is a phenomenon that the products are converted into services for sale, or process of transforming the consumers into users. This newly introduced way of business is defined as a new opportunity for operations management by focusing on; the reliability and durability of the products against the extreme repeatedly use, coordination problems in conducting the business, risk of encountering unknown negative customers, and moral hazard in product usage, operational difficulties, and the degradation of the products. All of the aforementioned issues in implementing the servicizing business models constitute a research question with the high impact of COVID-19 pandemics. Since people avoid using public transportation, car share programs are highly in demand. In addition, there is an obvious research gap in challenge analysis of the car-sharing programs' literature, an overall service network challenge analysis is missing in the field of study. Hence, the purpose of this study is proposing a novel neutrosophic set based hierarchical challenge analysis approach for the servicizing business models with a step by step guidance, and presenting a real world case study addressing a car sharing company's entire service network. A practitioner can understand which challenge should be dealt with first, what is the inter-relationship structure of these challenges in a hierarchy, and what is the expected final result of these interactions. The study also contributes to the theoretical background by using neutrosophic sets to propose a novel interpretive hierarchical challenge analysis. In this research, the evaluation of truth and indeterminacy membership degrees of a neutrosophic set are gathered separately instead of applying the defined linguistic variable schemes. The case study is a contemporary field of study attracting attention of innovative entrepreneurs, since there is an increased demand of individual car driving due to the isolation requirement arisen from COVID-19 pandemics.