Summary Object detection is a type of application that includes computer vision and image processing technologies, which deal with detecting, tracking, and classifying desired objects in images. Computer vision is a field of artificial intelligence that enables computers and systems to derive information from digital images and take action or suggestions based on that information. CNN is one of the current methods of object detection due to its ease of use and GPU‐supported parallel working features. Due to the aim of completing deep learning model training quickly or due to insufficient dataset, many studies using the transfer learning method are carried out in fields such as medicine, agriculture, and weapons. However, there are very few studies that use the fine‐tuning method and compare transfer learning in terms of effectiveness. By paying attention to the balanced distribution of the data, approximately 100 images of each chess piece type were included in the analysis and a dataset of at least 1000 images was created. The without transfer learning fine‐tune, fine‐tuned transfer learning, transfer learning, fully supervised learning (FSL) and weakly supervised learning (WSL) applied models performances compared. Experimental results show that the fine‐tuned transfer learning applied YOLO V4 model produces more accurate results than the other models in FSL and the transfer learning applied Faster R‐CNN model produces more accurate results than the other models in WSL.
Detection of small objects in natural scene images is a complicated problem due to the blur and depth found in the images. Detecting house numbers from the natural scene images in real-time is a computer vision problem. On the other hand, convolutional neural network (CNN) based deep learning methods have been widely used in object detection in recent years. In this study, firstly, a classical CNN-based approach is used to detect house numbers with locations from natural images in real-time. Faster R-CNN, MobileNet, YOLOv4, YOLOv5 and YOLOv7, among the commonly used CNN models, models were applied. However, satisfactory results could not be obtained due to the small size and variable depth of the door plate objects. A new approach using the fine-tuning technique is proposed to improve the performance of CNN-based deep learning models. Experimental evaluations were made on real data from Kayseri province. Classic Faster R-CNN, MobileNet, YOLOv4, YOLOv5 and YOLOv7 methods yield f1 scores of 0.763, 0.677, 0.880, 0.943 and 0.842, respectively. The proposed fine-tuned Faster R-CNN, MobileNet, YOLOv4, YOLOv5, and YOLOv7 approaches achieved f1 scores of 0.845, 0.775, 0.932, 0.972 and 0.889, respectively. Thanks to the proposed fine-tuned approach, the f1 score of all models has increased. Regarding the run time of the methods, classic Faster R-CNN detects 0.603 seconds, while fine-tuned Faster R-CNN detects 0.633 seconds. Classic MobileNet detects 0.046 seconds, while fine-tuned MobileNet detects 0.048 seconds. Classic YOLOv4 and fine-tuned YOLOv4 detect 0.235 and 0.240 seconds, respectively. Classic YOLOv5 and fine-tuned YOLOv5 detect 0.015 seconds, and classic YOLOv7 and fine-tuned YOLOv7 detect objects in 0.009 seconds. While the YOLOv7 model was the fastest running model with an average running time of 0.009 seconds, the proposed fine-tuned YOLOv5 approach achieved the highest performance with an f1 score of 0.972.
Summary Android malware has become a serious threat to mobile device users, and effective detection and defence architectures are needed to solve this problem. Recently, machine learning techniques have been widely used to deal with Android malicious apps. These methods are based on a simple feature set and have difficulty detecting up‐to‐date malware. Therefore, more robust and efficient classification methodologies are needed. In this article, AMD‐CNN, an Android malware detection tool, is proposed, and it uses graphical representations to detect malicious apks. In the first step, the features related to the androidmanifest.xml file are extracted and converted into a vector consisting of one or zero. The feature vector is then converted to 2D‐code images and used in training the CNN network. The model needs low‐resource consumption to run on mobile devices and allow real‐time applications to be analyzed. The experiments with 1920 malicious and benign apks show that the malware detection rate (accuracy) was 96.2% and precision, recall, and F‐score values were 97.9%, 98.2%, and 98.1%, respectively. The average time and memory space to analyze each application are 0.035 s and 3.38 MB. AMD‐CNN is an efficient and robust tool and has advantages over previous studies.
ÖzSanayideki gelişmeler, nüfus artışı, çarpık kentleşme gibi sebepler hava kirliliğini artırmaktadır. Hava kirliliği tüm ekolojiyi ve insan sağlığını olumsuz yönde etkilediği için küresel anlamda önemlidir. Hava kirliliğinden kaynaklı oluşabilecek tehlikeli durumları önleyebilmek için önceden tedbirler alınmalıdır. Hava kirliliğini etkileyen unsurların önceden tahmin edilmesi ile oluşabilecek tehlikeli durumları önlemek mümkün olabilir. Partikül madde (PM) değeri hava kirliliğinin derecesini belirtmek için yaygın olarak kullanılan bir parametredir. Aerodinamik çapı 10 µm'den küçük olan partiküller madde olarak tanımı yapılan PM10 parametresi, ülkemiz için belirlenen sınır değerleri aşmaktadır ve dolayısıyla PM10 konsantrasyonunun artışında önlem alınması ciddi önem taşımaktadır. Bu çalışmada hava kalitesinin belirlenmesinde büyük rolü olan PM10 parametresinin değerlerinin tahmini üzerine araştırmalar yapılmıştır. Bu çalışmada, Meteoroloji Genel Müdürlüğü'ne ait İç Anadolu Bölgesi ve çevresindeki istasyonlara ait gerçek ölçüm verileri kullanılmıştır. Hava kalitesi indeksinin hesaplanmasında kullanılan kirletici madde parametrelerinin değerleri kullanılarak PM10 parametresinin değeri tahmin edilmiştir. Son yıllarda tahmin işlemlerinde derin öğrenme yöntemleri sıklıkla kullanılmaktadır. Derin öğrenme yöntemlerinden uzun süreli kısa bellek ağı (LSTM) modeli zamansal olarak bir önceki durumdan etkilenen veri kümelerinde yaygın olarak kullanılmaktadır. Anlık hava kalitesi bir önceki durumlardan etkilendiğinden dolayı bu çalışmada LSTM derin öğrenme modeli bir ilin PM10 değerlerinin tahmin edilmesi için önerilmiştir. Önerilen LSTM tabanlı modelin performansı klasik derin öğrenme yöntemi (DL) ile karşılaştırılmıştır. Yöntemlerin başarım performansını irdelemek için değerlendirme kriteri ortalama hata kare kökü (RMSE) ve ortalama mutlak hata (MAE) değerleri kullanılmıştır. Deneysel değerlendirmeler, önerilen LSTM yönteminin DL yöntemine göre PM10 değerlerinin tahmininde daha başarılı tahminler elde ettiğini göstermektedir. Ayrıca LSTM yönteminin veri kaybı olduğu durumlarda DL yöntemine kıyasla veri sayısından daha az etkilendiği görülmüştür.
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