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In this paper, we consider the issue of digital camera identification based on images. This topic matches the area of digital forensics. The problem is well known in the literature and many algorithms based on camera’s fingerprint have been proposed. In this paper, we discuss the digital camera identification based on convolutional neural networks (CNN). CNNs are state-of-the-art method in computer vision and are widely utilized in many applications. Our goal is to find out whether it is possible to speed up the process of learning the networks by the images. We conduct a set of representative experiments which show that replacing the ReLU with SELU activation function and adjusting the network’s hyperparamethers (e.g. learning rate) may have a significant impact on reduction time of learning. We also consider using the dropout layer. The experiments are held on representative image dataset, consisting of many images coming from modern cameras and show effectiveness of our propositions.

With the advance of artificial intelligence (AI), the Internet of Things (IoT), and 5G communication technologies, various kinds of traffic data from diverse devices can be acquired nowadays, and they can help us look into intelligent transportation systems (ITSs) with a new eye. Graph-based machine learning holds out the potential as a powerful tool for modeling complex structural data relationships and also mining both useful information and temporal patterns which could be used for building powerful analytics for ITS construction. Considering the benefit of graph-based machine learning for ITS, some graph-based machine learning methods/architectures have been proposed. Even though these methods have achieved certain success, there exist various scientific and engineering challenges.

ABSTRACTContent-based image retrieval poses many problems to computer systems. The content of images has to be described by some feature extraction methods. As image databases are often very large, they are sometimes to complex to be processed by traditional computing methods. We have to use big data solutions to fast retrieve images. The paper presents a system for retrieving images in relational databases in a distributed environment. Content of the query image and images in the database is compared using global colour information and local image keypoints. Image keypoint descriptors are indexed by fuzzy sets directly in a relational database by our algorithm. The process is distributed to several machines thanks to the Apache Hadoop software framework with HDFS.