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Underwater object detection is a fascinating but challengeable subject in computer vision. Features are difficult to extract due to the color cast and blur of underwater images. Moreover, given the small scale of the underwater object, some details will be lost after several layers of convolution. Therefore, a multi-scale aggregation feature pyramid network is proposed to integrate multi-scale features and improve underwater object detection performance. Specifically, a lightweight and efficient network is used to extract the basic features. A special subnet is designed to improve the feature extraction capability of the backbone network to enrich the detailed features of small underwater objects. In addition, a multi-scale feature pyramid is proposed to enrich feature map. Each feature map enhances contextual information through a combination of up-sampling and down-sampling. The centerness strategy of the fully convolutional one-stage object detection head is improved by adding corner point regression to enhance the recall rate of small objects. Generalized intersection over union (GIoU) instead of IoU can better reflect the degree of coincidence between the actual box and the predicted box. Therefore, the regression loss is changed to GIoU loss. This paper evaluates the network on the underwater image dataset and obtains 78.90% mAP. Meanwhile, the experiment on the PASCAL VOC datasets is conducted and gets 84.3% mAP. [ABSTRACT FROM AUTHOR]

In order to address the problem of low efficiency and accuracy of artificial visual inspection for the quality of toy sets, this paper investigates the defect detection of toy sets based on machine vision. Firstly, an improved U-Net (IU-Net) is developed by introducing the Squeeze-and-Excitation (SE) block into the U-Net and modifying its objective function. Then, a convolutional neural network (CNN) is constructed for feature extraction and defect matching. Finally, a defect detection model for toy sets is created by integrating these two models. In addition, when it comes to a new-variety toy set, the IU-Net model is trained through transfer learning with a small number of new-variety data samples. This can reduce the dependence on new-variety data samples and improve the efficiency of model training process. The experimental results show that the defect detection accuracy of the proposed method is 100% and 99.43% for the whole toy set and single component, respectively, which meets the requirements of industrial automation quality inspection of toy sets. [ABSTRACT FROM AUTHOR]

The geometric distortion in panoramic images significantly mediates the performance of saliency detection method based on traditional CNN. The strategy of dynamically expanding convolution kernel can achieve good results, but it also produces a lot of computational overhead in the process of reading the adjacency list, which decreases the computational efficiency. The appearance of graph convolution provides a new way to solve such problems. Although using graph convolution can effectively extract the structural features of the graph, it reduces the accuracy of the model resulting from ignoring the spatial features of the image signal. To this end, this paper proposes a construction method of the multi-scale graph structure of the panoramic image and a panoramic image saliency detection model composed of an image saliency feature extraction network and multi-scale saliency feature fusion network combining the image structure information and spatial information in the panoramic image. First, we establish a graph structure consisting of root and leaf nodes obtained by super-pixel segmentation at different scales and spherical Fibonacci sampling, respectively. Then, a feature extraction network composed of two graph convolution layers and two one-dimensional auto-encoders with the same parameterization is used to extract the salient features of the multi-scale graph structure. Finally, the U-Net network fuses the multi-scale saliency features to get the final saliency map. The results show that the proposed model performs better than the state-of-the-art models in terms of calculation speed and accuracy. [ABSTRACT FROM AUTHOR]

Micro-expression is an expression that reveals one's true feelings and can be potentially applied in various domains such as healthcare, safety interrogation, and business negotiation. The micro-expression recognition is thus far being judged manually by psychologists and trained experts, which consumes a lot of human effort and time. Recently, the development of the deep learning network has proven promising performance in many computer vision-related tasks. Amongst, micro-expression recognition adopts the deep learning methodology to improve the feature learning capability and model generalization. This paper introduces a lightweight apex-based enhanced network that improves by extending one of the state-of-the-art, shallow triple stream three-dimensional CNN. Concretely, the network is first pre-trained with a macro-expression dataset to encounter the small data problem. The features were extracted from CASME II, SMIC, and SAMM datasets. Moreover, thorough recognition results comparison of the datasets are the optical flow-guided features. Besides, an eye masking technique is introduced to reduce noise interference such as eye blinking and glasses reflection issues. The results obtained have an accuracy of 79.19 % and an F1-score of 75.9 % . Comprehensive experimentation had been conducted on the composite dataset that consists of is provided by comparing it with recent methods. Detailed qualitative and quantitative results are reported and discussed. [ABSTRACT FROM AUTHOR]

The traditional local binary pattern (LBP) is susceptible to the influence of the centre pixel and noise and cannot accurately identify wire rope surface defects. To solve this problem, an image segmentation-based central multiscale local binary pattern (ISCM-LBP) and grey level cooccurrence matrix (GLCM) feature fusion method is proposed in this paper for defect recognition. Image segmentation and multiple scales are introduced into the local binary pattern algorithm to improve the image detail description and suppress noise sensitivity. Second, the centre pixel is connected with the neighbourhood pixel to enhance the robustness of the centre pixel. To further improve the image integrity description, PCA dimensionality reduction and GLCM feature fusion are performed on the features extracted by the ISCM-LBP algorithm, and the steel wire rope surface defects are identified by a support vector machine classifier. Experimental results show that the overall recognition rate reaches 97.5%, which is at least 5% higher than that of other algorithms and can effectively identify various defects on the surface of wire rope. [ABSTRACT FROM AUTHOR]

Microgrids are essential for developing the future energy systems. Microgrids can be utilized in grid-connected or island mode, enabling increased integration of renewable energy sources into a power system. However, due to the increased penetration of converter-based renewable energy sources, the quality of power in microgrids may be adversely affected. Therefore, finding an appropriate technique to classify and detect islanding and non-islanding events in microgrids is one of the major challenges associated with the design of renewable energy sources. This paper presents a new hybrid approach by using wavelet transform, Stockwell transform and residual neural networks for classification and detection of islanding and non-islanding events. The proposed hybrid approach consists of two main stages: in the first stage, optimum feature images of islanding and non-islanding events are obtained by performing wavelet transform and Stockwell transform. In the second stage, a residual neural network model which is fine-tuned with optimal hyperparameters is determined in order to detect and classify islanding and non-islanding events. Thus, feature image data obtained from microgrid test model are structured as input to residual neural network for classifying of islanding and non-islanding events. By employing the hybrid signal processing approaches with deep learning-based residual neural networks, the validation accuracy of 99.40% is obtained for islanding and non-islanding events with average detection time of 0.1260 s. The effectiveness of the hybrid approach was evaluated through comparative analysis with the results obtained for normal and noisy environments in approaches used by similar studies presented in the literature. Unlike traditional passive island detection techniques, the proposed deep learning-based hybrid approach is considered to have superior performance due to its more dynamic behavior. [ABSTRACT FROM AUTHOR]

Based on the training effect evaluation of swimmers, this paper proposes an infrared image detection method based on HOG feature extraction and infrared image detection technology. Since optical images are easily affected by illumination and other factors in complex environments, infrared images are chosen as the object of training effect evaluation in this study. Infrared images have the advantages of illumination insensitivity, through fog and so on, which can better reflect the posture and action of swimmers. The infrared image detection method based on HOG feature extraction is described in detail, and the feature vector of swimmers is obtained. By detecting the infrared images of the training set and the test set, the evaluation results of the training effect of the swimmers are obtained. The experimental results show that this method can accurately detect the swimmer's posture and movement, and provides an effective means for evaluating the training effect. [ABSTRACT FROM AUTHOR]

Infrared image technology has the advantages of high sensitivity and non-contact, which can be used to detect athletes' defects and improve the teaching quality and effect. This paper explores infrared image defect detection method based on data information visualization technology, adopts infrared image technology to obtain athletes image data, and uses data information visualization technology to process and analyze the image data. Firstly, the image data is preprocessed, and then the key features of athletes are extracted and defect detection is carried out by using image processing algorithm and pattern recognition algorithm. Finally, the test results are displayed visually, so that teachers and students can intuitively understand the athletes' defects. The research shows that the infrared image defect detection method based on data information visualization technology has achieved good results in physical education teaching. By processing and analyzing the infrared images of athletes, the defects of athletes can be accurately detected, and effective guidance and feedback can be provided for teachers. [ABSTRACT FROM AUTHOR]

The convolutional neural network has been proven to be a robust recognition and diagnosis model for modelling diseases. In general, a convolutional neural network consists of feature learning which is considered a vital process in terms of feature extraction and classifier algorithms used for decision-making. In recent years, various pooling operations considered the spine of the feature learning layer have been developed in the literature; unfortunately, few of them can be spread to tackle the overfitting problem. In this paper, a novel pooling approach termed whole image average pooling is proposed to hold over the most significant feature information on the learning layer. The proposed method consists of two main stages: in the first stage, the average of all the values of the input image is calculated, in the second stage, the input image is divided into regions that represent the feature set of the input image, and then, the values of the regions are calculated. While obtaining the values of these features, the average of all values of the input image is taken into account. The proposed whole image average pooling approach addresses the overfitting problem by processing all pixel values in the image data and is used to get the important feature information from the medical datasets. To evaluate the performance of the proposed whole image average pooling, four clinically applicable tumour datasets as medical datasets have been trained and tested on seven convolutional neural network models AlexNet, VGG16, ResNet18, GoogleNet, ResNet50, R-CNN, and Faster R-CNN. The experimental outcomes demonstrate that the proposed whole image average pooling consistently outperformed the state-of-the-art pooling operation in terms of accuracy, sensitivity, specificity, positive predictive value, false-positive rate, and F1-measure evaluation criteria in three datasets. [ABSTRACT FROM AUTHOR]

Purpose: To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies. Methods: We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated. Result: In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community. Conclusion: In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers. Critical relevance statement: A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning. Key points: • A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. • The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. • METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. • A web application has been developed to help with the calculation of the METRICS score (https://metricsscore.github.io/metrics/METRICS.html) and a repository created to collect feedback from the radiomics community (https://github.com/metricsscore/metrics). [ABSTRACT FROM AUTHOR]

The pooling layer in convolutional neural networks plays a crucial role in reducing spatial dimensions, and improving computational efficiency. However, standard pooling operations such as max pooling or average pooling are not suitable for all applications and data types. Therefore, developing custom pooling layers that can adaptively learn and extract relevant features from specific datasets is of great significance. In this paper, we propose a novel approach to design and implement customizable pooling layers to enhance feature extraction capabilities in CNNs. The proposed T-Max-Avg pooling layer incorporates a threshold parameter T, which selects the K highest interacting pixels as specified, allowing it to control whether the output features of the input data are based on the maximum values or weighted averages. By learning the optimal pooling strategy during training, our custom pooling layer can effectively capture and represent discriminative information in the input data, thereby improving classification performance. Experimental results show that the proposed T-Max-Avg pooling layer achieves good performance on three different datasets. When compared to LeNet-5 model with average pooling, max pooling, and Avg-TopK methods, the T-Max-Avg pooling method achieves the highest accuracy on CIFAR-10, CIFAR-100, and MNIST datasets. [ABSTRACT FROM AUTHOR]

Infrared small and dim target has posed great challenges on the target detection and tracking, especially for early warning, navigation and guidance. However, most common tracking algorithms are inefficient for infrared small target due to huge computation, and complicated feature analysis on such target is useless and needless. As such, a fast and useful algorithm based on the kernel correlation filter (KCF) is proposed in this paper. It supplies a solution for target without clear characteristic, target missing and analog interference in complex backgrounds. The proposed algorithm focuses on model update and scale update rules, fusion feature expression and multi-cues constrained re-detection module; all of the ideas aim to optimize process and improve practicability. Firstly, the improved ridge regression model with solving boundary effect and model consistency can get more precise results. Secondly, a useful strengthened feature description (SFD) including the fused feature extraction and complete confidence indicator is proposed to improve the reliability of tracking. Thirdly, the stabilized re-detection (SR) strategy is designed based on motion rules, which can cope with abnormal disturbance and missing states. Fourthly, an adaptive model update is proposed, which can reduce the error accumulation. The experiment results and the comparative studies with some common algorithms show that the proposed algorithm achieves the success rate of tracking above 98%, RMSE of center location below 5 pixels and speed above 120 fps and tends to be more effective. [ABSTRACT FROM AUTHOR]

Temporal modelling is still challenging for action recognition in video. To alleviate this problem, this paper proposes a new video architecture, called Adaptive Region Aware (ARP) network. The network focuses on combining short-range temporal information with long-range temporal information to perform effective action recognition. The core of our ARP is the Movement and Spatial-Temporal module (MST), which is made up of two modules, movement information and Spatial-Temporal information. MST uses the idea of difference for short-range temporal information extraction and adaptive region sensing and temporal convolution for long-range temporal information extraction. To extract temporal information for the entire video, our MST module contains two branches. Specifically, for local temporal information, we use the difference in motion between successive frames to extract a fine-grained representation of the motion, thus obtaining short-range temporal information. For the global temporal information, we use adaptive region awareness for feature extraction of the whole video to enhance the representation of the model in the spatio-temporal domain, and use temporal convolution for modelling to obtain our long-range temporal information. We insert the MST module into ResNet-50 to build our ARP network and experiment on the Something V1, Something V2 and Kinetics-400 datasets with optimal performance. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) is a technology that recognizes human emotions based on biometric markers. Over the past decade, FER has been a popular research area, particularly in the computer vision community. With deep learning (DL) development, FER can achieve impressive recognition accuracy. In addition, favorable Internet-of-Things (IoT) advancements generate massive amounts of visual data needed to enable reliable DL-based emotion analysis. However, training DL models can suffer from significant memory consumption and computational costs, complicating many vision tasks. Additionally, the direct use of RGB images during the training and inference stages might raise privacy concerns in various FER applications. On the other hand, adopting large deep networks hampers quick and accurate recognition on resource-constrained end devices such as smartphones. As a viable solution, edge computing can be employed to bring data storage and computation closer to end devices rather than relying on a central cloud server. As a result, it can potentially facilitate the deployment of real-time FER applications since the latency and efficiency problems are well addressed by utilizing the computing resources at the edge. In this paper, we develop an efficient FER framework that integrates DL with edge computing. Our framework relies on facial landmarks to enable privacy-preserving and low-latency FER. Accordingly, various landmark detection models and feature types are studied empirically to investigate their capabilities in capturing the dynamic information of facial expressions in videos. Then, using the extracted landmark-based features, we design lightweight DL models to classify human emotions on IoT devices. Extensive experiments performed on benchmark datasets further validate the efficiency and robustness of our framework. [ABSTRACT FROM AUTHOR]

Fast and accurate detection of traffic signs is crucial for the development of intelligent transportation systems. To address the issue of false detection and missing detection of small traffic signs in complex scenes, this paper proposes a YOLO-SG model based on YOLOv5. The YOLO-SG approach employs SPD-Conv as a down-sampling structure to mitigate the loss of feature information during the down-sampling process. This enhances the detection performance of small objects in complex scenes and improves the generalization and robustness of the model. The feature extraction architecture uses GhostNet, which effectively reduces the number of model parameters and weight, enhancing the feasibility of practical model deployment. Furthermore, this study optimizes the output feature structure by introducing a small object detection layer and removing the large object detection layer, enabling the detection of small objects. Extensive experiments conducted on the GTSDB and TT100K datasets demonstrate that YOLO-SG exhibits excellent detection performance. On the GTSDB dataset, YOLO-SG achieved a 2.3% increase in mAP compared to the baseline network, while reducing the number of parameters by 42%. Similarly, on the TT100K dataset, YOLO-SG increased mAP by 6.3% and reduced the number of parameters by 43%. These experimental results showcase the effectiveness and accuracy of YOLO-SG, particularly in detecting small traffic signs. [ABSTRACT FROM AUTHOR]

Traffic time series anomaly detection has been intensively studied for years because of its potential applications in intelligent transportation. However, classical traffic anomaly detection methods often overlook the evolving dynamic associations between road network nodes, which leads to challenges in capturing the long-term temporal correlations, spatial characteristics, and abnormal node behaviors in datasets with high periodicity and trends, such as morning peak travel periods. In this paper, we propose a mirror temporal graph autoencoder (MTGAE) framework to explore anomalies and capture unseen nodes and the spatiotemporal correlation between nodes in the traffic network. Specifically, we propose the mirror temporal convolutional module to enhance feature extraction capabilities and capture hidden node-to-node features in the traffic network. Morever, we propose the graph convolutional gate recurrent unit cell (GCGRU CELL) module. This module uses Gaussian kernel functions to map data into a high-dimensional space, and enables the identification of anomalous information and potential anomalies within the complex interdependencies of the traffic network, based on prior knowledge and input data. We compared our work with several other advanced deep-learning anomaly detection models. Experimental results on the NYC dataset illustrate that our model works best compared to other models for traffic anomaly detection. [ABSTRACT FROM AUTHOR]

SiamFC++ only extracts the object feature of the first frame as a tracking template, and only uses the highest level feature maps in both the classification branch and the regression branch, so that the respective characteristics of the two branches are not fully utilized. In view of this, the present paper proposes an object tracking algorithm based on SiamFC++. The algorithm uses the multi-layer features of the Siamese network to update template. First, FPN is used to extract feature maps from different layers of Backbone for classification branch and regression branch. Second, 3D convolution is used to update the tracking template of the object tracking algorithm. Next, a template update judgment condition is proposed based on mutual information. Finally, AlexNet is used as the backbone and GOT-10K as training set. Compared with SiamFC++, our algorithm obtains improved results on OTB100, VOT2016, VOT2018 and GOT-10k data sets, and the tracking process is real time. [ABSTRACT FROM AUTHOR]

Graphs are widely used to model real-life information, where graph similarity computation is one of the most significant applications, such as inferring the properties of a compound based on similarity to a known group. Definition methods (e.g., graph edit distance and maximum common subgraph) have extremely high computational cost, and the existing efficient deep learning methods suffer from the problem of inadequate feature extraction which would have a bad effect on similarity computation. In this paper, a double-branch model called DeepSIM was raised to deeply mine graph-level and node-level features to address the above problems. On the graph-level branch, a novel embedding relational reasoning network was presented to obtain interaction between pairwise inputs. Meanwhile, a new local-to-global attention mechanism is designed to improve the capability of CNN-based node-level feature extraction module on another path. In DeepSIM, double-branch outputs will be concatenated as the final feature. The experimental results demonstrate that our methods perform well on several datasets compared to the state-of-the-art deep learning models in related fields. [ABSTRACT FROM AUTHOR]

This paper introduces a novel method for satellite colour imagery retrieval, based on an adaptive Gaussian–Markov random field (AGMRF) model with the Bayes-driven deep convolutional neural network (AGMRF–BDCNN). The given input imagery is segregated into the structure, microstructure, and texture components, and the AGMRF-driven features and statistical features are extracted from the segregated components and are formulated as a feature vector of the query imagery. Cosine direction and Bhattacharyya distance measures are deployed to match the feature vector with the feature vector of the feature vector database. If the query imagery features match the feature-vector database's features, then the reference imagery in the database is marked and indexed. The indexed imageries are retrieved. Three different benchmark data sets, SceneSat, PatternNet, and UC Merced, have been used to validate the proposed AGMRF–BDCNN method. For the SceneSat data set, the AGMRF–BDCNN method results in 0.2319 scores for ANMRR and 0.7156 scores for mAP; for the UC Merced data set, it yields 0.2316 scores for ANMRR and 0.7816 scores for mAP; for PatternNet data set, it achieves 0.2405 scores for ANMRR and 0.6979 scores for mAP. The obtained results are comparable to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Stereo matching is a fundamental technique for many vision and robotics applications. State-of-the-art methods either employ convolutional neural networks with spatially-shared kernels or utilize content-dependent interactions (e.g., local or global attention) to augment convolution operations. Despite of great improvements being made, existing methods could either suffer from a high computational cost arising from global attention operations or a suboptimal performance at edge regions due to spatially-shared convolutions. In this paper, we propose a CoAtRS stereo matching method to exert the complementary advantages of convolution and attention to the full via region separation. Our method can adaptively adopt the most suitable feature extraction and aggregation patterns for smooth and edge regions with less computational cost. In addition, we propose D-global attention which performs global filtering on the disparity dimension to better fuse cost volumes of different regions and alleviate the locality defects of convolutions. Our CoAtRS stereo matching method can also be embedded conveniently in various existing 3D CNN stereo networks. The resulting networks can achieve significant improvements in terms of both accuracy and efficiency. Furthermore, we design an accurate network (named CoAtRSNet) which achieves the state-of-the-art results on five public datasets. At the time of writing, CoAtRSNet ranks 1st–3rd on all the metrics published on the ETH3D website, ranks 2nd on Scene Flow, and ranks 1st for the Root-Mean-Square metric, 2nd for the average error metric and 3rd for the bad 0.5 metric on the Middlebury benchmark. [ABSTRACT FROM AUTHOR]

This paper proposes an unconstrained human gaze estimation approach for medium-distance scene based on monocular vision. A recurrent convolution neural network for gaze estimation is designed to construct the mapping relationship from the face image to the 3D gaze vector in the camera space, so as to complete the estimation of the human gaze. Firstly, based on the VICON system and a monocular camera, the face image sequence and gaze vector sequence of human gaze behavior are collected in different situations of medium-distance scenes to construct the time-synchronized gaze estimation dataset. Secondly, the recurrent convolutional gaze estimation neural network is designed, and the head pose feature and eye appearance feature are extracted from single frame image with monocular vision image sequences as input. Thirdly, the extracted gaze features are fused in the spatial dimension and the temporal dimension to estimate the direction of the human gaze. Finally, the cross-validation and experimental evaluation of the estimation errors of the constructed gaze estimation model are carried out. The results show that the Cross-Person experimental average error is 7.65°, and the Person-Specific experimental error is 3.88°. Compared with the Gaze360 method, the proposed approach reduces the average evaluation error of the gaze angle by 5.0% in the Cross-Person experiments and 16.6% with the Person-Specific experiments. These experiments verify the effectiveness of the proposed approach, and it has better generalization ability and robustness. [ABSTRACT FROM AUTHOR]

Rolling bearings typically operate under time-varying conditions, which present challenges for fault diagnosis due to the presence of modulation effect and noise component in the bearing vibration signal. This paper introduces a novel feature extraction method that combines complementary ensemble empirical mode decomposition (CEEMD), Teager-Kaiser energy operator (TKEO), and self-organizing map (SOM) to enhance the identification accuracy of bearing characteristics by removing unwanted components from the vibration signal. Firstly, CEEMD and TKEO are used to generate demodulated instantaneous energies of the raw vibration signal and each decomposed mode, which represent bearing features. Next, relevant features are extracted by computing similarity distances using the SOM network. The feature signal is then reconstructed, and its power spectrum is used to evaluate the visualization of the characteristic frequencies of rolling bearings. The efficiency of the proposed method is confirmed through the analysis of simulated and experimental vibration signals collected from two types of bearings with different geometries. The results demonstrate that the proposed method can effectively suppress noise, extract characteristics, and identify the fault condition of rolling bearings compared to previous studies. [ABSTRACT FROM AUTHOR]

Text-to-image generation is a challenging task. Although diffusion models can generate high-quality images of complex scenes, they sometimes suffer from a lack of realism. Additionally, there is often a large diversity among images generated from different text with the same semantics. Furthermore, the generation of details is sometimes insufficient. Generative adversarial networks can generate realism images. These images are consistent with the text descriptions. And the networks can generate content-consistent images. In this paper, we argue that generating images that are more consistent with the text descriptions is more important than generating higher-quality images. Therefore, this paper proposes the pretrained model-based generative adversarial network (PMGAN). PMGAN utilizes multiple pre-trained models in both generator and discriminator. Specifically, in the generator, the deep attentional multimodal similarity model text encoder extracts word and sentence embeddings from the input text, and the contrastive language-image pre-training (CLIP) text encoder extracts initial image features from the input text. In the discriminator, a pre-trained CLIP image encoder extracts image features from the input image. The CLIP encoder can map text and images into a common semantic space, which is beneficial to generate high-quality images. Experimental results show that compared to the state-of-the-art methods, PMGAN achieves better scores on both inception score and Fréchet inception distance and can produce higher quality images while maintaining greater consistency with text descriptions. [ABSTRACT FROM AUTHOR]

Secure user authentication has grown importance in today’s modern culture. It is significant to authenticate the user identity in numerous consumer applications particularly financial transactions. Traditional authentication methods rely on easy-to-guess passwords, PIN numbers, or tokens with several security flaws, such as those printed on the back of credit cards for PIN numbers. As an alternative to current systems, biometric authentication techniques based on physical and behavioral characteristics have been proposed. Multibiometric systems, which combine several biometrics, are developed as a result of the difficulties that single-biometric authentication systems encountered in real-world applications including lack of precision and noisy data. The proposed system provides better performance and greater accuracy compared with other authentication techniques. The majority of them is inconvenient and demand complicated user interactions. This paper proposes Enhanced Elman Spike Neural Network along Glowworm Swarm Optimization (EESNN-GSO-AMT) for Multiple Transaction Authentication. The images are collected via SDUMLA-HMTalong CASIA V5 dataset. The pictures are provided to pre-processing to enhance the images quality utilizing Learnable Edge Collaborative Filter (LECF). The preprocessed images are fed to feature extraction using Adaptive and concise empirical wavelet transform (ACEWT) and the features are extracted such as entropy, homogeneity, energy and contrast. The extracting features are provided to EESNN classifier to categorize authorized or unauthorized persons. In general, the EESNN classifier does not express adapting optimization methods to determine ideal parameters to ensure accurately. Therefore, it is proposed to utilize the Glowworm Swarm Optimization to enhanceEESNN, which accurately categorizes the authorized and unauthorized person. The efficiency of the proposed approach is assessed usingsome metrics. The proposed EESNN-GSO-AMT method attains higher accuracy 20.54%, 21.76% and 23.89%; greater sensitivity 20.12% 20.34% and 21.43%; higher precision 23.34%, 22.68% and 24.34% are analyzed to the existing methods, like Optimal feature level fusion for safe human authentication in multimodal biometric scheme (OptGWO-AMT-FV), Joint attention network for finger vein authentication (JAnet-AMT-FV), Finger Vein Recognition Utilizing Deep Learning Technique (DCNN-AMT-FV) respectively. [ABSTRACT FROM AUTHOR]

In recent years, the incorporation of sensing technology into residential buildings has given rise to the concept of ”smart buildings”, aimed at enhancing resident comfort. These buildings are typically part of interconnected neighborhoods sharing common energy sources, which makes the energy consumption a critical consideration in decision-making processes. Consequently, optimizing energy usage in smart buildings has posed significant challenges for both enterprises and governments, prompting numerous studies to address this issue. One such challenge is organizing energy usage within neighborhood networks while ensuring the user comfort and without exceeding the total energy capacity. In this paper, we present a novel mechanism that predicts the future behavior of each house based on its historical consumption data, generating a weekly schedule annotated with hourly energy usage levels (high, normal, or low) tailored to individual user needs. Additionally, we introduce an incentive-based program that rewards users with bill discounts for adhering to high energy consumption periods. The scheduling process involves extracting features from data and utilizing a genetic algorithm for construction, coupled with dynamic programming to enhance efficiency by storing house features and schedules. This enables rapid provision of suitable schedules for similar houses. Evaluation results demonstrate that the proposed technique achieves an accuracy of 92%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$92\%$$\end{document} and improves the execution time of the optimization algorithm by 26%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$26\%$$\end{document}. [ABSTRACT FROM AUTHOR]

This paper provides a framework based on machine learning approach in I4.0 environment to predict the remaining useful life of an aviation engine. For illustration purpose, an industrial case study is presented which applies machine learning algorithms to analyze the data collected using the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) simulation which includes run-to-degradation data for a turbofan engine. The results obtained from the study validate the proposed framework to identify prominent features and perform sequential analysis on unstructured data for predicting the remaining useful life of an aviation engine. Six machine learning models are applied to the dataset containing four subsets: FD001, FD002, FD003 and FD004 in C-MAPSS dataset each working on different degradation conditions for turbofan engine. For FD001, random forest had the lowest RMSE (11.59), and for FD002, FD003 and FD004, the lowest RMSE was given by LGBM classifier (12.78, 7.95 and 11.04), respectively. From the findings, it is observed that LGBM performs better with higher AUC 89% and lowest RMSE. The proposed framework can be applied to a wide range of failure prediction applications. Regardless of the underlying physics, ML-based data-driven methodologies can be used to analyze a wide range of systems. [ABSTRACT FROM AUTHOR]

Traditional methods for unsupervised cross-domain classification learn a common low-dimensional subspace using images from a well-labeled source domain and an unlabeled target domain. To achieve domain-invariant feature extraction, researchers typically focus on minimizing the distribution discrepancy. However, these methods often overlook the fact that label information contains critical categorization information for discriminative subspace learning. This paper proposes a novel method, named domain-invariant feature learning with label information integration (DILI), which integrates metric learning and label information extraction to learn a cross-domain discriminant subspace. DILI first reduces the distances between the source and target domains to mitigate the marginal distribution discrepancy. Then, it reduces the distances between cross-domain samples from the same class to mitigate the conditional distribution discrepancy. Dual terms are imposed to balance the label information of both domains to learn common features, and a discriminant subspace is learned for cross-domain tasks. This method obtains domain-invariant features, while the label information of the source domain and pseudo labels from the target domain are used to improve the discriminant of subspaces. Experimental results on eight cross-domain datasets show that DILI outperforms some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The rolling elements of the induction motor are highly susceptible to faults. The detection and diagnosis of rolling element faults are accurate and reliable only when the extracted features are accurate. The paper proposes an approach for bearing and rotor fault diagnosis using deep optimal feature extraction and selection based on vibration signal analysis. The deep feature extraction is done using an ensemble deep models features extraction approach in which features are extracted from seven pretrained models are fused serially using serial-based feature fusion technique. This leads to a solution for a higher efficacy model, but at the cost of high processing time as the feature data set gets large. A unique approach termed Ensemble Feature Selection has been developed to address this issue and limit the harmful impact of unwanted features in data-driven diagnostics. The processing time is further reduced using the shallow classifier at the fully connected layer. The proposed model is tested using the data acquired in the laboratory and validated using the available online benchmark data sets. [ABSTRACT FROM AUTHOR]