Network Classifier Research Articles

Ensemble graph neural networks for structural classification of HIV inhibiting molecules

Ensemble graph neural networks for structural classification of HIV inhibiting molecules

STCM-GCN: a spatial-temproal prediction method for traffic crashes under road network constraints

Existing research faces challenges in accurately predicting crashes due to the unreasonable selection of spatial units, biased crash data collection, and insufficient integration of multi-source data. To address these issues, Graph Neural Networks (GNNs) for node classification are employed to predict crashes at macroscopic road level. Crash alarm data are incorporated as a supplement to official archive data to ensure the spatial–temporal distribution's authenticity and to mitigate data sparsity. Additionally, traffic violation data are included as a feature to enrich risk information. Finally, a multi-graph deep learning framework (STCM-GCN) with spatial, temporal, and spatial–temporal modules has been developed. Data from Shenzhen, China, demonstrates that the STCM-GCN outperforms baseline models and has a reasonable structure. The inclusion of crash alarm data and traffic violations contributes to performance improvement. Additionally, the model exhibits spatial–temporal robustness, and the analysis of computational efficiency provides comprehensive insights into the model's capabilities.

An adaptive assessment method of power system transient stability considering PMU data loss

AbstractTransient stability assessment (TSA) plays an important role in ensuring the reliable operation of power systems. With the popularity of phasor measurement units (PMUs), data‐driven TSA methods have been widely concerned. However, the performance of TSA model may deteriorate when data loss occurs due to PMU failure. This paper proposes an adaptive assessment method for transient stability of power systems considering PMU data loss. First, considering the importance of temporal features, a collection of PMU clusters is constructed to minimize the failure risk and maintain full observability of the whole buses of the grid. Secondly, a weighted integrated assessment model based on PMU clusters is constructed by using an improved eXplainable Convolutional neural network for Multivariate time series classification (XCM) as a TSA classifier. The model can make full use of time series information to carry out adaptive TSA and maintain the robustness of the assessment performance even when PMU failure occurs. Finally, it is verified in a modified IEEE 39‐bus system with wind power and solar power. The effect of the proposed method shows high accuracy and strong anti‐noise interference ability in case of data loss.

Designing a novel framework of email spam detection using an improved heuristic algorithm and dual-scale feature fusion-based adaptive convolution neural network

ABSTRACT Nowadays, e-mail is becoming more prevalent among individuals. In recent days, it has been declared to be the least expensive and quickest mode of communicating. In recent years, e-mail spam has become a big problem, so the number of e-mail spam has also increased, and they are used for unethical and illegal conduct, scams, and theft. As a result, the main objective of the study is to detect e-mail spam using an adaptive deep-learning model with feature fusion. Initially, the input image and text are collected from benchmark datasets. Further, the raw text undergoes the pre-processing stage, and it is then followed by extracting the features using Bidirectional Encoder Representations from Transformers (BERT). Similarly, the Vision Transformer (ViT) is employed for extracting the features from raw images. Finally, these features are given in Dual Scale Feature Fusion-based Adaptive Convolutional Neural Network (DFF-ACNNet), and it is then fused for performing the classification in Convolutional Neural Network (CNN). Here, the hyper-parameters are tuned by using Modified Squid Game Optimizer (MSGO). Finally, the evaluation is done with various metrics to estimate the performance of the model and the accuracy of the developed system is 98.46, which is higher than the other conventional approaches.

Time-frequency transformation integrated with a lightweight convolutional neural network for detection of myocardial infarction

Myocardial infarction (MI) is a life-threatening medical condition that necessitates both timely and precise diagnosis. The enhancement of automated method to detect MI diseases from Normal patients can play a crucial role in healthcare. This paper presents a novel approach that utilizes the Discrete Wavelet Transform (DWT) for the detection of myocardial signals. The DWT is employed to break down ECG signals into distinct frequency components and subsequently to selectively filter out noise by thresholding the high-frequency details, resulting in denoised ECG signals for myocardial signal detection. These denoised signals are fed into lightweight one-dimensional Convolutional Neural Networks (CNN) for binary classification into Myocardial Infarction (MI) and Normal categories. The paper explores three distinct approaches: utilizing all signals, incorporating under-sampling and up-sampling to address class imbalances, with both noised and denoised signals. Evaluation of the suggested model is done with the help of two publicly available datasets: PTB-XL, a large publicly available electrocardiography dataset and PTB Diagnostic ECG Database. Results obtained through 5-fold cross-validation on the trained model show that the model has achieved an accuracy of 96%, precision of 97% and F1 score of 95%. On cross-validation with the PTB-ECG dataset, this paper achieved an accuracy of 91.18%.

DeepYoga: Enhancing Practice with a Real-Time Yoga Pose Recognition System

The adoption of yoga as a holistic wellness practice is increasing throughout the world. However, in the absence of a personalized expert, especially in an online environment, there is a need for reliable and accurate methods for yoga posture recognition. Maintaining correct yoga postures is essential to reap holistic health benefits in the long term and address chronic medical issues. This paper presents DeepYoga, a novel approach to improve posture recognition accuracy with the support of deep learning models. The proposed approach uses a dataset of accurate yoga pose images encompassing five distinct poses. Landmarks extracted from the practitioner's body in the images are then used to train a Convolutional Neural Network (CNN) for accurate pose classification. The trained model is then used to detect yoga poses from real-time videos of yoga practitioners. Then, the system provides users with real-time feedback and visual suggestions, helping them improve physical alignment and reduce the risk of injury. The proposed method achieved an overall high accuracy of 99.02% in pose detection while trying to minimize the use of resources as much as possible to make it more accessible.

Uncertainty bidirectional guidance of multi-task mamba network for medical image classification and segmentation

Uncertainty bidirectional guidance of multi-task mamba network for medical image classification and segmentation

Generative Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease

This study reviews the recent progress of generative artificial intelligence for gastrointestinal disease (GID) from detection to diagnosis. The source of data was 16 original studies in PubMed. The search terms were ((gastro* [title]) or (endo* [title])) and ((GAN [title/abstract] or (transformer [title/abstract]). The eligibility criteria were as follows: (1) the dependent variable of gastrointestinal disease; (2) the interventions of generative adversarial network (GAN) and/or transformer for classification, detection and/or segmentation; (3) the outcomes of accuracy, intersection of union (IOU), structural similarity and/or Dice; (3) the publication period of 2021–2023; (4) the publication language of English. Based on the results of this study, different generative artificial intelligence methods would be appropriate for different tasks for the early diagnosis of gastrointestinal disease. For example, patch GAN (accuracy 91.9%) in the case of classification, bi-directional cycle GAN (structural similarity 98.8%) in the case of data generation and semi-supervised GAN (Dice 89.4%) in the case of segmentation. Their performance indicators reported varied within 87.1–91.9% for accuracy, 83.0–98.8% for structural similarity and 86.6–89.4% for Dice. Likewise, vision transformer (accuracy 96.9%) in the case of classification, multi-modal transformer (IOU 79.5%) in the case of detection and multi-modal transformer (Dice 89.5%) in the case of segmentation. Their performance measures reported registered a variation within 85.7–96.9% for accuracy, 79.5% for IOU and 77.8–89.5% for Dice. Synthesizing different kinds of generative artificial intelligence for different kinds of GID data would further the horizon of research on this topic. In conclusion, however, generative artificial intelligence provides an effective, non-invasive decision support system for the early diagnosis of gastrointestinal disease from detection to diagnosis.

Non-target feature filtering for weakly supervised semantic segmentation

Weakly supervised semantic segmentation (WSSS) utilizes weak labels to learn semantic segmentation models, significantly reducing reliance on pixel-level annotations. WSSS typically employs a multi-label classification network to extract image features for constructing localization maps. The quality of the localization map critically influences the performance of WSSS. However, non-target semantic noise within the features impedes the improvement of localization map quality. To address this issue, we propose a non-target feature filtering class activation mapping (NFF-CAM) for WSSS, which can reduce non-target semantic signals and generate higher-quality localization maps. Specifically, the class-constrained dual cosine clustering (CDCC) and channel identification (CI) modules are introduced in NFF-CAM. CDCC effectively addresses the issue of unsuitability in the clustering group relationships of the original features under specified class conditions. CI can efficiently identify channel features containing non-target semantic information. We conduct extensive evaluations of NFF-CAM on popular datasets, including PASCAL VOC 2012 and MS COCO 2014. Experimental results show that NFF-CAM can effectively improve the segmentation performance of off-the-shelf methods.

A relation network for plant disease detection based on fewshot learning

<span lang="EN-US">Accurate and timely disease detection remains a critical challenge in plant health management. Conventional methods often struggle to effectively differentiate between healthy and diseased plants, leading to compromised agricultural productivity and food security. In response to this pressing issue, this paper presents an innovative solution in the form of a novel Few-Shot Learning (FSL) classifier, based on Relation Network (RN) specifically designed for precise plant disease detection from limited image samples. Leveraging inherent relationships between samples, the proposed Relation Network for Plant Disease Classification (RN-PDC) enhances the detection performance by capturing intricate patterns within the data. Through comprehensive evaluation on a public image data subset, RN-PDC achieves exceptional detection accuracies of 0.9984 and 0.9967 in binary and multiclass classifications, respectively. This advancement holds great promise for revolutionizing disease diagnosis in the field of plant health, ultimately fostering more productive and sustainable agricultural practices.</span>

Hybrid improved fuzzy C-means and watershed segmentation to classify Alzheimer’s using deep learning

Brain damage and deficits in interactions among brain cells are the primary causes of dementia and Alzheimer’s disease (AD). Despite ongoing research, no effective medications have yet been developed for these conditions. Therefore, early detection is crucial for managing the progression of these disorders. In this study, we introduce a novel tool for detecting AD using non invasive medical tests, such as magnetic resonance imaging (MRI). Our method employs fuzzy C-means clustering to identify features that enhance image accuracy. The standard fuzzy C-means algorithm has been augmented with fuzzy components to improve clustering performance. This enhanced approach optimizes segmentation by extracting image information and utilizing a sliding window to calculate center coordinates and establish a stable group matrix. These critical features are subsequently integrated with a two-phase watershed segmentation process. The resulting segmented images are then used to train an optimal convolutional neural network (CNN) for AD classification. Our methodology demonstrated a 98.20% accuracy rate in the detection and classification of segmented MRI brain images, highlighting its efficacy in identifying disease types.

Unveiling DNA Sequences: A Comparison of Machine Learning and Deep Learning Techniques for Prediction

<div align="center"><table width="590" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>DNA is the biological macromolecule unit that carries the information of all protein, amino acid sequences. With the help of this protein sequence, we explore the mutated gene and disease-causing mutated genomic pattern. Currently, the progression of genomic innovation is the source of DNA arrangement information developing at a dangerous rate—external factors have stimulated the volume of research into DNA genomes. Initially, the development process of DNA sequencing is accomplished with the support of the Database, data structures, and sequence similarity. The method is capable of extracting a particular property in DNA. We employ the deep learning algorithm to pull out protein sequences' features. The DNA sequence is classified based on the in-build protein structures extracted into the Fasta file. Therefore, the DNA sequence of E.coli with 106 data sets and 57 nucleotides is tested experimentally. Finally, we compared the results with the existing decision tree algorithm, KNN- Classification, random forest, and neural networks. The deep learning algorithm yields higher efficiency of 98% compared to other machine learning algorithms. This highlights the potential of deep learning in genomics research and its ability to yield superior results in classifying DNA sequences.</p></td></tr></tbody></table></div>

Predicting Electricity Consumption using an Innovative Deep Energy Predictor Model for Enhanced Accuracy and Efficiency

Accurate forecasting of electricity consumption is crucial for efficient energy management and planning. Traditional models often struggle to capture the complex, nonlinear relationships inherent in energy consumption data, leading to less reliable predictions. In the quest to improve the accuracy and efficiency of electricity consumption predictions, we introduce the Deep Energy Predictor Model (DEPM). This innovative hybrid model combines the strengths of XGBoost for classification and Deep Neural Networks (DNN) for deep learning (DL) capabilities. The model leverages advanced feature extraction techniques using Cascaded ResNet, ensuring the capture of intricate patterns within the data. Implemented in Python, the DEPM highlights an impressive accuracy of 98%, with a precision of 0.97, recall of 0.99, and an F1-score of 0.98, setting a new standard for predictive models in the energy sector. This study elaborates on the model architecture, implementation details, and the evaluation metrics that underscore the model's superior performance. Our results demonstrate the potential of DEPM to significantly enhance the reliability of electricity consumption forecasts, providing a robust tool for energy management and planning.

Seeding precision: a mask region based convolutional neural networks classification approach for the classification of paddy seeds

The generation of sufficient training data that is accurately labelled for a deep neural network involves a significant amount of effort and frequently constitutes a bottleneck in the implementation process. For the purpose of this research, we are training a neural network model to perform instance segmentation and classification of crop seeds for various rice cultivars. Synthetically constructed dataset is used here. The concept of domain randomization, which offers a productive alternative to the laborious process of data annotation, serves as the basis for our methodology. We make use of the domain randomization technique in order to produce synthetic data, and the mask region-based convolutional neural network (Mask R-CNN) architecture is utilized in order to train our neural network models. A cultivar name is used to designate the seeds, and they are differentiated from one another using colors that are comparable to those used in the actual dataset of paddy cultivars. Our mission focuses on the identification and categorization of rice paddy varieties within automatically generated photographs. Farmers are able to accurately sort crop seeds from a variety of rice cultivars with the use of this approach, which is particularly useful for phenotyping and optimizing yields in laboratory settings.

Improving baseline reduction for emotion recognition based on electroencephalogram signals

The baseline reduction method has been widely used to define electroencephalogram (EEG) signal patterns. However, because the baseline signal in this approach contains artifacts, the baseline reduction approach cannot perform optimally. As a result, decreasing artifacts in the baseline signal is critical. The mean, Gaussian, and Savitzky-Golay filters will be compared in this study to minimize artifacts in the baseline signal. Three secondary datasets are utilized to evaluate these approaches' capacity to remove artifacts. These three strategies are also tested with the convolution neural network classification algorithms. When applied to the dataset for emotion analysis using physiological signals (DEAP) and a dataset for multimodal research of affect, personality traits, and mood on individuals and groups (AMIGOS) datasets, the mean filter can increase baseline reduction performance based on twenty-four test scenarios. On the data readiness for machine learning research (DREAMER) dataset, however, the Gaussian filter is preferable. The relative difference approach was employed in this study's baseline reduction process to generate EEG signal patterns that are easy to recognize throughout the classification phase, which impacts increasing accuracy.

Comparative exploration of deep convolutional neural networks using real-time endoscopy images

Until now various deep convolutional neural networks are designed and trained for the purpose of classifying different medical conditions related to the domain of gastroenterology. Most of the study carried out have considered publicly available datasets to train the classification networks. Nevertheless, the main motive for carrying out different works in the field gastroenterology is to administer the developed models in healthcare centers in real-world set-ups. For doing so, it is important to check the generalizing ability of the designed systems by regulating them so as to classify endoscopy images captured in a specific hospital. In this regard, the foremost work completed is the collection of the endoscopy data from the hospital and then correctly annotating the images taking the help of a senior endoscopist with experience of more than 5 years. Once the data annotation is completed, the images are segregated into the class of normal and abnormal endoscopy images. Four different models are designed in the current work based on deep learning models, transfer learning models and ensemble approaches, and trained to classify the hospital endoscopy data as normal or abnormal. The models are then tested and evaluated based on various performance measures. It is observed from the comparative analysis that the transfer learning-based ensemble model has the best generalizing ability and gives the best specificity of 100 ​%. It is believed that deep learning-based models can assist endoscopists in add-on to human prediction efficiency.

Learning From Human Attention for Attribute-Assisted Visual Recognition.

With prior knowledge of seen objects, humans have a remarkable ability to recognize novel objects using shared and distinct local attributes. This is significant for the challenging tasks of zero-shot learning (ZSL) and fine-grained visual classification (FGVC), where the discriminative attributes of objects have played an important role. Inspired by human visual attention, neural networks have widely exploited the attention mechanism to learn the locally discriminative attributes for challenging tasks. Though greatly promoted the development of these fields, existing works mainly focus on learning the region embeddings of different attribute features and neglect the importance of discriminative attribute localization. It is also unclear whether the learned attention truly matches the real human attention. To tackle this problem, this paper proposes to employ real human gaze data for visual recognition networks to learn from human attention. Specifically, we design a unified Attribute Attention Network (A 2Net) that learns from human attention for both ZSL and FGVC tasks. The overall model consists of an attribute attention branch and a baseline classification network. On top of the image feature maps provided by the baseline classification network, the attribute attention branch employs attribute prototypes to produce attribute attention maps and attribute features. The attribute attention maps are converted to gaze-like attentions to be aligned with real human gaze attention. To guarantee the effectiveness of attribute feature learning, we further align the extracted attribute features with attribute-defined class embeddings. To facilitate learning from human gaze attention for the visual recognition problems, we design a bird classification game to collect real human gaze data using the CUB dataset via an eye-tracker device. Experiments on ZSL and FGVC tasks without/with real human gaze data validate the benefits and accuracy of our proposed model. This work supports the promising benefits of collecting human gaze datasets and automatic gaze estimation algorithms learning from human attention for high-level computer vision tasks.

Ensemble transfer learning networks for disease classification from retinal optical coherence tomography images

Ensemble transfer learning networks for disease classification from retinal optical coherence tomography images

Lightweight convolutional neural network for chest X-ray images classification

In this study, we developed a lightweight and rapid convolutional neural network (CNN) architecture for chest X-ray images; it primarily consists of a redesigned feature extraction (FE) module and multiscale feature (MF) module and validated using publicly available COVID-19 datasets. Experiments were conducted on multiple updated versions of the COVID-19 Radiography Database, a publicly accessible dataset on Kaggle. The database contained images categorized into three classes: COVID-19 coronavirus, viral or bacterial pneumonia, and normal. The results revealed that the proposed method achieved a training accuracy of 99.85% and a validation accuracy of 96.28% when detecting the three classes. In the test set, the optimal results were 96.03% accuracy for COVID-19, 97.10% accuracy for viral or bacterial pneumonia, and 97.86% accuracy for normal individuals. By reducing the computational requirements and improving the speed of the model, the proposed method can achieve real-time, low-error performance to help medical professionals with rapid diagnosis of COVID-19.

Fuzzy-ER Net: Fuzzy-based Efficient Residual Network-based lung cancer classification

Globally, Lung Cancer (LC) continues to be the primary cause of cancer-related death. Effective diagnosis is essential to save the lives of people. Nevertheless, manual Computed Tomography (CT) scan analysis takes more time and is inaccurate. The principal intention of this paper is to establish a hybrid Fuzzy-based Efficient Residual Network (Fuzzy-ER Net) for LC classification. The prime phase is the acquisition of input CT images from the database and the obtained CT image is sent to the pre-processing stage where noise is eradicated utilizing a Double bilateral filter. Thereafter, segmentation of the lung lobe is done by using a Dual-Attention V-network (DAV-Net). Moreover, feature extraction is performed, where features that are extracted include area, irregularity index, Local Vector Pattern (LVP), Local Gabor XOR Pattern (LGXP), and Statistical Fuzzy Local Binary Pattern (SFLBP). Eventually, LC classification is done by utilizing the proposed hybrid Fuzzy-ER Net. Here, the proposed Fuzzy-ER Net is newly devised by assimilating fuzzy concepts, EfficientNet, and Deep Residual Network (DRN). Additionally, the evaluation of the Fuzzy-ER Net on the basis of various metrics shows that it achieved maximum accuracy, True Positive Rate (TPR), of 93.2 % and 94.8 %, minimum False Positive Rate (FPR) is 5.7 %, maximum precision of 92.6 %, and maximum F-measure of 93.7 %.