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Deep learning, a crucial technique for achieving artificial intelligence (AI), has been successfully applied in many fields. The gradual application of the latest architectures of deep learning in the field of time series forecasting (TSF), such as Transformers, has shown excellent performance and results compared to traditional statistical methods. These applications are widely present in academia and in our daily lives, covering many areas including forecasting electricity consumption in power systems, meteorological rainfall, traffic flow, quantitative trading, risk control in finance, sales operations and price predictions for commercial companies, and pandemic prediction in the medical field. Deep learning-based TSF tasks stand out as one of the most valuable AI scenarios for research, playing an important role in explaining complex real-world phenomena. However, deep learning models still face challenges: they need to deal with the challenge of large-scale data in the information age, achieve longer forecasting ranges, reduce excessively high computational complexity, etc. Therefore, novel methods and more effective solutions are essential. In this paper, we review the latest developments in deep learning for TSF. We begin by introducing the recent development trends in the field of TSF and then propose a new taxonomy from the perspective of deep neural network models, comprehensively covering articles published over the past five years. We also organize commonly used experimental evaluation metrics and datasets. Finally, we point out current issues with the existing solutions and suggest promising future directions in the field of deep learning combined with TSF. This paper is the most comprehensive review related to TSF in recent years and will provide a detailed index for researchers in this field and those who are just starting out. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a developmental disorder that encompasses difficulties in communication (both verbal and non-verbal), social skills, and repetitive behaviors. The diagnosis of autism spectrum disorder typically involves specialized procedures and techniques, which can be time-consuming and expensive. The accuracy and efficiency of the diagnosis depend on the expertise of the specialists and the diagnostic methods employed. To address the growing need for early, rapid, cost-effective, and accurate diagnosis of autism spectrum disorder, there has been a search for advanced smart methods that can automatically classify the disorder. Machine learning offers sophisticated techniques for building automated classifiers that can be utilized by users and clinicians to enhance accuracy and efficiency in diagnosis. Eye-tracking scan paths have emerged as a tool increasingly used in autism spectrum disorder clinics. This methodology examines attentional processes by quantitatively measuring eye movements. Its precision, ease of use, and cost-effectiveness make it a promising platform for developing biomarkers for use in clinical trials for autism spectrum disorder. The detection of autism spectrum disorder can be achieved by observing the atypical visual attention patterns of children with the disorder compared to typically developing children. This study proposes a deep learning model, known as T-CNN-Autism Spectrum Disorder (T-CNN-ASD), that utilizes eye-tracking scans to classify participants into ASD and typical development (TD) groups. The proposed model consists of two hidden layers with 300 and 150 neurons, respectively, and underwent 10 rounds of cross-validation with a dropout rate of 20%. In the testing phase, the model achieved an accuracy of 95.59%, surpassing the accuracy of other machine learning algorithms such as random forest (RF), decision tree (DT), K-Nearest Neighbors (KNN), and multi-layer perceptron (MLP). Furthermore, the proposed model demonstrated superior performance when compared to the findings reported in previous studies. The results demonstrate that the proposed model can accurately classify children with ASD from those with TD without human intervention. [ABSTRACT FROM AUTHOR]

Background: Dental caries, also known as tooth decay, is a widespread and long-standing condition that affects people of all ages. This ailment is caused by bacteria that attach themselves to teeth and break down sugars, creating acid that gradually wears away at the tooth structure. Tooth discoloration, pain, and sensitivity to hot or cold foods and drinks are common symptoms of tooth decay. Although this condition is prevalent among all age groups, it is especially prevalent in children with baby teeth. Early diagnosis of dental caries is critical to preventing further decay and avoiding costly tooth repairs. Currently, dentists employ a time-consuming and repetitive process of manually marking tooth lesions after conducting radiographic exams. However, with the rapid development of artificial intelligence in medical imaging research, there is a chance to improve the accuracy and efficiency of dental diagnosis. Methods: This study introduces a data-driven model for accurately diagnosing dental decay through the use of Bitewing radiology images using convolutional neural networks. The dataset utilized in this research includes 713 patient images obtained from the Samin Maxillofacial Radiology Center located in Tehran, Iran. The images were captured between June 2020 and January 2022 and underwent processing via four distinct Convolutional Neural Networks. The images were resized to 100 × 100 and then divided into two groups: 70% (4219) for training and 30% (1813) for testing. The four networks employed in this study were AlexNet, ResNet50, VGG16, and VGG19. Results: Among different well-known CNN architectures compared in this study, the VGG19 model was found to be the most accurate, with a 93.93% accuracy. Conclusion: This promising result indicates the potential for developing an automatic AI-based dental caries diagnostic model from Bitewing images. It has the potential to serve patients or dentists as a mobile app or cloud-based diagnosis service (clinical decision support system). [ABSTRACT FROM AUTHOR]

One of the most important features of a reliable power system is its capability to supply the demand continuously. This continuous supply has been maintained by the transient stability of the system against large disturbances. The study of this type of stability is examined through different indicators. One of the common indicators to evaluate the transient stability of the power system is the well-known Critical Clearing Time (CCT) index. Conventional methods for calculating CCT have presented good accuracy, however, their computational cost is very high which makes them not suitable for real-time applications and real large-scale networks. Considering their ability to feature extraction of big data, deep neural networks can be utilized as reliable tools to cover these deficiencies. In this regard, to cover the shortcomings of the conventional methods, this paper proposes a method based on deep Convolutional Neural Networks (CNN) to estimate the CCT index in real-time power system applications. Moreover, to analyze a realistic case, nonlinear limiters of the excitation systems which have a considerable effect on transient stability index are considered in this study. Thanks to the using of several deep layers and the comprehensive established database, the accuracy of proposed method is appropriately high. Numerical studies on IEEE standard networks as well as a real case in Iran Power Grid (IPG) represents the advantages of the proposed method. • A deep-learning-based convolutional neural network is designed to estimate CCT index of power system. • Nonlinear limiters of the excitation system are taken into account. • A real case study in Iran grid is considered for simulation. • As a result, the transient stability of system is accurately determined in on-line manner. [ABSTRACT FROM AUTHOR]