Computational Intelligence最新文献

Protein sequence classification needs to be performed quickly and accurately to progress bioinformatics advancements and the production of pharmaceutical products. Extensive comparisons between large databases of known proteins and unknown sequences are necessary in traditional protein classification methods, which can be time-consuming. This labour-intensive and slow manual matching and classification method depends on functional and biological commonalities. Protein classification is one of the many fields in which deep learning has recently revolutionized. The data on proteins are organized hierarchically and sequentially, and the most advanced algorithms, such as Deep Family-based Method (DeepFam) and Protein Convolutional Neural Network (ProtCNN), have shown promising results in classifying proteins into relative groups. On the other hand, these methods frequently refuse to acknowledge this fact. We propose a novel hybrid model called ProteinCNN-BLSTM to overcome these particular challenges. To produce more accurate protein sequence classification, it combines the techniques of amino acid embedding with bidirectional long short-term memory (BLSTM) and convolutional neural networks (CNNs). The CNN component is the most effective at capturing local features, while the BLSTM component is the most capable of modeling long-term dependencies across protein sequences. Through the process of amino acid embedding, sequences of proteins are transformed into numeric vectors, which significantly improves the precision of prediction and the representation of features. Using the standard protein samples PDB-14189 and PDB-2272, we analyzed the proposed ProteinCNN-BLSTM model and the existing deep-learning models. Compared to the existing models, such as CNN, LSTM, GCNs, CNN-LSTM, RNNs, GCN-RNN, DeepFam, and ProtCNN, the proposed model performed more accurately and better than the existing models.

Deep learning models have overcome traditional machine learning techniques for text classification domains in the field of natural language processing (NLP). Since, NLP is a branch of machine learning, used for interpreting language, classifying text of interest, and the same can be applied to analyse the medical clinical electronic health records. Medical text consists of lot of rich data which can altogether provide a good insight, by determining patterns from the clinical text data. In this paper, bidirectional-long short-term memory (Bi-LSTM), bi-LSTM attention and bidirectional encoder representations from transformers (BERT) base models are used to classify the text which are of privacy concern to a person and which should be extracted and can be tagged as sensitive. This text data which we might think not of privacy concern would majorly reveal a lot about the patient's integrity and personal life. Clinical data not only have patient demographic data but lot of hidden data which might go unseen and thus could arise privacy issues. Bi-LSTM with attention layer is also added on top to realize the importance of critical words which will be of great importance in terms of classification, we are able to achieve accuracy of about 92%. About 206,926 sentences are used out of which 80% are used for training and rest for testing we get accuracy of 90% approx. with Bi-LSTM alone. The same set of datasets is used for BERT model with accuracy of 93% approx.

In the field of single image super-resolution, the prevalent use of convolutional neural networks (CNN) typically assumes a simplistic bicubic downsampling model for image degradation. This assumption misaligns with the complex degradation processes encountered in medical imaging, leading to a performance gap when these algorithms are applied to real medical scenarios. Addressing this critical discrepancy, our study introduces a novel degradation comparative learning framework meticulously designed for the nuanced degradation characteristics of medical images within the Internet of Medical Things (IoMT). Unlike traditional CNN-based super-resolution approaches that homogeneously process image channels, our method acknowledges and leverages the disparity in informational content across channels. We present a blind image super-resolution technique, underpinned by edge reconstruction and an innovative image feature supplement module. This approach not only preserves but enriches texture details, crucial for the accurate analysis of medical images in the IoMT. Comparative evaluations of our model against existing blind super-resolution methods, utilizing both natural image testing datasets and medical images, demonstrate its superior performance. Notably, our approach exhibits remarkable proficiency in stably restoring various degraded super-resolution images, a critical requirement in the IoMT context. Experimental results demonstrate that our method is superior to the current state-of-the-art methods, marking a significant advancement in the field of medical image super-resolution.

Brain tumor segmentation from MRI is a challenging process that has positive ups and downs. The most crucial step for detection and treatment to save the patient's life is earlier diagnosis and classification of brain tumor (BT) with higher accuracy prediction. One of the deadliest cancers, malignant brain tumors is now the main cause of cancer-related death due to their extreme severity. To evaluate the tumors and help patients receive the appropriate treatment according to their classifications, it is essential to have a thorough understanding of brain diseases, such as classifying BT. In order to resolve the problem of low segmentation accuracy caused by an imbalance of model design and sample category in the process of brain tumor segmentation. In this research work, Multi-Dimensional Cascades Neural Network (MDCNet) is developed for multi-class BT classification. It is divided into two steps. In stage 1, an enhanced shallow-layer 3D locality net is used to conduct BT localization and rough segmentation on the preprocessed MRIs. It is also advised to use a unique circular inference module and parameter Dice loss to lower the uncertain probability and false positive border locations. In step 2, in order to compensate for mistakes and lost spatial information of a single view, morphological traits are investigated using a multi-view 2.5D net composed of three 2D refinement subnetworks. The suggested method outperforms the traditional model in segmentation, yielding an accuracy of 99.67%, 98.16%, and 99.76% for the three distinct datasets.

Modelling one-to-many type mappings in problems with a temporal component can be challenging. Backpropagation is not applicable to networks that perform discrete sampling and is also susceptible to gradient instabilities, especially when applied to longer sequences. In this paper, we propose two recurrent neural network architectures that leverage stochastic units and mixture models, and are trained with target propagation. We demonstrate that these networks can model complex conditional probability distributions, outperform backpropagation-trained alternatives, and do not rapidly degrade with increased time horizons. Our main contributions consist of the design and evaluation of the architectures that enable the networks to solve multi-model problems with a temporal dimension. This also includes the extension of the target propagation through time algorithm to handle stochastic neurons. The use of target propagation provides an additional computational advantage, which enables the network to handle time horizons that are substantially longer compared to networks fitted using backpropagation.

Question-driven automatic text summarization is a popular technique to produce concise and informative answers to specific questions using a document collection. Both query-based and question-driven summarization may not produce reliable summaries nor contain relevant information if they do not take advantage of extractive and abstractive summarization mechanisms to improve performance. In this article, we propose a novel extractive and abstractive hybrid framework designed for question-driven automatic text summarization. The framework consists of complimentary modules that work together to generate an effective summary: (1) discovering appropriate non-redundant sentences as plausible answers using an open-domain multi-hop question answering system based on a convolutional neural network, multi-head attention mechanism and reasoning process; and (2) a novel paraphrasing generative adversarial network model based on transformers rewrites the extracted sentences in an abstractive setup. Experiments show this framework results in more reliable abstractive summary than competing methods. We have performed extensive experiments on public datasets, and the results show our model can outperform many question-driven and query-based baseline methods (an R1, R2, RL increase of 6%–7% for over the next highest baseline).

There is a growing trend of using artificial intelligence, particularly deep learning algorithms, in medical diagnostics, revolutionizing healthcare by improving efficiency, accuracy, and patient outcomes. However, the use of artificial intelligence in medical diagnostics comes with the critical need to explain the reasoning behind artificial intelligence-based predictions and ensure transparency in decision-making. Explainable artificial intelligence has emerged as a crucial research area to address the need for transparency and interpretability in medical diagnostics. Explainable artificial intelligence techniques aim to provide insights into the decision-making process of artificial intelligence systems, enabling clinicians to understand the factors the algorithms consider in reaching their predictions. This paper presents a detailed review of saliency-based (visual) methods, such as class activation methods, which have gained popularity in medical imaging as they provide visual explanations by highlighting the regions of an image most influential in the artificial intelligence's decision. We also present the literature on non-visual methods, but the focus will be on visual methods. We also use the existing literature to experiment with infrared breast images for detecting breast cancer. Towards the end of this paper, we also propose an “attention guided Grad-CAM” that enhances the visualizations for explainable artificial intelligence. The existing literature shows that explainable artificial intelligence techniques are not explored in the context of infrared medical images and opens up a wide range of opportunities for further research to make clinical thermography into assistive technology for the medical community.

In order to solve the huge impact of the digital information age on many technical and industrial fields, a periodic fast search genetic algorithm is proposed. Based on the reconnaissance mission, this paper introduces the common allocation strategy into mission planning, and constructs the mathematical model of multi unmanned aerial vehicle (UAV) cooperative reconnaissance mission planning decision-making. The proposed periodic fast search genetic algorithm is used to solve the problem of multi UAV cooperative reconnaissance mission planning. In 2020, the industry growth rate of global UAV technology expenditure was as high as 30.6%, and the compound growth rate of UAV in China reached 63.5%, which is enough to see the great prospect of the integrated development of UAV technology and different industries. The experiment evaluates the log verification module of UAV by comparing the two data structures of Merkle tree and linear, and the time and memory overhead of storing and verifying logs, which shows the effectiveness of the log verification scheme in this paper.

Electroencephalogram (EEG), as a tool capable of objectively recording brain electrical signals during emotional expression, has been extensively utilized. Current technology heavily relies on datasets, with its performance being limited by the size of the dataset and the accuracy of its annotations. At the same time, unsupervised learning and contrastive learning methods largely depend on the feature distribution within datasets, thus requiring training tailored to specific datasets for optimal results. However, the collection of EEG signals is influenced by factors such as equipment, settings, individuals, and experimental procedures, resulting in significant variability. Consequently, the effectiveness of models is heavily dependent on dataset collection efforts conducted under stringent objective conditions. To address these challenges, we introduce a novel approach: employing a self-supervised pre-training model, to process data across different datasets. This model is capable of operating effectively across multiple datasets. The model conducts self-supervised pre-training without the need for direct access to specific emotion category labels, enabling it to pre-train and extract universally useful features without predefined downstream tasks. To tackle the issue of semantic expression confusion, we employed a masked prediction model that guides the model to generate richer semantic information through learning bidirectional feature combinations in sequence. Addressing challenges such as significant differences in data distribution, we introduced adaptive clustering techniques that manage by generating pseudo-labels across multiple categories. The model is capable of enhancing the expression of hidden features in intermediate layers during the self-supervised training process, enabling it to learn common hidden features across different datasets. This study, by constructing a hybrid dataset and conducting extensive experiments, demonstrated two key findings: (1) our model performs best on multiple evaluation metrics; (2) the model can effectively integrate critical features from different datasets, significantly enhancing the accuracy of emotion recognition.

In recent years, with the development of low-earth orbit broadband satellites, the combination of multi-path transmission and software-defined networking (SDN) for satellite networks has seen rapid advancement. The integration of SDN and multi-path transmission contributes to improving the efficiency of transmission and reducing network congestion. However, the current SDN controllers do not support the multi-path QUIC protocol (MPQUIC), and the routing algorithm used in current satellite networks based on minimum hop count struggles to meet the real-time requirements for some applications. Therefore, this paper designs and implements an SDN controller that supports the MPQUIC protocol and proposes a multi-objective optimization-based routing algorithm. This algorithm selects paths with lower propagation delays and higher available bandwidth for subflow transmission to improve transmission throughput. Considering the high-speed mobility of satellite nodes and frequent link switching, this paper also designs a flow table update algorithm based on the predictability of satellite network topology. It enables proactive rerouting upon link switching, ensuring stable transmission. The performance of the proposed solution is evaluated through satellite network simulation environments. The experimental results highlight that SDN-MPQUIC significantly improves performance metrics: it reduces average completion time by 37.3% to 59.3% compared to QSMPS and by 52.8% to 72.4% compared to Disjoint for files with different sizes. Additionally, SDN-MPQUIC achieves an average throughput improvement of 81.4% compared to QSMPS and 147.8% compared to Disjoint, while demonstrating a 26.3% lower retransmission rate than QSMPS.