Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

One of the biggest dangers to society today is terrorism, where attacks have become one of the most significantrisks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) havebecome the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management,medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related,initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terroristattacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database(GTD) can influence the accuracy of the model’s classification of terrorist attacks, where each part of the datacan provide vital information to enrich the ability of classifier learning. Each data point in a multiclass taxonomyhas one or more tags attached to it, referred as “related tags.” We applied machine learning classifiers to classifyterrorist attack incidents obtained from the GTD. A transformer-based technique called DistilBERT extracts andlearns contextual features from text attributes to acquiremore information from text data. The extracted contextualfeatures are combined with the “key features” of the dataset and used to perform the final classification. Thestudy explored different experimental setups with various classifiers to evaluate the model’s performance. Theexperimental results show that the proposed framework outperforms the latest techniques for classifying terroristattacks with an accuracy of 98.7% using a combined feature set and extreme gradient boosting classifier.

Artificial intelligence-driven radiomics study in cancer:the role of feature engineering and modeling

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.

An intelligent prediction model of epidemic characters based on multi-feature

The epidemic characters of Omicron(e.g.large-scale transmission)are significantly different from the initial variants of COVID-19.The data generated by large-scale transmission is important to predict the trend of epidemic characters.However,the re-sults of current prediction models are inaccurate since they are not closely combined with the actual situation of Omicron transmission.In consequence,these inaccurate results have negative impacts on the process of the manufacturing and the service industry,for example,the production of masks and the recovery of the tourism industry.The authors have studied the epidemic characters in two ways,that is,investigation and prediction.First,a large amount of data is collected by utilising the Baidu index and conduct questionnaire survey concerning epidemic characters.Second,theβ-SEIDR model is established,where the population is classified as Susceptible,Exposed,Infected,Dead andβ-Recovered persons,to intelligently predict the epidemic characters of COVID-19.Note thatβ-Recovered persons denote that the Recovered persons may become Sus-ceptible persons with probabilityβ.The simulation results show that the model can accurately predict the epidemic characters.

Current Status and Development Trends of Chinese Intelligent Furniture Industry

In this work,the current status,technical capabilities,and development trends of the Chinese intelligent furniture industry were in focus.Based on combining a literature review with field investigations and analysis of major scientific research projects in Zhejiang Province,China,an in-depth overview and discussion about previous experience,features,technologies,products and control methods in the intelligent furniture industry in China were conducted.The key technologies in current Chinese intelligent furniture industry include embedded systems,sensors,short-range wireless communication,artificial intelligence and intelligent interaction techniques.This work also mentions the challenges and opportunities for the industry,pointing out how to innovate and dominate the furniture market in the era of transformation from the traditional furniture industry to intelligent upgrading.The results are proposed to help readers from all over the world to understand the development trend and progress of the Chinese intelligent furniture industry.

Smart prediction of liquefaction-induced lateral spreading

The prediction of liquefaction-induced lateral spreading/displacement(Dh)is a challenging task for civil/geotechnical engineers.In this study,a new approach is proposed to predict Dh using gene expression programming(GEP).Based on statistical reasoning,individual models were developed for two topographies:free-face and gently sloping ground.Along with a comparison with conventional approaches for predicting the Dh,four additional regression-based soft computing models,i.e.Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimization regression(SMOR),and M5-tree,were developed and compared with the GEP model.The results indicate that the GEP models predict Dh with less bias,as evidenced by the root mean square error(RMSE)and mean absolute error(MAE)for training(i.e.1.092 and 0.815;and 0.643 and 0.526)and for testing(i.e.0.89 and 0.705;and 0.773 and 0.573)in free-face and gently sloping ground topographies,respectively.The overall performance for the free-face topology was ranked as follows:GEP>RVM>M5-tree>GPR>SMOR,with a total score of 40,32,24,15,and 10,respectively.For the gently sloping condition,the performance was ranked as follows:GEP>RVM>GPR>M5-tree>SMOR with a total score of 40,32,21,19,and 8,respectively.Finally,the results of the sensitivity analysis showed that for both free-face and gently sloping ground,the liquefiable layer thickness(T_(15))was the major parameter with percentage deterioration(%D)value of 99.15 and 90.72,respectively.

Traffic Sign Detection with Low Complexity for Intelligent Vehicles Based on Hybrid Features

Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians.Consequently,traffic signs have been of great importance for every civilized country,which makes researchers give more focus on the automatic detection of traffic signs.Detecting these traffic signs is challenging due to being in the dark,far away,partially occluded,and affected by the lighting or the presence of similar objects.An innovative traffic sign detection method for red and blue signs in color images is proposed to resolve these issues.This technique aimed to devise an efficient,robust and accurate approach.To attain this,initially,the approach presented a new formula,inspired by existing work,to enhance the image using red and green channels instead of blue,which segmented using a threshold calculated from the correlational property of the image.Next,a new set of features is proposed,motivated by existing features.Texture and color features are fused after getting extracted on the channel of Red,Green,and Blue(RGB),Hue,Saturation,and Value(HSV),and YCbCr color models of images.Later,the set of features is employed on different classification frameworks,from which quadratic support vector machine(SVM)outnumbered the others with an accuracy of 98.5%.The proposed method is tested on German Traffic Sign Detection Benchmark(GTSDB)images.The results are satisfactory when compared to the preceding work.

Coupling Analysis of Multiple Machine Learning Models for Human Activity Recognition

Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study introduces a robust coupling analysis framework that integrates four AI-enabled models,combining both machine learning(ML)and deep learning(DL)approaches to evaluate their effectiveness in HAR.The analytical dataset comprises 561 features sourced from the UCI-HAR database,forming the foundation for training the models.Additionally,the MHEALTH database is employed to replicate the modeling process for comparative purposes,while inclusion of the WISDM database,renowned for its challenging features,supports the framework’s resilience and adaptability.The ML-based models employ the methodologies including adaptive neuro-fuzzy inference system(ANFIS),support vector machine(SVM),and random forest(RF),for data training.In contrast,a DL-based model utilizes one-dimensional convolution neural network(1dCNN)to automate feature extraction.Furthermore,the recursive feature elimination(RFE)algorithm,which drives an ML-based estimator to eliminate low-participation features,helps identify the optimal features for enhancing model performance.The best accuracies of the ANFIS,SVM,RF,and 1dCNN models with meticulous featuring process achieve around 90%,96%,91%,and 93%,respectively.Comparative analysis using the MHEALTH dataset showcases the 1dCNN model’s remarkable perfect accuracy(100%),while the RF,SVM,and ANFIS models equipped with selected features achieve accuracies of 99.8%,99.7%,and 96.5%,respectively.Finally,when applied to the WISDM dataset,the DL-based and ML-based models attain accuracies of 91.4%and 87.3%,respectively,aligning with prior research findings.In conclusion,the proposed framework yields HAR models with commendable performance metrics,exhibiting its suitability for integration into the healthcare services system through AI-driven applications.

A Review on Intelligent Detection and Classification of Power Quality Disturbances:Trends,Methodologies,and Prospects

With increasing global concerns about clean energy in smart grids,the detection of power quality disturbances(PQDs)caused by energy instability is becoming more and more prominent.It is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous,which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent grids.In order to ensure safe and reliable equipment implementation,appropriate PQDdetection technologiesmust be adopted to avoid such adverse effects.This paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start to new researchers in the related field,where specific scenarios and events for which each technique is applicable are also clearly presented.Finally,comments on the future evolution of PQD detection techniques are given.Unlike the published review articles,this paper focuses on the new techniques from the last five years while providing a brief recap on traditional PQD detection techniques so as to supply researchers with a systematic and state-of-the-art review for PQD detection.

Prognostic factors of breast cancer brain metastasis

In this editorial we comment on the article by Chen et al published in the recent issue of the World Journal of Clinical Oncology.Brain metastasis is one of the most serious complications of breast cancer and causes high morbidity and mortality.Brain metastases may involve the brain parenchyma and/or leptomeninges.Symptomatic brain metastases develop in 10%-16%of newly recognized cases each year,and this rate increases to 30%in autopsy series.Depending on the size of the metastatic foci,it may be accompanied by extensive vasogenic edema or may occur as small tumor foci.Since brain metastases are a significant cause of morbidity and mortality,early diagnosis can have significant effects on survival and quality of life.The risk of developing brain metastases emerges progressively due to various patient and tumor characteristics.Patient variability may be particularly important in the susceptibility and distribution of brain metastases because malignant blood must cross the brain barrier and move within the brain parenchyma.Some characteristics of the tumor,such as gene expression,may increase the risk of brain metastasis.Clinical growth,tumor stage,tumor grade,growth receptor positivity,HER2 positivity,molecular subtype(such as triple negative status,luminal/nonluminal feature)increase the risk of developing breast cancer metastasis.Factors related to survival due to breast cancer brain metastasis include both tumor/patient characteristics and treatment characteristics,such as patient age,lung metastasis,surgery for brain metastasis,and HER2 positivity.If cases with a high risk of developing brain metastasis can be identified with the help of clinical procedures and artificial intelligence,survival and quality of life can be increased with early diagnosis and treatment.At the same time,it is important to predict the formation of this group in order to develop new treatment methods in cases with low survival expectancy with brain metastases.

Impact of Portable Executable Header Features on Malware Detection Accuracy

One aspect of cybersecurity,incorporates the study of Portable Executables(PE)files maleficence.Artificial Intelligence(AI)can be employed in such studies,since AI has the ability to discriminate benign from malicious files.In this study,an exclusive set of 29 features was collected from trusted implementations,this set was used as a baseline to analyze the presented work in this research.A Decision Tree(DT)and Neural Network Multi-Layer Perceptron(NN-MLPC)algorithms were utilized during this work.Both algorithms were chosen after testing a few diverse procedures.This work implements a method of subgrouping features to answer questions such as,which feature has a positive impact on accuracy when added?Is it possible to determine a reliable feature set to distinguish a malicious PE file from a benign one?when combining features,would it have any effect on malware detection accuracy in a PE file?Results obtained using the proposed method were improved and carried few observations.Generally,the obtained results had practical and numerical parts,for the practical part,the number of features and which features included are the main factors impacting the calculated accuracy,also,the combination of features is as crucial in these calculations.Numerical results included,finding accuracies with enhanced values,for example,NN_MLPC attained 0.979 and 0.98;for DT an accuracy of 0.9825 and 0.986 was attained.

A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm

Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.

Intelligent Fault Diagnosis of Rotary Machinery Based on Unsupervised Multiscale Representation Learning

The performance of traditional vibration based fault diagnosis methods greatly depends on those hand- crafted features extracted using signal processing algo- rithms, which require significant amounts of domain knowledge and human labor, and do not generalize well to new diagnosis domains. Recently, unsupervised represen- tation learning provides an alternative promising solution to feature extraction in traditional fault diagnosis due to its superior learning ability from unlabeled data. Given that vibration signals usually contain multiple temporal struc- tures, this paper proposes a multiscale representation learning （MSRL） framework to learn useful features directly from raw vibration signals, with the aim to capture rich and complementary fault pattern information at dif- ferent scales. In our proposed approach, a coarse-grained procedure is first employed to obtain multiple scale signals from an original vibration signal. Then, sparse filtering, a newly developed unsupervised learning algorithm, is applied to automatically learn useful features from each scale signal, respectively, and then the learned features at each scale to be concatenated one by one to obtain multi- scale representations. Finally, the multiscale representa- tions are fed into a supervised classifier to achieve diagnosis results. Our proposed approach is evaluated using two different case studies： motor bearing and wind turbine gearbox fault diagnosis. Experimental results show that the proposed MSRL approach can take full advantages of the availability of unlabeled data to learn discriminative features and achieved better performance with higher accuracy and stability compared to the traditional approaches.

A system for detection of cervical precancerous in field emission scanning electron microscope images using texture features

This study develops a novel cervical precancerous detection system by using texture analysis of field emission scanning electron microscopy(FE-SEM)images.The processing scheme adopted in the proposed system focused on two steps.The first step was to enhance cervical cell FE-SEM images in order to show the precancerous characterization indicator.A problem arises from the question of how to extract features which characterize cervical precancerous cells.For the first step,a preprocessing technique called intensity transformation and morphological operation(ITMO)algorithm used to enhance the quality of images was proposed.The algo-rithm consisted of contrast stretching and morphological opening operations.The second step was to characterize the cervical cells to three classes,namely normal,low grade intra-epithelial squamous lesion(LSIL),and high grade intra-epithelial squamous lesion(HSIL).To differen-tiate between normal and precancerous cells of the cervical cell FE-SEM images,human papillomavirus(HPV)contained in the surface of cells were used as indicators.In this paper,we investigated the use of texture as a tool in determining precancerous cell images based on the observation that cell images have a distinct visual texture.Gray level co-occurrences matrix(GLCM)technique was used to extract the texture features.To confirm the system's perfor-mance,the system was tested using 150 cervical cell FE-SEM images.The results showed that the accuracy,sensitivity and specificity of the proposed system are 95.7%,95.7%and 95.8%,respectively.

Analysis of the Relationship between Image and Blood Examinations in an Artificial Intelligence System for the Molecular Diagnosis of Breast Cancer

Molecular subtype classification based on tumor genotype has recently been used for differential diagnosis of breast cancer. The shift from conventional tissue classification to molecular genetics-based classification is primarily because objective genetic information can ensure a biologically clear classification system and patient groups may be created for a given set of diagnoses and suitable treatments. Given the stressful nature of biopsy, radiomic studies are conducted to determine breast cancer subtypes using non-invasive imaging tests. Minimally invasive blood tests using microRNAs (miRNAs) contained in exosomes have been developed. We investigated the usefulness of radiomic features and miRNAs in distinguishing triple-negative breast cancer (TNBC) from other cancer types. Fat suppression T2-weighted magnetic resonance images and miRNAs of 60 cases (9 TNBC and 51 others) were retrieved from the Cancer Genome Atlas Breast Invasive Carcinoma. Six radiomic features and six miRNAs were selected by least absolute shrinkage and selection operator. Linear discriminant analysis was employed to distinguish between TNBC and others. With miRNAs, TNBC and others were completely separated, whereas with radiomic features, TNBC overlapped with other types of breast cancer. Receiver operating characteristic curve analysis results showed that the area under the curve of radiomic features and miRNAs was 0.85 and 1.0, respectively. miRNAs showed a higher discrimination performance than radiomic features. Although gene analysis is expensive and facilities for performing it are limited, miRNAs for blood tests may be useful in artificial intelligence systems for the molecular diagnosis of breast cancer.

Fault diagnosis of rolling bearings with noise signal based on modified kernel principal component analysis and DC‐ResNet

In view of the influence of aliasing noise on the effectiveness and accuracy of bearing fault diagnosis,a bearing fault diagnosis algorithm based on the spatial decoupling method of modified kernel principal component analysis(MKPCA)and the residual network with deformable convolution(DC‐ResNet)is innovatively proposed.Firstly,the Gaussian noise with different signal‐to‐noise ratios(SNRs)is added to the data to simulate the different degrees of noise in the actual data acquisition process.The MKPCA is used to project the fault signal with different SNRs in the kernel space to reduce the data dimension and eliminate some noise effects.Finally,the DC‐ResNet model is used to further filter the noise effects and fully extract the fault features through the training of the preprocessed data.The proposed algorithm is tested on the Case Western Reserve University(CWRU)and Xi'an Jiaotong University and Changxing Sumyoung Technology Co.,Ltd.(XJTU‐SY)bearing data sets with different SNR noise.The fault diagnosis accuracy can reach 100%within 30 min,which has better performance than most of the existing methods.The experimental results show that the algorithm has an excellent effect on accuracy and computation complexity under different noise levels.

Intelligent Student Mental Health Assessment Model on Learning Management System

A learning management system(LMS)is a software or web based application,commonly utilized for planning,designing,and assessing a particular learning procedure.Generally,the LMS offers a method of creating and delivering content to the instructor,monitoring students’involvement,and validating their outcomes.Since mental health issues become common among studies in higher education globally,it is needed to properly determine it to improve mental stabi-lity.This article develops a new seven spot lady bird feature selection with opti-mal sparse autoencoder(SSLBFS-OSAE)model to assess students’mental health on LMS.The major aim of the SSLBFS-OSAE model is to determine the proper health status of the students with respect to depression,anxiety,and stress(DAS).The SSLBFS-OSAE model involves a new SSLBFS model to elect a useful set of features.In addition,OSAE model is applied for the classification of mental health conditions and the performance can be improved by the use of cuckoo search optimization(CSO)based parameter tuning process.The design of CSO algorithm for optimally tuning the SAE parameters results in enhanced classifica-tion outcomes.For examining the improved classifier results of the SSLBFS-OSAE model,a comprehensive results analysis is done and the obtained values highlighted the supremacy of the SSLBFS model over its recent methods interms of different measures.

Deepfake Video Detection Employing Human Facial Features

Deepfake technology can be used to replace people’s faces in videos or pictures to show them saying or doing things they never said or did. Deepfake media are often used to extort, defame, and manipulate public opinion. However, despite deepfake technology’s risks, current deepfake detection methods lack generalization and are inconsistent when applied to unknown videos, i.e., videos on which they have not been trained. The purpose of this study is to develop a generalizable deepfake detection model by training convoluted neural networks (CNNs) to classify human facial features in videos. The study formulated the research questions: “How effectively does the developed model provide reliable generalizations?” A CNN model was trained to distinguish between real and fake videos using the facial features of human subjects in videos. The model was trained, validated, and tested using the FaceForensiq++ dataset, which contains more than 500,000 frames and subsets of the DFDC dataset, totaling more than 22,000 videos. The study demonstrated high generalizability, as the accuracy of the unknown dataset was only marginally (about 1%) lower than that of the known dataset. The findings of this study indicate that detection systems can be more generalizable, lighter, and faster by focusing on just a small region (the human face) of an entire video.

生成式人工智能算法专利保护的理据与进路

生成式人工智能算法是ChatGPT类应用的核心,基于其技术特征,现有著作权保护容易忽视其与传统算法的异同,只保护其具有差异性的代码内容。采用商业秘密保护,不仅会加剧算法的“不可解释性”,还会导致私人利益与公众权益的失衡。适用行业自律保护,则易诱发知识产权侵权风险,不符合市场追求效益与效率的宗旨。而以“公开换保护”的专利保护既能给予其充足的保护空间,也能规制其引发的社会风险。在专利保护体系下,应厘清生成式人工智能算法与相关客体的差异以确定其专利客体属性,从“正向界定”与“反向排除”入手以划定专利授权范围,立足于其技术特征以修正“专利三性”审查标准,明确其可专利性规则,回应ChatGPT类应用保护的现实需求,赋能我国数字经济发展。

基于脑电图的帕金森轻度认知障碍功能网络特征分析

帕金森氏轻度认知障碍(PDMCI)是帕金森氏症患者痴呆的先兆,这对使用神经评分量表和医生经验等传统方法进行准确诊断提出了挑战。利用26名PDMCI患者和23名正常人的脑电信号,基于定向传递函数构建了Delta、Theta、Alpha、Beta和Gamma频段的脑功能网络。引入了一种新颖的图论特征——效率密度来捕获网络密度和传输效率。研究结果揭示了独特的连接模式,Delta和Theta波段的连接更紧密,而Alpha、Beta和Gamma波段的连接更稀疏。帕金森病(PD)患者与对照组之间的Theta、Alpha、Beta和Gamma频带存在显著差异(p<0.05)。因此,脑功能网络可以有效反映PD脑功能异常状态,效率密度特征可以反映PD脑功能异常活动的特征量。

无人机高空航拍视角下小尺度车辆精确检测方法

无人机高空航拍图像中车辆像素占比极低,目标可视化信息较少,在目标检测任务中容易漏检和误检。因此,本文提出一种基于改进YOLOX(You Only Look Once X)的无人机高空航拍视角下小尺度车辆精确检测方法。首先,为增强网络对低级特征的提取能力,在原始YOLOX预测头部增加一个160 pixel×160 pixel的浅层特征提取网络;其次,在骨干网络后端嵌入基于归一化的注意力机制模块(Normalization-based Attention Module,NAM),以抑制冗余的非显著特征表达;最后,为了增大小尺度车辆的相对像素比,提升网络捕捉有效特征信息的能力,提出一种基于滑动窗口的图像切分检测方法。试验结果表明,改进YOLOX网络表现出良好的检测效能,检测精度达到了84.58%,优于典型的目标检测网络Faster R-CNN(79.95%)、YOLOv3(83.69%)、YOLOv5(84.31%)及YOLOX(83.10%)。此外,改进YOLOX能够有效解决无人机高空航拍图像中小尺度车辆的漏检和误检问题,且预测框更贴合车辆的实际轮廓;同时,在不同航拍高度的目标检测任务中具有较高的鲁棒性。