Chinese Clinical Named Entity Recognition Using Multi-Feature Fusion and Multi-Scale Local Context Enhancement

Chinese Clinical Named Entity Recognition(CNER)is a crucial step in extracting medical information and is of great significance in promoting medical informatization.However,CNER poses challenges due to the specificity of clinical terminology,the complexity of Chinese text semantics,and the uncertainty of Chinese entity boundaries.To address these issues,we propose an improved CNER model,which is based on multi-feature fusion and multi-scale local context enhancement.The model simultaneously fuses multi-feature representations of pinyin,radical,Part of Speech(POS),word boundary with BERT deep contextual representations to enhance the semantic representation of text for more effective entity recognition.Furthermore,to address the model’s limitation of focusing just on global features,we incorporate Convolutional Neural Networks(CNNs)with various kernel sizes to capture multi-scale local features of the text and enhance the model’s comprehension of the text.Finally,we integrate the obtained global and local features,and employ multi-head attention mechanism(MHA)extraction to enhance the model’s focus on characters associated with medical entities,hence boosting the model’s performance.We obtained 92.74%,and 87.80%F1 scores on the two CNER benchmark datasets,CCKS2017 and CCKS2019,respectively.The results demonstrate that our model outperforms the latest models in CNER,showcasing its outstanding overall performance.It can be seen that the CNER model proposed in this study has an important application value in constructing clinical medical knowledge graph and intelligent Q&A system.

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.

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.

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.

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.

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.

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.

Classification and Extraction of Urban Land-Use Information from High-Resolution Image Based on Object Multi-features

Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noticeable. Urban administrators and decision-makers seek modern methods and technology to provide information support for urban growth. Recently, with the fast development of high-resolution sensor technology, more relevant data can be obtained, which is an advantage in studying the sustainable development of urban land-use. However, these data are only information sources and are a mixture of ＂information＂ and ＂noise＂. Processing, analysis and information extraction from remote sensing data is necessary to provide useful information. This paper extracts urban land-use information from a high-resolution image by using the multi-feature information of the image objects, and adopts an object-oriented image analysis approach and multi-scale image segmentation technology. A classification and extraction model is set up based on the multi-features of the image objects, in order to contribute to information for reasonable planning and effective management. This new image analysis approach offers a satisfactory solution for extracting information quickly and efficiently.

Research on Facial Fatigue Detection of Drivers with Multi-feature Fusion

In order to solve the shortcomings of current fatigue detection methods such as low accuracy or poor real-time performance,a fatigue detection method based on multi-feature fusion is proposed.Firstly,the HOG face detection algorithm and KCF target tracking algorithm are integrated and deformable convolutional neural network is introduced to identify the state of extracted eyes and mouth,fast track the detected faces and extract continuous and stable target faces for more efficient extraction.Then the head pose algorithm is introduced to detect the driver’s head in real time and obtain the driver’s head state information.Finally,a multi-feature fusion fatigue detection method is proposed based on the state of the eyes,mouth and head.According to the experimental results,the proposed method can detect the driver’s fatigue state in real time with high accuracy and good robustness compared with the current fatigue detection algorithms.

Ensemble Classifier Design Based on Perturbation Binary Salp Swarm Algorithm for Classification

Multiple classifier system exhibits strong classification capacity compared with single classifiers,but they require significant computational resources.Selective ensemble system aims to attain equivalent or better classification accuracy with fewer classifiers.However,current methods fail to identify precise solutions for constructing an ensemble classifier.In this study,we propose an ensemble classifier design technique based on the perturbation binary salp swarm algorithm(ECDPB).Considering that extreme learning machines(ELMs)have rapid learning rates and good generalization ability,they can serve as the basic classifier for creating multiple candidates while using fewer computational resources.Meanwhile,we introduce a combined diversity measure by taking the complementarity and accuracy of ELMs into account;it is used to identify the ELMs that have good diversity and low error.In addition,we propose an ECDPB with powerful optimizing ability;it is employed to find the optimal subset of ELMs.The selected ELMs can then be used to forman ensemble classifier.Experiments on 10 benchmark datasets have been conducted,and the results demonstrate that the proposed ECDPB delivers superior classification capacity when compared with alternative methods.

SA-Model:Multi-Feature Fusion Poetic Sentiment Analysis Based on a Hybrid Word Vector Model

Sentiment analysis in Chinese classical poetry has become a prominent topic in historical and cultural tracing,ancient literature research,etc.However,the existing research on sentiment analysis is relatively small.It does not effectively solve the problems such as the weak feature extraction ability of poetry text,which leads to the low performance of the model on sentiment analysis for Chinese classical poetry.In this research,we offer the SA-Model,a poetic sentiment analysis model.SA-Model firstly extracts text vector information and fuses it through Bidirectional encoder representation from transformers-Whole word masking-extension(BERT-wwmext)and Enhanced representation through knowledge integration(ERNIE)to enrich text vector information;Secondly,it incorporates numerous encoders to remove text features at multiple levels,thereby increasing text feature information,improving text semantics accuracy,and enhancing the model’s learning and generalization capabilities;finally,multi-feature fusion poetry sentiment analysis model is constructed.The feasibility and accuracy of the model are validated through the ancient poetry sentiment corpus.Compared with other baseline models,the experimental findings indicate that SA-Model may increase the accuracy of text semantics and hence improve the capability of poetry sentiment analysis.

Real and Altered Fingerprint Classification Based on Various Features and Classifiers

Biometric recognition refers to the identification of individuals through their unique behavioral features(e.g.,fingerprint,face,and iris).We need distinguishing characteristics to identify people,such as fingerprints,which are world-renowned as the most reliablemethod to identify people.The recognition of fingerprints has become a standard procedure in forensics,and different techniques are available for this purpose.Most current techniques lack interest in image enhancement and rely on high-dimensional features to generate classification models.Therefore,we proposed an effective fingerprint classification method for classifying the fingerprint image as authentic or altered since criminals and hackers routinely change their fingerprints to generate fake ones.In order to improve fingerprint classification accuracy,our proposed method used the most effective texture features and classifiers.Discriminant Analysis(DCA)and Gaussian Discriminant Analysis(GDA)are employed as classifiers,along with Histogram of Oriented Gradient(HOG)and Segmentation-based Feature Texture Analysis(SFTA)feature vectors as inputs.The performance of the classifiers is determined by assessing a range of feature sets,and the most accurate results are obtained.The proposed method is tested using a Sokoto Coventry Fingerprint Dataset(SOCOFing).The SOCOFing project includes 6,000 fingerprint images collected from 600 African people whose fingerprints were taken ten times.Three distinct degrees of obliteration,central rotation,and z-cut have been performed to obtain synthetically altered replicas of the genuine fingerprints.The proposal achieved massive success with a classification accuracy reaching 99%.The experimental results indicate that the proposed method for fingerprint classification is feasible and effective.The experiments also showed that the proposed SFTA-based GDA method outperformed state-of-art approaches in feature dimension and classification accuracy.

A Multi-Feature Weighting Based K-Means Algorithm for MOOC Learner Classification

Massive open online courses(MOOC)have recently gained worldwide attention in the field of education.The manner of MOOC provides a new option for learning various kinds of knowledge.A mass of data miming algorithms have been proposed to analyze the learner’s characteristics and classify the learners into different groups.However,most current algorithms mainly focus on the final grade of the learners,which may result in an improper classification.To overcome the shortages of the existing algorithms,a novel multi-feature weighting based K-means(MFWK-means)algorithm is proposed in this paper.Correlations between the widely used feature grade and other features are first investigated,and then the learners are classified based on their grades and weighted features with the proposed MFWK-means algorithm.Experimental results with the Canvas Network Person-Course(CNPC)dataset demonstrate the effectiveness of our method.Moreover,a comparison between the new MFWK-means and the traditional K-means clustering algorithm is implemented to show the superiority of the proposed method.

Clustering-Aided Supervised Malware Detection with Specialized Classifiers and Early Consensus

One of the most common types of threats to the digital world is malicious software.It is of great importance to detect and prevent existing and new malware before it damages information assets.Machine learning approaches are used effectively for this purpose.In this study,we present a model in which supervised and unsupervised learning algorithms are used together.Clustering is used to enhance the prediction performance of the supervised classifiers.The aim of the proposed model is to make predictions in the shortest possible time with high accuracy and f1 score.In the first stage of the model,the data are clustered with the k-means algorithm.In the second stage,the prediction is made with the combination of the classifier with the best prediction performance for the related cluster.While choosing the best classifiers for the given clusters,triple combinations of ten machine learning algorithms(kernel support vector machine,k-nearest neighbor,naive Bayes,decision tree,random forest,extra gradient boosting,categorical boosting,adaptive boosting,extra trees,and gradient boosting)are used.The selected triple classifier combination is positioned in two stages.The prediction time of the model is improved by positioning the classifier with the slowest prediction time in the second stage.The selected triple classifier combination is positioned in two tiers.The prediction time of the model is improved by positioning the classifier with the highest prediction time in the second tier.It is seen that clustering before classification improves prediction performance,which is presented using Blue Hexagon Open Dataset for Malware Analysis(BODMAS),Elastic Malware Benchmark for Empowering Researchers(EMBER)2018 and Kaggle malware detection datasets.The model has 99.74%accuracy and 99.77%f1 score for the BODMAS dataset,99.04%accuracy and 98.63%f1 score for the Kaggle malware detection dataset,and 96.77%accuracy and 96.77%f1 score for the EMBER 2018 dataset.In addition,the tiered positioning of classifiers shortened the average prediction time by 76.13%for the BODMAS dataset and 95.95%for the EMBER 2018 dataset.The proposed method’s prediction performance is better than the rest of the studies in the literature in which BODMAS and EMBER 2018 datasets are used.

Multi-Feature Fusion Book Recommendation Model Based on Deep Neural Network

The traditional recommendation algorithm represented by the collaborative filtering algorithm is the most classical and widely recommended algorithm in the practical industry.Most book recommendation systems also use this algorithm.However,the traditional recommendation algorithm represented by the collaborative filtering algorithm cannot deal with the data sparsity well.This algorithm only uses the shallow feature design of the interaction between readers and books,so it fails to achieve the high-level abstract learning of the relevant attribute features of readers and books,leading to a decline in recommendation performance.Given the above problems,this study uses deep learning technology to model readers’book borrowing probability.It builds a recommendation system model through themulti-layer neural network and inputs the features extracted from readers and books into the network,and then profoundly integrates the features of readers and books through the multi-layer neural network.The hidden deep interaction between readers and books is explored accordingly.Thus,the quality of book recommendation performance will be significantly improved.In the experiment,the evaluation indexes ofHR@10,MRR,andNDCGof the deep neural network recommendation model constructed in this paper are higher than those of the traditional recommendation algorithm,which verifies the effectiveness of the model in the book recommendation.

Multi-Features Disease Analysis Based Smart Diagnosis for COVID-19

Coronavirus 2019(COVID-19)is the current global buzzword,putting the world at risk.The pandemic’s exponential expansion of infected COVID-19 patients has challenged the medical field’s resources,which are already few.Even established nations would not be in a perfect position to manage this epidemic correctly,leaving emerging countries and countries that have not yet begun to grow to address the problem.These problems can be solved by using machine learning models in a realistic way,such as by using computer-aided images during medical examinations.These models help predict the effects of the disease outbreak and help detect the effects in the coming days.In this paper,Multi-Features Decease Analysis(MFDA)is used with different ensemble classifiers to diagnose the disease’s impact with the help of Computed Tomography(CT)scan images.There are various features associated with chest CT images,which help know the possibility of an individual being affected and how COVID-19 will affect the persons suffering from pneumonia.The current study attempts to increase the precision of the diagnosis model by evaluating various feature sets and choosing the best combination for better results.The model’s performance is assessed using Receiver Operating Characteristic(ROC)curve,the Root Mean Square Error(RMSE),and the Confusion Matrix.It is observed from the resultant outcome that the performance of the proposed model has exhibited better efficient.

Single-qubit quantum classifier based on gradient-free optimization algorithm

A single-qubit quantum classifier(SQC)based on a gradient-free optimization(GFO)algorithm,named the GFO-based SQC,is proposed to overcome the effects of barren plateaus caused by quantum devices.Here,a rotation gate R_(X)(φ)is applied on the single-qubit binary quantum classifier,and the training data and parameters are loaded intoφin the form of vector multiplication.The cost function is decreased by finding the value of each parameter that yields the minimum expectation value of measuring the quantum circuit.The algorithm is performed iteratively for all parameters one by one until the cost function satisfies the stop condition.The proposed GFO-based SQC is demonstrated for classification tasks in Iris and MNIST datasets and compared with the Adam-based SQC and the quantum support vector machine(QSVM).Furthermore,the performance of the GFO-based SQC is discussed when the rotation gate in the quantum device is under different types of noise.The simulation results show that the GFO-based SQC can reach a high accuracy in reduced time.Additionally,the proposed GFO algorithm can quickly complete the training process of the SQC.Importantly,the GFO-based SQC has a good performance in noisy environments.