ASD-GResTM: Deep Learning Framework for ASD classification using Gramian Angular Field.

Many human activities consist of physical gestures that tend to be performed in certain sequences. Wearable inertial sensor data have as a consequence been employed to automatically detect human activities, lately predominantly with deep learning methods. This article focuses on the necessity of recurrent layers—more specifically Long Short-Term Memory (LSTM) layers—in common Deep Learning architectures for Human Activity Recognition (HAR). Our experimental pipeline investigates the effects of employing none, one, or two LSTM layers, as well as different layers' sizes, within the popular DeepConvLSTM architecture. We evaluate the architecture's performance on five well-known activity recognition datasets and provide an in-depth analysis of the per-class results, showing trends which type of activities or datasets profit the most from the removal of LSTM layers. For 4 out of 5 datasets, an altered architecture with one LSTM layer produces the best prediction results. In our previous work we already investigated the impact of a 2-layered LSTM when dealing with sequential activity data. Extending upon this, we now propose a metric, rGP, which aims to measure the effectiveness of learned temporal patterns for a dataset and can be used as a decision metric whether to include recurrent layers into a network at all. Even for datasets including activities without explicit temporal processes, the rGP can be high, suggesting that temporal patterns were learned, and consequently convolutional networks are being outperformed by networks including recurrent layers. We conclude this article by putting forward the question to what degree popular HAR datasets contain unwanted temporal dependencies, which if not taken care of, can benefit networks in achieving high benchmark scores and give a false sense of overall generability to a real-world setting.

Over the years, well test analysis or pressure transient analysis (PTA) methods have progressed from straight lines via type curve analysis to pressure derivatives and deconvolution methods. Today, analysis of the log-log (pressure and its derivative) response is the most used method for PTA. Although these methods are widely available through commercial software, they are not fully automated, and human interaction is needed for their application. Furthermore, PTA is described as an inverse problem, whose solution in general is non-unique, and several models (well, reservoir and boundary) can be found applicable to similar pressure-derivative response. This tends to always bring about confusion in choosing the correct model using the conventional approach. This results in multiple iterations that are time consuming and requires constant human interaction. Our approach automates the process of PTA using a Siamese neural network (SNN) architecture comprised of Convolutional neural network (CNN) and Long Short-Term Memory (LSTM) layers. The SNN model is trained on simulated experimental data created using a design of experiments (DOE) approach involving most common 14 interpretation scenarios across well, reservoir, and boundary model types. Across each model type, parameters such as permeability, horizontal well length, skin factor, and distance to the boundary were sampled to compute 560 different pressure derivative responses. SNN is trained using a self-supervised training strategy where the positive and negative pairs are generated from the training data. We use transformations such as compression and expansion to generate positive pairs and negative pairs for the well test model responses. For a given well test model response, similarity scores are computed against the candidates in each model class, and the best match from each class is identified. These matches are then ranked according to the similarity scores to identify optimal candidates. Experimental analysis indicated that the true model class frequently appeared among the top ranked classes. The model achieves an accuracy of 93% for the top one model recommendations when tested on 70 samples from the 14 interpretation scenarios. Prior information on the top ranked probable well test models, significantly reduces the manual effort involved in the analysis. This machine learning (ML) approach can be integrated with any PTA software or function as a standalone application in the interpreter's system. Current work using SNN with LSTM layers can be used to speed up the process of detecting the pressure derivative response explained by a certain combination of well, reservoir and boundary models and produce models with less user interaction. This methodology will facilitate the interpretation engineer in making the model recognition faster for detailed integration with additional information from sources such as geophysics, geology, petrophysics, drilling, and production logging.

In recent years, supervised video summarization has achieved promising progress with various recurrent neural networks (RNNs) based methods, which treats video summarization as a sequence-to-sequence learning problem to exploit temporal dependency among video frames across variable ranges. However, RNN has limitations in modelling the long-term temporal dependency for summarizing videos with thousands of frames due to the restricted memory storage unit. Therefore, in this paper we propose a stacked memory network called SMN to explicitly model the long dependency among video frames so that redundancy could be minimized in the video summaries produced. Our proposed SMN consists of two key components: Long Short-Term Memory (LSTM) layer and memory layer, where each LSTM layer is augmented with an external memory layer. In particular, we stack multiple LSTM layers and memory layers hierarchically to integrate the learned representation from prior layers. By combining the hidden states of the LSTM layers and the read representations of the memory layers, our SMN is able to derive more accurate video summaries for individual video frames. Compared with the existing RNN based methods, our SMN is particularly good at capturing long temporal dependency among frames with few additional training parameters. Experimental results on two widely used public benchmark datasets: SumMe and TVsum, demonstrate that our proposed model is able to clearly outperform a number of state-of-the-art ones under various settings.

Human action recognition in videos is an important task with a broad range of applications. In this study, we improve the performance of recurrent attention convolutional neural network (RACNN) by proposing a novel model, “attention-again”. We consider the nature of video frames as sequences, which will cause the change of regions of interest in the frame, thus we cannot use an attention mechanism similar to that in images. “Attention-again” model is a variant from traditional attention model for recognizing human activities and is embedded in two long short-term memory (LSTM) layers. Different from hierarchal LSTM which change the LSTM structure to combine the hidden states from two LSTM layers, our proposals introduce “attention-again” model to avoid the change of LSTM structure. Furthermore, this model not only learns the relations in each frame, but also obtains the relations among all frames, and these relations instruct the next learning stage. Therefore, our proposed model outperform the baseline and is superior to methods with the same experimental conditions on three benchmark datasets: UCF-11, HMDB-51 and UCF-101. To understand how the model works, we also visualize the region of interest in the frame.

Packet loss may affect a wide range of applications that use voice over IP (VoIP), e.g. video conferencing. In this paper, we investigate a time-domain convolutional recurrent network (CRN) for online packet loss concealment. The CRN comprises a convolutional encoder-decoder structure and long short-term memory (LSTM) layers, which have been shown to be suitable for real-time speech enhancement applications. Moreover, we propose lookahead and masked training to further improve the performance of the CRN framework. Experimental results show that the proposed system outperforms a baseline system using only LSTM layers in terms of two objective metrics – perceptual evaluation of speech quality (PESQ) and short-term objective intelligibility (STOI); it also reduces the word error rate (WER) more than the baseline when used as a frontend for speech recognition. The advantage of the proposed system is also verified in a subjective evaluation by the mean opinion score (MOS).

Resting-state brain functional connectivity (FC) patterns play an essential role in the development of autism spectrum disorder (ASD) classification models based on functional magnetic resonance imaging (fMRI) data. Due to the limited number of models in the literature for identifying ASD subtypes, a multiclass classification is introduced in this study. The aim of this study is to develop an ASD diagnosis model using convolutional neural networks (CNN) with dynamic FC as inputs. The rs-fMRI dataset used in this study consists of 35 individuals from multiple sites labeled based on autistic disorder subtypes (ASD, APD, and PDD-NOS) and normal control (NC). The Atlas for Automated Anatomical Labeling (AAL) is selected as the brain atlas for defining brain nodes. The BOLD signals of the nodes are extracted and then the dynamic FC between brain nodes is determined using our new metric wavelet coherence (WCF), where WCF quantifies the overall variability of coherence in specific low-frequency scales over the time. Based on the statistical analysis of WCF values between ASD and NC, 6 pairwise nodes are identified. Classification algorithm is developed using CNN, and wavelet coherence maps (scalogram) of pairwise nodes. The training and testing of the CNN is using a cross-validation framework. The results of the multiclass classification provided an average accuracy of 88.6%. The results of this study illustrate the good potential of the wavelet coherence technique in analysing dynamics FC and open up possibilities for its application in diagnostic models, not only for ASD but also for other neuropsychiatric disorders.

Hybrid blockchain-based spectrum sharing algorithm for dynamic channel selection in cognitive radio

In this paper, the influence of Deep Neural Network (DNN) in predicting both the channel parameters and the power factors for users in a Power Domain Multi-Input Single-Output Non-Orthogonal Multiple Access (MISO-NOMA) system is inspected. In channel prediction based Deep Learning (DL) approach, we integrate the Long Short Term Memory (LSTM) learning network into NOMA system in order that LSTM can be utilized to predict the channel coefficients. In addition, in Deep Learning based power estimation method, we introduce an algorithm based on Convolutional Neural Network (CNN) to predict and allocate the power factor for each user in MISO-NOMA cell. DNN is trained online using channel statistics in order to approximate the channel coefficients and allocate the power factors for each user, so that these parameters can be utilized by the receiver to recover the desired data. Besides, this paper demonstrates the framework where channel prediction based on LSTM layer and power approximation based on CNN can be jointly employed for multiuser detection in MISO-NOMA. In this work, Power factors are optimized analytically based on maximizing the sum-rate of users to derive the optimum power factors. Simulation outcomes for distinct metrics have verified the dominance of the channel estimation and power predication based DNN over standard approaches.

Forecasting stock prices has always been a complex and challenging task due to its high volatility, noise in data, and abrupt price fluctuations caused by different factors. Traditional approaches, such as quantitative, fundamental, and technical analysis, often struggle to accurately predict stock price movements due to their difficulty in processing non-linear data. The evolution of machine learning, particularly neural network models, opened new opportunities for improving prediction accuracy by handling non-linear and multidimensional data with high effectiveness. This study focuses on evaluating and comparing three neural network architectures, in which the last is a combination of the first two - Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN-LSTM model, for forecasting stock prices. Using ten years of historical stock data, taken for Apple Inc. (AAPL) stocks as an example, ranging from 2015 to 2025, the models were trained and tested on key performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R². Results show that while both CNN and LSTM models show good performance individually, the hybrid CNN-LSTM model can significantly outperform them, achieving the highest accuracy with the lowest error rates. By combining the strengths of CNN for feature extraction and LSTM for temporal sequence processing, the hybrid model effectively captures complex patterns in stock price movements. By iterating over the types of layers to use, we came to the conclusion, that by combining both models into one hybrid model, some layers, like one CNN layer, and one LSTM layer should be removed. This research demonstrates the potential of hybrid neural network models in financial time-series forecasting and comprehensively analyzes their advantages over non-hybrid models.

As the environment issue is put on the agenda, air pollution also concerns a lot. Nitrogen oxide (NOx) an is important factor which affects air pollution and is also the main gas emissions of the smoke and waste gas of FCC unit in petrochemical industry. It is important to accurately predict the NOx emission in advance for petrochemical industry to avoid air pollution incidents. In this paper, convolutional neural network (CNN) and long short-term memory (LSTM) are combined to predict the NOx emission in Fluid Catalytic Cracking unit (FCC unit). Convolutional-LSTM (CLSTM) is able to extract the spatial and temporal features which are essential information in the prediction of the NOx emission. The features in the factors of production which would affect the NOx emission are extracted by CNN which prepares time series data for LSTM. The LSTM layer is connected after CNN to model the irregular trends in time series. CNN, Multi-layer perception (MLP), rand forest (RF), support vector machine (SVM) and LSTM are implemented as baseline models. The results from the proposed CLSTM model showed better performance than all the baseline models. The mean absolute error and root mean square error for CLSTM were calculated with the values of 16.8267 and 23.7089 which are the lowest among all the models. The Pearson correlation coefficient and R2 for the proposed CLSTM model are calculated with the value of 0.9263, 0.8237 which are the highest among all the models. Furthermore, the residual graphs indicate the well matched performance between the observations and the predictions. The study provides a model reference for forecasting the NOx concentration emitted by FCC unit in petrochemical industry.

Ocean surface currents (OSC), abiotic features of the environment, are continuous and directed movements of ocean water. Prediction to OSC is of significant interests in physical oceanography. Recently, deep learning technology has shown feasibility in mining the intrinsic change pattern of marine numerical values, such as SSS, SST and SSHA, but not been applied to OSC prediction yet. In this work, a deep learning method, named skipped dual path network (SDPNet), is proposed for OSC prediction. Specifically, SDPNet has a convolutional neural network (CNN) module with a one-dimensional convolution layer, and a recurrent neural network (RNN) module with a dual-path structure. Each path consists of a stack of three layers of long short-term memory (LSTM) and gated recurrent unit (GRU), respectively. As well, a skipped-connection structure is added in the both paths. It aims to gradually mine the intrinsic change pattern contained in OSC time series data itself. Experiments are conducted on the South China Sea OSC data set in REDOS. SDPNet achieves accuracy 80.83%, 75.9%, 74.9%, 73.9%, 72.58%, 70.35%, 69.93% in predicting the coming 7 days OSC values and directions. It performs better than state-of-the-art machine learning methods, include Artificial Neural Network, Simple RNN, LSTM and GRU.

Long-short term memory (LSTM) is a state-of-art and widely used model to forecast financial time series. However, primitive LSTM networks do not perform well due to over-fitting problems of the deep learning model and nonlinear and non-stationary characteristics of financial time series data. In addition, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is an outstanding data frequency decomposition technique that can decompose original time series into several intrinsic mode functions and a residue. Thus, this paper proposed a novel hybrid network CEEMDAN-LSTM-BN based on LSTM. Specifically, to avoid over-fitting, the modified LSTM-BN network consists of two LSTM layers, two Batch Normalization (BN) layers following each LSTM layer, and a dropout layer. Each of the intrinsic mode functions and the residue would be processed by CEEMDAN-LSTM-BN and the final prediction results are obtained by reconstructing each predictive series. The advantages of the proposed CEEMDANLSTM-BN networks are verified by comparing them to primitive LSTM, other hybrid models, and some famous machine learning models. Moreover, the robustness of the networks is assessed by numerical experiments on different stock indices datasets.

The use of functional magnetic resonance imaging (fMRI) data in machine learning (ML)-based classification of autism spectrum disorder (ASD) has been a topic of increasing research interest in past years due to the noninvasiveness of the fMRI technique and its potential for providing valuable biomarkers. However, there are still controversies surrounding some fMRI data preprocessing steps, such as bandpass filtering and global signal regression (GSR). It still needs to be determined whether or how these preprocessing steps impact the classification accuracy of ML algorithms. This paper uses fMRI signals from the ABIDE-I dataset to train a long short-term memory (LSTM) network to classify subjects into ASD or healthy controls (HC). We considered 18 preprocessing pipelines comprising all combinations of with and without filtering, with and without global signal regression, and three different segment lengths of 1 min, 2 min, and 3 min. The best model was obtained when using a segment length of 2 min. Our results suggest that not filtering produces significantly higher classification accuracies than filtering, whereas there were no significant differences in classification accuracies when removing or not the global signal.

Artificial intelligence models have been widely applied for natural gas consumption forecasting over the past decades, especially for short-term consumption forecasting. This paper proposes a three-layer neural network forecasting model that can extract key information from input factors and improve the weight optimization mechanism of long short-term memory (LSTM) neural network to effectively forecast short-term consumption. In the proposed model, a convolutional neural network (CNN) layer is adopted to extract the features among various factors affecting natural gas consumption and improve computing efficiency. The LSTM layer is able to learn and save the long-distance state through the gating mechanism and overcomes the defects of gradient disappearance and explosion in the recurrent neural network. To solve the problem of encoding input sequences as fixed-length vectors, the layer of attention (ATT) is used to optimize the assignment of weights and highlight the key sequences. Apart from the comparisons with other popular forecasting models, the performance and robustness of the proposed model are validated on datasets with different fluctuations and complexities. Compared with traditional two-layer models (CNN-LSTM and LSTM-ATT), the mean absolute range normalized errors (MARNE) of the proposed model in Athens and Spata are improved by more than 16% and 11%, respectively. In comparison with single LSTM, back propagation neural network, support vector regression, and multiple linear regression methods, the improvement in MARNE exceeds 42% in Athens. The coefficient of determination is improved by more than 25%, even in the high-complexity dataset, Spata.

Machine learning based predictions of protein–protein interactions (PPIs) could provide valuable insights into protein functions, disease occurrence, and therapy design on a large scale. The intensive feature engineering in most of these methods makes the prediction task more tedious and trivial. The emerging deep learning technology enabling automatic feature engineering is gaining great success in various fields. However, the over-fitting and generalization of its models are not yet well investigated in most scenarios. Here, we present a deep neural network framework (DNN-PPI) for predicting PPIs using features learned automatically only from protein primary sequences. Within the framework, the sequences of two interacting proteins are sequentially fed into the encoding, embedding, convolution neural network (CNN), and long short-term memory (LSTM) neural network layers. Then, a concatenated vector of the two outputs from the previous layer is wired as the input of the fully connected neural network. Finally, the Adam optimizer is applied to learn the network weights in a back-propagation fashion. The different types of features, including semantic associations between amino acids, position-related sequence segments (motif), and their long- and short-term dependencies, are captured in the embedding, CNN and LSTM layers, respectively. When the model was trained on Pan’s human PPI dataset, it achieved a prediction accuracy of 98.78% at the Matthew’s correlation coefficient (MCC) of 97.57%. The prediction accuracies for six external datasets ranged from 92.80% to 97.89%, making them superior to those achieved with previous methods. When performed on Escherichia coli, Drosophila, and Caenorhabditis elegans datasets, DNN-PPI obtained prediction accuracies of 95.949%, 98.389%, and 98.669%, respectively. The performances in cross-species testing among the four species above coincided in their evolutionary distances. However, when testing Mus Musculus using the models from those species, they all obtained prediction accuracies of over 92.43%, which is difficult to achieve and worthy of note for further study. These results suggest that DNN-PPI has remarkable generalization and is a promising tool for identifying protein interactions.