Night Vision Object Tracking System Using Correlation Aware LSTM-Based Modified Yolo Algorithm

Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions,such as mist,fog,dust etc.The pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient envir-onments.In recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities.However,the iden-tification of objects in the night world also poses further problems because of the distorted backdrop and dim light.The Correlation aware LSTM based YOLO(You Look Only Once)classifier method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this work.In order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerfilter for removing the noise in the image.The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set Segmentation.Finally,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalfindings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism.Our approach may therefore satisfy the true demands of night scene target detection applications.We very much believe that our method will help future research.

An Intrusion Detection System for SDN Using Machine Learning

Software Defined Networking(SDN)has emerged as a promising and exciting option for the future growth of the internet.SDN has increased the flexibility and transparency of the managed,centralized,and controlled network.On the other hand,these advantages create a more vulnerable environment with substantial risks,culminating in network difficulties,system paralysis,online banking frauds,and robberies.These issues have a significant detrimental impact on organizations,enterprises,and even economies.Accuracy,high performance,and real-time systems are necessary to achieve this goal.Using a SDN to extend intelligent machine learning methodologies in an Intrusion Detection System(IDS)has stimulated the interest of numerous research investigators over the last decade.In this paper,a novel HFS-LGBM IDS is proposed for SDN.First,the Hybrid Feature Selection algorithm consisting of two phases is applied to reduce the data dimension and to obtain an optimal feature subset.In thefirst phase,the Correlation based Feature Selection(CFS)algorithm is used to obtain the feature subset.The optimal feature set is obtained by applying the Random Forest Recursive Feature Elimination(RF-RFE)in the second phase.A LightGBM algorithm is then used to detect and classify different types of attacks.The experimental results based on NSL-KDD dataset show that the proposed system produces outstanding results compared to the existing methods in terms of accuracy,precision,recall and f-measure.

An Efficient Cyber Security and Intrusion Detection System Using CRSR with PXORP-ECC and LTH-CNN

Intrusion Detection System(IDS)is a network security mechanism that analyses all users’and applications’traffic and detectsmalicious activities in real-time.The existing IDSmethods suffer fromlower accuracy and lack the required level of security to prevent sophisticated attacks.This problem can result in the system being vulnerable to attacks,which can lead to the loss of sensitive data and potential system failure.Therefore,this paper proposes an Intrusion Detection System using Logistic Tanh-based Convolutional Neural Network Classification(LTH-CNN).Here,the Correlation Coefficient based Mayfly Optimization(CC-MA)algorithm is used to extract the input characteristics for the IDS from the input data.Then,the optimized features are utilized by the LTH-CNN,which returns the attacked and non-attacked data.After that,the attacked data is stored in the log file and non-attacked data is mapped to the cyber security and data security phases.To prevent the system from cyber-attack,the Source and Destination IP address is converted into a complex binary format named 1’s Complement Reverse Shift Right(CRSR),where,in the data security phase the sensed data is converted into an encrypted format using Senders Public key Exclusive OR Receivers Public Key-Elliptic Curve Cryptography(PXORP-ECC)Algorithm to improve the data security.TheNetwork Security Laboratory-Knowledge Discovery inDatabases(NSLKDD)dataset and real-time sensor are used to train and evaluate the proposed LTH-CNN.The suggested model is evaluated based on accuracy,sensitivity,and specificity,which outperformed the existing IDS methods,according to the results of the experiments.

Data-driven unsupervised anomaly detection and recovery of unmanned aerial vehicle flight data based on spatiotemporal correlation

Anomaly detection is crucial to the flight safety and maintenance of unmanned aerial vehicles(UAVs)and has attracted extensive attention from scholars.Knowledge-based approaches rely on prior knowledge,while model-based approaches are challenging for constructing accurate and complex physical models of unmanned aerial systems(UASs).Although data-driven methods do not require extensive prior knowledge and accurate physical UAS models,they often lack parameter selection and are limited by the cost of labeling anomalous data.Furthermore,flight data with random noise pose a significant challenge for anomaly detection.This work proposes a spatiotemporal correlation based on long short-term memory and autoencoder(STCLSTM-AE)neural network data-driven method for unsupervised anomaly detection and recovery of UAV flight data.First,UAV flight data are preprocessed by combining the Savitzky-Golay filter data processing technique to mitigate the effect of noise in the original historical flight data on the model.Correlation-based feature subset selection is subsequently performed to reduce the reliance on expert knowledge.Then,the extracted features are used as the input of the designed LSTM-AE model to achieve the anomaly detection and recovery of UAV flight data in an unsupervised manner.Finally,the method's effectiveness is validated on real UAV flight data.

Apple leaf disease identification using genetic algorithm and correlation based feature selection method

Apple leaf disease is one of the main factors to constrain the apple production and quality.It takes a long time to detect the diseases by using the traditional diagnostic approach,thus farmers often miss the best time to prevent and treat the diseases.Apple leaf disease recognition based on leaf image is an essential research topic in the field of computer vision,where the key task is to find an effective way to represent the diseased leaf images.In this research,based on image processing techniques and pattern recognition methods,an apple leaf disease recognition method was proposed.A color transformation structure for the input RGB(Red,Green and Blue)image was designed firstly and then RGB model was converted to HSI(Hue,Saturation and Intensity),YUV and gray models.The background was removed based on a specific threshold value,and then the disease spot image was segmented with region growing algorithm(RGA).Thirty-eight classifying features of color,texture and shape were extracted from each spot image.To reduce the dimensionality of the feature space and improve the accuracy of the apple leaf disease identification,the most valuable features were selected by combining genetic algorithm(GA)and correlation based feature selection(CFS).Finally,the diseases were recognized by SVM classifier.In the proposed method,the selected feature subset was globally optimum.The experimental results of more than 90%correct identification rate on the apple diseased leaf image database which contains 90 disease images for there kinds of apple leaf diseases,powdery mildew,mosaic and rust,demonstrate that the proposed method is feasible and effective.

A new multi-model coding algorithm based on MBE and spectral amplitude correlation between successive frames

The researches on spectral amplitude correlation between successive frames based on MBE (Multi-Band Excitation) are presented and a new MBE coding algorithm of muitimodels is proposed. It has been shown that the magnitude spectrum in MBE coding algorithm can be estimated in very high accuracy by using a simple linear predictive model of the spectral amplitude correlation between successive frames. This model employs oniy one gain coefficient and one predictive coefficient. The accuracy of estimated magnitude spectrum in this model is higher than that of the conventional MBE algorithm using the 10-th order LPC (Linear Predictive Coding) Inodel without the spectral amplitude correlation between successlve frames.This model is superior to the conventional MBE coding model in volced speech, but it is iess effective in unvoiced speech, so that the conventional MBE coding algorithm is still used for unvoiced speech. The new multi-model coding algorithm improves the quality of coding speech,and obtains good results

3D image reconstruction with controllable spatial filtering based on correlation of multiple periodic functions in computational integral imaging

We propose a novel method of slice image reconstruction with controllable spatial filtering by using the correlation of periodic delta-function arrays （PDFAs） with elemental images in computational integral imaging. The multiple PDFAs, whose spatial periods correspond to object＇s depths with the elemental image array （EIA）, can generate a set of spatially filtered EIAs for multiple object depths compared with the conventional method for the depth of a single object. We analyze a controllable spatial filtering effect by the proposed method. To show the feasibility of the proposed method, we carry out preliminary experiments for multiple objects and present the results.