The convolutional neural networks (CNNs) are frequently used in the segmentation of histopathological whole slide image- (WSI) acquired breast lymph nodes. The first layers in deep network architectures generally encode the geometric and color properties of objects in the training set, while the last layers encode the distinctive and detailed properties between classes. Modern segmentation approaches (DeepLabV3+, SegNet, PSPNet) are realized by evaluating these layers together. However, having a high parameter space of all these networks increases the calculation costs and prevents the researchers from working more effectively. In this study, we present a new pyramid-structured segmentation network (NonLocalSeg). Although the proposed network has low parameter space, its segmentation performance is similar to current architectures. The integration of the Nonlocal Module (NLM-a form of attention mechanism) or Asymmetric Pyramid Nonlocal block (APNB) into classical pyramid-built architectures has led to the reduction of network depth and narrowing of the parameter space while enabling coding of low and high image features. These mechanisms suppressed the unfocused background image, emphasizing the focused foreground object. As a result of a series of ablation experiments carried out, it is seen that the NLM and APNL mechanisms give the succeeded results. Although the network architectures adapting these mechanisms contain fewer parameters than current networks, it is observed that they have a similar accuracy (mean intersection over union [IoU]) range.

Liselere Giriş Sınavı (LGS) ortaokul 8. sınıf öğrencilerinin girdikleri ve aldıkları puanlara göre okuyacakları liseleri belirlemektedir. Bu sınav ile öğrencinin eğitim öğretim becerileri sınanmaktadır. Öğrencilerin sınav başarısını, öğrencinin sosyal-ekonomik durumu, aile ortamı, ebeveyn eğitim durumu, cinsiyeti ve önceki yıllara ait eğitim verilerinde gibi faktörler etkilemektedir. Bu faktörlerin sınav başarısındaki etkilerinin araştırılması, mevcut durumun tahmin edilebilmesi, gelecek dönemlerdeki öğrenci başarısını iyileştirmede çok önemlidir. Bu amaçla ortaokul öğrenci bilgileri güncel makine ögrenme yöntemleri kullanılarak analiz edilmiştir. Öğrencilerin sınav performansını etkileyen faktörler ön plana çıkarılmıştır. Aynı zamanda öğrencinin sınav performans tahmini yapılmıştır. Çalışmamızın sonuçları alanda daha önce yapılan araştırma sonuçlarını desteklemektedir. Çeşitli optimizasyon yöntemleri kullanarak sınav performansını etkileyen ortak faktörler tespit edilmiştir. Bunlar; ortaokul öğrencisinin yıllara göre okul puan ortalaması, aile gelir durumu, kardeş sayısı ve sıralaması ve özürsüz devamsızlık sayısıdır. Devamında birden fazla makine ögrenme yöntemi kullanılarak öğrenci sınav performans tahmini yapılmıştır. Yapılan deneylerde optimizasyon algoritması olan Guguk Kuşu Arama Algoritmaları (GKAA) ile seçilen etkin faktörler, Destek Vektör Regresyonu analizi ile 0.866 (R2 doğruluk) sınav başarı tahminin skoru elde edilmiştir. Aynı zamanda sınav performansında kardeş sıralamasının etkisi gözlenmiş, ileri çalışmalar için hedefler belirlenmiştir.

Ambient sound analysis has become more prominent with the rise of portable and wearable devices. It provides valuable insights into a person's environment by analyzing surrounding sounds. Recently, deep learning methods, frequently used in image and text processing, have been applied to this field and are proving more effective than traditional machine learning techniques. In this study, we evaluated the performance of different deep learning models using mel-spectrograms of 3 classes of stage sounds based on TAU Acoustic Scene 2023 dataset. Our results indicate that a simple Convolutional Neural Network (CNN) model gives better classification results compared to other more complex models in classification tasks. Despite having the fewest parameters, the CNN model achieved the highest success with 59% accuracy. This suggests that simpler models can be highly effective for acoustic scene classification, highlighting the value of more efficient and computationally feasible approaches in this domain

Deep learning is used in all areas of the image processing like object detection/localization, synthetic image generation, segmentation, tracking, and others. It is frequently used especially in medical image segmentation field since it provides rapid response during the treatment process. The fact that natural images contain different types of noise, patterns, and structures and the lack of distinctive quantitative information still makes the segmentation problem very challenging. The classical networks having high parameters have a long training time. The need of less training time for high parameter networks and high segmentation accuracy has led us to develop a new network. In this study, a state-of-the-art autoencoder network (MSRSegNet) is proposed to perform segmentation. Unlike conventional autoencoder approaches, it consists of encoder, fusion and decoder blocks. In encoder and decoder blocks, Multi-scale Residual Blocks are used to share information between blocks and to detect features on different scales. In fusion block, Atrous Spatial Pyramid Pooling (ASPP) module is used to preserve multi-scale contextual information. Information sharing between blocks has increased the ability of the proposed method to capture global features. The performance parameters of mean intersection over unit (mIOU) and pixel accuracy (PA) is used to compare the results. As a result, it was observed that the proposed segmentation network has high accuracy (69% mIoU) and fast segmentation performance (0.061sec. for an image with 256x256)

Mineral and organic fertilizers differ in nutrient-release patterns and effects on soil biological processes, yet their combined influence on soybean produ