Long-noncoding RNAs (LncRNAs) play important roles in physiological and pathological processes. Accurately predicting lncRNA-protein interactions (LPIs) is vital strategy for clarify functions and pathogenic mechanism...
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Long-noncoding RNAs (LncRNAs) play important roles in physiological and pathological processes. Accurately predicting lncRNA-protein interactions (LPIs) is vital strategy for clarify functions and pathogenic mechanisms of lncRNAs. Current computational methods for evaluating LPIs with their utility and generalization have significant room for improvement. In this study, data splitting by incorporating protein clusters as group information reveals that lots of LPI prediction methods suffer from generalization flaws due to data leakage caused by ignoring LPI biological properties. To address the issue, we present LPItabformer, a tabular Transformer framework for predicting LPIs, that incorporates a domain shifts with uncertainty (DSU) module for generalization enhancement. The LPItabformer demonstrates a capacity to alleviate the generalization challenges associated with biases in LPI data and preferences in protein binding patterns. In addition, LPItabformer shows greater robustness and generalization on human and mouse LPI datasets compared to state-of-the-art methods. Ultimately, we have verified that the LPItabformer is capable of predicting novel LPIs. Code is available at https://***/Ci-TJ/LPItabformer.
The research introduced a new method for land-use classification by merging deep convolutional neural networks with a modified variant of a metaheuristic optimization technique. The methodology involved utilizing the ...
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The research introduced a new method for land-use classification by merging deep convolutional neural networks with a modified variant of a metaheuristic optimization technique. The methodology involved utilizing the VGG-19 model for feature extraction, dimensionality reduction, and a stacked autoencoder optimized with a boosted version of the Big Bang Crunch Theory. Through testing on the Aerial Image Dataset the and UC Merced Land Use Dataset and comparing it with other published works, the approach showed higher classification accuracy compared to current state-of-the-art methods. The study revealed that incorporating boosted Big-Bang Crunch significantly enhances the performance of stacked autoencoder in land-use classification tasks. Moreover, comparisons with other techniques, including convolutional neural networks, Cascaded Residual Dilated Networks, hierarchical convolutional recurrent neural networks, Fusion Region Proposal Networks, and multi-level context-guided classification techniques using Object-Based Convolutional Neural Networks, emphasized the benefits of using Convolutional Neural Network models over traditional methods. The proposed model achieved an accuracy of 92.49% on the AID dataset and 95.93% on the UC Merced dataset, with precision scores of 98.64% and 98.93%, respectively. These results emphasize the importance of integrating deep learning architectures with sophisticated optimization techniques, contributing to enhanced land-use classification accuracy.
This paper successfully tackles the problem of processing a vast amount of security related data for the task of network intrusion detection. It employs Apache Spark, as a big data processing tool, for processing a la...
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This paper successfully tackles the problem of processing a vast amount of security related data for the task of network intrusion detection. It employs Apache Spark, as a big data processing tool, for processing a large size of network traffic data. Also, we propose a hybrid scheme that combines the advantages of deep network and machine learning methods. Initially, stacked autoencoder network is used for latent feature extraction, which is followed by several classification-based intrusion detection methods, such as support vector machine, random forest, decision trees, and naive Bayes which are used for fast and efficient detection of intrusion in massive network traffic data. A real time UNB ISCX 2012 dataset is used to validate our proposed method and the performance is evaluated in terms of accuracy, f-measure, sensitivity, precision and time.
Due to rapid growth and tremendous advancement, the Internet of Things (IoT) has been considered as a superior technology in recent years and increased its demand over the internet world because of its smart services....
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Due to rapid growth and tremendous advancement, the Internet of Things (IoT) has been considered as a superior technology in recent years and increased its demand over the internet world because of its smart services. IoT has a wide range of applications over the industry, business, and academia. Still, security and routing remain a huge challenging task because of some reasons, such as heterogeneity issues, large energy consumption, and uncontrollable environment. In order to cope up with such security issues, it is essential to develop an energy-efficient routing protocol. Hence, this article presents an Adaptive energy harvesting and Trust aware routing (EHTARA) algorithm for optimal routing such that it prolongs the lifetime of the network. The optimal secure routing path is chosen to exploit the cost metric function. Moreover, the classification of big data is performed at MapReduce framework using stacked autoencoder, which is trained using the proposed Adaptive E-2-Bat algorithm. Moreover, the proposed Adaptive E-2-Bat algorithm is derived by incorporating adaptive principle with E-2-Bat algorithm. The proposed Adaptive EHTARA achieved an energy of 0.948 J that reveals the superiority of the proposed scheme.
With the fast development of power grid the usage of electrical equipments is increased which led to importance of power quality disturbance sensing for reliable and smooth operation. In this paper, a deep neural netw...
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With the fast development of power grid the usage of electrical equipments is increased which led to importance of power quality disturbance sensing for reliable and smooth operation. In this paper, a deep neural network has been designed using stacked autoencoder (SAE) for deep feature extraction from time-frequency spectrum of single and combined PQ disturbances in electrical power system network. For this purpose, synthetic PQ signals are analyzed in time-frequency domain through hyperbolic window stockwell transform (HWST). Thereafter, PQ signal converted HWST time-frequency matrix has been grouped into time-frequency blocks and subsequently fed as input to 3-layer stacked autoencoder model (SAE) for deep feature learning. Finally, the extracted deep features are classified through several machine learning classifier. The results indicate that proposed framework using XGboost classifier can classify 18 different single and combined PQ event with a 99.86% accuracy. The proposed framework also yields satisfactory outcome with real life PQ data. Therefore, proposed framework can be implemented for Power quality monitoring in electrical power system.
The demand on devices and systems empowered by biometric verification and identification mechanisms has been increasing in recent years as they have become a significant part of our lives. Palm vein biometric is an em...
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The demand on devices and systems empowered by biometric verification and identification mechanisms has been increasing in recent years as they have become a significant part of our lives. Palm vein biometric is an emerging technology that has drawn considerable attention from researchers and scientists over the last decade. In the present work, a novel feature extraction methodology named GPWLD combing the Gabor features with positional Weber's local descriptor (PWLD) features is proposed. WLD is a highly representative micro-pattern descriptor that performs well against noise and illumination changes in images. However, it lacks the ability to capture the vein pattern features at different orientations. Moreover, its descriptor packs the local information content into a single histogram that does not take the spatial locality of micro-patterns into consideration. To solve these two issues, firstly, the palm vein image is passed through Gabor filter with different orientations to capture the salient rotational features found in the output feature maps. Secondly, the spatiality is achieved by uniformly dividing each feature map into several blocks. Next, Weber's law feature descriptor (WLD) histogram is computed for each block in every feature map. Finally, these histograms are concatenated to compose the final feature vector. Due to the high dimensionality of the final feature vector, Principal component analysis (PCA) algorithm is utilized for feature size reduction. In the classification stage, a deep neural network (DNN) comprising an optimized stacked autoencoder (SAE) with Bayesian optimization and a softmax layer is used. Optimization of the SAE is carried out by using Bayesian optimization to find the optimal SAE structure and the options of the training algorithm. The Experimental results verify the discriminative power of the extracted features and the accuracy of the proposed DNN. For both Identification and verification experiments, the proposed method a
In modern chemical process control, the application of data-driven soft sensor has become increasingly extensive. Feature extraction is an important step in soft sensor. A novel feature extraction and integration meth...
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In modern chemical process control, the application of data-driven soft sensor has become increasingly extensive. Feature extraction is an important step in soft sensor. A novel feature extraction and integration method based on stacked autoencoders (SAE) and mutual information (MI)-weighted principle component analysis (PCA) was proposed to solve the loss of information on shallow depth features and original variables in neural network models. First, an SAE model was trained to extract the features of the original variables with varying depths. Second, through an MI indicator, the original variables and features with strong dependency on the outputs were selected. Then, MI was used to assign varied weights to the features and original variables, and the PCA method was used to remove any possible redundancy between the original variables and features of varying depths to obtain the principle components. Finally, the principle components were used to construct a regressor, such as a neural network. The model was first tested using the Boston housing dataset as a benchmark and then applied to the soft sensor of a constant top oil dry point. The proposed model achieved optimal results in terms of the root mean squared error and r indicators in the experiments and was thus proved feasible and useful.
To address the problems of existing passenger flow prediction methods such as low accuracy, inadequate learning of spatial features of station topology, and inability to apply to large networks, a SAE-GCN-BiLSTM-based...
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To address the problems of existing passenger flow prediction methods such as low accuracy, inadequate learning of spatial features of station topology, and inability to apply to large networks, a SAE-GCN-BiLSTM-based passenger flow forecasting method for urban rail transit is proposed. First, the external features are extracted layer by layer using stacked autoencoder (SAE). Then, graph convolutional network (GCN) is used to capture the spatial features of station topology, and bi-directional long and short-term memory network (BiLSTM) is used to extract the bi-directional temporal features, realizing the extraction of the spatio-temporal features. Finally, external features and spatio-temporal features are fused for accurate prediction of urban rail transit passenger flow. The experimental results show that the proposed method is higher than several other advanced models in the evaluation indexes under different granularities, indicating that the model effectively develops the accuracy and robustness of urban rail transit passenger flow prediction.
In this work, a seismocardiogram (SCG) based breathing-state measuring method is proposed for m-health applications. The aim of the proposed framework is to assess the human respiratory system by identifying degree-of...
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In this work, a seismocardiogram (SCG) based breathing-state measuring method is proposed for m-health applications. The aim of the proposed framework is to assess the human respiratory system by identifying degree-of-breathings, such as breathlessness, normal breathing, and long and labored breathing. For this, it is needed to measure cardiac-induced chest-wall vibrations, reflected in the SCG signal. Orthogonal subspaceprojection is employed to extract the SCG cycles with the help of a concurrent ECG signal. Subsequently, fifteen statistically significant morphological-features are extracted from each of the SCG cycles. These features can efficiently characterize physiological changes due to varying respiratory-rates. stacked autoencoder (SAE) based architecture is employed for the identification of different respiratory-effort levels. The performance of the proposed method is evaluated and compared with other standard classifiers for 1147 analyzed SCG- beats. The proposed method gives an overall average accuracy of 91.45% in recognizing three different breathing states. The quantitative analysis of the performance results clearly shows the effectiveness of the proposed framework. It may be employed in various healthcare applications, such as pre-screening medical sensors and IoT based remote health-monitoring systems.
Universities play an important role in exploring new concepts and knowledge transfer. University research naturally forms heterogeneous graphs through all real-life academic communication activities. In recent years, ...
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Universities play an important role in exploring new concepts and knowledge transfer. University research naturally forms heterogeneous graphs through all real-life academic communication activities. In recent years, there have been many large scholarly graph datasets containing web-scale nodes and edges. However, so far, for these graph data, characterizing research about university output is focusing on counting the volume or evaluating the excellence of research articles and providing a ranking. This paper proposes a novel University Profiling Framework (UPF) from the production and complexity point of view which is different from other straightforward solutions. The framework includes a novel Recurrent Deep Clustering Model (Recurrent-DC) for the learning of deep representations and clusters. In our model, successive operations in a clustering algorithm are expressed as steps in a recurrent process, stacked on top of representations output by a stacked autoencoder (SAE). Our key idea behind this model is that good representations for university clustering task-specific problem can be learned over multiple timesteps. Experimental results illustrate the stability and effectiveness of the proposed model comparing with the other deep clustering and classical clustering methods. (C) 2020 Elsevier B.V. All rights reserved.
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