جستجوی مقالات مرتبط با کلیدواژه "روش های یادگیری عمیق" در نشریات گروه "برق"

The development of smart devices in many aspects of our daily lives is accompanied by the increasing use of appropriate mechanisms to counter them against various attacks and applications in the Internet of Things environment. In this context, it is emerging as one of the most successful and suitable techniques for use in various aspects of IoT security. The aim of this is to systematically review and analyze research studies on research eyes conducted in different Internet of Things security scenarios. The reviewed researches are classified according to different perspectives in a coherent and structured classification to identify the gap in this research area. This research has been published on articles related to the keywords "concept learning", "security" and "Internet of Things" in the four main databases IEEEXplore, ScienceDirect, SpringerLink, and ACM Digital Library. In the end, 90 articles have been selected and reviewed. These studies are conducted according to three main research questions, i.e. the security aspects involved, the network architectures used, and the datasets used in IoT security. The final discussion explores the research gaps and acknowledges the outstanding flaws and vulnerabilities in the IoT security scenario.

Financial time series are fundamentally complex, dynamic, noisy, non-linear, non-parametric and chaotic, so forecasting financial markets is one of the most challenging fields in engineering and economics. With the increasing progress of artificial intelligence and the emergence of deep learning methods, the problem of stock market forecasting has been faced with significant developments, especially in the field of prediction models and big data. Four important steps to create a systematic intelligent forecasting model include model inputs, selection of forecasting algorithms and design of the general structure of the forecasting model, using appropriate loss functions to train the algorithm and finally suitable evaluation of the results, according to the desired criteria. In this paper, a comprehensive review of recent approaches to stock market forecasting is provided, focusing on the above steps. The most important achievements of this paper are: 1- A comprehensive review of the problem, including: reviewing the types of model inputs, different prediction structures, training the model and types of loss functions used, and the evaluation metric of the results, in a fully classified and structured way to easily provide the road map and existing challenges for the enthusiasts and also an important research field of each section for the researchers. 2- Complete analysis of each part, specifying the application of each method and discussing their advantages and disadvantages based on the latest developments and providing perspectives on the research boundaries. 3- Determining the ongoing research path, future approaches and open issues for researchers interested in this field.