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Social networks have drastically changed how people obtain information. News in social networks is accompanied by images and videos and thus receives more attention from readers as opposed to traditional sources. Unfortunately, fake-news publishers often misuse these advantages to spread false information rapidly. Therefore, the early detection of fake news is crucial. The best way to address this issue is to design an automatic detector based on fake-news content. Thus far, many fake-news recognition systems, including both traditional machine learning and deep learning models, have been proposed. Given that manual feature-extraction methods are very time-consuming, deep learning methods are the preferred tools. This study aimed to enhance the performance of existing approaches by utilizing an ensemble of deep learners based on attention mechanisms. To a great extent, the success of an ensemble model depends on the variety of its learners. To this end, we propose a novel loss function that enforces each learner to attend to different parts of news content on the one hand and obtain good classification accuracy on the other hand. Also, the learners are built on a common deep-feature extractor and only differ in their attention modules. As a result, the number of parameters is reduced efficiently and the overfitting problem is addressed. We conducted several experiments on some widely used fake-news detection datasets. The results confirm that the proposed method consistently surpasses the existing peer methods.

Now-a-days, the great challenge of adaptive e-learning systems is to recommend an individualized learning scenario according to the specific needs of learners. Therefore, the perfect adaptive e-learning system is the one that is based on a deep learner profile to recommend the most appropriate learning objects for that learner. Yet, the majority of existing adaptive e-learning systems do not give high importance to the adequacy of the real learner profile and its update with the one taken into account in the learning path recommendation. In this paper, we proposed an intelligent adaptive e-learning system, based on machine learning and reinforcement learning. The objectives of this system are the creation of a deep profile of a given learner, via the implementation of K-means and linear regression, and the recommendation of adaptive learning paths according to this deep profile, by implementing the Q-learning algorithm. The proposed system is decomposed into three principal modules, data preprocessing module, learner deep profile creation module, and learning path recommendation module. These three modules interact with each other to provide a personalized adaptation according to the learner's deep profile. The results obtained indicate that taking into account the learner's deep profile improves the quality of learning for learners.

It has been demonstrated that modified denoising stacking autoencoders (MSDAEs) serve to implement high-performance missing value imputation schemes. On the other hand, complete MSDAE (CMSDAE) classifiers, which extend their inputs with target estimates from an auxiliary classifier and are layer by layer trained to recover both the observation and the target estimates, offer classification results that are better than those provided by MSDAEs. As a consequence, investigating whether CMSDAEs can improve the MSDAEs imputation processes has an obvious practical importance. In this correspondence, two types of imputation mechanisms with CMSDAEs are considered. The first is a direct procedure in which the CMSDAE output is just the target. The second mechanism is suggested by the presence of the targets in the vectors to be autoencoded, and it uses the well-known multitask learning (MTL) ideas, including the observations as a secondary task. Experimental results show that these CMSDAE structures increase the quality of the missing value imputations, in particular the MTL versions. They give the best result in 5 out of 6 missing value problems.

Specific emitter identification (SEI), which classifies radio transmitters by extracting hardware-intrinsic radio frequency fingerprints (RFFs), faces critical challenges in noise robustness, generalization under limited training data and class imbalance. To address these limitations, we propose adaptive deep ensemble learning (ADEL)—a framework that integrates heterogeneous neural networks including convolutional neural networks (CNN), multilayer perception (MLP) and transformer for hierarchical feature extraction. Crucially, ADEL also adopts adaptive weighted predictions of the three base classifiers based on reconstruction errors and hybrid losses for robust classification. The methodology employs (1) three heterogeneous neural networks for robust feature extraction; (2) the hybrid losses refine feature space structure and preserve feature integrity for better feature generalization; and (3) collaborative decision-making via adaptive weighted reconstruction errors of the base learners for precise inference. Extensive experiments are performed to validate the effectiveness of ADEL. The results indicate that the proposed method significantly outperforms other competing methods. ADEL establishes a new SEI paradigm through robust feature extraction and adaptive decision integrity, enabling potential deployment in space target identification and situational awareness under limited training samples and imbalanced classes conditions.

WebShell is a common network backdoor attack that is characterized by high concealment and great harm. However, conventional WebShell detection methods can no longer cope with complex and flexible variations of WebShell attacks. Therefore, this paper proposes a deep super learner for attack detection. First, the collected data are deduplicated to prevent the influence of duplicate data on the result. Second, to detect the results of the algorithm, static and dynamic feature are taken as the feature of the algorithm to construct a comprehensive feature set. We then use the Word2Vec algorithm to vectorize the features. During this period, to prevent the outbreak of the number of features, we use a genetic algorithm to extract the validity of the feature dimension. Finally, we use a deep super learner to detect WebShell. The experimental results show that this algorithm can effectively detect WebShell, and its accuracy and recall are greatly improved.

The author of the best-selling What the Best College Teachers Do is back with humane, doable, and inspiring help for students who want to get the most out of their education. The first thing they should do? Think beyond the transcript. Use these four years to cultivate habits of thought that enable learning, growth, and adaptation throughout life.

The dramatic impact of the COVID-19 pandemic has resulted in the closure of physical classrooms and teaching methods being shifted to the online medium.To make the online learning environment more interactive, just like traditional offline classrooms, it is essential to ensure the proper engagement of students during online learning sessions.This paper proposes a deep learning-based approach using facial emotions to detect the real-time engagement of online learners. This is done by analysing the students’ facial expressions to classify their emotions throughout the online learning session. The facial emotion recognition information is used to calculate the engagement index (EI) to predict two engagement states “Engaged” and “Disengaged” . Different deep learning models such as Inception-V3, VGG19 and ResNet-50 are evaluated and compared to get the best predictive classification model for real-time engagement detection. Varied benchmarked datasets such as FER-2013, CK+ and RAF-DB are used to gauge the overall performance and accuracy of the proposed system. Experimental results showed that the proposed system achieves an accuracy of 89.11%, 90.14% and 92.32% for Inception-V3, VGG19 and ResNet-50, respectively, on benchmarked datasets and our own created dataset. ResNet-50 outperforms all others with an accuracy of 92.3% for facial emotions classification in real-time learning scenarios.

In problem-based learning (PBL), implemented worldwide, students learn by discussing professionally relevant problems enhancing application and integration of knowledge, which is assumed to encourage students towards a deep learning approach in which students are intrinsically interested and try to understand what is being studied. This review investigates: (1) the effects of PBL on students’ deep and surface approaches to learning, (2) whether and why these effects do differ across (a) the context of the learning environment (single vs. curriculum wide implementation), and (b) study quality. Studies were searched dealing with PBL and students’ approaches to learning. Twenty-one studies were included. The results indicate that PBL does enhance deep learning with a small positive average effect size of .11 and a positive effect in eleven of the 21 studies. Four studies show a decrease in deep learning and six studies show no effect. PBL does not seem to have an effect on surface learning as indicated by a very small average effect size (.08) and eleven studies showing no increase in the surface approach. Six studies demonstrate a decrease and four an increase in surface learning. It is concluded that PBL does seem to enhance deep learning and has little effect on surface learning, although more longitudinal research using high quality measurement instruments is needed to support this conclusion with stronger evidence. Differences cannot be explained by the study quality but a curriculum wide implementation of PBL has a more positive impact on the deep approach (effect size .18) compared to an implementation within a single course (effect size of −.05). PBL is assumed to enhance active learning and students’ intrinsic motivation, which enhances deep learning. A high perceived workload and assessment that is perceived as not rewarding deep learning are assumed to enhance surface learning.

In recent years, artificial intelligence (AI) and learning analytics (LA) have been introduced into the field of education, where their use has great potential to enhance the teaching and learning processes. Researchers have focused on applying these technologies to teacher education, as they see the value of technology for educating. Therefore, a systematic review of the literature on AI and LA in teacher education is necessary to understand their impact in the field. Our methodology follows the PRISMA guidelines, and 30 studies related to teacher education were identified. This review analyzes and discusses the several ways in which AI and LA are being integrated in teacher education based on the studies’ goals, participants, data sources, and the tools used to enhance teaching and learning activities. The findings indicate that (a) there is a focus on studying the behaviors, perceptions, and digital competence of pre- and in-service teachers regarding the use of AI and LA in their teaching practices; (b) the main data sources are behavioral data, discourse data, and statistical data; (c) machine learning algorithms are employed in most of the studies; and (d) the ethical clearance is mentioned by few studies. The implications will be valuable for teachers and educational authorities, informing their decisions regarding the effective use of AI and LA technologies to support teacher education.

In soybean, there is a lack of research aiming to compare the performance of machine learning (ML) and deep learning (DL) methods to predict more than one agronomic variable, such as days to maturity (DM), plant height (PH), and grain yield (GY). As these variables are important to developing an overall precision farming model, we propose a machine learning approach to predict DM, PH, and GY for soybean cultivars based on multispectral bands. The field experiment considered 524 genotypes of soybeans in the 2017/2018 and 2018/2019 growing seasons and a multitemporal–multispectral dataset collected by embedded sensor in an unmanned aerial vehicle (UAV). We proposed a multilayer deep learning regression network, trained during 2000 epochs using an adaptive subgradient method, a random Gaussian initialization, and a 50% dropout in the first hidden layer for regularization. Three different scenarios, including only spectral bands, only vegetation indices, and spectral bands plus vegetation indices, were adopted to infer each variable (PH, DM, and GY). The DL model performance was compared against shallow learning methods such as random forest (RF), support vector machine (SVM), and linear regression (LR). The results indicate that our approach has the potential to predict soybean-related variables using multispectral bands only. Both DL and RF models presented a strong (r surpassing 0.77) prediction capacity for the PH variable, regardless of the adopted input variables group. Our results demonstrated that the DL model (r = 0.66) was superior to predict DM when the input variable was the spectral bands. For GY, all machine learning models evaluated presented similar performance (r ranging from 0.42 to 0.44) for each tested scenario. In conclusion, this study demonstrated an efficient approach to a computational solution capable of predicting multiple important soybean crop variables based on remote sensing data. Future research could benefit from the information presented here and be implemented in subsequent processes related to soybean cultivars or other types of agronomic crops.