Detection Problem Research Articles

Machine learning model with output correction: Towards reliable bradycardia detection in neonates

Bradycardia is a commonly occurring condition in premature infants, often causing serious consequences and cardiovascular complications. Reliable and accurate detection of bradycardia events is pivotal for timely intervention and effective treatment. Excessive false alarms pose a critical problem in bradycardia event detection, eroding trust in machine learning (ML)-based clinical decision support tools designed for such detection. This could result in disregarding the algorithm’s accurate recommendations and disrupting workflows, potentially compromising the quality of patient care. This article introduces an ML-based approach incorporating an output correction element, designed to minimise false alarms. The approach has been applied to bradycardia detection in preterm infants. We applied five ML-based autoencoder techniques, using recurrent neural network (RNN), long-short-term memory (LSTM), gated recurrent unit (GRU), 1D convolutional neural network (1D CNN), and a combination of 1D CNN and LSTM. The analysis is performed on ∼440 hours of real-time preterm infant data. The proposed approach achieved 0.978, 0.73, 0.992, 0.671 and 0.007 in AUC-ROC, AUC-PRC, recall, F1 score, and false positive rate (FPR) respectively and a false alarms reduction of 36% when compared with methods without the correction approach. This study underscores the imperative of cultivating solutions that alleviate alarm fatigue and encourage active engagement among healthcare professionals.

Uncertainty Quantification for Scale-Space Blob Detection

We consider the problem of blob detection for uncertain images, such as images that have to be inferred from noisy measurements. Extending recent work motivated by astronomical applications, we propose an approach that represents the uncertainty in the position and size of a blob by a region in a three-dimensional scale space. Motivated by classic tube methods such as the taut-string algorithm, these regions are obtained from level sets of the minimizer of a total variation functional within a high-dimensional tube. The resulting non-smooth optimization problem is challenging to solve, and we compare various numerical approaches for its solution and relate them to the literature on constrained total variation denoising. Finally, the proposed methodology is illustrated on numerical experiments for deconvolution and models related to astrophysics, where it is demonstrated that it allows to represent the uncertainty in the detected blobs in a precise and physically interpretable way.

Adaptive target detection for an FDA-MIMO radar in a mainlobe deceptive jamming and a partially homogeneous noise

In this paper, we investigate the adaptive target detection problem for a collocated frequency diverse array multiple-input multiple-output (FDA-MIMO) radar in the presence of mainlobe deceptive jamming and partially homogeneous noise with an unknown covariance matrix and scaling factor. Precisely, we establish a received signal model that includes the true target and false counterparts echoes in the presence of Gaussian noise (it includes thermal noise, interference suppression, and clutter after range compensation.). Furthermore, we consider that the range information of the true target and mainlobe deceptive targets is not identical. Therefore, we project the corresponding transmit steering vectors onto different subspaces with known subspace matrices but unknown coordinates. On the other hand, the true and false targets' receive steering vectors are considered to be from the same subspace. Finally, following the generalized likelihood ratio test (GLRT), the Rao and the Wald criteria, we design three adaptive detectors with two-step criteria, i.e., TGLRT, TRao, and TWald. We note that the proposed detectors maintain a constant false alarm rate (CFAR) against the scaling factor and the noise covariance matrix. The extensive numerical simulation results have validated the effectiveness of the proposed detectors.

Backdoor DNFs

We introduce backdoor DNFs, as a tool to measure the theoretical hardness of CNF formulas. Like backdoor sets and backdoor trees, backdoor DNFs are defined relative to a tractable class of CNF formulas. Each conjunctive term of a backdoor DNF defines a partial assignment that moves the input CNF formula into the base class. Backdoor DNFs are more expressive and potentially smaller than their predecessors backdoor sets and backdoor trees. We establish the fixed-parameter tractability of the backdoor DNF detection problem. Our results hold for the fundamental base classes Horn and 2CNF, and their combination. We complement our theoretical findings by an empirical study. Our experiments show that backdoor DNFs provide a significant improvement over their predecessors.

Vehicle detection algorithm for foggy based on improved AOD-Net

To strengthen the safety monitoring of foggy road traffic and maintain the safety of vehicle driving on foggy roads, image dehazing algorithms are used to improve the clarity of road images detected in foggy environments, thereby improving the detection ability and monitoring efficiency of intelligent transportation systems for vehicle targets. Due to the low accuracy of vehicle detection and serious problem of missed detections in haze environments, this paper proposes an improved All-in-One Dehazing Network (AOD-Net) algorithm for detecting foggy vehicles, which adds batch normalization (BN) layers after each layer of convolution in AOD-Net, accelerating the convergence of the model and controlling overfitting. To enhance image detail information, an effective pyramid-shaped PSA attention module is embedded to extract richer feature information, enrich model representation, and improve the loss function to a multi-scale structural similarity (MS-SSIM) + L1 mixed loss function, thereby improving the quality, brightness, and contrast of dehazing images. Compared with current image dehazing algorithms, the dehazing quality of our algorithm is superior to other dehazing algorithms, such as dark channel prior (DCP), Dehaze-Net, and Fusion Feature Attention Network (FFA-Net). Compared with AOD-Net, the improved algorithm has increased the peak signal-to-noise ratio by 3.23 dB. At the same time, after the improved AOD-Net image dehazing processing, YOLOv7 object detection was performed and experimentally validated on a real foggy dataset. The results showed that compared with the previous method, it had better recognition performance in foggy detection and recognition, and higher detection accuracy for vehicles.

A Pseudo-Deterministic Noisy Extremal Optimization algorithm for the pairwise connectivity Critical Node Detection Problem

Abstract The critical node detection problem is a central task in computational graph theory due to its large applicability, consisting in deleting $k$ nodes to minimize a certain graph measure. In this article, we propose a new Extremal Optimization-based approach, the Pseudo-Deterministic Noisy Extremal Optimization (PDNEO) algorithm, to solve the Critical Node Detection variant in which the pairwise connectivity is minimized. PDNEO uses an adaptive pseudo-deterministic parameter to switch between random nodes and articulation points during the search, as well as other features, such as noise induction to preserve diversity, greedy search to better exploit the search space and a greater search space exploration mechanism. Numerical experiments on synthetic and real-world networks show the effectiveness of the proposed algorithm compared with existing methods.

Long-Time Coherent Integration for the Spatial-Based Bistatic Radar Based on Dual-Scale Decomposition and Conditioned CPF

This paper addresses the problem of weak maneuvering target detection in the space-based bistatic radar system through long-time coherent integration (LTCI). The space-based bistatic radar is vulnerable to the high-order range migration (RM) and Doppler frequency migration (DFM), since the target, the receiver and the transmitter all can play fast movement independently. To correct high- order RM and DFM, this usually involves joint high-dimensional parameter searching, incurring a large computational burden. In our previous work, a dual-scale (DS) decomposition of motion parameters was proposed, in which the optimal GRFT is conditionally decoupled into two cascade procedures called the modified generalized inverse Fourier transform (GIFT) and generalized Fourier transform (GFT), resulting in the DS-GRFT detector. However, even if the DS-GRFT detector preserves the superior performance and dramatically decreases the complexity, high-dimensional searching is still required. In this paper, by analyzing the structure of the DS-GRFT detector, we further designed a conditioned cubic phase function (CCPF) tailored to the range–slow-time signal after GIFT, breaking the joint high-dimensional searching into independent one-dimensional searching. Then, by connecting the proposed CCPF with the GIFT, we achieved a new LTCI detector called the DS-GIFT-CCPF detector, which obtained a significant computational cost reduction with acceptable performance loss, as demonstrated in numerical experiments.

SURVEY ON WBC CLASSIFICATION AND DETECTION USING YOLO

Our heart pumps red blood constantly, carrying waste products, hormones, nutrition, oxygen, and other essential health markers. Blood cells in this fluid are made up of many components, including platelets, RBCs, and WBCs. Neutrophils, Lymphocytes, Monocytes, Eosinophils, and Basophils are further subdivided into WBCs. To precisely identify and treat numerous kinds of blood-related illnesses, the detection and analysis of these blood cells is necessary for advanced diagnostics in medicine. Convolutional neural networks (CNNs), for example, are highly relied upon in modern medical diagnostics for the examination of blood cells. The precision and potency of blood cell identification procedures have been greatly improved by CNNs and Additional approaches to deep learning like RNNs and YOLO. The persistence of this research is to list challenges in White Blood cell detection and classification problem. Deep learning-based blood cell identification and classification approaches based on Single Stage approaches. Single Stage Approach involve You only look Once (YOLO). It explores the various architectures for feature selection and classification starting from YoloV1 toV5 and it’s various modifications. In this paper would be looking at things like network parameters, training procedure, and transfer learning application for each variation. It also assesses model complexity, accuracy, performance, generalization, computational efficiency, and transfer learning capabilities, in addition to variables like data amount and source.

Jellyfish detection algorithm based on multi-gradient flow feature fusion

Jellyfish detection is a challenge in the field of underwater biological identification. Although early works have solved some of the problems of jellyfish detection, there are still shortcomings. Earlier jellyfish datasets were not as extensive, necessitating heavy reliance on data augmentation. Moreover, the detection capability is limited, especially in scenarios involving jellyfish gatherings and occlusions. To overcome these limitations, we establish a high-quality dataset with more jellyfish species and propose a more robust real-time detection algorithm. Our algorithm primarily consists of a multi-gradient flow backbone and a feature fusion module GFPN. Additionally, we have designed a receptive field expansion module SPPFCSPC_G. The entire network employs the FReLU activation function, while the bounding box regression utilizes the WIOU loss function. Our methods demonstrate accuracy and run-time performance in comparison with the state-of-the-art yolo series algorithms. Results show that our algorithm achieves the highest Precision, Recall and mAP50, exceeding the baseline yolov5 by 1.1%, 4.1%, and 4.5%, and outperforming the latest yolov8 by 0.9%, 1.3%, and 2.5%. Importantly, our method effectively addresses aggregation, occlusion, and deformation issues commonly encountered in jellyfish detection.

Research on Improved YOLOv5 Vehicle Target Detection Algorithm in Aerial Images

Aerial photoelectric imaging payloads have become an important means of reconnaissance and surveillance in recent years. However, aerial images are easily affected by external conditions and have unclear edges, which greatly reduces the accuracy of imaging target recognition. This paper proposes the M-YOLOv5 model, which uses a shallow feature layer. The RFBs module is introduced to improve the receptive field and detection effect of small targets. In the neck network part, the BiFPN structure is used to reuse the underlying features to integrate more features, and a CBAM attention mechanism is added to improve detection accuracy. The experimental results show that the detection effect of this method on the DroneVehicle dataset is better than that of the original network, with the precision rate increased by 2.8%, the recall rate increased by 16%, and the average precision increased by 2.3%. Considering the real-time problem of target detection, based on the improved model, the Clight-YOLOv5 model is proposed, by lightweighting the network structure and using the depth-separable convolution optimization module. After lightweighting, the number of model parameters is decreased by 71.3%, which provides a new idea for lightweight target detection and proves the model’s effectiveness in aviation scenarios.

Point based weakly semi-supervised biomarker detection with cross-scale and label assignment in retinal OCT images

Background and ObjectiveOptical coherence tomography (OCT) is currently one of the most advanced retinal imaging methods. Retinal biomarkers in OCT images are of clinical significance and can assist ophthalmologists in diagnosing lesions. Compared with fundus images, OCT can provide higher resolution segmentation. However, image annotation at the bounding box level needs to be performed by ophthalmologists carefully and is difficult to obtain. In addition, the large variation in shape of different retinal markers and the inconspicuous appearance of biomarkers make it difficult for existing deep learning-based methods to effectively detect them. To overcome the above challenges, we propose a novel network for the detection of retinal biomarkers in OCT images. MethodsWe first address the issue of labeling cost using a novel weakly semi-supervised object detection method with point annotations which can reduce bounding box-level annotation efforts. To extend the method to the detection of biomarkers in OCT images, we propose multiple consistent regularizations for point-to-box regression network to deal with the shortage of supervision, which aims to learn more accurate regression mappings. Furthermore, in the subsequent fully supervised detection, we propose a cross-scale feature enhancement module to alleviate the detection problems caused by the large-scale variation of biomarkers. We also propose a dynamic label assignment strategy to distinguish samples of different importance more flexibly, thereby reducing detection errors due to the indistinguishable appearance of the biomarkers. ResultsWhen using our detection network, our regressor also achieves an AP value of 20.83 s when utilizing a 5 % fully labeled dataset partition, surpassing the performance of other comparative methods at 5 % and 10 %. Even coming close to the 20.87 % result achieved by Point DETR under 20 % full labeling conditions. When using Group R-CNN as the point-to-box regressor, our detector achieves 27.21 % AP in the 50 % fully labeled dataset experiment. 7.42 % AP improvement is achieved compared to our detection network baseline Faster R-CNN. ConclusionsThe experimental findings not only demonstrate the effectiveness of our approach with minimal bounding box annotations but also highlight the enhanced biomarker detection performance of the proposed module. We have included a detailed algorithmic flow in the supplementary material.

Anomaly Detection for Industrial Time Series Data Based on Correlation Analysis and CNN-BiLSTM with Self-attention

This paper aims to propose an anomaly detection model for industrial time series data based on correlation analysis and CNN-BiLSTM with self-attention to solve the problem of abnormal data detection in the field of industrial data analysis. Industrial data anomaly detection is an important task in the industrial field, which can help people to timely understand the production operation status and real-time record and perception of the operating environment. This paper introduces two key technologies: correlation analysis and CNN-BiLSTM with self-attention, and how to combine them to build an effective anomaly detection model for industrial time series data. Through experimental evaluation, this paper proves the effectiveness and superiority of the proposed model in industrial data anomaly detection tasks.

SEEI: spherical evolution with feedback mechanism for identifying epistatic interactions

BackgroundDetecting epistatic interactions (EIs) involves the exploration of associations among single nucleotide polymorphisms (SNPs) and complex diseases, which is an important task in genome-wide association studies. The EI detection problem is dependent on epistasis models and corresponding optimization methods. Although various models and methods have been proposed to detect EIs, identifying EIs efficiently and accurately is still a challenge.ResultsHere, we propose a linear mixed statistical epistasis model (LMSE) and a spherical evolution approach with a feedback mechanism (named SEEI). The LMSE model expands the existing single epistasis models such as LR-Score, K2-Score, Mutual information, and Gini index. The SEEI includes an adaptive spherical search strategy and population updating strategy, which ensures that the algorithm is not easily trapped in local optima. We analyzed the performances of 8 random disease models, 12 disease models with marginal effects, 30 disease models without marginal effects, and 10 high-order disease models. The 60 simulated disease models and a real breast cancer dataset were used to evaluate eight algorithms (SEEI, EACO, EpiACO, FDHEIW, MP-HS-DHSI, NHSA-DHSC, SNPHarvester, CSE). Three evaluation criteria (pow1, pow2, pow3), a T-test, and a Friedman test were used to compare the performances of these algorithms. The results show that the SEEI algorithm (order 1, averages ranks = 13.125) outperformed the other algorithms in detecting EIs.ConclusionsHere, we propose an LMSE model and an evolutionary computing method (SEEI) to solve the optimization problem of the LMSE model. The proposed method performed better than the other seven algorithms tested in its ability to identify EIs in genome-wide association datasets. We identified new SNP–SNP combinations in the real breast cancer dataset and verified the results. Our findings provide new insights for the diagnosis and treatment of breast cancer.Availability and implementation: https://github.com/scutdy/SSO/blob/master/SEEI.zip.

An Examination of Mental Stress in College Students: Utilizing Intelligent Perception Data and the Mental Stress Scale

In order to solve the problems of traditional mental stress detection in college students that are time-consuming, random, and subjective, this paper proposes an intelligent perception-driven mental stress assessment method for college students. First, we analyze the factors in SRQ and SCL-90, which can be measured by intelligent sensing methods, including sleep, exercise, social interaction, and environment, and then perform feature extraction. Secondly, we use machine learning methods to build a mental stress assessment model. The Shapley additive explanations (SHAP) model is used to explain the training results. Experimental results show that the model proposed in this article can effectively assess the mental stress state of college students. This means that the collection of intelligent perception data based on the mental stress scale can effectively evaluate the mental stress state of college students and provide a new research idea for further developing a non-intrusive and real-time mental stress assessment for college students.

Contingency detection in modern power systems: A stochastic hybrid system method

This paper introduces a new stochastic hybrid system (SHS) framework for contingency detection in modern power systems (MPS). The framework uses stochastic hybrid system representations in state space models to expand and facilitate capability of contingency detection. In typical microgrids (MGs), buses may contain various synchronous generators, renewable generators, controllable loads, battery systems, regular loads, etc. For development of SHS models in power systems, this paper introduces the concept of dynamic and non-dynamic buses. By converting a physical power grid into a virtual linearized state space model and representing contingencies as random switching of system structures and parameters, this paper formulates the contingency detection problem as a joint estimation problem of discrete events and continuous states in stochastic hybrid systems. This method offers unique advantages, including using common measurement signals on voltage and current synchrophasors to detect different types and locations of contingencies, avoiding expensive local direct fault measurements and detecting certain contingencies that cannot be directly measured. The method employs a small and suitably-designed probing signal to sustain the ability of persistent contingency detection. Joint estimation algorithms are presented with their proven convergence and reliability properties. Examples that use an IEEE 5-Bus system demonstrate the main ideas and derivation steps. Simulation case studies on an IEEE 33-Bus system are used for detecting transmission line faults and sensor interruptions.

A Multi-Objective Pigeon-Inspired Optimization Algorithm for Community Detection in Complex Networks

Community structure is a very interesting attribute and feature in complex networks, which has attracted scholars’ attention and research on community detection. Many single-objective optimization algorithms have been migrated and modified to serve community detection problems. Due to the limitation of resolution, the final algorithm implementation effect is not ideal. In this paper, a multi-objective community detection method based on a pigeon-inspired optimization algorithm, MOPIO-Net, is proposed. Firstly, the PIO algorithm is discretized in terms of the solution space representation, position, and velocity-updating strategies to adapt to discrete community detection scenarios. Secondly, by minimizing the two objective functions of community score and community fitness at the same time, the community structure with a tight interior and sparse exterior is obtained. Finally, for the misclassification caused by boundary nodes, a mutation strategy is added to improve the accuracy of the final community recognition. Experiments on synthetic and real networks verify that the proposed algorithm is more accurate in community recognition compared to 11 benchmark algorithms, confirming the effectiveness of the proposed method.

FocusDet: an efficient object detector for small object

The object scale of a small object scene changes greatly, and the object is easily disturbed by a complex background. Generic object detectors do not perform well on small object detection tasks. In this paper, we focus on small object detection based on FocusDet. FocusDet refers to the small object detector proposed in this paper. It consists of three parts: backbone, feature fusion structure, and detection head. STCF-EANet was used as the backbone for feature extraction, the Bottom Focus-PAN for feature fusion, and the detection head for object localization and recognition.To maintain sufficient global context information and extract multi-scale features, the STCF-EANet network backbone is used as the feature extraction network.PAN is a feature fusion module used in general object detectors. It is used to perform feature fusion on the extracted feature maps to supplement feature information.In the feature fusion network, FocusDet uses Bottom Focus-PAN to capture a wider range of locations and lower-level feature information of small objects.SIOU-SoftNMS is the proposed algorithm for removing redundant prediction boxes in the post-processing stage. SIOU multi-dimension accurately locates the prediction box, and SoftNMS uses the Gaussian algorithm to remove redundant prediction boxes. FocusDet uses SIOU-SoftNMS to address the missed detection problem common in dense tiny objects.The VisDrone2021-DET and CCTSDB2021 object detection datasets are used as benchmarks, and tests are carried out on VisDrone2021-det-test-dev and CCTSDB-val datasets. Experimental results show that FocusDet improves mAP@.5% from 33.6% to 46.7% on the VisDrone dataset. mAP@.5% on the CCTSDB2021 dataset is improved from 81.6% to 87.8%. It is shown that the model has good performance for small object detection, and the research is innovative.

Resistance spot welding defect detection based on vectorized dynamic resistance signal and LightGBM classifier

The problem of real-time detection of welding defects is a difficult problem in resistance spot welding. It is found that the dynamic resistance has a strong connection with the growth of the nugget. The dynamic resistance signals with welding defects are significantly different from those of normal welding, and the dynamic resistance signals between different welding defects show different characteristics, and the dynamic resistance signals of the same kind of welding defects may also differ from each other. The most common practice today to realize the detection of resistive defects is by extracting the time-domain features of the signal waveforms. However, this approach is highly subjective, so this article proposes a double-size mesh division method to process the dynamic resistance signal. Experiments prove that the method can retain the characteristics of the signal curve well, and it is also improves the training speed and accuracy compared with the mesh division method. Finally, the processed signals are classified using the light gradient boosting machine classifier with an accuracy of 98.55%.

Deep Domain Adaptation With Max-Margin Principle for Cross-Project Imbalanced Software Vulnerability Detection

Software vulnerabilities (SVs) have become a common, serious, and crucial concern due to the ubiquity of computer software. Many AI-based approaches have been proposed to solve the software vulnerability detection (SVD) problem to ensure the security and integrity of software applications (in both the development and testing phases). However, there are still two open and significant issues for SVD in terms of i) learning automatic representations to improve the predictive performance of SVD, and ii) tackling the scarcity of labeled vulnerability datasets that conventionally need laborious labeling effort by experts. In this paper, we propose a novel approach to tackle these two crucial issues. We first exploit the automatic representation learning with deep domain adaptation for SVD. We then propose a novel cross-domain kernel classifier leveraging the max-margin principle to significantly improve the transfer learning process of SVs from imbalanced labeled into imbalanced unlabeled projects. Our approach is the first work that leverages solid body theories of the max-margin principle, kernel methods, and bridging the gap between source and target domains for imbalanced domain adaptation (DA) applied in cross-project SVD . The experimental results on real-world software datasets show the superiority of our proposed method over state-of-the-art baselines. In short, our method obtains a higher performance on F1-measure, one of the most important measures in SVD, from 1.83% to 6.25% compared to the second highest method in the used datasets.

Kernel-Based Multivariate Nonparametric CUSUM Multi-Chart for Detection of Abrupt Changes

In many cases, it is difficult to obtain precise distributional information on multivariate sequences. Therefore, there is a need to propose nonparametric methods for monitoring multivariate sequences. This article discusses the multivariate change detection problem and utilizes the kernel function as the statistic to construct the nonparametric Multivariate Cumulative Sum multi-chart, under the assumption that there is prior information about the abrupt changes. Through theoretical and numerical analysis, we show that the proposed control chart is more effective compared to other existing control charts. The good monitoring effect of this method demonstrates a strong potential for application.