Optimal Fusion Research Articles

MFAM-AD: an anomaly detection model for multivariate time series using attention mechanism to fuse multi-scale features.

Multivariate time series anomaly detection has garnered significant attention in fields such as IT operations, finance, medicine, and industry. However, a key challenge lies in the fact that anomaly patterns often exhibit multi-scale temporal variations, which existing detection models often fail to capture effectively. This limitation significantly impacts detection accuracy. To address this issue, we propose the MFAM-AD model, which combines the strengths of convolutional neural networks (CNNs) and bi-directional long short-term memory (Bi-LSTM). The MFAM-AD model is designed to enhance anomaly detection accuracy by seamlessly integrating temporal dependencies and multi-scale spatial features. Specifically, it utilizes parallel convolutional layers to extract features across different scales, employing an attention mechanism for optimal feature fusion. Additionally, Bi-LSTM is leveraged to capture time-dependent information, reconstruct the time series and enable accurate anomaly detection based on reconstruction errors. In contrast to existing algorithms that struggle with inadequate feature fusion or are confined to single-scale feature analysis, MFAM-AD effectively addresses the unique challenges of multivariate time series anomaly detection. Experimental results on five publicly available datasets demonstrate the superiority of the proposed model. Specifically, on the datasets SMAP, MSL, and SMD1-1, our MFAM-AD model has the second-highest F1 score after the current state-of-the-art DCdetector model. On the datasets NIPS-TS-SWAN and NIPS-TS-GECCO, the F1 scores of MAFM-AD are 0.046 (6.2%) and 0.09 (21.3%) higher than those of DCdetector, respectively(the value ranges from 0 to 1). These findings validate the MFAMAD model's efficacy in multivariate time series anomaly detection, highlighting its potential in various real-world applications.

Face Recognition Algorithm based on Optimal Fusion of LBP and HOG Features

Face Recognition Algorithm based on Optimal Fusion of LBP and HOG Features

Multi-biometric fusion for enhanced human authentication in information security

In the digital age, information security has taken on greater importance, necessitating the development of strong and trustworthy human authentication techniques. Traditional single-factor authentication schemes, such passwords or PINs, are prone to a number of security flaws. Multi-biometric fusion approaches have become a possible remedy to these restrictions. In comparison to other fusion techniques, feature level fusion is a very popular technique. Features are taken from every biometric attribute in this fusion. Then, the retrieved features are concatenated to create a high dimension final feature vector. In this study, we present a novel method for feature-level fusion employing optimal feature level fusion. The essential features are chosen and identified using a Binary chimp optimized adaptive kernel support vector machine (BCO-AKSVM). According to f1-score (99 %), recall (98 %), accuracy (97 %), precision (88 %), and time computation (1000 ms), the experimental results of the suggested method are analyzed. When compared to current methods, it can show that the proposed BCO-AKSVM achieves the highest performance of information security in human biometric authentication. Multi-biometric fusion is anticipated to play a significant role as technology develops in maintaining secure access control and safeguarding sensitive information in numerous sectors.

Optimal Fusion of Multispectral Optical and SAR Images for Flood Inundation Mapping through Explainable Deep Learning

In the face of increasing flood risks intensified by climate change, accurate flood inundation mapping is pivotal for effective disaster management. This study introduces a novel explainable deep learning architecture designed to generate precise flood inundation maps from diverse satellite data sources. A comprehensive evaluation of the proposed model is conducted, comparing it with state-of-the-art models across various fusion configurations of Multispectral Optical and Synthetic Aperture Radar (SAR) images. The proposed model consistently outperforms other models across both Sentinel-1 and Sentinel-2 images, achieving an Intersection Over Union (IOU) of 0.5862 and 0.7031, respectively. Furthermore, analysis of the different fusion combinations reveals that the use of Sentinel-1 in combination with RGB, NIR, and SWIR achieves the highest IOU of 0.7053 and that the inclusion of the SWIR band has the greatest positive impact on the results. Gradient-weighted class activation mapping is employed to provide insights into its decision-making processes, enhancing transparency and interpretability. This research contributes significantly to the field of flood inundation mapping, offering an efficient model suitable for diverse applications. This study not only advances flood inundation mapping but also provides a valuable tool for improved understanding of deep learning decision-making in this area, ultimately contributing to improved disaster management strategies.

Error modeling and hierarchical Bayesian fusion for spaceborne and ground radar rainfall data

The optimal fusion of multisource rainfall data can improve the accuracy and resolution of a posteriori estimates, which is beneficial for hydrological and meteorological applications. In this study, the errors of ground-based dual-polarization radar (GR) and dual-frequency precipitation radar (DPR) on global precipitation measurement (GPM) satellite rainfall estimation were modeled, and a hierarchical Bayesian (HB) framework was developed for GR and DPR rainfall data fusion. According to the matched optimal estimated GR rainfall and rain gauge data, the GR rainfall error was analyzed, and the error variance varying with rain type and intensity was described and modeled. The rain types were classified based on disdrometer and dual-polarization radar data. By comparing the DPR rainfall product with GR rainfall, the DPR rainfall error was divided into systematic and random errors. The systematic error was linearly fitted, and the probability distributions for the random error and the variable error parameters were analyzed and modeled. Because the parameters for GR and DPR rainfall errors varied with rain type and intensity, we developed a HB fusion approach considering the varying parameters in rainfall error modeling. The performance of the fusion algorithm was evaluated using matched GR and DPR cases, which showed that the HB fusion can effectively improve the consistency between the original DPR rainfall product and rain gauge data, reducing the relative bias by an average of 30% and improving the correlation by an average of 20%. Compared with the conventional Bayesian (CB) fusion algorithm, which is based on overall error modeling, the HB fusion results can improve the root mean square error by an average of 24% and relative bias by 14%.

Sketch-fusion: A gradient compression method with multi-layer fusion for communication-efficient distributed training

Gradient compression is an effective technique for improving the efficiency of distributed training. However, introducing gradient compression can reduce model accuracy and training efficiency. Furthermore, we also find that using a layer-wise gradient compression algorithm would lead to significant compression and communication overhead, which can negatively impact the scaling efficiency of the distributed training system. To address these issues, we propose a new method called Sketch−Fusion SGD, which leverages the Count-Sketch data structure to enhance the scalability and training speed of distributed deep learning systems. Moreover, our method employs LayerFusion to optimize gradient compression algorithms' scalability and convergence efficiency by formulating an optimal multi-layer fusion strategy without introducing extra hyperparameters. We evaluate our method on a cluster of 16 GPUs and demonstrate that it can improve training efficiency by up to 18.6% without compromising the model's accuracy. In addition, we find that applying our LayerFusion algorithm to other gradient compression methods improved their scalability by up to 2.87×.

Multi-view clustering using a flexible and optimal multi-graph fusion method

Recently, many multi-view clustering (MVC) methods based on graphs have been proposed to address prevalent multi-view data. For these methods, the multi-graph fusion step, aim of which is to obtain a consensus graph is vital for obtaining good clustering performance. However, these methods still have two problems. First, the column sum of the consensus graph is restricted to one, which is not flexible enough for practical applications. Affected by the sum-to-one constraint, the similarity between some noisy points may be large, which may cause these abnormal points to be grouped into a separate cluster. Second, the cluster structure of the consensus graph is often not considered. To address these problems, in this paper, we propose a novel multi-view clustering using a flexible and optimal multi-graph fusion method (MVC/FOMF). Specifically, we first obtain the similarity graph of each view by using red the self-expressive method. Second, we fuse these graphs into a consensus graph whose column sum is constrained to s(0<s≤1), and we can adjust s to look for the best clustering performance. Third, we impose a rank constraint on the Laplacian matrix of the consensus graph to learn the best clustering structure. Finally, all these steps are unified into a framework and the corresponding optimization procedure, which is based on the alternating multiplier method, is also designed. More importantly, the complexity of our algorithm is also lower than those of many representative algorithms. Compared with that of the state-of-the-art algorithms, our algorithm shows very encouraging performance on some datasets. The code can be found at https://github.com/wulala2233/MVCFOMF.

Optimal Deep Convolutional Neural Network based Fusion Model for Soil Nutrient Analysis

The vast majority of people in India, agriculture is their main line of work, and it has a large economic impact.. Soil is important for supplying vital nutrients to crops for better yield. Determining soil nutrients is certainly essential for selecting appropriate crops and monitoring growth. Common methods used by agriculturalists are inadequate to satisfy increasing demands and have to obstruct cultivating soil. For a better crop yield, agriculturalists must have an awareness regarding the soil nutrients for a specific crop. There comes a need for using Deep learning methods in soil analysis that would help farmers in the domain. This study introduces an Optimal Deep Convolutional Neural Network Fusion Model for Soil nutrient Type Classification (ODCNNF-STC) technique. The presented ODCNNF-STC technique examines the input soil images to classify them into different nutrients present in the soil. In this approach, the noise present in the soil images are initially filtered using a bilateral filter (BF) followed by contrast enhancement. The preprocessed soil images are fed to the model formed by the fusion of DenseNet201 and InceptionResNetV2 models extracting the soil images that can successfully differentiate soil nutrients. Finally, classification of soil nutrients were performed by three classifiers namely extreme learning machine (ELM), RMSProp optimizer-based 1DCNN, and RMSProp optimizer-based Stacked Auto Encoder (SAE). The experimental validation of ODCNNF-STC method is examined on real-time dataset of soil images with a maximum accuracy of 99.39% over recent methods.

Efficient Non-Destructive Detection for External Defects of Kiwifruit

External defects of kiwifruit seriously affect its added commercialization. To address the existing problems, kiwifruit external defects detection has a few methods for detecting multi-category defects and weak adaptability to complex images. In this study, we proposed ResNet combined with CBAM for the automatic detection of external defects in kiwifruit. The experiment first built an acquisition device to obtain high-quality images. The optimal fusion scheme of ResNet and CBAM was investigated, the network training parameters were optimized, and Adam was used to accelerate the convergence speed of the model. It was found that the average recognition accuracy of ResNet34 + CBAM for kiwifruit was 99.6%, and all evaluation metrics were greater than 99%. Meanwhile, the experiment selected AlexNet, VGG16, InceptionV3, ResNet34, and ResNet34 + CBAM for comparison. The results showed that the recognition accuracy of ResNet34 + CBAM was 7.9%, 12.7%, 11.8%, and 4.3% higher than that of AlexNet, VGG16, InceptionV3, and ResNet34, respectively. Therefore, it can be concluded that ResNet34 + CBAM has the advantages of high recognition accuracy and good stability for kiwifruit external defect sample detection. It provides a technical guarantee for online detection and sorting of kiwifruit and other fruit defects.

OF-WFBP: A near-optimal communication mechanism for tensor fusion in distributed deep learning

The communication bottleneck has severely restricted the scalability of distributed deep learning. Tensor fusion improves the scalability of data parallelism by overlapping computation and communication tasks. However, existing tensor fusion schemes only result in suboptimal training performance. In this paper, we propose an efficient communication mechanism (OF-WFBP) to find the optimal tensor fusion scheme for synchronous data parallelism. We present the mathematical model of OF-WFBP and prove it is an NP-hard problem. We mathematically solve the mathematical model of OF-WFBP in two cases. We propose an improved sparrow search algorithm (GradSSA) to find the near-optimal tensor fusion scheme efficiently in other cases. Experimental results on two different GPU clusters show that OF-WFBP achieves up to 1.43x speedup compared to the state-of-the-art tensor fusion mechanisms.

Synthesis of lithium slag-based ANA zeolite and molecular dynamics study of carbon dioxide adsorption performance

In this paper, lithium slag was used as raw material to prepare zeolite framework with porous structure by alkali activation and hydrothermal synthesis. The alkali activation of raw materials and the phase analysis of zeolite products were carried out by XRD characterization methods, and the effects of different alkali fusion conditions and hydrothermal conditions on zeolite synthesis were investigated. At the same time, the effects of different temperatures on the adsorption capacity and adsorption heat of carbon dioxide adsorbed by zeolite skeleton model under normal pressure were studied by using Materials studio software. The results showed that the optimum alkali fusion conditions were: 500 °C, 6 h; the best hydrothermal condition is 220 °C, 6 h. The average adsorption capacity of cristobalite for carbon dioxide is 2 mmol/g, and the adsorption heat is 11.98 kJ/mol.

A Dual Convolutional Neural Network with Attention Mechanism for Thermal Infrared Image Enhancement

In industrial applications, thermal infrared images, which are commonly used, often suffer from issues such as low contrast and blurred details. Traditional image enhancement algorithms are limited in their effectiveness in improving the visual quality of thermal infrared images due to the specific nature of the application. Therefore, we propose a dual Convolutional Neural Network (CNN) combined with an attention mechanism to address the challenges of enhancing low-quality thermal infrared images and improving their visual quality. Firstly, we employ two parallel sub-networks to extract both global and local features. In one sub-network, we utilize a sparse mechanism incorporating dilated convolutions, while the other sub-network employs Feature Attention (FA) blocks based on channel attention and pixel attention. This architecture significantly enhances the feature extraction capability. The use of attention mechanisms allows the network to filter out irrelevant background information, enabling more flexible feature extraction. Finally, through a simple yet effective fusion block, we thoroughly integrate the extracted features to achieve an optimal fusion strategy, ensuring the highest quality enhancement of the final image. Extensive experiments on benchmark datasets and real images demonstrate that our proposed method outperforms other state-of-the-art models in terms of objective evaluation metrics and subjective assessments. The generated images also exhibit superior visual quality.

Monitoring of simultaneous saccharification and fermentation of ethanol by multi-source data deep fusion strategy based on near-infrared spectra and electronic nose signals

Fuel ethanol represents a future energy trajectory, and the simultaneous saccharification and fermentation (SSF) technique emerges as the principal approach for ethanol production. This scholarly inquiry offers an innovative means to monitor the SSF process for ethanol meticulously. Employing a profound fusion strategy that effectively amalgamates diverse data sources. The convolutional neural network and recurrent neural network (RNN) architectures are thoughtfully crafted and designed to enable autonomous feature self-learning from near-infrared spectra and electronic nose data. These intricately devised networks further implement data fusion strategies at the granular level of features. Ultimately, a deep fusion correction model was devised and rigorously validated using two distinct data sources, namely near-infrared spectroscopy and electronic nose data. The obtained results demonstrate a discernible improvement in the overall predictive accuracy of the model when employing the fusion feature strategy, surpassing the model constructed solely on a single technical data source. Regarding the monitoring of ethanol content, the optimal RNN fusion model exhibited remarkable performance metrics, with a root mean square error of prediction (RMSEP) value of 3.2265, a coefficient of determination (R2) value of 0.9880, and a relative percent deviation (RPD) value of 9.2662. In terms of monitoring glucose content, the optimal RNN fusion model also demonstrated commendable performance, with the following respective parameters: RMSEP was 3.2770, R2 was 0.9840, and RPD was 8.0085. The overall results indicate that the multi-sensor data fusion strategy not only improves the performance of the model but also provides valuable insights into the fermentation process.

Automatic simulated dental implants method with implant parameter estimation based on CBCT images

PurposeThis paper aims to automatically obtain the implant parameter from the CBCT images to improve the outcome of implant planning.Design/methodology/approachThis paper proposes automatic simulated dental implant positioning on CBCT images, which can significantly improve the efficiency of implant planning. The authors introduce the fusion point calculation method for the missing tooth's long axis and root axis based on the dental arch line used to obtain the optimal fusion position. In addition, the authors proposed a semi-interactive visualization method of implant parameters that be automatically simulated by the authors' method. If the plan does not meet the doctor's requirements, the final implant plan can be fine-tuned to achieve the optimal effect.FindingsA series of experimental results show that the method proposed in this paper greatly improves the feasibility and accuracy of the implant planning scheme, and the visualization method of planting parameters improves the planning efficiency and the friendliness of system use.Originality/valueThe proposed method can be applied to dental implant planning software to improve the communication efficiency between doctors, patients and technicians.

Research on New Word Recognition Based on Weibo Information and SVM

In order to effectively recognize Weibo new words, this paper proposes a Weibo new word recognition method based on Weibo information and SVM. Firstly, positive and negative samples are extracted from the Weibo corpus and the training corpus annotated with part of speech. Next, the word features of these samples are vectorized and trained through support vector machines to obtain support vectors for new word classification. Finally, it is vectorized and inputted into the trained support vector machine classifier to obtain the recognized Weibo new words. By comparing the experimental results, the optimal fusion feature combination and SVM kernel function are obtained.

Structural health monitoring via a group-theoretic WSA for optimal feature selection and data fusion

In this study, two binary versions of the Water Strider Algorithm (WSA) are proposed and applied to optimal feature selection in classification problems. In the new binary versions, the formulations of WSA in continuous space are converted into binary space using group-theoretic operators (in AWSA) and sigmoid function (in BWSA). AWSA, BWSA, genetic algorithm (GA), and binary particle swarm optimization (BPSO) are selected and compared over eighteen well-known datasets from the University of California, Irvine repository. The results of AWSA indicate its satisfactory performance compared to those of other algorithms. Then, they are applied to find optimal features of a structural health monitoring classification problem using two well-known machine learning classifiers, namely k-Nearest Neighbor (KNN) and Naïve Bayes (NB) algorithms. To further improve the accuracy of the classification models, a decision-level data fusion technique is proposed based on the improved Dempster-Shafer theory. It is demonstrated that the AWSA presents superior results compared to the other algorithms and the suggested decision-level data fusion provides a reliable detection of damage.

Development of novel recombinant peroxidase secretion system from Pseudomonas putida for lignin valorisation

Pseudomonas putida is a promising strain for lignin valorisation. However, there is a dearth of stable and efficient systems for secreting enzymes to enhance the process. Therefore, a novel secretion system for recombinant lignin–depolymerising peroxidase was developed. By adopting a flagellar type III secretion system, P. putida KT-M2, a secretory host strain, was constructed and an optimal secretion signal fusion partner was identified. Application of the dye-decolourising peroxidase of P. putida to this system resulted in efficient oxidation activity of the cell-free supernatant against various chemicals, including lignin model compounds. This peroxidase-secreting strain was examined to confirm its lignin utilisation capability, resulting in the efficient assimilation of various lignin substrates with 2.6-fold higher growth than that of the wild-type strain after 72 h of cultivation. Finally, this novel system will lead efficient bacterial lignin breakdown and utilization through enzyme secretion, paving the way for sustainable lignin-consolidated bioprocessing.

Feature fusion-based ensemble CNN learning optimization for automated detection of pediatric pneumonia

Detecting pediatric pneumonia accurately and rapidly is crucial for timely treatment, especially considering its association with seasonal changes and potentially fatal outcomes. However, medical image analysis using convolutional neural network (CNN) models faces challenges such as limited labeled data, image noise, class imbalance, and overfitting. Regularization techniques are often insufficient, necessitating advanced approaches for successful pneumonia detection. Our study aims to accurately detect pneumonia by proposing an ensemble CNN framework that incorporates optimal feature fusion. A novel image preprocessing algorithm has been developed that applies hierarchical template-matching to reduce image noise and improves the learning of relevant features. Instead of relying solely on a few pre-defined CNN models combined through majority voting, multiple CNN models with different architectures are trained on the pneumonia dataset using fine-tuning and transfer learning techniques. To obtain an optimal feature set, Chi-Square and mRMR methods iteratively eliminate irrelevant features from the fully connected layer of each CNN model. These optimal feature sets are then concatenated to enhance feature vector diversity for classification. The study’s results illustrate that when compared to state-of-the-art approaches, this framework achieves exceptional accuracy (98.94%) and F1 score (99.12%) on a public test dataset. These findings strongly support the conclusion that the proposed feature fusion-based model outperforms individual models and the majority voting ensemble method in terms of performance. The significance of this successful approach to pneumonia detection lies in its potential to provide clinicians and healthcare professionals with an effective solution for accurate and rapid diagnosis, particularly in pediatric cases.

Text detection and recognition through deep learning-based fusion neural network

&lt;p&gt;Text recognition task involves recognizing the text from the natural image; it possesses various application, which aids information extraction through data mining from street view like images. Scene text recognition involves two stages i.e., text detection and text recognition, in the past several mechanisms has been proposed for accurate identification, these mechanisms are either traditional approach or deep learning-based. All the existing deep-learning methodology fails as this comprises character data and image data, further this research develops an optimal architecture fusion neural network (FNN) for text identification and recognition. FNN comprises several layers of convolutional neural network as well as recurrent neural network. Within FNN architecture convolutional layer is utilized for the feature extraction and recurrent layer is utilized for attaining the feature classification prediction. Further, an optimal training architecture is established for the enhancement of classification accuracy. Here Devanagari MLT-19 dataset is utilized for the evaluation of FNN. Three different parameters are considered during evaluation i.e., script word identification, character recognition rate (CRR) and word recognition rate (WRR). Further comparison with existing models is performed to establish the proposed model efficiency and it shows FNN methodology observes 98.67% of script identification accuracy, 84.65% of WRR and 92.93% of CRR.&lt;/p&gt;

Optimal Information Fusion-Based Strong Tracking Filter for State of Charge Estimation of Lithium-Ion Batteries

State of charge (SOC) plays a crucial role in battery management systems, which is of paramount importance in safety of lithium-ion batteries. However, incorrect charging/discharging, electromagnetic interference, electrochemical rebound characteristics of the battery, or battery faults can lead to sudden and unexpected variations in SOC, posing hazards on systems with lithium-ion batteries. To achieve rapid and accurate tracking of such variations, this paper proposes a robust strong tracking filter based on optimal information fusion, which can address the issue of estimation accuracy degradation caused by the over-adjustment of the fading factor in traditional strong tracking filters, while maintaining strong tracking capability for SOC variations. The effectiveness of the proposed method has been demonstrated by discharge experiments and dynamic stress testing.