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View AllSymmetry-based multi-objective AI/ML driven optimization framework for sustainable building performance
Research on the Symmetry Optimization and Detection for Architecture (SODA) framework in Dutch buildings has emerged as a critical area of inquiry due to the fundamental role symmetry plays in architectural design, heritage conservation, and urban modeling. This research develops and validates an integrated framework combining symmetry-based geometric optimization with Progressive Transfer Learning (PTL) for construction performance enhancement in Dutch buildings. The SODA framework employs computer vision algorithms to automatically identify geometric patterns from diverse data sources including point clouds and facade images. A modified Continuous Symmetry Measure adapted from molecular chemistry provides quantitative symmetry assessment, while multiobjective optimization algorithms balance competing objectives including material efficiency, structural performance, construction economy, and architectural preservation. Progressive Transfer Learning mechanisms enable systematic knowledge transfer across building projects, reducing analysis time for subsequent optimizations while maintaining solution quality. Empirical validation across 50 Dutch buildings encompassing 2.287 million m² demonstrates substantial optimization potential through systematic symmetry-based design interventions, achieving average material savings of 11.3%, construction time reduction of 10.7%, total cost savings of €87.3 million, and carbon emission reduction of 21,450 tons CO₂. The integrated SODA-PTL framework advances architectural computation toward genuine practical impact on construction industry sustainability and efficiency, providing validated design guidelines for practitioners pursuing resource-efficient building optimization.
Derin öğrenme kullanilarak cilt kanserinin tespiti ve siniflandirilmasi
One of the worst malignancies is skin cancer. It is expected to spread to other parts of the body if it is not identified and treated at the outset. The method for successfully treating skin cancer uses both image processing and deep learning. In this research, three different kinds of skin cancer, including melanoma, pigmented benign keratoses, and basal cell carcinoma are introduced for detection and classification using efficient methods in Machine Learning (ML) such as K-Mean clustering and Multi-class support vector machine (M-SVM) algorithm, and then Deep Learning (DL) techniques such as AlexNet and ResNet are used. Additionally, deep convolutional neural networks (CNN) effectiveness and capacity are seen. The data set contains 1176 images of skin cancer with different classes of disease, this data set is used in both ML and DL. In ML the data set is divided into training and testing sets, these sets are pass through a few steps of enhancement using a canny edge detection filter and extracting the features using the Gray-Level Co-occurrence Matrix (GLCM) method. Then, these images are segmented into three clustering using K-mean clustering algorithms. In DL two models are used AlexNet and ResNet, in these models the data sets are divided into training and testing sets. Then, an augmentation technique has been proposed, it's very useful for small data sets, and the results show changes in accuracy result. The results show an accuracy of 99.03%, and 97.02% for AlexNet, and ResNet respectively.
Elektrik akıllı şebeke stabilitesi için yapay zeka modellerinin uygulanması
This study does a comparative analysis of several models, including XGBoost, SVM, Random Forest, KNN, Logistic Regression, Decision Tree, Neural Network, SimpleRNN, LSTM, and GRU. The performance of these models is evaluated based on metrics such as accuracy, precision, recall, and F1-score. The XGBoost Classifier is considered the optimal model due to its superior combination of precision, computational effectiveness, and interpretability. The findings presented in this study underscore the considerable capacity of machine learning in facilitating predictive analytics within the context of smart grids. Moreover, these results establish a robust basis for further research endeavours in this domain. Nevertheless, the literature highlights some challenges, such as the intricate nature of models, their limited interpretability, and the substantial computational demands they impose. These issues underscore the necessity for more research and enhancements in this field. As the study concludes, this paper offers strategic recommendations for effectively integrating the findings into actual applications of smart grids. Additionally, it outlines potential avenues for future research in this field.
Yüksek hızlı insansız hava aracı modellemesi ve simülasyonu
Unmanned Aerial Vehicles (UAVs) have appeared as a pivotal industry in contemporary times, attributed to their adeptness at executing intricate tasks in both civilian and military domains without endangered human lives. Consequently, numerous researchers have passionately studied these aircraft. Especially under stringent and challenging conditions, to ascertain their structural integrity post-manufacture. The focal point of this thesis is an in-depth analysis of the Boeing X-45C aircraft, with particular emphasis on its fuselage. The aircraft will be meticulously designed and rendered using the SOLIDWORKS software, adhering to its authentic dimensions. After this design phase, the aircraft will be positioned within a precisely defined fluidic environment to facilitate a comprehensive CFD simulation using the ANSYS Fluent software, simulating the aircraft's flight at peak velocities. This will be followed by an FSI simulation, intertwining the insights gleaned from the CFD analysis with the potential ramifications on the aircraft's aluminium structure. The culmination of the thesis will present a thorough commentary on the results, evaluating the success and efficacy of the simulations and analyses conducted. The findings underscored the robustness of the aircraft, confirming its resilience under the tested conditions. Keywords: FSI,
Kullanilan iot cihazlarinda kullanici gizliliğimakine ve derin öğrenme yaklaşimlar
The swift expansion of Internet of Things (IoT) technology has sparked concerns regarding the privacy of users, since these devices often collect and transmit vast amounts of personal information. To address these issues, this thesis will look at the use of machine and deep learning technologies to improve user privacy on IoT devices. First, the research will examine the existing state of user privacy on IoT devices, as well as the issues associated with protecting privacy. This will include a discussion of the many types of data gathered and communicated by IoT devices, as well as the numerous ways in which this data might be exploited or hacked. The research will also look at current legislative frameworks and best practices in the sector for preserving user privacy on IoT devices. The thesis will then investigate the application of machine learning approaches to improve user privacy on IoT devices. This will take a look at the many machine learning techniques that may be used for this, such as decision tree algorithms and ANNs. The research will also look at the possible benefits and drawbacks of utilizing these algorithms for privacy protection, such as the trade-offs between privacy and other objectives like performance or accuracy. Besides machine learning, the project will look into the use of deep learning technologies for improving user privacy on IoT devices. Deep learning models, a specific category of machine learning techniques, have demonstrated significant potential across various applications. The research will examine the potential benefits and challenges of applying deep learning algorithms for privacy protection on IoT devices, as well as the present related works in this field. Finally, the dissertation will conclude with a discussion of the potential future direction of research in this area, including the potential for integrating machine and deep learning approaches with other privacy-enhancing technologies and the potential for additional regulatory or industry-led efforts to improve user privacy on IoT devices. This dissertation intends to offer a complete assessment of the present status of user privacy on IoT devices, as well as the possibilities for enhancing privacy via the application of machine and deep learning technologies. The study intends to contribute to continuing efforts to secure user privacy in the rapidly developing realm of IoT by addressing these challenges.
Santrifüj pompalarda iki fazlı akış özelliklerinin sayısal incelenmesi
Due to its important applications, particularly in the oil and gas industry, two-phase pumping has been the subject of several studies. This study includes a numerical investigation of two-phase flow inside a centrifugal impeller with seven blades, at a rotational speed of 2500 rpm and an inlet pressure of 100000 Pa. The simulation includes investigating the effect of flow rate, gas volume fraction GVF, and bubble sizes on the pump performance curves and also on the phase distribution through the impeller. Four flow rate values of 2.4, 3.1, 3.9, and 4.6 kg/s, four GVF values of 0.1, 0.15, 0.2, and 0.3, and four bubble sizes of 0.1, 0.2, 0.3, and 0.5 mm are considered in the simulation. The governing equation and the turbulent kinetic energy dissipation (k - ε) model are solved using ANSYS software. The results show that increasing the flow rate leads to a decrease in the pump head and hydraulic efficiency under single- and two-phase operations, with reduced performance in the case of the two-phase condition. The head and efficiency are also significantly affected by increasing the GVF and bubble diameter. Increasing the GVF from 0.1 to 0.3 causes the head to decrease by about 26% and hydraulic efficiency by up to 25%. The impact of bubble size, on the other hand, depends on the flow rate and bubble diameter. A significant reduction in pump performance by increasing bubble diameter has been reported for low flow rates, while only a limited impact has been observed for high flow rates, particularly at large bubble sizes. Gas pocket formation has been visualized at the inlet upper pressure side of the impeller blade in cases of high GVF and bubble diameters.