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
The unseen struggle-depression and associated factors in geriatric cancer patients
(Frontiers Media, 2025) Doğan, Özlem; Şahinli, Hayriye; Yazılıtaş, Doğan; Kantarcı, Selen
Background: The objective of this study was to investigate the frequency of depression and its associations, rather than causal relationships, in patients aged 65 years and older receiving chemotherapy, using the Geriatric Depression Scale (GDS). Methods: This prospective study was conducted between January 2023 and December 2023 at Ankara Etlik City Hospital, including 501 chemotherapy patients aged 65 years and older. Patients receiving only oral therapy, those under palliative care, those with brain metastases, or those with insufficient cognitive functionality were excluded. Demographic and clinical data were collected from medical records. Depression was assessed using the 15-item Yesavage Geriatric Depression Scale (GDS), with scores ≥5 indicating high depression symptoms. Results: Among the 501 patients included in the study, 204 (40.7%) were female, with a median age of 69 years (range: 65–84 years). A total of 214 patients (42.7%) had high depressive symptom scores (GDS ≥ 5). A multivariable logistic regression analysis identified the following as independent predictors of depression: being female (odds ratio (OR): 1.481, 95% confidence interval (CI): 1.011–2.168, p = 0.04), body mass index (BMI) ≥ 21 (OR: 1.665, 95% CI: 1.081–2.564, p = 0.02), higher pain scores (OR: 1.269, 95% CI: 1.122–1.436, p < 0.001), insomnia (OR: 1.626, 95% CI: 1.109–2.384, p = 0.01), and weak social support (OR: 2.004, 95% CI: 1.046–3.839, p = 0.03). Conclusion: Our study highlights the high prevalence of depressive symptoms among geriatric cancer patients. In this population, early diagnosis and management of depression, with particular attention to independent risk factors such as pain and insomnia, as well as strengthening social support mechanisms, may be crucial for enhancing quality of life and improving treatment adherence.
BCI-DRONE CONTROL BASED ON THE CONCENTRATION LEVEL AND EYE BLINK SIGNALS USING A NEUROSKY HEADSET
(University of Kufa, 2025) Mohammed, Ali Abdulwahhab; Abdulwahhab, Ali H.; Abdulaal, Alaa Hussein; Mahmood, Musaria Karim; Myderrizi, Indrit; Yassin, Riyam Ali; Abdulridha, Taha Talib; Valizadeh, Morteza
Brain neurons activate Human movements by producing electrical bio-signals. Neuron activity is used in several technologies by operating their applications based on mind waves. The Brain-Computer Interface (BCI) technology enables a processor to connect with the brain using a signal received from the brain. This study proposes a drone controlled using EEG signals acquired by a Neurosky device based on the BCI system. Two active signals are adapted for controlling the drone motions: concentration brain signals portrayed by attention level and the eye blinks as an integer value. A dynamic classification method is implemented via a Linear Regression algorithm for attention-level code. The eye blinking generates a binary code to control the drone's motions. The accuracy of this code is improved through Artificial Neural Networks and Machine Learning techniques. These codes (attention level and eye blink codes) drive two controlling layers and manipulate nine possible drone movements. The experiment was evaluated with several users and showed high performance for the classification methods and developed algorithm. The experiment shows a 90.37% accuracy control that outperforms most existing experiments. Also, the experiment can support 16 commands, making the algorithm appropriate for various applications.
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.
