Payment channels support off-chain transactions by enhancing transaction speed and reducing fees in the main blockchain. However, the costs and complexity of the network increase as we increase the size of the network...
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This study employs transfer learning using a fine-tuned pretrained EfficientNetB0 convolutional neural network (CNN) model to accurately detect the various stages of Diabetic Retinopathy. The training process involved...
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Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and t...
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Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.
Epileptic seizure is a highly severe neurological disorder that abruptly interrupts the daily life of those afflicted. In order to advance the technology utilized for managing epileptic seizures, new diagnostic proced...
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Automatic Human Action Recognition (HAR) using RGB-D (Red, Green, Blue, and Depth) videos captivated a lot of attention in the pattern classification field due to low-cost depth cameras. Feature extraction in action r...
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Deep neural networks have played a vital role in developing automated methods for addressing medical image segmentation. However, their reliance on labeled data impedes the practicability. Semi-Supervised learning is ...
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technology-enhanced learning has the potential to increase educational quality. In this study, we examine the integration of augmented reality (AR) technology in rural primary schools in India. We listed articles asso...
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This paper presents an efficient prediction model for a good learning environment using Random Forest(RF)*** consists of a series of modules;data preprocessing,data normalization,data split andfinally classification o...
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This paper presents an efficient prediction model for a good learning environment using Random Forest(RF)*** consists of a series of modules;data preprocessing,data normalization,data split andfinally classification or prediction by the RF *** preprocessed data is normalized using minmax normalization often used before *** the input data or variables are measured at different scales,it is necessary to normalize them to contribute equally to the ***,the RF classifier is employed for course selection which is an ensemble learning method and k-fold cross-validation(k=10)is used to validate the *** proposed Prediction Model for Course Selection(PMCS)system is considered a multi-class problem that predicts the course for a particular learner with three complexity levels,namely low,medium and *** is operated under two modes;locally and *** former considers the gender of the learner and the later does not consider the gender of the *** database comprises the learner opinions from 75 males and 75 females per category(low,medium and high).Thus the system uses a total of 450 samples to evaluate the performance of the PMCS *** show that the system’s performance,while using locally i.e.,gender-wise has slightly higher performance than the global *** RF classifier with 75 decision trees in the global system provides an average accuracy of 97.6%,whereas in the local system it is 97%(male)and 97.6%(female).The overall performance of the RF classifier with 75 trees is better than 25,50 and 100 decision trees in both local and global systems.
Accurately predicting crop yield is essential for optimizing agricultural practices and ensuring food security. However, existing approaches often struggle to capture the complex interactions between various environme...
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Background: Emotion is a strong feeling such as love, anger, fear, etc. Emotion can be recognized in two ways, i.e., External expression and Biomedical data-based. Nowadays, various research is occurring on emotion cl...
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Background: Emotion is a strong feeling such as love, anger, fear, etc. Emotion can be recognized in two ways, i.e., External expression and Biomedical data-based. Nowadays, various research is occurring on emotion classification with biomedical data. Aim: One of the most current studies in the medical sector, gaming-based applications, education sector, and many other domains is EEG-based emotion identification. The existing research on emotion recognition was published using models like KNN, RF Ensemble, SVM, CNN, and LSTM on biomedical EEG data. In general, only a few works have been published on ensemble or concatenation models for emotion recognition on EEG data and achieved better results than individual ones or a few machine learning approaches. Various papers have observed that CNN works better than other approaches for extracting features from the dataset, and LSTM works better on the sequence data. Methods: Our research is based on emotion recognition using EEG data, a mixed-model deep learning methodology, and its comparison with a machine learning mixed-model methodology. In this study, we introduced a mixed model using CNN and LSTM that classifies emotions in valence and arousal on the DEAP dataset with 14 channels across 32 people. Result and Discussion: We then compared it to SVM, KNN, and RF Ensemble, and concatenated these models with it. First preprocessed the raw data, then checked emotion classification using SVM, KNN, RF Ensemble, CNN, and LSTM individually. After that with the mixed model of CNN-LSTM, and SVM-KNN-RF Ensemble results are compared. Proposed model results have better accuracy as 80.70% in valence than individual ones with CNN, LSTM, SVM, KNN, RF Ensemble and concatenated models of SVM, KNN and RF Ensemble. Conclusion: Overall, this paper concludes a powerful technique for processing a range of EEG data is the combination of CNNs and LSTMs. Ensemble approach results show better performance in the case of valence at 80.70% and 78.24
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