A classification method of building structures based on multi-feature fusion of UAV remote sensing images

In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.

Improved Whale Optimization with Local-Search Method for Feature Selection

Various feature selection algorithms are usually employed to improve classification models’overall performance.Optimization algorithms typically accompany such algorithms to select the optimal set of features.Among the most currently attractive trends within optimization algorithms are hybrid metaheuristics.The present paper presents two Stages of Local Search models for feature selection based on WOA(Whale Optimization Algorithm)and Great Deluge(GD).GD Algorithm is integrated with the WOA algorithm to improve exploitation by identifying the most promising regions during the search.Another version is employed using the best solution found by the WOA algorithm and exploited by the GD algorithm.In addition,disruptive selection(DS)is employed to select the solutions from the population for local search.DS is chosen to maintain the diversity of the population via enhancing low and high-quality solutions.Fifteen(15)standard benchmark datasets provided by the University of California Irvine(UCI)repository were used in evaluating the proposed approaches’performance.Next,a comparison was made with four population-based algorithms as wrapper feature selection methods from the literature.The proposed techniques have proved their efficiency in enhancing classification accuracy compared to other wrapper methods.Hence,the WOA can search effectively in the feature space and choose the most relevant attributes for classification tasks.

Morpho-structural Features and Structural Classification of Chromite Pods in the Tropoje-Has Ophiolite Massif, Albania

Tropoje-Has ophiolitic massif of eastern Mirdita(Albania)ophiolitic belt,is a major source for metallurgical chromite ore in Albania.Massif consists of a thick mantle section of SSZ type,8-10 km thick and

A Deep CNN-LSTM-Based Feature Extraction for Cyber-Physical System Monitoring

A potential concept that could be effective for multiple applications is a“cyber-physical system”(CPS).The Internet of Things(IoT)has evolved as a research area,presenting new challenges in obtaining valuable data through environmental monitoring.The existing work solely focuses on classifying the audio system of CPS without utilizing feature extraction.This study employs a deep learning method,CNN-LSTM,and two-way feature extraction to classify audio systems within CPS.The primary objective of this system,which is built upon a convolutional neural network(CNN)with Long Short Term Memory(LSTM),is to analyze the vocalization patterns of two different species of anurans.It has been demonstrated that CNNs,when combined with mel-spectrograms for sound analysis,are suitable for classifying ambient noises.Initially,the data is augmented and preprocessed.Next,the mel spectrogram features are extracted through two-way feature extraction.First,Principal Component Analysis(PCA)is utilized for dimensionality reduction,followed by Transfer learning for audio feature extraction.Finally,the classification is performed using the CNN-LSTM process.This methodology can potentially be employed for categorizing various biological acoustic objects and analyzing biodiversity indexes in natural environments,resulting in high classification accuracy.The study highlights that this CNNLSTM approach enables cost-effective and resource-efficient monitoring of large natural regions.The dissemination of updated CNN-LSTM models across distant IoT nodes is facilitated flexibly and dynamically through the utilization of CPS.

SCChOA:Hybrid Sine-Cosine Chimp Optimization Algorithm for Feature Selection

Feature Selection(FS)is an important problem that involves selecting the most informative subset of features from a dataset to improve classification accuracy.However,due to the high dimensionality and complexity of the dataset,most optimization algorithms for feature selection suffer from a balance issue during the search process.Therefore,the present paper proposes a hybrid Sine-Cosine Chimp Optimization Algorithm(SCChOA)to address the feature selection problem.In this approach,firstly,a multi-cycle iterative strategy is designed to better combine the Sine-Cosine Algorithm(SCA)and the Chimp Optimization Algorithm(ChOA),enabling a more effective search in the objective space.Secondly,an S-shaped transfer function is introduced to perform binary transformation on SCChOA.Finally,the binary SCChOA is combined with the K-Nearest Neighbor(KNN)classifier to form a novel binary hybrid wrapper feature selection method.To evaluate the performance of the proposed method,16 datasets from different dimensions of the UCI repository along with four evaluation metrics of average fitness value,average classification accuracy,average feature selection number,and average running time are considered.Meanwhile,seven state-of-the-art metaheuristic algorithms for solving the feature selection problem are chosen for comparison.Experimental results demonstrate that the proposed method outperforms other compared algorithms in solving the feature selection problem.It is capable of maximizing the reduction in the number of selected features while maintaining a high classification accuracy.Furthermore,the results of statistical tests also confirm the significant effectiveness of this method.

Information Classification and Extraction on Official Web Pages of Organizations

As a real-time and authoritative source,the official Web pages of organizations contain a large amount of information.The diversity of Web content and format makes it essential for pre-processing to get the unified attributed data,which has the value of organizational analysis and mining.The existing research on dealing with multiple Web scenarios and accuracy performance is insufficient.This paper aims to propose a method to transform organizational official Web pages into the data with attributes.After locating the active blocks in the Web pages,the structural and content features are proposed to classify information with the specific model.The extraction methods based on trigger lexicon and LSTM(Long Short-Term Memory)are proposed,which efficiently process the classified information and extract data that matches the attributes.Finally,an accurate and efficient method to classify and extract information from organizational official Web pages is formed.Experimental results show that our approach improves the performing indicators and exceeds the level of state of the art on real data set from organizational official Web pages.

Ensemble feature selection integrating elitist roles and quantum game model

To accelerate the selection process of feature subsets in the rough set theory （RST）, an ensemble elitist roles based quantum game （EERQG） algorithm is proposed for feature selec- tion. Firstly, the multilevel elitist roles based dynamics equilibrium strategy is established, and both immigration and emigration of elitists are able to be self-adaptive to balance between exploration and exploitation for feature selection. Secondly, the utility matrix of trust margins is introduced to the model of multilevel elitist roles to enhance various elitist roles＇ performance of searching the optimal feature subsets, and the win-win utility solutions for feature selec- tion can be attained. Meanwhile, a novel ensemble quantum game strategy is designed as an intriguing exhibiting structure to perfect the dynamics equilibrium of multilevel elitist roles. Finally, the en- semble manner of multilevel elitist roles is employed to achieve the global minimal feature subset, which will greatly improve the fea- sibility and effectiveness. Experiment results show the proposed EERQG algorithm has superiority compared to the existing feature selection algorithms.

Automatic modulation classification using modulation fingerprint extraction

An automatic method for classifying frequency shift keying(FSK),minimum shift keying(MSK),phase shift keying(PSK),quadrature amplitude modulation(QAM),and orthogonal frequency division multiplexing(OFDM)is proposed by simultaneously using normality test,spectral analysis,and geometrical characteristics of in-phase-quadrature(I-Q)constellation diagram.Since the extracted features are unique for each modulation,they can be considered as a fingerprint of each modulation.We show that the proposed algorithm outperforms the previously published methods in terms of signal-to-noise ratio(SNR)and success rate.For example,the success rate of the proposed method for 64-QAM modulation at SNR=11 dB is 99%.Another advantage of the proposed method is its wide SNR range;such that the probability of classification for 16-QAM at SNR=3 dB is almost 1.The proposed method also provides a database for geometrical features of I-Q constellation diagram.By comparing and correlating the data of the provided database with the estimated I-Q diagram of the received signal,the processing gain of 4 dB is obtained.Whatever can be mentioned about the preference of the proposed algorithm are low complexity,low SNR,wide range of modulation set,and enhanced recognition at higher-order modulations.

Impact of Genetic Algorithm for the Diagnosis of Breast Cancer: Literature Review

In recent research from the total number of new cancer cases in Africa about 29.46% and in Ethiopia 31.85% are breast cancer cases. 25.84% of all cancer related death is from breast cancer. One of the challenges in the treatment of breast cancer is early detection. Researchers agreed that, improving the preventive mechanism of breast cancer is an early predicting and detecting model. Research efforts are continuing to present different solution approaches using advanced techniques of Artificial intelligence (AI), Machine learning (ML), Deep Learning (DL), and Computational Intelligence as well. A genetic algorithm is a hyper-parameter optimization algorithm that belongs to the class of evolutionary algorithms. Genetic Algorithm (GA) is used for complex search spaces for search and optimization. This reviewed literature paper shows the positive effect of GA in the diagnosis of breast cancer on AI algorithms.

Brake Fault Diagnosis Through Machine Learning Approaches–A Review

Diagnosis is the recognition of the nature and cause of a certain phenomenon.It is generally used to determine cause and effect of a problem. Machine fault diagnosis isa field of finding faults arising in machines. To identify the most probable faults leadingto failure, many methods are used for data collection, including vibration monitoring,thermal imaging, oil particle analysis, etc. Then these data are processed using methodslike spectral analysis, wavelet analysis, wavelet transform, short-term Fourier transform,high-resolution spectral analysis, waveform analysis, etc. The results of this analysis areused in a root cause failure analysis in order to determine the original cause of the fault.This paper presents a brief review about one such application known as machine learningfor the brake fault diagnosis problems.

An Efficient Scheme for Data Pattern Matching in IoT Networks

The Internet has become an unavoidable trend of all things due to the rapid growth of networking technology,smart home technology encompasses a variety of sectors,including intelligent transportation,allowing users to communicate with anybody or any device at any time and from anywhere.However,most things are different now.Background:Structured data is a form of separated storage that slows down the rate at which everything is connected.Data pattern matching is commonly used in data connectivity and can help with the issues mentioned above.Aim:The present pattern matching system is ineffective due to the heterogeneity and rapid expansion of large IoT data.The method requires a lot of manual work and has a poor match with real-world applications.In the modern IoT context,solving the challenge of automatic pattern matching is complex.Methodology:A three-layer mapping matching is proposed for heterogeneous data from the IoT,and a hierarchical pattern matching technique.The feature classification matching,relational feature clustering matching,and mixed element matching are all examples of feature classification matching.Through layer-by-layer matching,the algorithm gradually narrows the matching space,improving matching quality,reducing the number of matching between components and the degree of manual participation,and producing a better automatic mode matching.Results:The algorithm’s efficiency and performance are tested using a large number of data samples,and the results show that the technique is practical and effective.Conclusion:the proposed algorithm utilizes the instance information of the data pattern.It deploys three-layer mapping matching approach and mixed element matching and realizes the automatic pattern matching of heterogeneous data which reduces the matching space between elements in complex patterns.It improves the efficiency and accuracy of automatic matching.

Repairing the human with artificial intelligence in oncology

Artificial intelligence is a groundbreaking tool to learn and analyse higher features extracted from any dataset at large scale.This ability makes it ideal to facing any complex problem that may generally arise in the biomedical domain or oncology in particular.In this work,we envisage to provide a global vision of this mathematical discipline outgrowth by linking some other related subdomains such as transfer,reinforcement or federated learning.Complementary,we also introduce the recently popular method of topological data analysis that improves the performance of learning models.

Video Analytics Framework for Human Action Recognition

Human action recognition(HAR)is an essential but challenging task for observing human movements.This problem encompasses the observations of variations in human movement and activity identification by machine learning algorithms.This article addresses the challenges in activity recognition by implementing and experimenting an intelligent segmentation,features reduction and selection framework.A novel approach has been introduced for the fusion of segmented frames and multi-level features of interests are extracted.An entropy-skewness based features reduction technique has been implemented and the reduced features are converted into a codebook by serial based fusion.A custom made genetic algorithm is implemented on the constructed features codebook in order to select the strong and wellknown features.The features are exploited by a multi-class SVM for action identification.Comprehensive experimental results are undertaken on four action datasets,namely,Weizmann,KTH,Muhavi,and WVU multi-view.We achieved the recognition rate of 96.80%,100%,100%,and 100%respectively.Analysis reveals that the proposed action recognition approach is efficient and well accurate as compare to existing approaches.

Affective State Recognition Using Thermal-Based Imaging: A Survey

The thermal-based imaging technique has recently attracted the attention of researchers who are interested in the recognition of human affects dueto its ability to measure the facial transient temperature, which is correlated withhuman affects and robustness against illumination changes. Therefore, studieshave increasingly used the thermal imaging as a potential and supplemental solution to overcome the challenges of visual (RGB) imaging, such as the variation oflight conditions and revealing original human affect. Moreover, the thermal-basedimaging has shown promising results in the detection of psychophysiological signals, such as pulse rate and respiration rate in a contactless and noninvasive way.This paper presents a brief review on human affects and focuses on the advantages and challenges of the thermal imaging technique. In addition, this paper discusses the stages of thermal-based human affective state recognition, such asdataset type, preprocessing stage, region of interest (ROI), feature descriptors,and classification approaches with a brief performance analysis based on a number of works in the literature. This analysis could help beginners in the thermalimaging and affective recognition domain to explore numerous approaches usedby researchers to construct an affective state system based on thermal imaging.

Real-Time Brain-Computer Interface System Based on Motor Imagery

Abstract-A brain-computer interface （BCI） real- time system based on motor imagery translates the user＇s motor intention into a real-time control signal for peripheral equipments. A key problem to be solved for practical applications is real-time data collection and processing. In this paper, a real-time BCI system is implemented on computer with electroencephalogram amplifier. In our implementation, the on-line voting method is adopted for feedback control strategy, and the voting results are used to control the cursor horizontal movement. Three subjects take part in the experiment. The results indicate that the best accuracy is 90%.

Classification of Remote Sensing Images Based on Band Selection and Multi-mode Feature Fusion

As feature data in multimodal remote sensing images belong to multiple modes and are complementary to each other,the traditional method of single-mode data analysis and processing cannot effectively fuse the data of different modes and express the correlation between different modes.In order to solve this problem,make better fusion of different modal data and the relationship between the said features,this paper proposes a fusion method of multiple modal spectral characteristics and radar remote sensing imageaccording to the spatial dimension in the form of a vector or matrix for effective integration,by training the SVM model.Experimental results show that the method based on band selection and multi-mode feature fusion can effectively improve the robustness of remote sensing image features.Compared with other methods,the fusion method can achieve higher classification accuracy and better classification effect.

Double DQN Method For Botnet Traffic Detection System

In the face of the increasingly severe Botnet problem on the Internet,how to effectively detect Botnet traffic in realtime has become a critical problem.Although the existing deepQnetwork(DQN)algorithminDeep reinforcement learning can solve the problem of real-time updating,its prediction results are always higher than the actual results.In Botnet traffic detection,although it performs well in the training set,the accuracy rate of predicting traffic is as high as%;however,in the test set,its accuracy has declined,and it is impossible to adjust its prediction strategy on time based on new data samples.However,in the new dataset,its accuracy has declined significantly.Therefore,this paper proposes a Botnet traffic detection system based on double-layer DQN(DDQN).Two Q-values are designed to adjust the model in policy and action,respectively,to achieve real-time model updates and improve the universality and robustness of the model under different data sets.Experiments show that compared with the DQN model,when using DDQN,the Q-value is not too high,and the detectionmodel has improved the accuracy and precision of Botnet traffic.Moreover,when using Botnet data sets other than the test set,the accuracy and precision of theDDQNmodel are still higher than DQN.

IMPROVED MAN-COMPUTER INTERACTIVE CLASSIFICATION OF CLOUDS BASED ON BISPECTRAL SATELLITE IMAGERY

In this paper,improvement on man-computer interactive classification of clouds based on hispeetral satellite imagery has been synthesized by using the maximum likelihood automatic clustering(MLAC)and the unit feature space classification(UFSC)approaches.The improved classification not only shortens the time of sample-training in UFSC method,but also eliminates the inevitable shortcomings of the MLAC method.(e.g.,1.sample selecting and training is confined only to one cloud image:2.the result of clustering is pretty sensitive to the selection of initial cluster center:3.the actual classification basically can not satisfy the supposition of normal distribution required by MLAC method;4.errors in classification are difficult to be modified.) Moreover,it makes full use of the professionals'accumulated knowledge and experience of visual cloud classifications and the cloud report of ground observation,having ensured both the higher accuracy of classification and its wide application as well.

Hyperspectral image classification based on volumetric texture and dimensionality reduction

A novel approach using volumetric texture and reduced-spectral features is presented for hyperspectral image classification. Using this approach, the volumetric textural fea^res were extracted by volumetric gray-level co-occurrence matrices （VGLCM）. The spectral features were extracted by minimum estimated abundance covar- iance （MEAC） and linear prediction （LP）-based band selection, and a semi-supervised k-means （SKM） cluster- ing method with deleting the worst cluster （SKMd） band- clustering algorithms. Moreover, four feature combination schemes were designed for hyperspectral image classifica- tion by using spectral and textural features. It has been proven that the proposed method using VGLCM outper- forms the gray-level co-occurrence matrices （GLCM） method, and the experimental results indicate that the combination of spectral information with volumetric textural features leads to an improved classification performance in hyperspectral imagery.

Classification of Spectra of Emission Line Stars Using Machine Learning Techniques

Advances in the technology of astronomical spectra acquisition have resulted in an enormous amount of data available in world-wide telescope archives. It is no longer feasible to analyze them using classical approaches, so a new astronomical discipline,astroinformatics, has emerged. We describe the initial experiments in the investigation of spectral line profiles of emission line stars using machine learning with attempt to automatically identify Be and B[e] stars spectra in large archives and classify their types in an automatic manner. Due to the size of spectra collections, the dimension reduction techniques based on wavelet transformation are studied as well. The result clearly justifies that machine learning is able to distinguish different shapes of line profiles even after drastic dimension reduction.