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There are more than 962 million people aged 60 and up globally. Physical activity declines as people get older, as does their capacity to undertake everyday tasks, effecting both physical and mental health. Many researchers use machine learning and deep learning methods to recognize human activities, but very few studies have been focused on human activity recognition of elderly people. This paper focuses on providing assistance to elderly people by monitoring their activities in different indoor and outdoor environments using gyroscope and accelerometer data collected from a smart phone. Smart phones have been routinely used to monitor the activities of persons with impairments; routine activities such as sitting, walking, going upstairs, going downstairs, standing, and lying are included in the dataset. Conventional Machine Learning and Deep Learning algorithms such as k-Nearest Neighbors, Random Forest, Support Vector Machine, Artificial Neural Network, and Long Short-Term Memory Network are used for human activity recognition. Long Short-Term Memory is a recurrent neural network variation that is best suited to handling temporal sequences. Two-fold and ten-fold cross-validation methods were performed to show the effect of changing the data in the training and testing dataset. Among all the classification techniques, the proposed Long Short-Term Memory Network gave the best accuracy of 95.04%. However, Support Vector Machine gave 89.07% accuracy with a very low computational time of 0.42 min using 10-fold cross-validation. [ABSTRACT FROM AUTHOR]

Traditional time series forecasting techniques can not extract good enough sequence data features, and their accuracies are limited. The deep learning structure SeriesNet is an advanced method, which adopts hybrid neural networks, including dilated causal convolutional neural network (DC-CNN) and Long-short term memory recurrent neural network (LSTM-RNN), to learn multi-range and multi-level features from multi-conditional time series with higher accuracy. However, they didn't consider the attention mechanisms to learn temporal features. Besides, the conditioning method for CNN and RNN is not specific, and the number of parameters in each layer is tremendous. This paper proposes the conditioning method for two types of neural networks, and respectively uses the gated recurrent unit network (GRU) and the dilated depthwise separable temporal convolutional networks (DDSTCNs) instead of LSTM and DC-CNN for reducing the parameters. Furthermore, this paper presents the lightweight RNN-based hidden state attention module (HSAM) combined with the proposed CNN-based convolutional block attention module (CBAM) for time series forecasting. Experimental results show our model is superior to other models from the viewpoint of forecasting accuracy and computation efficiency. [ABSTRACT FROM AUTHOR]

The proliferation of new technologies and advancements in existing ones are altering our perspective of the world. So, continuous improvements are needed. A connected world filled with a vast amount of data was created as a result of the integration of these advanced technologies in the financial sector. The advantages of this connection came at the cost of more sophisticated and advanced attacks, such as fraudulent transactions. To address these illegal transactions, researchers and engineers have created and implemented various systems and models to detect fraudulent transactions; many of them produce better results than others. On the other hand, criminals change their strategies and technologies to imitate legitimate transactions. In this article, the objective is to propose an intelligent system for detecting fraudulent transactions using various deep learning architectures, including artificial neural networks (ANNs), recurrent neural networks (RNNs), and long short-term memory (LSTM). Furthermore, the Bayesian optimization algorithm is used for hyperparameter optimization. For the evaluation, a credit card fraudulent transaction dataset was used. Based on the many experiments conducted, the RNN architecture demonstrated better efficiency and yielded better results in a shorter computational time than the ANN LSTM architectures. [ABSTRACT FROM AUTHOR]

The paper focuses on what two different types of Recurrent Neural Networks, namely a recurrent Long Short-Term Memory and a recurrent variant of self-organizing memories, a Temporal Self-Organizing Map, can tell us about speakers' learning and processing a set of fully inflected verb forms selected from the top-frequency paradigms of Italian and German. Both architectures, due to the re-entrant layer of temporal connectivity, can develop a strong sensitivity to sequential patterns that are highly attested in the training data. The main goal is to evaluate learning and processing dynamics of verb inflection data in the two neural networks by focusing on the effects of morphological structure on word production and word recognition, as well as on word generalization for untrained verb forms. For both models, results show that production and recognition, as well as generalization, are facilitated for verb forms in regular paradigms. However, the two models are differently influenced by structural effects, with the Temporal Self-Organizing Map more prone to adaptively find a balance between processing issues of learnability and generalization, on the one side, and discriminability on the other side. [ABSTRACT FROM AUTHOR]

Domain generation algorithms (DGAs) represent a class of malware used to generate large numbers of new domain names to achieve command-and-control (C2) communication between the malware program and its C2 server to avoid detection by cybersecurity measures. Deep learning has proven successful in serving as a mechanism to implement real-time DGA detection, specifically through the use of recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This paper compares several state-of-the-art deep-learning implementations of DGA detection found in the literature with two novel models: a deeper CNN model and a one-dimensional (1D) Capsule Networks (CapsNet) model. The comparison shows that the 1D CapsNet model performs as well as the best-performing model from the literature. [ABSTRACT FROM AUTHOR]

This survey paper describes a literature review of deep learning (DL) methods for cyber security applications. A short tutorial-style description of each DL method is provided, including deep autoencoders, restricted Boltzmann machines, recurrent neural networks, generative adversarial networks, and several others. Then we discuss how each of the DL methods is used for security applications. We cover a broad array of attack types including malware, spam, insider threats, network intrusions, false data injection, and malicious domain names used by botnets. [ABSTRACT FROM AUTHOR]

Out-of-vocabulary (OOV) words are the most challenging problem in automatic speech recognition (ASR), especially for morphologically rich languages. Most end-to-end speech recognition systems are performed at word and character levels of a language. Amharic is a poorly resourced but morphologically rich language. This paper proposes hybrid connectionist temporal classification with attention end-to-end architecture and a syllabification algorithm for Amharic automatic speech recognition system (AASR) using its phoneme-based subword units. This algorithm helps to insert the epithetic vowel እ[ɨ], which is not included in our Grapheme-to-Phoneme (G2P) conversion algorithm developed using consonant–vowel (CV) representations of Amharic graphemes. The proposed end-to-end model was trained in various Amharic subwords, namely characters, phonemes, character-based subwords, and phoneme-based subwords generated by the byte-pair-encoding (BPE) segmentation algorithm. Experimental results showed that context-dependent phoneme-based subwords tend to result in more accurate speech recognition systems than the character-based, phoneme-based, and character-based subword counterparts. Further improvement was also obtained in proposed phoneme-based subwords with the syllabification algorithm and SpecAugment data augmentation technique. The word error rate (WER) reduction was 18.38% compared to character-based acoustic modeling with the word-based recurrent neural network language modeling (RNNLM) baseline. These phoneme-based subword models are also useful to improve machine and speech translation tasks. [ABSTRACT FROM AUTHOR]

Subject-independent emotion recognition based on physiological signals has become a research hotspot. Previous research has proved that electrodermal activity (EDA) signals are an effective data resource for emotion recognition. Benefiting from their great representation ability, an increasing number of deep neural networks have been applied for emotion recognition, and they can be classified as a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), or a combination of these (CNN+RNN). However, there has been no systematic research on the predictive power and configurations of different deep neural networks in this task. In this work, we systematically explore the configurations and performances of three adapted deep neural networks: ResNet, LSTM, and hybrid ResNet-LSTM. Our experiments use the subject-independent method to evaluate the three-class classification on the MAHNOB dataset. The results prove that the CNN model (ResNet) reaches a better accuracy and F1 score than the RNN model (LSTM) and the CNN+RNN model (hybrid ResNet-LSTM). Extensive comparisons also reveal that our three deep neural networks with EDA data outperform previous models with handcraft features on emotion recognition, which proves the great potential of the end-to-end DNN method. [ABSTRACT FROM AUTHOR]

As students' behaviors are important factors that can reflect their learning styles and living habits on campus, extracting useful features of them plays a helpful role in understanding the students' learning process, which is an important step towards personalized education. Recently, the task of predicting students' performance from their campus behaviors has aroused the researchers' attention. However, existing studies mainly focus on extracting statistical features manually from the pre-stored data, resulting in hysteresis in predicting students' achievement and finding out their problems. Furthermore, due to the limited representation capability of these manually extracted features, they can only understand the students' behaviors shallowly. To make the prediction process timely and automatically, we treat the performance prediction task as a short-term sequence prediction problem, and propose a two-stage classification framework, i.e., Sequence-based Performance Classifier (SPC), which consists of a sequence encoder and a classic data mining classifier. More specifically, to deeply discover the sequential features from students' campus behaviors, we first introduce an attention-based Hybrid Recurrent Neural Network (HRNN) to encode their recent behaviors by giving a higher weight to the ones that are related to the students' last action. Then, to conduct student performance prediction, we further involve these learned features to the classic Support Vector Machine (SVM) algorithm and finally achieve our SPC model. We conduct extensive experiments in the real-world student card dataset. The experimental results demonstrate the superiority of our proposed method in terms of Accuracy and Recall. [ABSTRACT FROM AUTHOR]

Social media and health-related forums, including the expression of customer reviews, have recently provided data sources for adverse drug reaction (ADR) identification research. However, in the existing methods, the neglect of noise data and the need for manually labeled data reduce the accuracy of the prediction results and greatly increase manual labor. We propose a novel architecture named the weakly supervised mechanism (WSM) convolutional neural network (CNN) long-short-term memory (WSM-CNN-LSTM), which combines the strength of CNN and bi-directional long short-term memory (Bi-LSTM). The WSM applies the weakly labeled data to pre-train the parameters of the model and then uses the labeled data to fine-tune the initialized network parameters. The CNN employs a convolutional layer to study the characteristics of the drug reviews and active features at different scales, and then the feed-forward and feed-back neural networks of the Bi-LSTM utilize these salient features to output the regression results. The experimental results effectively demonstrate that our model marginally outperforms the comparison models in ADR identification and that a small quantity of labeled samples results in an optimal performance, which decreases the influence of noise and reduces the manual data-labeling requirements. [ABSTRACT FROM AUTHOR]

The existing short-term traffic flow prediction models fail to provide precise prediction results and consider the impact of different traffic conditions on the prediction results in an actual traffic network. To solve these problems, a hybrid Long Short–Term Memory (LSTM) neural network is proposed, based on the LSTM model. Then, the structure and parameters of the hybrid LSTM neural network are optimized experimentally for different traffic conditions, and the final model is compared with the other typical models. It is found that the prediction error of the hybrid LSTM model is obviously less than those of the other models, but the running time of the hybrid LSTM model is only slightly longer than that of the LSTM model. Based on the hybrid LSTM model, the vehicle flows of each road section and intersection in the actual traffic network are further predicted. The results show that the maximum relative error between the actual and predictive vehicle flows of each road section is 1.03%, and the maximum relative error between the actual and predictive vehicle flows of each road intersection is 1.18%. Hence, the hybrid LSTM model is closer to the accuracy and real-time requirements of short-term traffic flow prediction, and suitable for different traffic conditions in the actual traffic network. [ABSTRACT FROM AUTHOR]