FusionNet- A Hybrid Deep Learning Approach for Accurate Drug-Target Binding Prediction

Nowadays, due to the increase in the demand for electrical energy and the development of technology, the electrical devices have a more complex structure. This situation has increased the importance of concept of the power quality in the electrical power system. This paper presents a deep learning-based system to recognize the power quality disturbances (PQDs) in the solar photovoltaic (SPV) plant integrated with power system networks. The PQDs are analyzed using continuous wavelet transform (CWT) and image files are obtained from scalograms of CWT. Then, these image files are used to recognize PQDs with the help of a hybrid deep learning approach based on convolutional neural network (CNN), neighbor component analysis (NCA), and support vector machine (SVM). In this hybrid deep learning approach, the image files are given as input to AlexNet and GoogLeNet. The NCA is applied to the features obtained from the last dropout layer of each architecture. The distinctive features obtained from the NCA process are classified using the SVM algorithm. In order to evaluate the proposed approach, PQD data are obtained from a modified IEEE 13-bus test system including the SPV system. Several analyses and comparisons are carried out to verify the success of the proposed approach. It has been found that the proposed hybrid deep learning approach has the ability to accurately recognize the PQDs even if the SPV plant integrated power system has a negative effect on power quality.

The rising frequency and complexity of Distributed Denial of Service (DDoS) attacks pose a severe threat to network security. This study aims to develop an effective and interpretable DDoS detection framework using a hybrid deep learning approach. The proposed method integrates Convolutional Neural Networks (CNN) to capture local traffic patterns and Long Short-Term Memory (LSTM) networks to model temporal dependencies. The CICIDS 2017 dataset, after preprocessing steps including data cleaning, standardization, and class balancing with SMOTE, was used to train and evaluate the model. Experimental results show that the framework achieved 99.98% accuracy and a 99.83% F1-Score, with minimal false positive and false negative rates. This study integrates SHAP to improve model interpretability, aligning feature importance with network security expertise. Future research will focus on real-time deployment, cross-dataset validation, and exploring alternative explainable AI techniques for improved scalability.

SSHF-DTI: Leveraging structural similarity and hierarchical features through a fusion network for drug-target interaction prediction.

The overheating of heat transfer wall is a primary contributor to boiler tube failures in coal-fired boilers. A fusion model that integrates sparse self-attention (SSA), convolutional neural networks (CNN), and bidirectional long short-term memory networks (BiLSTM) is proposed to predict the wall temperature of the final reheater, based on their dynamic characteristics. Firstly, the original candidate variables are subjected to filtering and dimensionality reduction through the KPCA algorithm, resulting in the selection of the top 26 principal component variables as the final input for the model. Secondly, leveraging the strengths of convolutional neural networks (CNN) in capturing local correlations and bidirectional long short-term memory networks (BiLSTM) in modeling long-term sequential dependencies, a CNN-BiLSTM architecture is employed to effectively capture both short-term and long-term dependencies within time series data. Additionally, a sparse self-attention mechanism (SSA) is incorporated to enhance CNN-BiLSTM’s performance by assigning varying weights to different components based on their significance. Simulations are performed utilizing historical data from an operational 1000MW ultra-supercritical boiler. The results indicate that the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) for the CNN-BiLSTM-SSA model are 4.92°C, 3.81°C, and 0.624%, respectively, demonstrating superior performance compared to CNN, LSTM, BiLSTM, CNN-LSTM, and CNN-BiLSTM models. The presented deep learning fusion model effectively predicts high-temperature reheater wall temperatures and holds significant theoretical and engineering value for addressing overheating issues in advance.

MultiscaleDTA: A multiscale-based method with a self-attention mechanism for drug-target binding affinity prediction.

Drug-target interactions provide insight into the drug-side effects and drug repositioning. However, wet-lab biochemical experiments are time-consuming and labor-intensive, and are insufficient to meet the pressing demand for drug research and development. With the rapid advancement of deep learning, computational methods are increasingly applied to screen drug-target interactions. Many methods consider this problem as a binary classification task (binding or not), but ignore the quantitative binding affinity. In this paper, we propose a new end-to-end deep learning method called DeepMHADTA, which uses the multi-head self-attention mechanism in a deep residual network to predict drug-target binding affinity. On two benchmark datasets, our method outperformed several current state-of-the-art methods in terms of multiple performance measures, including mean square error (MSE), consistency index (CI), , and PR curve area (AUPR). The results demonstrated that our method achieved better performance in predicting the drug–target binding affinity.

Biological sequence data mining is hot spot in bioinformatics. A biological sequence can be regarded as a set of characters. Time series is similar to biological sequences in terms of both representation and mechanism. Therefore, in the article, biological sequences are represented with time series to obtain biological time sequence (BTS). Hybrid ensemble learning framework (SaPt-CNN-LSTM-AR-EA) for BTS is proposed. Single-sequence and multi-sequence models are respectively constructed with self-adaption pre-training one-dimensional convolutional recurrent neural network and autoregressive fractional integrated moving average fused evolutionary algorithm. In DNA sequence experiments with six viruses, SaPt-CNN-LSTM-AR-EA realized the good overall prediction performance and the prediction accuracy and correlation respectively reached 1.7073 and 0.9186. SaPt-CNN-LSTM-AR-EA was compared with other five benchmark models so as to verify its effectiveness and stability. SaPt-CNN-LSTM-AR-EA increased the average accuracy by about 30%. The framework proposed in this article is significant in biology, biomedicine, and computer science, and can be widely applied in sequence splicing, computational biology, bioinformation, and other fields.

Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms

Nowadays, most smartphones feature a number of strong sensors, such as direction, network, location, and motion sensors. Motion and inertial sensors (i.e., accelerometer, gyroscope, etc.) are particularly popular in human activity recognition (HAR) for detecting human physical activity which helps in many research areas of the Internet of healthcare things (IoHT) including several patient’s diseases such as Parkinson, obesity, cardiovascular, and diabetes. Deep learning (DL) techniques have been increasingly popular over the years as a result of their significant outcomes. The use of deep learning techniques to recognize human physical activity in wearable and mobile sensor situations has also gotten a lot of attention from around the world. In this paper, a deep neural network (DNN) combining bidirectional long short-term memory (Bi-LSTM) and convolutional neural network (CNN) is proposed. The performance of the model has been evaluated on two publicly available datasets: WISDM and UCI-HAR. The model has achieved 97.96 and 97.15% accuracy for the WISDM and UCI-HAR, respectively. Moreover, the simulation results show the effectiveness of the proposed work compared to other state-of-the-art methods.KeywordsHuman activity recognitionDeep learningIoHTConvolutional neural networks (CNN)Bidirectional long short-term memory networks (Bi-LSTM)

In human-machine interaction systems, continuous movement estimation methods occupy an important position because they are more natural and intuitive than pattern-recognition methods. Essentially, arm position is decided by the shoulder and elbow joint angles. However, the various deformations of muscles around the shoulder and elbow often lead to difficulties in sensor fixation, which results in a loss of synchronization between the surface electromyography (sEMG) signals and joint angles. In order to accurately estimate movement angles using sEMG in situations where the sEMG is not synchronized with joint angles, we utilized a bi-directional long short-term memory (Bi-LSTM) network rather than other deep learning methods to estimate non-dominant arm movements, based on the sEMG signal from the dominant arm. This estimation protocol was designed to avoid a multiplicity of sensors and to simulate more complicated loss of synchronization problems). The performance of the Bi-LSTM was compared with multilayer perceptrons (MLPs), convolutional neural networks (CNNs), and a long short-term memory network (LSTM). The Pearson correlation coefficient (cc) between the estimated and target joint angle sequences was calculated to evaluate the performance of each neural network. The Wilcoxon signed-rank results showed that the Bi-LSTM model significantly outperformed the MLP, CNN, and LSTM models (tested with completely untrained newly recorded free movements).

A deep CNN-LSTM model for predicting interface depth from gravity data over thrust and fold belts of North East India

BackgroundLong short-term memory (LSTM) is one of the most attractive deep learning methods to learn time series or contexts of input data. Increasing studies, including biological sequence analyses in bioinformatics, utilize this architecture. Amino acid sequence profiles are widely used for bioinformatics studies, such as sequence similarity searches, multiple alignments, and evolutionary analyses. Currently, many biological sequences are becoming available, and the rapidly increasing amount of sequence data emphasizes the importance of scalable generators of amino acid sequence profiles.ResultsWe employed the LSTM network and developed a novel profile generator to construct profiles without any assumptions, except for input sequence context. Our method could generate better profiles than existing de novo profile generators, including CSBuild and RPS-BLAST, on the basis of profile-sequence similarity search performance with linear calculation costs against input sequence size. In addition, we analyzed the effects of the memory power of LSTM and found that LSTM had high potential power to detect long-range interactions between amino acids, as in the case of beta-strand formation, which has been a difficult problem in protein bioinformatics using sequence information.ConclusionWe demonstrated the importance of sequence context and the feasibility of LSTM on biological sequence analyses. Our results demonstrated the effectiveness of memories in LSTM and showed that our de novo profile generator, SPBuild, achieved higher performance than that of existing methods for profile prediction of beta-strands, where long-range interactions of amino acids are important and are known to be difficult for the existing window-based prediction methods. Our findings will be useful for the development of other prediction methods related to biological sequences by machine learning methods.

A hybrid deep learning approach for Assamese toxic comment detection in social media

To better mine the effective information contained in massive data and improve the accuracy of short-term load forecasting, this paper proposes a hybrid model based on convolutional neural network and long short-term memory network (CNN-LSTM) for short-term load forecasting (STLF), which takes massive historical load data, meteorological data, date information, and peak-valley electric price data as input by constructing continuous feature maps with time sliding window. Convolutional neural network (CNN) is used to extract features and reshape them into vectors. The feature vectors are constructed in temporal and fed into long short-term memory network (LSTM) which is used to predict STLF. To the best of our knowledge, this is the first work to predict STLF using deep learning in both spatial and temporal domains. It is shown that the forecasting accuracy can be notably improved by CNN-LSTM hybrid model method. The effectiveness of the proposed method is validated through extensive comparison studies on a real-world dataset.

EA-based hyperparameter optimization of hybrid deep learning models for effective drug-target interactions prediction