The enhancement of fabric quality prediction in the textile manufacturing sector is achieved by utilizing information derived from sensors within the Internet of Things (IoT) and Enterprise Resource Planning (ERP) systems linked to sensors embedded in textile machinery. The integration of Industry 4.0 concepts is instrumental in harnessing IoT sensor data, which, in turn, leads to improvements in productivity and reduced lead times in textile manufacturing processes. This study addresses the issue of imbalanced data pertaining to fabric quality within the textile manufacturing industry. It encompasses an evaluation of seven open-source automated machine learning (AutoML) technologies, namely FLAML (Fast Lightweight AutoML), AutoViML (Automatically Build Variant Interpretable ML models), EvalML (Evaluation Machine Learning), AutoGluon, H2OAutoML, PyCaret, and TPOT (Tree-based Pipeline Optimization Tool). The most suitable solutions are chosen for certain circumstances by employing an innovative approach that finds a compromise among computational efficiency and forecast accuracy. The results reveal that EvalML emerges as the top-performing AutoML model for a predetermined objective function, particularly excelling in terms of mean absolute error (MAE). On the other hand, even with longer inference periods, AutoGluon performs better than other methods in measures like mean absolute percentage error (MAPE), root mean squared error (RMSE), and r-squared. Additionally, the study explores the feature importance rankings provided by each AutoML model, shedding light on the attributes that significantly influence predictive outcomes. Notably, sin/cos encoding is found to be particularly effective in characterizing categorical variables with a large number of unique values. This study includes useful information about the application of AutoML in the textile industry and provides a roadmap for employing Industry 4.0 technologies to enhance fabric quality prediction. The research highlights the importance of striking a balance between predictive accuracy and computational efficiency, emphasizes the significance of feature importance for model interpretability, and lays the groundwork for future investigations in this field.

This review paper presents a comprehensive examination of automated program repair (APR) and its significant contribution to the field of modern software engineering. It elucidates how APR methodologies markedly mitigate manual debugging needs by automating the detection and resolution of software glitches. The study encompasses an in-depth exploration of three primary categories of APR techniques: template-based, machine learning, and deep learning approaches, drawing from an exhaustive evaluation of 41 APR tools. Each category showcases distinct strategies for managing diverse software errors, underscoring the breadth and effectiveness of current APR methodologies. Template-based APR solutions utilize pre-established patterns to efficiently tackle common coding issues, while machine learning-driven approaches dynamically devise repair strategies from historical bug-fix datasets. Deep learning methods extend error rectification boundaries by delving into the semantic context of code, yielding more precise adjustments. The ongoing advancement of APR technologies necessitates researchers to address critical challenges, including the integration of semantic-syntactic analyses, mitigation of data scarcity, optimization of cross-platform tools, development of context-aware approaches, enhancement of fault localization and patch validation processes, and establishment of standardized performance evaluation metrics. This comprehensive analysis underscores the pivotal role of APR in enhancing software efficiency and reliability, representing significant progress in software development and maintenance practices.

This study proposes an efficient algorithm for improving flattening result of triangular mesh surface patches having a convex shape. The proposed approach, based on barycentric mapping technique, incorporates a dynamic virtual boundary, which considerably improves initial mapping result. The dynamic virtual boundary approach is utilized to reduce the distortions for the triangles near the boundary caused by the nature of convex combination technique. Mapping results of the proposed algorithm and the base technique are compared by area and shape accuracy metrics measured for several sample surfaces. The results prove the success of the proposed approach with respect to the base method.

Objective: The aim of this study is to identify cancer earlier in life using machine learning methods. Methods: For this purpose, the Wisconsin Diagnostic Breast Cancer dataset was classified using Naive Bayes, decision trees, artificial neural networks algorithms and comparison of these machine learning methods was made. KNIME Analytics Platform was used for applications. Before the classification process, the dataset was preprocessed. After the pre-processing stage, three different classifier methods were applied to the dataset. Accuracy, sensitivity, specificity and confusion matrices were used to measure the success of the methods. Results: The results show that Naive Bayes and artificial neural network methods classify tumors with 96.5% accuracy. The success of the decision tree method in classification was 92.6%. Conclusion: The machine learning algorithms can be used successfully in breast cancer diagnosis to determine whether the tumors are malign or benign.

Gayrimenkul sektörünün hızlı ekonomik büyümesi nedeniyle, konut fiyatlarının tahmini geleceğe yönelik planlamalar için önemlidir. Bu çalışmanın amacı makine öğrenmesi yöntemlerini kullanarak bir konutun potansiyel fiyatını tahmin etmektir. Makine öğrenmesi yöntemleri olarak Yapay sinir ağları ve Polinomsal regresyon kullanılarak bunların tahmin performansları karşılaştırılmıştır. Makine öğrenmesi yöntemlerinin uygulanabilmesi için KNIME veri analiz platformu kullanılmıştır. Yöntemlerin başarısını ölçmek için R Kare performans metriği kullanılmıştır. Uygulama sonuçları, Yapay sinir ağları yönteminin Polinomsal regresyon yöntemine göre ev fiyatlarını daha yüksek doğrulukla tahmin ettiğini göstermektedir. Yapılan çalışmanın ev değerlendirilmesi için kullanılan uygulamaların geliştirilmesine ve bu alanda yapılan bilimsel çalışmalara katkı sağlayacağı düşünülmektedir. Sonraki çalışmalarda farklı yöntemler veya ev özniteliklerinin bulunduğu veri setleri kullanılarak çalışmanın genişletilmesi hedeflenmektedir.

This review examines the methods, determinants, and forecasting horizons used in electricity demand forecasting in Türkiye. The study investigates how Türkiye's electricity demand is influenced by economic, climatic, socio-demographic, and technological factors, and explores the evolving landscape of forecasting techniques, from traditional statistical models to advanced machine learning and hybrid approaches. The research addresses three key questions: The significant determinants of electricity demand in Türkiye, the most effective forecasting methods, and the application of these insights to improve energy planning and policy development. Through a systematic analysis of peer-reviewed literature, official reports, and case studies, the study reveals the complex interplay of factors affecting electricity demand and the increasing sophistication of forecasting methodologies. Economic growth, industrial production, climate change, urbanization, and technological advancements emerge as primary drivers of demand, while artificial neural networks and hybrid models demonstrate superior forecasting capabilities. The study highlights the importance of integrated modeling approaches, sector-specific strategies, and the incorporation of climate projections in long-term planning. It also emphasizes the need for aligning energy policies with broader economic and environmental objectives. This review provides valuable insights for researchers, policymakers, and industry stakeholders, offering a comprehensive framework for understanding and improving electricity demand forecasting.