İHÜ Araştırma ve Akademik Performans Sistemi
DSpace@İHÜ, İbn Haldun Üniversitesi’nin bilimsel araştırma ve akademik performansını izleme, analiz etme ve raporlama süreçlerini tek çatı altında buluşturan bütünleşik bilgi sistemidir.
Güncel Gönderiler
Retraction Note: Does environmental sustainability afect the renewable energy consumption? Nexus among trade openness, CO2 emissions, income inequality, renewable energy, and economic growth in OECD countries
(Springer Nature, 2026) Muhammad, Iftikhar; Özcan, Rasim; Jain, Vipin; Sharma, Paritosh; Shabbir, Malik Shahzad; Yönetim Bilimleri Fakültesi, İktisat Bölümü
The Publisher has retracted this article in agreement with the Editor-in-Chief. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised peer review process, inappropriate or irrelevant references, containing nonstandard phrases or not being in scope of the journal. Based on the investigation's findings the publisher, in consultation with the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article. Iftikhar Muhammad stated on behalf of all authors that they agree with this retraction.
The predictive role of artificial intelligence applications in enhancing psychological flourishing & sustainable development among Fayoum University students
(Springer Nature, 2026) Elwakeel, Sayed; Bulut, Sefa; Hamza, Tarek; Ewees, Arzak; Eğitim Bilimleri Fakültesi, Rehberlik ve Psikolojik Danışmanlık Bölümü
Purpose: This study examines the extent to which artificial intelligence (AI) applications predict psychological flourishing and sustainable development among Fayoum University students. It further explores gender differences in AI usage, flourishing levels, and sustainability orientations, as well as the interrelationships among the three variables. Methods: A quantitative correlational design was employed using validated scales measuring AI application use, psychological flourishing, and sustainable development. The sample consisted of 190 university students from Fayoum University (Egypt) there ages were between 20 and 25 years. The mean age was 22.54 years, with a standard deviation of 3.22 years. Data were analyzed using t-tests, Pearson’s correlation coefficients, and simple linear regression to test the study hypotheses. Results: Findings revealed no significant gender differences in AI application use, while significant differences were found in psychological flourishing and sustainable development in favor of male students. Significant positive correlations emerged among AI use, flourishing, and sustainability. Regression analyses showed that AI application use moderately predicted psychological flourishing (R2 = 0.098) but strongly predicted sustainable development (R2 = 0.604), indicating that AI plays a substantial role in shaping students’ sustainability-related behaviors. Conclusions: The study demonstrates that AI applications contribute meaningfully to enhancing students’ well-being and sustainability practices, with a stronger influence on sustainability outcomes. These findings underscore the growing relevance of AI-driven learning environments in promoting sustainable education and student development. Implications: The results highlight the need for integrating AI-based tools into higher education policies, fostering responsible AI use, and developing institutional initiatives that enhance students’ well-being and sustainability competencies.
A randomized mixed-methods pilot feasibility trial of CBT group therapy with 4T-Integrated religious psychoeducation for religious obsessions and compulsions
(Taylor & Francis, 2026) Toprak, Taha Burak; Türkçapar, M. Hakan; İnsan ve Toplum Bilimleri Fakültesi, Psikoloji Bölümü
Objective: To evaluate the feasibility, acceptability, and religious congruence of integrating the 4T religiouspsychoeducation model into CBT group therapy for religious OCD, with exploratory analyses of clinical change.Method: Twenty-three adults with religious OCD were randomly assigned to standard CBGT or a 4T-integrated group.Assessments were conducted at pre-test, post-test, and 1-, 3-, and 12-month follow-ups using the Y-BOCS, OBQ-44,TAFS, PIOS, BDI, and BAI. Linear mixed-effects and nonparametric analyses explored within-group change. Feasibilityindicators included recruitment, retention, adherence, and participant feedback. Semi-structured interviews werethematically analyzed to examine acceptability and cultural fit.Results: Both interventions were feasible and well-tolerated, with adequate recruitment and retention. Participants in bothgroups showed within-group improvements across symptom and cognitive measures. Exploratory trends suggested greaterreductions in thought–action fusion (likelihood) and additional late-phase cognitive shifts in the 4T group. Qualitativefindings highlighted positive perceptions of the 4T model’s religious congruence, clearer understanding of intrusivethoughts, and enhanced motivation.Conclusion: This randomized pilot feasibility trial supports the practicality and acceptability of integrating religiouslygrounded psychoeducation into CBGT for religious OCD. Preliminary trends suggest the need for a larger definitivetrial, and qualitative data highlight the contextual relevance of religiously integrated psychoeducation for treatmentengagement.
Boosted LightFace: A hybrid DNN and GBM model for boosted facial recognition
(Gazi University, 2026) Serengil, Sefik Ilkin; Özpınar, Mustafa Alper; Yönetim Bilimleri Fakültesi, İşletme Bölümü
Facial recognition technology has seen significant advancements, impacting security, surveillance, and personal identification. Deep neural networks have enhanced accuracy and reliability, with integration into everyday devices further accelerating adoption. Researchers explore combining Deep Neural Networks with Gradient Boosting Machines for improved performance. This paper proposes Boosted LightFace, a hybrid Deep Neural Networks and Gradient Boosting Machines model leveraging robust facial recognition and face detection models. The architecture first integrates predictions from five high-performing DNN models. Their distance metrics and classification outcomes are engineered into a tabular dataset of 6,000 image pairs with 13 features. This dataset is then trained using a highly efficient LightGBM model with a low learning rate of 0.01 and 1000 estimators, incorporating an early stopping mechanism, and employing 10-fold cross-validation to maximize generalization. Recent research identifies FaceNet512d as a robust model, surpassing human recognition on the Labeled Faces in The Wild dataset with 98.4% score. Boosted LightFace achieves 99.1% accuracy, surpassing human recognition by 1.6% and outperforming the best single model in LightFace by 0.7%, underscoring the potential of integrating Deep Neural Networks and Gradient Boosting Machines models in advancing facial recognition technology. Furthermore, Boosted LightFace not only outperforms individual models in terms of accuracy but also surpasses them in precision, recall, F1, and AUC scores, highlighting its comprehensive superiority.
et-Tekâlîdü'l-ilmiyyetü'l-İslâmiyye fi't-tarih
(Ma'hedü'ş-Şârika li't-Türâs, 2026) Açıkgenç, Alparslan; İnsan ve Toplum Bilimleri Fakültesi, Felsefe Bölümü
[No Abstract Available]
