A WEIGHTED FEATURE REDUCTION METHOD FOR POWER SPECTRA OF RADAR HRRPS

Feature reduction is a key process in pattern recognition. This paper deals with the feature reduction methods for a time-shift invariant feature, power spectrum, in Radar Automatic Target Recognition (RATR) using High-Resolution Range Profiles (HRRPs). Several existing feature reduction methods in pattern recognition are analyzed, and a weighted feature reduction method based on Fisher's Discriminant Ratio (FDR) is proposed in this paper. According to the characteristics of radar HRRP target recognition, this proposed method searches the optimal weight vector for power spectra of HRRPs by means of an iterative algorithm, and thus reduces feature dimensionality. Compared with the method of using raw power spectra and some existing feature reduction methods, the weighted feature reduction method can not only reduce feature dimensionality, but also improve recognition performance with low computation complexity. In the recognition experiments based on measured data, the proposed method is robust to different test data and achieves good recognition results.

TCP/IP Feature Reduction in Intrusion Detection

Due to the amount of data that an IDS needs to examine is very large, it is necessary to reduce the audit features and neglect the redundant features. Therefore, we investigated the performance to reduce TCP/IP features based on the decision tree rule-based statistical method（DTRS）. Its main idea is to create n decision trees in n data subsets, extract the rules, work out the relatively important features in accordance with the frequency of use of different features and demonstrate the performance of reduced features better than primary features by experimental resuits.

Dynamic Forecasting of Traffic Event Duration in Istanbul:A Classification Approach with Real-Time Data Integration

Today,urban traffic,growing populations,and dense transportation networks are contributing to an increase in traffic incidents.These incidents include traffic accidents,vehicle breakdowns,fires,and traffic disputes,resulting in long waiting times,high carbon emissions,and other undesirable situations.It is vital to estimate incident response times quickly and accurately after traffic incidents occur for the success of incident-related planning and response activities.This study presents a model for forecasting the traffic incident duration of traffic events with high precision.The proposed model goes through a 4-stage process using various features to predict the duration of four different traffic events and presents a feature reduction approach to enable real-time data collection and prediction.In the first stage,the dataset consisting of 24,431 data points and 75 variables is prepared by data collection,merging,missing data processing and data cleaning.In the second stage,models such as Decision Trees(DT),K-Nearest Neighbour(KNN),Random Forest(RF)and Support Vector Machines(SVM)are used and hyperparameter optimisation is performed with GridSearchCV.In the third stage,feature selection and reduction are performed and real-time data are used.In the last stage,model performance with 14 variables is evaluated with metrics such as accuracy,precision,recall,F1-score,MCC,confusion matrix and SHAP.The RF model outperforms other models with an accuracy of 98.5%.The study’s prediction results demonstrate that the proposed dynamic prediction model can achieve a high level of success.

Automated Algorithms for Detecting and Classifying X-Ray Images of Spine Fractures

This paper emphasizes a faster digital processing time while presenting an accurate method for identifying spinefractures in X-ray pictures. The study focuses on efficiency by utilizing many methods that include picturesegmentation, feature reduction, and image classification. Two important elements are investigated to reducethe classification time: Using feature reduction software and leveraging the capabilities of sophisticated digitalprocessing hardware. The researchers use different algorithms for picture enhancement, including theWiener andKalman filters, and they look into two background correction techniques. The article presents a technique forextracting textural features and evaluates three picture segmentation algorithms and three fractured spine detectionalgorithms using transformdomain, PowerDensity Spectrum(PDS), andHigher-Order Statistics (HOS) for featureextraction.With an emphasis on reducing digital processing time, this all-encompassing method helps to create asimplified system for classifying fractured spine fractures. A feature reduction program code has been built toimprove the processing speed for picture classification. Overall, the proposed approach shows great potential forsignificantly reducing classification time in clinical settings where time is critical. In comparison to other transformdomains, the texture features’ discrete cosine transform (DCT) yielded an exceptional classification rate, and theprocess of extracting features from the transform domain took less time. More capable hardware can also result inquicker execution times for the feature extraction algorithms.

Multi-state Information Dimension Reduction Based on Particle Swarm Optimization-Kernel Independent Component Analysis

The precision of the kernel independent component analysis( KICA) algorithm depends on the type and parameter values of kernel function. Therefore,it's of great significance to study the choice method of KICA's kernel parameters for improving its feature dimension reduction result. In this paper, a fitness function was established by use of the ideal of Fisher discrimination function firstly. Then the global optimal solution of fitness function was searched by particle swarm optimization( PSO) algorithm and a multi-state information dimension reduction algorithm based on PSO-KICA was established. Finally,the validity of this algorithm to enhance the precision of feature dimension reduction has been proven.

DAMAGE CLASSIFICATION BY PROBABILISTIC NEURAL NETWORKS BASED ON LATENT COMPONENTS FOR TIME-VARYING SYSTEM

A new approach to damage classification for health monitoring of a time-varylng system is presented. The functional-series time-dependent auto regressive moving average （FS-TARMA） time series model is applied to the vibration signal observed in the time-varying system for estimating the TAR/TMA parameters and the innovation variance. These parameters are the functions of the time, represented by a group of projection coefficients on the certain functional subspace with specific basis functions. The estimated TAR/TMA parameters and the innovation variance are further used to calculate the latent components （LCs） as the more informative data for health monitoring evaluation, based on an eigenvalue decomposition technique. LCs are then combined and reduced to numerical values （NVs） as feature sets, which are input to a probabilistic neural network （PNN） for the damage classification. For the evaluation of the proposed method, numerical simulations of the damage classification for a tlme-varylng system are used, in which different classes of damage are modeled by the mass or stiffness reductions. It is demonstrated that the method can identify the damages in the course of operation and the change of parameters on the time-varying background of the system.

Novel design concepts for network intrusion systems based on dendritic cells processes

An abstraction and an investigation to the worth of dendritic cells (DCs) ability to collect, process and present antigens are presented. Computationally, this ability is shown to provide a feature reduction mechanism that could be used to reduce the complexity of a search space, a mechanism for development of highly specialized detector sets as well as a selective mechanism used in directing subsets of detectors to be activated when certain danger signals are present. It is shown that DCs, primed by different danger signals, provide a basis for different anomaly detection pathways. Different antigen-peptides are developed based on different danger signals present, and these peptides are presented to different adaptive layer detectors that correspond to the given danger signal. Experiments are then undertaken that compare current approaches, where a full antigen structure and the whole repertoire of detectors are used, with the proposed approach. Experiment results indicate that such an approach is feasible and can help reduce the complexity of the problem by significant levels. It also improves the efficiency of the system, given that only a subset of detectors are involved during the detection process. Having several different sets of detectors increases the robustness of the resulting system. Detectors developed based on peptides are also highly discriminative, which reduces the false positives rates, making the approach feasible for a real time environment.

Parametric modeling of carbon nanotubes and estimating nonlocal constant using simulated vibration signals-ARMA and ANN based approach

Nonlocal continuum mechanics is a popular growing theory for investigating the dynamic behavior of Carbon nanotubes(CNTs).Estimating the nonlocal constant is a crucial step in mathematical modeling of CNTs vibration behavior based on this theory.Accordingly,in this study a vibration-based nonlocal parameter estimation technique,which can be competitive because of its lower instrumentation and data analysis costs,is proposed.To this end,the nonlocal models of the CNT by using the linear and nonlinear theories are established.Then,time response of the CNT to impulsive force is derived by solving the governing equations numerically.By using these time responses the parametric model of the CNT is constructed via the autoregressive moving average(ARMA)method.The appropriate ARMA parameters,which are chosen by an introduced feature reduction technique,are considered features to identify the value of the nonlocal constant.In this regard,a multi-layer perceptron(MLP)network has been trained to construct the complex relation between the ARMA parameters and the nonlocal constant.After training the MLP,based on the assumed linear and nonlinear models,the ability of the proposed method is evaluated and it is shown that the nonlocal parameter can be estimated with high accuracy in the presence/absence of nonlinearity.

Combining rough set theory and instance selection in ontology mapping

This paper presents a novel ontology mapping approach based on rough set theory and instance selection.In this appoach the construction approach of a rough set-based inference instance base in which the instance selection(involving similarity distance,clustering set and redundancy degree)and discernibility matrix-based feature reduction are introduced respectively;and an ontology mapping approach based on multi-dimensional attribute value joint distribution is proposed.The core of this mapping aI overlapping of the inference instance space.Only valuable instances and important attributes can be selected into the ontology mapping based on the multi-dimensional attribute value joint distribution,so the sequently mapping efficiency is improved.The time complexity of the discernibility matrix-based method and the accuracy of the mapping approach are evaluated by an application example and a series of analyses and comparisons.

Feature-reduction and semi-simulated data in functional connectivity-based cortical parcellation

Recently, restingstate functional magnetic resonance imaging has been used to parcellate the brain into functionally distinct regions based on the information available in functional connectivity maps. However, brain voxels are not independent units and adjacent voxels are always highly correlated, so functional connectivity maps contain redundant information, which not only impairs the computational efficiency during clustering, but also reduces the accuracy of clustering results. The aim of this study was to propose featurereduction approaches to reduce the redundancy and to develop semisimulated data with defined ground truth to evaluate these approaches. We proposed a featurereduction approach based on the Affinity Propagation Algorithm （APA） and compared it with the classic feature reduction approach based on Principal Component Analysis （PCA）. We tested the two approaches to the parcellation of both semisimulated and real seed regions using the Kmeans algorithm and designed two experiments to evaluate their noise resistance. We found that all functional connectivitymaps （with/without feature reduction） provided correct information for the parcellation of the semi simulated seed region and the computational efficiency was greatly improved by both feature reduction approaches. Meanwhile, the APAbased featurereduction approach outperformed the PCA based approach in noiseresistance. The results suggested that functional connectivity maps can provide correct information for cortical parcellation, and featurereduction does not significantly change the information. Considering the improvement in computational efficiency and the noiseresistance, featurereduction of functional connectivity maps before cortical parcellation is both feasible and necessary.

IoT and Blockchain-Based Mask Surveillance System for COVID-19 Prevention Using Deep Learning

On the edge of the worldwide public health crisis,the COVID-19 disease has become a serious headache for its destructive nature on humanity worldwide.Wearing a facial mask can be an effective possible solution to mitigate the spreading of the virus and reduce the death rate.Thus,wearing a face mask in public places such as shopping malls,hotels,restaurants,homes,and offices needs to be enforced.This research work comes up with a solution of mask surveillance system utilizing the mechanism of modern computations like Deep Learning(DL),Internet of things(IoT),and Blockchain.The absence or displacement of the mask will be identified with a raspberry pi,a camera module,and the operations of DL and Machine Learning(ML).The detected information will be sent to the cloud server with the mechanism of IoT for real-time data monitoring.The proposed model also includes a Blockchain-based architecture to secure the transactions of mask detection and create efficient data security,monitoring,and storage fromintruders.This research further includes an IoT-based mask detection scheme with signal bulbs,alarms,and notifications in the smartphone.To find the efficacy of the proposed method,a set of experiments has been enumerated and interpreted.This research work finds the highest accuracy of 99.95%in the detection and classification of facial masks.Some related experiments with IoT and Block-chain-based integration have also been performed and calculated the corresponding experimental data accordingly.ASystemUsability Scale(SUS)has been accomplished to check the satisfaction level of use and found the SUS score of 77%.Further,a comparison among existing solutions on three emergent technologies is included to track the significance of the proposed scheme.However,the proposed system can be an efficient mask surveillance system for COVID-19 and workable in real-time mask detection and classification.

Fusion-Based Deep Learning Model for Hyperspectral Images Classification

A crucial task in hyperspectral image(HSI)taxonomy is exploring effective methodologies to effusively practice the 3-D and spectral data delivered by the statistics cube.For classification of images,3-D data is adjudged in the phases of pre-cataloging,an assortment of a sample,classifiers,post-cataloging,and accurateness estimation.Lastly,a viewpoint on imminent examination directions for proceeding 3-D and spectral approaches is untaken.In topical years,sparse representation is acknowledged as a dominant classification tool to effectually labels deviating difficulties and extensively exploited in several imagery dispensation errands.Encouraged by those efficacious solicitations,sparse representation(SR)has likewise been presented to categorize HSI’s and validated virtuous enactment.This research paper offers an overview of the literature on the classification of HSI technology and its applications.This assessment is centered on a methodical review of SR and support vector machine(SVM)grounded HSI taxonomy works and equates numerous approaches for this matter.We form an outline that splits the equivalent mechanisms into spectral aspects of systems,and spectral–spatial feature networks to methodically analyze the contemporary accomplishments in HSI taxonomy.Furthermore,cogitating the datum that accessible training illustrations in the remote distinguishing arena are generally appropriate restricted besides training neural networks(NNs)to necessitate an enormous integer of illustrations,we comprise certain approaches to increase taxonomy enactment,which can deliver certain strategies for imminent learnings on this issue.Lastly,numerous illustrative neural learning-centered taxonomy approaches are piloted on physical HSI’s in our experimentations.

Knowledge Access Based on the Rough Set Theory

During the procedure of fault diagnosis for large-scale complicated equipment, the existence of redundant and fuzzy information results in the difficulty of knowledge access. Aiming at this characteristic, this paper brought forth the Rough Set （RS） theory to the field of fault diagnosis. By means of the RS theory which is predominant in the way of dealing with fuzzy and uncertain information, knowledge access about fault diagnosis was realized. The foundation ideology of the RS theory was exhausted in detail, an amended RS algorithm was proposed, and the process model of knowledge access based on the amended RS algorithm was researched. Finally, we verified the correctness and the practicability of this method during the procedure of knowledge access.

Health monitoring of time-varying stochastic structures by latent components and fuzzy expert system

In this paper, a novel parametric model-based decomposition method is proposed for structural health monitoring of time-varying structures. For this purpose, the advanced Functional-Series Time-dependent Auto Regressive Moving Average （FS-TARMA） technique is used to estimate the parameters and innovation variance used in the parametric signal decomposition scheme. Additionally, a unique feature extraction and reduction method based on the decomposed signals, known as Latent Components （LCs）, is proposed. To evaluate the efficiency of the proposed method, numerical simulation and an experimental study in the laboratory were conducted on a time-varying structure, where various types of damage were introduced. The Fuzzy Expert System （FES） was used as a classification toot to demonstrate that the proposed method successfully identifies different structural conditions when compared with other methods based on non-reduced and ordinary feature extraction.

A Novel Image Retrieval Method with Improved DCNN and Hash

In large-scale image retrieval,deep features extracted by Convolutional Neural Network(CNN)can effectively express more image information than those extracted by traditional manual methods.However,the deep feature dimensions obtained by Deep Convolutional Neural Network(DCNN)are too high and redundant,which leads to low retrieval efficiency.We propose a novel image retrieval method,which combines deep features selection with improved DCNN and hash transform based on high-dimension features reduction to gain low-dimension deep features and realizes efficient image retrieval.Firstly,the improved network is based on the existing deep model to build a more profound and broader network by adding multiple groups of different branches.Therefore,it is named DFS-Net(Deep Feature Selection Network).The adaptive learning deep features of the Network can effectively alleviate the influence of over-fitting and improve the feature expression of image content.Secondly,the information gain rate method is used to filter the extracted deep features to reduce the feature dimension and ensure the information loss is small.The last step of the method,hash Transform,sparsifies and binarizes this representation to reduce the computation and storage pressure while maintaining the retrieval accuracy.Finally,the scheme is based on the distinguished ResNet50,InceptionV3,and MobileNetV2 models,and studied and evaluated deeply on the CIFAR10 and Caltech256 datasets.The experimental results show that the novel method can train the deep features with stronger recognition ability on limited training samples,and improve the accuracy and efficiency of image retrieval effectively.

Rapid and label-free classification of pathogens based on light scattering,reduced power spectral features and support vector machine

The rapid identification of pathogens is crucial in controlling the food quality and safety.The proposed system for the rapid and label-free identification of pathogens is based on the principle of laser scattering from the bacterial microbes.The clinical prototype consists of three parts:the laser beam,photodetectors,and the data acquisition system.The bacterial testing sample was mixed with 10 mL distilled water and placed inside the machine chamber.When the bacterial microbes pass by the laser beam,the scattering of light occurs due to variation in size,shape,and morphology.Due to this reason,different types of pathogens show their unique light scattering patterns.The photo-detectors were arranged at the surroundings of the sample at different angles to collect the scattered light.The photodetectors convert the scattered light intensity into a voltage waveform.The waveform features were acquired by using the power spectral characteristics,and the dimensionality of extracted features was reduced by applying minimal-redundancy-maximal-relevance criterion(mRMR).A support vector machine(SVM)classifier was developed by training the selected power spectral features for the classification of three different bacterial microbes.The resulting average identification accuracies of E.faecalis,E.coli and S.aureus were 99%,87%,and 94%,respectively.The ove rall experimental results yield a higher accuracy of 93.6%,indicating that the proposed device has the potential for label-free identification of pathogens with simplicity,rapidity,and cost-effectiveness.

Appropriate Sub-band Selection in Wavelet Packet Decomposition for Automated Glaucoma Diagnoses

The most common reason for blindness among human beings is Glaucoma.The increase of fluid pressure damages the optic nerve which gradually leads to irreversible loss of vision.A technique for automated screening of Glaucoma from the fundal retinal images is presented in this paper.This paper intends to explore the significance of both the approximate and detail coefficients through wavelet packet decomposition(WPD).Decomposition is done with "db3" wavelet function and the images are decomposed up to level-3producing 84 sub-bands.Two features,the energy and the entropy are calculated for each sub-band producing two feature matrices(158 images × 84 features).The above step is purely a statistical measure based on WPD.To enhance the diagnostic accuracy,the second phase considers the structural(biological) region of interest(ROI) in the image and then extracts the same features.It is worthy to note that direct biological features are not extracted to eliminate the drawbacks of segmentation whereas the biologically significant region is taken as biological-ROI.Interestingly,the detailed coefficient sub-bands(prominent edges) show more significance in the biological-ROI phase.Apart from enhancing the diagnostic accuracy by feature reduction,the paper intends to mark the significance indices,uniqueness and discrimination capability of the significant features(sub-bands) in both the phases.Then,the crisp inputs are fed to the classifier ANN.Finally,from the significant features of the biological-ROI feature matrices,the accuracy is raised to 85%which is notable than the accuracy of 79%achieved without considering the ROI.