Altınbaş Üniversitesi Kurumsal Akademik Arşivi
DSpace@Altınbaş, Altınbaş Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
Integrative machine learning approaches for enhanced cardiovascular disease prediction: a comparative analysis of XGBoost and ANFIS algorithms
(Springer, 2025) Muhyi, Diyar Fadhil; Ata, Oğuz
Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, highlighting the urgent need for advanced diagnostic tools to improve early detection and patient outcomes. This study evaluates the predictive performance of two machine learning models-Extreme Gradient Boosting (XGBoost) and the Adaptive Neuro-Fuzzy Inference System (ANFIS)-across five datasets from the UCI Machine Learning Repository: Cleveland, Hungary, Switzerland, Long Beach VA, and Statlog Heart. Comprehensive preprocessing steps-including imputation, standardization, one-hot encoding, and SMOTEENN-were applied to ensure data consistency and address class imbalance. XGBoost achieved perfect accuracy (100%) on the Switzerland and Statlog datasets, reflecting its strength in structured data environments and consistent predictive performance. Conversely, ANFIS outperformed XGBoost on the Cleveland dataset, demonstrating its effectiveness in modeling complex, nonlinear relationships. Performance evaluation metrics included accuracy, precision, recall, F1 score, F2 score, and ROC-AUC. XGBoost consistently delivered high precision and recall, which are essential for minimizing false positives and negatives in clinical settings. ANFIS yielded high F2 scores, indicating a stronger emphasis on reducing false negatives-a critical concern in CVD diagnosis. This comparative analysis suggests that while XGBoost is well suited for scalable, high-throughput diagnostic applications, ANFIS offers greater interpretability and is more effective in nuanced clinical scenarios. These findings underscore the potential of integrating advanced machine learning models into cardiovascular disease prediction frameworks to enhance diagnostic accuracy and support real-world healthcare decision-making.
Boosting Multiverse Optimizer by Simulated Annealing for Dimensionality Reduction
(Airlangga University, 2025) Mutlag, Wamidh K.; Mazher, Wamidh Jalil; Ibrahim, Hadeel Tariq; Uçan, Osman Nuri
Background: Because of The Multi-Verse Optimizer (MVO) has gained popularity in feature selection due to its strong global and local search capabilities. However, its effectiveness diminishes when tackling high-dimensional datasets due to the exponential growth of the search space and a tendency for premature convergence. Objective: This study aims to enhance MVO’s performance by integrating it with the Simulated Annealing Algorithm (SAA), creating a hybrid model that improves search convergence and optimizes feature selection efficiency. Methods: A High-level Relay Hybrid (HRH) architecture is proposed, where MVO identifies promising regions of the feature space and passes them to SAA for local refinement. The resulting MVOSA-FS model was evaluated on ten high-dimensional benchmark datasets from the Arizona State University (ASU) repository. Support Vector Machine (SVM) classifiers were used to assess the classification accuracy. MVOSA-FS achieved superior performance compared to six state-of-the-art feature selection algorithms: Atom Search Optimization (ASO), Equilibrium Optimizer (EO), Emperor Penguin Optimizer (EPO), Monarch Butterfly Optimization (MBO), Satin Bowerbird Optimizer (SBO), and Sine Cosine Algorithm (SCA). Results: The proposed model yielded the lowest average classification error rate (1.45%), smallest standard deviation (0.008), and most compact feature subset (0.91%). The hybrid MVOSA-FS model effectively balances exploration and exploitation, delivering robust and scalable performance in feature selection for high-dimensional data. Conclusion: This hybridization approach demonstrates improved classification accuracy and reduced computational burden.
An intelligent atrous convolution-based cascaded deep learning framework for enhanced privacy preservation performance in edge computing
(IOS Press, 2025) Siryeh, Fatima Abu; Ibrahim, Abdullahi Abdu
A system without any communication delays, called edge computing, has been introduced for nearer and faster services. The major concern in the edge computing scenario is its privacy risks. A user, as well as a cloud data preservation scheme, is the main aim of this paperwork. Test data is given by the user to access the cloud-based data processing framework. The training of the suitable model is carried out by utilizing the data stored in the cloud. The suggested model divides the entire model into two sections, namely, the untrusted cloud and the trusted edge. On the trusted edge side the data is directly provided to the developed advanced deep learning model called the Atrous Convolution based Cascaded Deep Temporal Convolution Network (ACC-DTCN) for the data analysis process. However, instead of giving the whole data directly to the untrusted cloud side, the test data is protected on the cloud side by utilizing a hybrid encryption technique called the Optimal Hybrid Encryption Model (OHEM). Both Attribute-Based Encryption (ABE) and Homomorphic Encryption (HE) are utilized in the recommended OHEM scheme. The OHEM variables are tuned with the help of an advanced algorithm called the Enhanced Ladybug Beetle Optimization algorithm (ELBOA). The confidence score vector among the testing and training data is predicted by the implemented ACC-DTCN model by utilizing the encrypted data on the cloud side. The suggested privacy preservation scheme provides higher prediction accuracy and prevents interference attacks while contrasting it against conventional methods during extensive experimentations.
Donor impact on allogeneic transplant outcomes with PTCy for severe aplastic anemia: a study of the SAAWP EBMT
(Scientific & Medical Division, 2025) Montoro, Juan; Eikema, Dirk-Jan; Piepenbroek, Brian; Tuffnell, Joe; Halahleh, Khalid; Kulagin, Alexander; AlAhmari, Ali; Adaklı Aksoy, Başak; Remenyi, Peter; Itala-Remes, Maija; Gülbaş, Zafer; McDonald, Andrew; Apte, Shashikant; Kwon, Mi; Rovira, Montserrat; Kharya, Gaurav; Potter, Victoria; Gambella, Massimilano; Schroeder, Thomas; Giammarco, Sabrina; Bazarbachi, Ali; Aljurf, Mahmoud; Ho, Aloysius; Dalle, Jean-Hugues; Vydra, Jan; Sanz, Jaime; Perez-Simon, Jose Antonio; Colita, Anca; Collin, Matthew; Tanase, Alina; Halkes, Constantijn; Kulasekararaj, Austin; Risitano, Antonio; de Latour, Regis Peffault
The use of post-transplant cyclophosphamide (PTCy) for graft-versus-host disease (GVHD) prophylaxis in severe aplastic anemia (SAA) remains understudied, particularly beyond haploidentical transplants. We analyzed outcomes of SAA patients who underwent stem cell transplantation (SCT) with PTCy from haploidentical donors (n = 209), HLA-matched sibling donors (MSD, n = 70), and unrelated donors (UD, n = 69) using EBMT data from 2010 to 2022. Median age was 22 years, and median time to transplantation was 8.6 months. For haploidentical, MSD, and UD cohorts, the 100-day cumulative incidence of grade II-IV acute GVHD was 19%, 11%, and 14% (p = 0.15), while grade III-IV was 6%, 3%, and 2% (p = 0.1). Two-year chronic and extensive chronic GVHD were 14%, 13%, and 14% (p = 0.1) and 5%, 6%, and 2% (p = 0.5), respectively. Non-relapse mortality at two years was 24% for haploidentical, 7% for MSD, and 10% for UD (p = 0.003). Two-year overall survival (OS) and GVHD- and relapse-free survival were 66% and 54% for haploidentical, 92% and 70% for MSD, and 81% and 66% for UD (p < 0.001, p = 0.06). In multivariable analysis, MSD and UD were associated with superior OS and GRFS compared to haploidentical. PTCy is safe and effective in SAA patients, though haploidentical SCT had higher NRM, leading to lower survival.
An enhanced attention and dilated convolution-based ensemble model for network intrusion detection system against adversarial evasion attacks
(Springer, 2025) Awad, Omer Fawzi; Çevik, Mesut; Farhan, Hameed Mutlag
Network Intrusion Detection System (NIDS) is a system for recognizing suspicious activities in the network traffic. Numerous machines learning and deep learning-aided IDSs have been implemented in the past, however, most of these techniques face challenges based on class imbalance issues and high false positive rates. Other primary problems of the conventional techniques are their vulnerability to adversarial attacks and also there is no analysis done on how NIDS sustain their performance over various attacks. Moreover, recent studies have demonstrated that while handling the attackers in real-time, the deep learning-based IDS shows slight variations in accuracy. To defend against adversarial evasion attacks, an enhanced deep learning-based NIDS model is designed in this work. For this purpose, at first, the required data is collected from available websites. From the collected data, effective features are extracted to improve the accuracy of the process. To select the optimal features, this work employed the Improved Cheetah Optimizer (ICO) that eliminates the unwanted features efficiently. Further, an Attention and Dilated Convolution based Ensemble Network (ADCEN) is implemented to detect the intrusions from the optimal features. The Deep Temporal Convolutional Neural Network (DTCN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) models are integrated to develop the ADCEN. The outcomes from each technique are considered for the fuzzy ranking mechanism to generate the final detected outcome. Thus, recognized intrusion is attained as the outcome and to demonstrate how well the recommended deep learning-based NIDS defends against adversarial evasion assaults, experiments are conducted against conventional models. The accuracy and the FPR values of the recommended model are 95 and 4.9 when considering the first dataset which is superior to the conventional techniques. Thus, the findings indicated that the implemented NIDS against adversarial evasion attacks attained more effective solutions than the baseline approaches.
