Detection of Turbulence Anomalies Using a Symbolic Classifier Algorithm in Airborne Quick Access Record(QAR)Data Analysis

As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.

Intrusion Detection System Using Classification Algorithms with Feature Selection Mechanism over Real-Time Data Traffic

The key objective of intrusion detection systems(IDS)is to protect the particular host or network by investigating and predicting the network traffic as an attack or normal.These IDS uses many methods of machine learning(ML)to learn from pastexperience attack i.e.signatures based and identify the new ones.Even though these methods are effective,but they have to suffer from large computational costs due to considering all the traffic features,together.Moreover,emerging technologies like the Internet of Things(Io T),big data,etc.are getting advanced day by day;as a result,network traffics are also increasing rapidly.Therefore,the issue of computational cost needs to be addressed properly.Thus,in this research,firstly,the ML methods have been used with the feature selection technique(FST)to reduce the number of features by picking out only the important ones from NSL-KDD,CICIDS2017,and CIC-DDo S2019datasets later that helped to build IDSs with lower cost but with the higher performance which would be appropriate for vast scale network.The experimental result demonstrated that the proposed model i.e.Decision tree(DT)with Recursive feature elimination(RFE)performs better than other classifiers with RFE in terms of accuracy,specificity,precision,sensitivity,F1-score,and G-means on the investigated datasets.

基于Extra Tree Classifier的水质安全建模预测

随着工业化和城市化的快速发展,水质安全问题日益受到关注。本研究利用一个包含7999条数据记录的水质分析数据集,涵盖多种化学物质浓度测量值与安全阈值,以及“是否安全”分类变量,运用Extr aTree Classifier模型进行水质安全建模预测及数据分析。本研究目的在于提供一个可靠的模型,以帮助决策者和相关部门更好地监测和维护水质安全,从而保障公众健康和环境可持续发展。

Using Cross Entropy as a Performance Metric for Quantifying Uncertainty in DNN Image Classifiers: An Application to Classification of Lung Cancer on CT Images

Cross entropy is a measure in machine learning and deep learning that assesses the difference between predicted and actual probability distributions. In this study, we propose cross entropy as a performance evaluation metric for image classifier models and apply it to the CT image classification of lung cancer. A convolutional neural network is employed as the deep neural network (DNN) image classifier, with the residual network (ResNet) 50 chosen as the DNN archi-tecture. The image data used comprise a lung CT image set. Two classification models are built from datasets with varying amounts of data, and lung cancer is categorized into four classes using 10-fold cross-validation. Furthermore, we employ t-distributed stochastic neighbor embedding to visually explain the data distribution after classification. Experimental results demonstrate that cross en-tropy is a highly useful metric for evaluating the reliability of image classifier models. It is noted that for a more comprehensive evaluation of model perfor-mance, combining with other evaluation metrics is considered essential. .

RefluxClassifier分离细颗粒的技术发展与应用前景

矿石综采技术带来诸多便利的同时,也导致了矿石中细颗粒比例增多。细颗粒分离成为了国内外矿物加工领域面临的难题。由于细颗粒质量小、比表面积大、表面能高、容易团聚,进而难以有效分离。本世纪初,由澳大利亚学者Galvin所研制的RefluxClassifier(回流分级机,简称RC)作为一种新型重力分选设备进入到矿物加工设备行列。该设备由液固流化床与倾斜通道组成,分为垂直段与倾斜段,具有操作简单、成本低廉和高效节能等优点。据研究,RC因其特殊的结构与工作机理可以有效解决细颗粒分离问题。本文首先归纳了国内外有关RC的理论研究,详细描述了RC倾斜段中颗粒在流体中的运动状态,阐明了倾斜通道内颗粒运动与流体流动特性之间的关系,简要分析了颗粒性质与流体之间的力与速度关系。此外,本文对目前现有RC的水速预测模型(经典动力学模型、经验模型、弱化粒度模型、平衡模型)进行了总结,并综合分析了各模型的适用范围。结合试验案例,介绍了RC在煤炭、黑金属、砂石骨料等领域的应用现状,举例分析不同试验条件下RC对细颗粒回收的分离情况。最后结合我国资源现状与现代设备发展趋势,提出如何深入优化RC分选理论模型、拓展更广阔的应用领域是国内外学者的长期研究目标,并展望RC在工业范围内的全面推广。

CL2ES-KDBC:A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.

An Expert System to Detect Political Arabic Articles Orientation Using CatBoost Classifier Boosted by Multi-Level Features

The number of blogs and other forms of opinionated online content has increased dramatically in recent years.Many fields,including academia and national security,place an emphasis on automated political article orientation detection.Political articles(especially in the Arab world)are different from other articles due to their subjectivity,in which the author’s beliefs and political affiliation might have a significant influence on a political article.With categories representing the main political ideologies,this problem may be thought of as a subset of the text categorization(classification).In general,the performance of machine learning models for text classification is sensitive to hyperparameter settings.Furthermore,the feature vector used to represent a document must capture,to some extent,the complex semantics of natural language.To this end,this paper presents an intelligent system to detect political Arabic article orientation that adapts the categorical boosting(CatBoost)method combined with a multi-level feature concept.Extracting features at multiple levels can enhance the model’s ability to discriminate between different classes or patterns.Each level may capture different aspects of the input data,contributing to a more comprehensive representation.CatBoost,a robust and efficient gradient-boosting algorithm,is utilized to effectively learn and predict the complex relationships between these features and the political orientation labels associated with the articles.A dataset of political Arabic texts collected from diverse sources,including postings and articles,is used to assess the suggested technique.Conservative,reform,and revolutionary are the three subcategories of these opinions.The results of this study demonstrate that compared to other frequently used machine learning models for text classification,the CatBoost method using multi-level features performs better with an accuracy of 98.14%.

Mammogram Classification with HanmanNets Using Hanman Transform Classifier

Breast cancer is a deadly disease and radiologists recommend mammography to detect it at the early stages. This paper presents two types of HanmanNets using the information set concept for the derivation of deep information set features from ResNet by modifying its kernel functions to yield Type-1 HanmanNets and then AlexNet, GoogLeNet and VGG-16 by changing their feature maps to yield Type-2 HanmanNets. The two types of HanmanNets exploit the final feature maps of these architectures in the generation of deep information set features from mammograms for their classification using the Hanman Transform Classifier. In this work, the characteristics of the abnormality present in the mammograms are captured using the above network architectures that help derive the features of HanmanNets based on information set concept and their performance is compared via the classification accuracies. The highest accuracy of 100% is achieved for the multi-class classifications on the mini-MIAS database thus surpassing the results in the literature. Validation of the results is done by the expert radiologists to show their clinical relevance.

Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malware detection.However,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection.Addressing this gap can provide valuable insights for enhancing cybersecurity strategies.While numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware detection.Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures.This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems.The objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows malware.Evaluating the accuracy,efficiency,and suitability of each classifier for real-world malware detection scenarios.Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers.Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence.The study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and evaluation.Exploratory data analysis involves understanding the dataset’s characteristics and identifying preprocessing requirements.Data preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for training.Model training utilizes various supervised classifiers,and their performance is evaluated using metrics such as accuracy,precision,recall,and F1 score.The study’s outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection.Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware.Additionally,insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses.Overall,this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats.

Model-Free Ultra-High-Dimensional Feature Screening for Multi-Classified Response Data Based on Weighted Jensen-Shannon Divergence

In ultra-high-dimensional data, it is common for the response variable to be multi-classified. Therefore, this paper proposes a model-free screening method for variables whose response variable is multi-classified from the point of view of introducing Jensen-Shannon divergence to measure the importance of covariates. The idea of the method is to calculate the Jensen-Shannon divergence between the conditional probability distribution of the covariates on a given response variable and the unconditional probability distribution of the covariates, and then use the probabilities of the response variables as weights to calculate the weighted Jensen-Shannon divergence, where a larger weighted Jensen-Shannon divergence means that the covariates are more important. Additionally, we also investigated an adapted version of the method, which is to measure the relationship between the covariates and the response variable using the weighted Jensen-Shannon divergence adjusted by the logarithmic factor of the number of categories when the number of categories in each covariate varies. Then, through both theoretical and simulation experiments, it was demonstrated that the proposed methods have sure screening and ranking consistency properties. Finally, the results from simulation and real-dataset experiments show that in feature screening, the proposed methods investigated are robust in performance and faster in computational speed compared with an existing method.

Changes in classified precipitation in the urban, suburban, and mountain areas of Beijing

In this paper, based on hourly precipitation observations in 1977e2013 in the Beijing area, China, hourly precipitation in summer (June?August) is classified into three categories: light (below the 50th percentile values), moderate (the 50th to 95th percentile values), and heavy (above the 95th percentile values). Results reveal that both light and moderate precipitation decreased significantly during the research period and thereby caused the decrease in summer totals. By contrast, pronounced trends failed to be detected in the heavy category. Since 2004, the contribution of heavy rainfall to the summer total precipitation in the urban area increased as compared to the suburban area, which is opposite to light rainfall. There are obvious differences in the diurnal variations of classified precipitation. Light precipitation shows a double peak structure in the early morning and at night, while moderate and heavy rainfall show a single peak at night. Light precipitation at the early morning peak time decreased significantly in the whole Beijing area. Compared with the suburban area, light precipitation in the urban area occurred less frequently whereas heavy precipitation occurred more frequently at evening peak time after 2004. The asymmetry of the rainfall is obvious, especially, for heavy precipitation. The asymmetry of heavy precipitation events in the urban area exhibits a significant increasing trend.

An Improved LSTM-PCA Ensemble Classifier for SQL Injection and XSS Attack Detection

The Repository Mahasiswa(RAMA)is a national repository of research reports in the form of final assignments,student projects,theses,dissertations,and research reports of lecturers or researchers that have not yet been published in journals,conferences,or integrated books from the scientific repository of universities and research institutes in Indonesia.The increasing popularity of the RAMA Repository leads to security issues,including the two most widespread,vulnerable attacks i.e.,Structured Query Language(SQL)injection and cross-site scripting(XSS)attacks.An attacker gaining access to data and performing unauthorized data modifications is extremely dangerous.This paper aims to provide an attack detection system for securing the repository portal from the abovementioned attacks.The proposed system combines a Long Short–Term Memory and Principal Component Analysis(LSTM-PCA)model as a classifier.This model can effectively solve the vanishing gradient problem caused by excessive positive samples.The experiment results show that the proposed system achieves an accuracy of 96.85%using an 80%:20%ratio of training data and testing data.The rationale for this best achievement is that the LSTM’s Forget Gate works very well as the PCA supplies only selected features that are significantly relevant to the attacks’patterns.The Forget Gate in LSTM is responsible for deciding which information should be kept for computing the cell state and which one is not relevant and can be discarded.In addition,the LSTM’s Input Gate assists in finding out crucial information and stores specific relevant data in the memory.

A novel molecular classification method for osteosarcoma based on tumor cell differentiation trajectories

Subclassification of tumors based on molecular features may facilitate therapeutic choice and increase the response rate of cancer patients.However,the highly complex cell origin involved in osteosarcoma(OS)limits the utility of traditional bulk RNA sequencing for OS subclassification.Single-cell RNA sequencing(sc RNA-seq)holds great promise for identifying cell heterogeneity.However,this technique has rarely been used in the study of tumor subclassification.By analyzing sc RNA-seq data for six conventional OS and nine cancellous bone(CB)samples,we identified 29 clusters in OS and CB samples and discovered three differentiation trajectories from the cancer stem cell(CSC)-like subset,which allowed us to classify OS samples into three groups.The classification model was further examined using the TARGET dataset.Each subgroup of OS had different prognoses and possible drug sensitivities,and OS cells in the three differentiation branches showed distinct interactions with other clusters in the OS microenvironment.In addition,we verified the classification model through IHC staining in 138 OS samples,revealing a worse prognosis for Group B patients.Furthermore,we describe the novel transcriptional program of CSCs and highlight the activation of EZH2 in CSCs of OS.These findings provide a novel subclassification method based on sc RNA-seq and shed new light on the molecular features of CSCs in OS and may serve as valuable references for precision treatment for and therapeutic development in OS.

REMOTE SENSING IMAGE CODING METHOD COMBINING WAVELET TRANSFORM WITH CLASSIFIED VECTOR QUANTIZATION

A new remote sensing image coding scheme based on the wavelet transform and classified vector quantization (CVQ) is proposed. The original image is first decomposed into a hierarchy of 3 layers including 10 subimages by DWT. The lowest frequency subimage is compressed by scalar quantization and ADPCM. The high frequency subimages are compressed by CVQ to utilize the similarity among different resolutions while improving the edge quality and reducing computational complexity. The experimental results show that the proposed scheme has a better performance than JPEG, and a PSNR of reconstructed image is 31～33 dB with a rate of 0.2 bpp.

Classified recognition for metal magnetic memory signals of welding defects in API 5L X65 pipeline steel

Feature extraction and selection from signals is a key issue for metal magnetic memory testing technique. In order to realize the classification of metal magnetic memory signals of welding defects, four fractal analysis methods, such as box- counting, detrended fluctuation, minimal cover and rescaled-range analysis, were used to extract the feature signal after the original metal magnet memory signal was de-noising and differential processing, then the Karhunen-Lo^e transformation was adopted as classification tool to identify the defect signals. The result shows that this study can provide an efficient classification method for metal magnetic memory signal of welding defects.

Investigation of Inhabitants' Wishes on Classified Collection of Waste in Wanghua District of Fushun

In order to disclose present situation and problem of classified collection of municipal solid waste in Wanghua District of Fushun and ana- lyze its practicability, questionnaire was designed in this paper, random research was adopted in Wanghua District, and statistic analysis of investi- gation result was conducted. This investigation could provide basis for popularizing classified collection of municipal solid waste in the whole nation.

BS-SC Model:A Novel Method for Predicting Child Abuse Using Borderline-SMOTE Enabled Stacking Classifier

For a long time,legal entities have developed and used crime prediction methodologies.The techniques are frequently updated based on crime evaluations and responses from scientific communities.There is a need to develop type-based crime prediction methodologies that can be used to address issues at the subgroup level.Child maltreatment is not adequately addressed because children are voiceless.As a result,the possibility of developing a model for predicting child abuse was investigated in this study.Various exploratory analysis methods were used to examine the city of Chicago’s child abuse events.The data set was balanced using the Borderline-SMOTE technique,and then a stacking classifier was employed to ensemble multiple algorithms to predict various types of child abuse.The proposed approach successfully predicted crime types with 93%of accuracy,precision,recall,and F1-Score.The AUC value of the same was 0.989.However,when compared to the Extra Trees model(17.55),which is the second best,the proposed model’s execution time was significantly longer(476.63).We discovered that Machine Learning methods effectively evaluate the demographic and spatial-temporal characteristics of the crimes and predict the occurrences of various subtypes of child abuse.The results indicated that the proposed Borderline-SMOTE enabled Stacking Classifier model(BS-SC Model)would be effective in the real-time child abuse prediction and prevention process.

Design of Hierarchical Classifier to Improve Speech Emotion Recognition

Automatic Speech Emotion Recognition(SER)is used to recognize emotion from speech automatically.Speech Emotion recognition is working well in a laboratory environment but real-time emotion recognition has been influenced by the variations in gender,age,the cultural and acoustical background of the speaker.The acoustical resemblance between emotional expressions further increases the complexity of recognition.Many recent research works are concentrated to address these effects individually.Instead of addressing every influencing attribute individually,we would like to design a system,which reduces the effect that arises on any factor.We propose a two-level Hierarchical classifier named Interpreter of responses(IR).Thefirst level of IR has been realized using Support Vector Machine(SVM)and Gaussian Mixer Model(GMM)classifiers.In the second level of IR,a discriminative SVM classifier has been trained and tested with meta information offirst-level classifiers along with the input acoustical feature vector which is used in primary classifiers.To train the system with a corpus of versatile nature,an integrated emotion corpus has been composed using emotion samples of 5 speech corpora,namely;EMO-DB,IITKGP-SESC,SAVEE Corpus,Spanish emotion corpus,CMU's Woogle corpus.The hierarchical classifier has been trained and tested using MFCC and Low-Level Descriptors(LLD).The empirical analysis shows that the proposed classifier outperforms the traditional classifiers.The proposed ensemble design is very generic and can be adapted even when the number and nature of features change.Thefirst-level classifiers GMM or SVM may be replaced with any other learning algorithm.

Observation points classifier ensemble for high-dimensional imbalanced classification

In this paper,an Observation Points Classifier Ensemble(OPCE)algorithm is proposed to deal with High-Dimensional Imbalanced Classification(HDIC)problems based on data processed using the Multi-Dimensional Scaling(MDS)feature extraction technique.First,dimensionality of the original imbalanced data is reduced using MDS so that distances between any two different samples are preserved as well as possible.Second,a novel OPCE algorithm is applied to classify imbalanced samples by placing optimised observation points in a low-dimensional data space.Third,optimization of the observation point mappings is carried out to obtain a reliable assessment of the unknown samples.Exhaustive experiments have been conducted to evaluate the feasibility,rationality,and effectiveness of the proposed OPCE algorithm using seven benchmark HDIC data sets.Experimental results show that(1)the OPCE algorithm can be trained faster on low-dimensional imbalanced data than on high-dimensional data;(2)the OPCE algorithm can correctly identify samples as the number of optimised observation points is increased;and(3)statistical analysis reveals that OPCE yields better HDIC performances on the selected data sets in comparison with eight other HDIC algorithms.This demonstrates that OPCE is a viable algorithm to deal with HDIC problems.

Phytodiversity and Vulnerability of Protected Areas in Burkina Faso: Case of Péni Classified Forest

Protected areas contain most of Burkina Faso’s plant biodiversity which confer different benefits for the communities. However, the composition of some of them remains unknown. In a context of overexploitation and climate change, it is important to have a detailed knowledge of the vegetation of forests that have not been studied, such as Péni Classified Forest (PCF) to develop better preservation protocols. The aim of this study is to contribute to the knowledge of the flora of Burkina Faso. Phytosociological surveys were carried out in 213 plots, have identified 475 species distributed in 321 genera and 87 families. We identified during this study 201 woody species representing 38% of the woody flora of Burkina Faso. 64% of this flora is confined to shrub savannahs and 61% to tree savannahs. Among the vegetation units, shrub savannahs and tree savannahs have respectively 56.21% and 44.67% of very rare species. Poaceae (11.90%), Fabaceae-Faboideae (11.27%) and Rubiaceae (6.26%) are the most dominant families. The dominant biological types of the flora are phanerophytes (42.32%) and therophytes (30.32%), and Sudanian species (20.63%) are the best represented. Logging is the most frequent disturbance factor (100%) in the PCF. The PCF is a particular ecosystem with a great diversity but subject to many disturbances. Actions to strengthen its protection are necessary.