Ara
Toplam kayıt 17, listelenen: 11-17
Subset selection for tuning of hyper-parameters in artificial neural networks
(IEEE, 2017)
Hyper-parameters of a machine learning architecture define its design. Tuning of hyper-parameters is costly and for large data sets outright impractical, whether it is performed manually or algorithmically. In this study ...
Do?rudan pazarlama amaçlı hedef kitle analizi
(Institute of Electrical and Electronics Engineers Inc., 2018-07-05)
Doğrudan pazarlama, uygun ürünleri uygun kişilerle en kısa yoldan buluşturma sürecidir. Son yılların en popüler pazarlama yaklaşımlarından birisidir. Bu çalışmada turizm sektörüne ait isimsizleştirilmiş bir veri tabanını ...
Adaptive convolution kernel for artificial neural networks
(Academic Press Inc., 2021-02)
Many deep neural networks are built by using stacked convolutional layers of fixed and single size (often 3 × 3) kernels. This paper describes a method for learning the size of convolutional kernels to provide varying size ...
Hotel sales forecasting with LSTM and N-BEATS
(IEEE, 2023-09-15)
Time series forecasting aims to model the change in data points over time. It is applicable in many areas, such as energy consumption, solid waste generation, economic indicators (inflation, currency), global warming (heat, ...
Convolutional neural network (CNN) algorithm based facial emotion recognition (FER) system for FER-2013 dataset
(IEEE, 2022-11-18)
Facial expression recognition (FER) is the key to understanding human emotions and feelings. It is an active area of research since human thoughts can be collected, processed, and used in customer satisfaction, politics, ...
Assessing dyslexia with machine learning: a pilot study utilizing Google ML Kit
(IEEE, 2023-12-19)
In this study, we explore the application of Google ML Kit, a machine learning development kit, for dyslexia detection in the Turkish language. We collected face-tracking data from two groups: 49 dyslexic children and 22 ...
Closeness and uncertainty aware adversarial examples detection in adversarial machine learning
(Elsevier Ltd, 2022-07)
While deep learning models are thought to be resistant to random perturbations, it has been demonstrated that these architectures are vulnerable to deliberately crafted perturbations, albeit being quasi-imperceptible. These ...