Fast Filtering Algorithm Research Articles

Enhancing metastatic colorectal cancer prediction through advanced feature selection and machine learning techniques

Background and aimsColorectal cancer (CRC) is the third most prevalent cancer globally, posing a significant challenge due to its high rate of metastasis. Approximately 20% of patients with CRC present with distant metastases at diagnosis, and over 50% develop metastases within five years. Accurate prediction of metastasis is crucial for improving survival outcomes in patients with CRC. MethodsThis study introduces an innovative cost-sensitive fast correlation-based filter (CS-FCBF) algorithm for feature selection, integrated with machine learning techniques to predict metastatic CRC. The CS-FCBF algorithm effectively reduced the number of genomic features from 184 to 9 critical genes: CXCL9, C2CD4B, RGCC, GFI1, BEX2, CXCL3, FOXQ1, PBK, and PLAG1. The methodology combined in vitro, in vivo, and analysis of publicly available single-cell RNA-seq datasets to validate the findings. ResultsThe application of the CS-FCBF algorithm led to a significant improvement in prediction model performance, with an average 21.16% increase in the area under the precision-recall curve. The nine identified genes hold potential as diagnostic biomarkers and therapeutic targets for metastatic CRC. ConclusionsThis study highlights the critical role of advanced feature selection methods, combined with machine learning, in addressing the challenge of class imbalance in medical diagnosis, particularly for CRC. Early detection of metastasis is vital, and the identified genes underscore their importance in the metastatic process of CRC. The methodology applied here offers valuable insights and paves the way for future research in other cancers or diseases that face similar diagnostic challenges.

Lung adenocarcinoma identification based on hybrid feature selections and attentional convolutional neural networks.

Lung adenocarcinoma, a chronic non-small cell lung cancer, needs to be detected early. Tumor gene expression data analysis is effective for early detection, yet its challenges lie in a small sample size, high dimensionality, and multi-noise characteristics. In this study, we propose a lung adenocarcinoma convolutional neural network (LATCNN), a deep learning model tailored for accurate lung adenocarcinoma prediction and identification of key genes. During the feature selection stage, we introduce a hybrid algorithm. Initially, the fast correlation-based filter (FCBF) algorithm swiftly filters out irrelevant features, followed by applying the k-means-synthetic minority over-sampling technique (k-means-SMOTE) method to address category imbalance. Subsequently, we enhance the particle swarm optimization (PSO) algorithm by incorporating fast-decay dynamic inertia weights and utilizing the classification and regression tree (CART) as the fitness function for the second stage of feature selection, aiming to further eliminate redundant features. In the classifier construction stage, we present an attention convolutional neural network (atCNN) that incorporates an attention mechanism. This improved model conducts feature selection post lung adenocarcinoma gene expression data analysis for classification and prediction. The results show that LATCNN effectively reduces the feature dimensions and accurately identifies 12 key genes with accuracy, recall, F1 score, and MCC of 99.70%, 99.33%, 99.98%, and 98.67%, respectively. These performance metrics surpass those of other comparative models, highlighting the significance of this research for advancing lung adenocarcinoma treatment.

Machine learning-based classification of physiological and pathological high-frequency oscillations recorded by stereoelectroencephalography

ObjectiveHigh-frequency oscillations (HFOs) are an efficient indicator to locate the epileptogenic zone (EZ). However, physiological HFOs produced in the normal brain region may interfere with EZ localization. The present study aimed to build a machine learning-based classifier to distinguish the properties of each HFO event based on features in different domains. MethodsHFOs were detected in focal epilepsy patients from two different hospitals who underwent stereoelectroencephalography and subsequent resection surgery. Subsequently, 37 features in four different domains (time, frequency and time-frequency, entropy-based and nonlinear) were extracted for each HFO. After extraction, a fast correlation-based filter (FCBF) algorithm was applied for feature selection. The machine learning classifier was trained on the feature matrix with and without FCBF and then tested on the data set from patients in another hospital. ResultsA dataset was compiled, consisting of 89,844 pathological HFOs and 23,613 physiological HFOs from 17 patients assigned to the training dataset. Additionally, 12,695 pathological HFOs and 5,599 physiological HFOs from 9 patients were assigned to the testing dataset. Four features (ripple band power, arithmetic mean, Petrosian fractal dimension and zero crossings) were obtained for classifier training after FCBF. The classifier showed an area under the curve (AUC) of 0.95/0.98 for FCBF/no FCBF features in the training dataset and AUC of 0.82/0.90 for FCBF/no FCBF features in the testing dataset. Our findings indicated that the classifier utilizing all features demonstrated superior performance compared to the one relying on FCBF-processed features. ConclusionOur classifier could reliably differentiate pathological HFOs from physiological ones, which could promote the development of HFOs in EZ localization.

Fast Kalman filter synchronization algorithm for cooperative synchronization in time sensitive networks

Clock synchronization is crucial in time-sensitive networks (TSN) to ensure the normal operation of devices. However, traditional synchronization techniques face challenges such as the accumulation of synchronization errors over multiple hops and slow synchronization speed, which hinder the development of large-scale and multi-hop TSN. In this paper, we propose a cooperative synchronization scheme based on two-way timestamp exchange to reduce error accumulation and enable synchronization information exchange between any node in the network and the reference node. Moreover, we develop a fast Kalman filter (FKF) algorithm to estimate clock offset and clock skew. To reduce time complexity, we replace the calculation of gain matrices with extremely high dimension with a small matrix based on the randomization matrix approximation in the updating phase of the Kalman Filter. Simulation results show that the proposed method significantly reduces synchronization complexity without sacrificing clock synchronization accuracy, making it highly promising for future TSN deployments.

A two-stage hybrid biomarker selection method based on ensemble filter and binary differential evolution incorporating binary African vultures optimization

BackgroundIn the field of genomics and personalized medicine, it is a key issue to find biomarkers directly related to the diagnosis of specific diseases from high-throughput gene microarray data. Feature selection technology can discover biomarkers with disease classification information.ResultsWe use support vector machines as classifiers and use the five-fold cross-validation average classification accuracy, recall, precision and F1 score as evaluation metrics to evaluate the identified biomarkers. Experimental results show classification accuracy above 0.93, recall above 0.92, precision above 0.91, and F1 score above 0.94 on eight microarray datasets.MethodThis paper proposes a two-stage hybrid biomarker selection method based on ensemble filter and binary differential evolution incorporating binary African vultures optimization (EF-BDBA), which can effectively reduce the dimension of microarray data and obtain optimal biomarkers. In the first stage, we propose an ensemble filter feature selection method. The method combines an improved fast correlation-based filter algorithm with Fisher score. obviously redundant and irrelevant features can be filtered out to initially reduce the dimensionality of the microarray data. In the second stage, the optimal feature subset is selected using an improved binary differential evolution incorporating an improved binary African vultures optimization algorithm. The African vultures optimization algorithm has excellent global optimization ability. It has not been systematically applied to feature selection problems, especially for gene microarray data. We combine it with a differential evolution algorithm to improve population diversity.ConclusionCompared with traditional feature selection methods and advanced hybrid methods, the proposed method achieves higher classification accuracy and identifies excellent biomarkers while retaining fewer features. The experimental results demonstrate the effectiveness and advancement of our proposed algorithmic model.

A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms.

An IA is an abnormal swelling of cerebral vessels, and a subset of these IAs can rupture causing aneurysmal subarachnoid hemorrhage (aSAH), often resulting in death or severe disability. Few studies have used an appropriate method of feature selection combined with machine learning by analyzing transcriptomic sequencing data to identify new molecular biomarkers. Following gene ontology (GO) and enrichment analysis, we found that the distinct status of IAs could lead to differential innate immune responses using all 913 differentially expressed genes, and considering that there are numerous irrelevant and redundant genes, we propose a mixed filter- and wrapper-based feature selection. First, we used the Fast Correlation-Based Filter (FCBF) algorithm to filter a large number of irrelevant and redundant genes in the raw dataset, and then used the wrapper feature selection method based on the he Multi-layer Perceptron (MLP) neural network and the Particle Swarm Optimization (PSO), accuracy (ACC) and mean square error (MSE) were then used as the evaluation criteria. Finally, we constructed a novel 10-gene signature (YIPF1, RAB32, WDR62, ANPEP, LRRCC1, AADAC, GZMK, WBP2NL, PBX1, and TOR1B) by the proposed two-stage hybrid algorithm FCBF-MLP-PSO and used different machine learning models to predict the rupture status in IAs. The highest ACC value increased from 0.817 to 0.919 (12.5% increase), the highest area under ROC curve (AUC) value increased from 0.87 to 0.94 (8.0% increase), and all evaluation metrics improved by approximately 10% after being processed by our proposed gene selection algorithm. Therefore, these 10 informative genes used to predict rupture status of IAs can be used as complements to imaging examinations in the clinic, meanwhile, this selected gene signature also provides new targets and approaches for the treatment of ruptured IAs.

Construction of Dynamic Multiparallel Foreign Language Teaching Model Based on Multicore Processor

Heterogeneous multicore processor systems, as one of the highlights of multicore processor systems, are widely loved by people for their high efficiency and low cost, and they have also become the most commonly used processor systems in embedded systems. In the process of research on heterogeneous multicore processor systems, system task scheduling is particularly important. A good task scheduling algorithm can give full play to system performance. In this paper, the intelligent approximation algorithm is applied to the task scheduling problem of heterogeneous multicore processor system, and the heterogeneous multicore processor system is obtained as a highlight in the multicore processor system. Relying on the characteristics of high efficiency and low cost, it is widely loved by people, and at the same time, it has become the most commonly used processor system in embedded systems. In the process of research on heterogeneous multicore processor systems, the system task scheduling problem is particularly important. A good task scheduling algorithm can give full play to system performance. Some commonly used heuristic task scheduling algorithms are insufficient in solving such problems. This paper combines the granularity-based wavefront parallel decoding algorithm and the fast fusion loop filter algorithm to apply pipeline parallel technology between pixels. Decoding reconstruction module and fast loop filter module realize the fusion of multilevel parallel decoding. Based on the multicore platform, a dynamic multiparallel scheduling algorithm is designed to realize two-way video real-time parallel high-speed decoding, which improves the core resource utilization rate and decoding execution efficiency of the multicore processing platform. This paper also designs an indicator collector, a read-write hit collector, and a cache block priority determiner to implement a dynamic generation strategy with low hardware overhead. Multimodal teaching has certain feasibility and effectiveness and can have a positive impact on the English reading motivation and English reading comprehension ability of university students. Multimodal teaching improves students’ English reading comprehension ability, deepens students’ understanding and memory of words, and broadens the scope of knowledge, which has a significant promoting effect. The research results show that multimodality can be applied to college English teaching, and it can achieve better results than traditional teaching methods. By comparing the test results before and after the test, there are obvious differences between the experimental class and the control class. Whether it is the paired sample T-test of the experimental class or the independent sample T-test of two teaching methods in two classes, it proves that the scores under the multimodal English teaching mode are higher than the traditional teaching mode. Multimodal classrooms provide students with many opportunities to participate in classroom activities and form a competitive learning atmosphere. This competitive learning atmosphere has become a driving force to promote student learning. Multimodal teaching methods help to cultivate students’ independent and cooperative learning. In a multimodal classroom, many activities require cooperation and discussion among students. When they encounter difficulties, they can help each other, discuss with each other, and cooperate to complete tasks. Therefore, the multimodal teaching method will stimulate students’ interest in learning, give full play to students’ initiative, and improve students’ English ability.

Partial Update Simplified Fast Transversal Filter Algorithms for Acoustic Echo Cancellation

Robust algorithms applied in Acoustic Echo Cancellation systems present an excessive calculation load that has to be minimized. In the present paper, we propose two different low complexity fast least squares algorithms, called Partial Update Simplified Fast Transversal Filter (PU-SMFTF) algorithm and Reduced Partial Update Simplified Fast Transversal Filter (RPU-SMFTF) algorithm. The first algorithm reduces the computational complexity in both filtering and prediction parts using the M-Max method for coefficients selection. Moreover, the second algorithm applies the partial update technique on the filtering part, joined to the P-size forward predictor, to get more complexity reduction. The obtained results show a computational complexity reduction from (7L+8) to (L+6M+8) and from (7L+8) to (L+M+4P+17) for the PU-SMFTF algorithm and RPU-SMFTF algorithm, respectively compared to the original Simplified Fast Transversal Filter (SMFTF). Furthermore, experiments picked out in the context of acoustic echo cancellation, have demonstrated that the proposed algorithms provide better convergence speed, good tracking capability and steady-state performances than the NLMS and SMFTF algorithms.

Short term wind energy prediction model based on data decomposition and optimized LSSVM

The uncertainty and instability of wind power make it difficult to develop wind energy, wind farm grid connection, and power system stability, so accurate forecasting of wind power is very important. How to reduce wind power forecast errors and improve wind power forecast accuracy has become an urgent problem to be solved. The combined model proposed in this paper uses fast filtering algorithm (FCBF) and variational mode decomposition (VMD) to extract features of wind resource data, and uses least squares support vector machine (LSSVM) to predict short-term wind speed. Finally, according to the point prediction results and the prediction error, the wind speed range is estimated. In this paper, three groups of wind speed data at different times and regions are used for experiments. The experimental results show that the prediction error of the model designed in this paper is significantly less than that of the comparison model. The results show that the prediction system proposed in this paper is superior to all comparative prediction schemes, which can reduce the fluctuation risk and improve the stability and security of wind power system.

Simplified fast transversal filter algorithms for multichannel active noise control

In recent years, more attention has been paid to the performance of algorithm in active noise control (ANC). Compared with filtered-x LMS (FxLMS) algorithm based on stochastic gradient descent, filtered-x RLS (FXRLS) algorithm has faster convergence speed and better tracking performance at the cost of high computational complexity. In order to reduce the computation, fast transversal filter (FTF) algorithm can be used in ANC system. In this paper, simplified multi-channel FXFTF algorithms are presented, and the convergence speed and noise reduction performance of different multichannel algorithms are simulated and compared, and the numerical stability of the algorithms are analyzed.

An Effective Algorithm for Intrusion Detection Using Random Shapelet Forest

Detection of abnormal network traffic is an important issue when builds intrusion detection systems. An effective way to address this issue is time series mining, in which the network traffic is naturally represented as a set of time series. In this paper, we propose a novel efficient algorithm, called RSFID (Random Shapelet Forest for Intrusion Detection), to detect abnormal traffic flow patterns in periodic network packets. Firstly, the Fast Correlation‐based Filter (FCBF) algorithm is employed to remove irrelevant features to decrease the overfitting as well as the time complexity. Then, a random forest which is built upon a set of shapelet candidates is used to classify the normal and abnormal traffic flow patterns. Specifically, the Symbolic Aggregate approXimation (SAX) and random sampling technique are adopted to mitigate the high time complexity caused by enumerating shapelet candidates. Experimental results show the effectiveness and efficiency of the proposed algorithm.

Accelerating Haze Removal Algorithm Using CUDA

The dark channel prior (DCP)-based single image removal algorithm achieved excellent performance. However, due to the high complexity of the algorithm, it is difficult to satisfy the demands of real-time processing. In this article, we present a Graphics Processing Unit (GPU) accelerated parallel computing method for the real-time processing of high-definition video haze removal. First, based on the memory access pattern, we propose a simple but effective filter method called transposed filter combined with the fast local minimum filter algorithm and integral image algorithm. The proposed method successfully accelerates the parallel minimum filter algorithm and the parallel mean filter algorithm. Meanwhile, we adopt the inter-frame atmospheric light constraint to suppress the flicker noise in the video haze removal and simplify the estimation of atmospheric light. Experimental results show that our implementation can process the 1080p video sequence with 167 frames per second. Compared with single thread Central Processing Units (CPU) implementation, the speedup is up to 226× with asynchronous stream processing and qualified for the real-time high definition video haze removal.

Эквалайзеры с дробной задержкой и обратной связью на базе быстрых RLS-алгоритмов

Эквалайзеры с дробной задержкой и обратной связью на базе быстрых RLS-алгоритмов

A new simplified fast transversal filter algorithm based on subband approach (SSFTF) for acoustic echo cancellation

This paper addresses the problem of acoustic echo cancellation (AEC) by adaptive filtering algorithms with low complexity. In this paper, we propose a subband implementation of the simplified fast transversal filter (SFTF) algorithm. The proposed subband SFTF (SSFTF) is derived for acoustic echo cancellation (AEC) application. The efficient proposed subband decomposition leads to an improvement of the convergence speed performance even in situation when very non-stationary signal is present at the input. The proposed SSFTF algorithm has shown robustness in the background noise presence case. The complexity and comparison of the proposed SSFTF algorithm with state of the art algorithms have shown its superiority in terms of convergence speed performance that is evaluated by the mean square error (MSE) and system mismatch (SM) objective criteria. Intensive experiments have been conducted on the new algorithm to show all the performance with different scenarios in AEC application. All the obtained results have shown the best performances and efficiency of the new SSFTF algorithm.

RETRACTED: Flow online identification method for the encrypted Skype

Abstract The machine learning algorithm is gaining prominence in traffic identification research as it offers a way to overcome the shortcomings of port-based and deep packet inspection, especially for P2P-based Skype. However, recent studies have focused mainly on traffic identification based on a full-packet dataset, which poses great challenges to identifying online network traffic. This study aims to provide a new flow identification algorithm by taking the sampled flow records as the object. The study constructs flow records from a Skype set as the dataset, considers the inherent NETFLOW and extended flow metrics as features, and uses a fast correlation-based filter algorithm to select highly correlated features. The study also proposes a new NFI method that adopts a Bayesian updating mechanism to improve the classifier model. The experimental results show that the proposed scheme can achieve much better identification performance than existing state-of-the-art traffic identification methods, and a typical feature metric is analyzed in the sampling environment. The NFI method improves identification accuracy and reduces false positives and false negatives compared to other methods.

A new efficient two-channel fast transversal adaptive filtering algorithm for blind speech enhancement and acoustic noise reduction

This paper addresses the problem of speech enhancement and acoustic noise reduction by blind structures. Recently, the backward blind source separation (BBSS) structure has shown efficiency in cancelling the acoustic noise and improving corrupted speech signals form very noisy observations without any a priori information of source signals. In this paper, we propose a new algorithm based on the combination between the BBSS structure and the simplified fast transversal filter (SFTF) algorithm. The proposed two-channel simplified fast transversal filter (TCSFTF) algorithm succeeded an important blind improvement of steady state and convergence speed performances in diverse noisy situations when only the noisy signals are known. The performances of the new TCSFTF algorithm are compared with four state-of-the-art algorithms in different noisy conditions. This comparison is evaluated in terms of cepstral distance (CD), system mismatch (SM), segmental signal to noise ratio (SegSNR), and segmental mean square error (SegMSE) criteria.

Fast Non-Local Mean Filter Algorithm Based on Recursive Calculation of Similarity Weights

Abstract—A theoretically derived technique for acceleration of the original non-local means image denoising algorithm based on calculation of recursive patch similarity weights is proposed. A significant amount of computation in the non-local means scheme is dedicated to estimation of the patch similarity between pixel neighborhoods. The proposed recursive weights calculation scheme adopts the classic recursive mean calculation scheme for a multidimensional shift-vector in order to lower the computational complexity of the original non-local means method, thus speeding up this algorithm more than tenfold. Note that the output of the proposed algorithm is exactly the same as that of the original non-local means method. Hence this algorithm belongs to the class of true fast algorithms, unlike methods approaching to a certain degree the resul of the original algorithm.

Fast high-order matched filter for hyperspectral image target detection

Hyperspectral image target detection is an important application for both of its civil and military uses. Traditional hyperspectral image target detection algorithms are usually designed based on the second-order statistics of the data where it is assumed that the target follows a Gaussian distribution. However, due to the low spatial resolution of the hyperspectral sensor, sometimes the targets of interest only occupy a few pixels. In this case, targets are more suitable to be described by high-order statistics. In this paper, we propose a fast high-order matched filter (FHMF) algorithm which describes the essence of the data more properly and then formulate the detection problem as an optimization problem. To solve the optimization problem efficiently, a fixed-point algorithm is proposed inspired by the FastICA algorithm of the signal processing field. The experiments are conducted with a synthetic hyperspectral image and a real hyperspectral image. The experiment results show that FHMF has better detection performance than other classical detection algorithms. In addition, the fast convergence speed also demonstrates the effectiveness of the proposed fixed-point algorithm.

RETRACTED ARTICLE: Unsupervised video multi-target tracking based on fast resampling particle filter

In order to improve non-supervision and monitoring effect for moving target of video, target tracking algorithm without supervision video based on resampling particle filter is proposed. A fast resampling particle filter algorithm designed in the paper is adopted, and it is based on quasi-Monte Carlo method which owns the property of low difference for determining sequences for acquiring more uniform sample distribution in space and is able to avoid degeneracy of sampling particle for improving calculation efficiency and accuracy of particle filter algorithm; subsequently, background difference algorithm is used for reducing algorithm for frame of video supervision image. Dynamic particle filter is established according to quantity of moving target in image with color distribution character to trace it, and the establishment of filter and reducing algorithm is used for reducing algorithm redundancy; eventually, according to simulation test in instance of supervision image, it shows that target tracking accuracy and calculation efficiency of the proposed method are better.

A Fast Adaptive-Gain Complementary Filter Algorithm for Attitude Estimation of an Unmanned Aerial Vehicle

A novel fast adaptive-gain complementary filter algorithm is developed for Unmanned Aerial Vehicle (UAV) attitude estimation. This approach provides an accurate, robust and simple method for attitude estimation with minimised attitude errors and reduced computation. UAV attitude data retrieved from accelerometer data is transformed to the solution of a linearly discrete dynamic system. A novel complementary filter is designed to fuse accelerometer and gyroscope data, with a self-adjusted gain to achieve a good performance in accuracy. The performance of the proposed algorithm is compared with an Adaptive-gain Complementary Filter (ACF) and Extended Kalman Filtering (EKF). Simulation and experimental results show that the accuracy of the proposed filter has the same performance as an EKF in high dynamic operating conditions. Therefore, the proposed algorithm can balance accuracy and time consumption, and it has a better price/performance ratio in engineering applications.