Automated Arabic Text Classification Using Hyperparameter Tuned Hybrid Deep Learning Model

The text classification process has been extensively investigated in various languages,especially English.Text classification models are vital in several Natural Language Processing(NLP)applications.The Arabic language has a lot of significance.For instance,it is the fourth mostly-used language on the internet and the sixth official language of theUnitedNations.However,there are few studies on the text classification process in Arabic.A few text classification studies have been published earlier in the Arabic language.In general,researchers face two challenges in the Arabic text classification process:low accuracy and high dimensionality of the features.In this study,an Automated Arabic Text Classification using Hyperparameter Tuned Hybrid Deep Learning(AATC-HTHDL)model is proposed.The major goal of the proposed AATC-HTHDL method is to identify different class labels for the Arabic text.The first step in the proposed model is to pre-process the input data to transform it into a useful format.The Term Frequency-Inverse Document Frequency(TF-IDF)model is applied to extract the feature vectors.Next,the Convolutional Neural Network with Recurrent Neural Network(CRNN)model is utilized to classify the Arabic text.In the final stage,the Crow Search Algorithm(CSA)is applied to fine-tune the CRNN model’s hyperparameters,showing the work’s novelty.The proposed AATCHTHDL model was experimentally validated under different parameters and the outcomes established the supremacy of the proposed AATC-HTHDL model over other approaches.

Pervasive Attentive Neural Network for Intelligent Image Classification Based on N-CDE’s

The utilization of visual attention enhances the performance of image classification tasks.Previous attentionbased models have demonstrated notable performance,but many of these models exhibit reduced accuracy when confronted with inter-class and intra-class similarities and differences.Neural-Controlled Differential Equations(N-CDE’s)and Neural Ordinary Differential Equations(NODE’s)are extensively utilized within this context.NCDE’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with enhanced clarity.To this end,an attentive neural network has been proposed to generate attention maps,which uses two different types of N-CDE’s,one for adopting hidden layers and the other to generate attention values.Two distinct attention techniques are implemented including time-wise attention,also referred to as bottom N-CDE’s;and element-wise attention,called topN-CDE’s.Additionally,a trainingmethodology is proposed to guarantee that the training problem is sufficiently presented.Two classification tasks including fine-grained visual classification andmulti-label classification,are utilized to evaluate the proposedmodel.The proposedmethodology is employed on five publicly available datasets,including CUB-200-2011,ImageNet-1K,PASCAL VOC 2007,PASCAL VOC 2012,and MS COCO.The obtained visualizations have demonstrated that N-CDE’s are better appropriate for attention-based activities in comparison to conventional NODE’s.

A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification.

Decisive Role of Gravitational Parameter G in Cosmology

In 1937, P. Dirac proposed the Large Number Hypothesis and the Hypothesis of the variable gravitational “constant”, and later added the notion of continuous creation of Matter in the World. The Hypersphere World-Universe Model (WUM) follows these ideas, albeit introducing a different mechanism of Matter creation. In this paper, we show that Gravitational parameter G that can be measured directly makes measurable all Cosmological parameters, which cannot be measured directly.

Individualization of Data-Segment-Related Parameters for Improvement of EEG Signal Classification in Brain-Computer Interface

In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in an EEG-based brain-computer interface (BCI) was studied. An auto search algorithm was developed to study four datasegment-related parameters in each trial of 12 subjects’ EEG. The length of data segment (LDS), the start position of data (SPD) segment, AR order, and number of trials (NT) were used to build the model. The study showed that, compared with the classification ratio (CR) without parameter selection, the CR was increased by 20% to 30% with proper selection of these data-segment-related parameters, and the optimum parameter values were subject-dependent. This suggests that the data-segment-related parameters should be individualized when building models for BCI.

Support vector classification algorithm based on variable parameter linear programming

To solve the problems of SVM in dealing with large sample size and asymmetric distributed samples, a support vector classification algorithm based on variable parameter linear programming is proposed. In the proposed algorithm, linear programming is employed to solve the optimization problem of classification to decrease the computation time and to reduce its complexity when compared with the original model. The adjusted punishment parameter greatly reduced the classification error resulting from asymmetric distributed samples and the detailed procedure of the proposed algorithm is given. An experiment is conducted to verify whether the proposed algorithm is suitable for asymmetric distributed samples.

A new discriminative sparse parameter classifier with iterative removal for face recognition

Face recognition has been widely used and developed rapidly in recent years.The methods based on sparse representation have made great breakthroughs,and collaborative representation-based classification(CRC)is the typical representative.However,CRC cannot distinguish similar samples well,leading to a wrong classification easily.As an improved method based on CRC,the two-phase test sample sparse representation(TPTSSR)removes the samples that make little contribution to the representation of the testing sample.Nevertheless,only one removal is not sufficient,since some useless samples may still be retained,along with some useful samples maybe being removed randomly.In this work,a novel classifier,called discriminative sparse parameter(DSP)classifier with iterative removal,is proposed for face recognition.The proposed DSP classifier utilizes sparse parameter to measure the representation ability of training samples straight-forward.Moreover,to avoid some useful samples being removed randomly with only one removal,DSP classifier removes most uncorrelated samples gradually with iterations.Extensive experiments on different typical poses,expressions and noisy face datasets are conducted to assess the performance of the proposed DSP classifier.The experimental results demonstrate that DSP classifier achieves a better recognition rate than the well-known SRC,CRC,RRC,RCR,SRMVS,RFSR and TPTSSR classifiers for face recognition in various situations.

ADASYN与类别逆比例加权法在阿尔茨海默病不平衡数据中的应用

目的利用自适应合成抽样(adaptive synthetic sampling,ADASYN)与类别逆比例加权法处理类别不平衡数据,结合分类器构建模型对阿尔茨海默病(alzheimer′s disease,AD)患者疾病进程进行分类预测。方法数据源自阿尔茨海默病神经影像学计划(Alzheimer′s disease neuroimaging initiative,ADNI),经随机森林填补缺失值,弹性网络筛选特征子集后,利用ADASYN与类别逆比例加权法处理类别不平衡数据。分别结合随机森林(random forest,RF)、支持向量机(support vector machine,SVM)构建四种模型:ADASYN-RF、ADASYN-SVM、加权随机森林(weighted random forest,WRF)、加权支持向量机(weighted support vector machine,WSVM),与RF、SVM比较分类性能。模型评价指标为宏观平均精确率(macro-average of precision,macro-P)、宏观平均召回率(macro-average of recall,macro-R)、宏观平均F1值(macro-average of F1-score,macro-F1)、准确率(accuracy,ACC)、Kappa值和AUC(area under the ROC curve)。结果ADASYN-RF的分类性能最优(Kappa值为0.938,AUC为0.980),ADASYN-SVM次之。利用ADASYN-RF预测得到的重要分类特征分别为CDRSB、LDELTOTAL、MMSE,在临床上均可得到证实。结论ADASYN与类别逆比例加权法都能辅助提升分类器性能,但ADASYN算法更优。

在片S参数计量比对结果浅析

中国电子科技集团公司第十三研究所作为主导实验室开展了在片S参数计量比对工作,对参比实验室提交的在片S参数测量结果进行了汇总分析,并用E_n值对各参比实验室测量结果进行了评价。通过在片S参数计量比对,确保了量值传递的准确、可靠,特别是对在片S参数测量不确定度的主要来源统一了认识。同时也为业内提供了在片S参数测量一致性的比较平台。

高速S-CO_(2)向心透平几何参数优化及变工况特性分析

透平是热力循环中实现热功转换的核心设备,优化透平几何参数以改善气动性能,是提升系统热效率的关键。该文以面向某船舶主机烟气余热利用的超临界二氧化碳布雷顿循环透平设计参数为基础,给出向心透平主要几何参数,采用耦合CO_(2)真实物性参数的CFX仿真方法,分析几何参数动、静叶包角以及叶轮出口几何角对透平性能的影响规律并进行参数优化,进一步研究非设计工况下的透平运行特性。结果表明:增大静叶包角会使动叶前缘出现扰动涡流并减少余速损失,增大叶轮出口几何角,动叶包角对透平效率的影响规律不变,但效率峰值点会沿动叶包角减小的方向移动;动、静叶包角和叶轮出口几何角分别为49°、21.5°、25°时透平设计点总-静等熵效率高达87.99%,相比优化前,提高0.54个百分点;透平偏离设计点±20%工况下等熵效率大于80%。结果可为超临界二氧化碳高速向心透平设计提供一定参考。

基于遗传算法优化C-LSTM模型的心律失常分类方法

结合遗传算法全局寻优的特点提出一种GC-LSTM模型,该模型通过特定遗传策略的遗传算法自动迭代搜寻C-LSTM模型最佳超参数配置。利用遗传迭代结果配置模型,并按照医疗仪器促进协会制定分类标准在MIT-BIH心律失常数据库上进行验证。经过测试,本文提出的GC-LSTM模型在分类准确率(99.37%)、灵敏度(95.62%)、精确度(95.17%)、F1值(95.39%)上相较于手动搭建模型均有所提升,且与现有主流方法相比亦具备一定优势。实验结果表明该方法在避免大量实验调参的同时取得较好的分类性能。

Phylogeny,character evolution,and classification of Selaginellaceae(lycophytes)

Selaginella is the largest and most taxonomically complex genus in lycophytes.The fact that over 750 species are currently treated in a single genus makes Selaginellales/Selaginellaceae unique in pteridophytes.Here we assembled a dataset of six existing and newly sampled plastid and nuclear loci with a total of 684 accessions(74%increase of the earlier largest sampling)representing ca.300 species to infer a new phylogeny.The evolution of 10 morphological characters is studied in the new phylogenetic context.Our major results include:(1)the nuclear and plastid phylogenies are congruent with each other and combined analysis well resolved and strongly supported the relationships of all but two major clades;(2)the Sinensis group is resolved as sister to S.subg.Pulviniella with strong support in two of the three analyses;(3)most morphological characters are highly homoplasious but some characters alone or combinations of characters well define the major clades in the family;and(4)an infrafamilial classification of Selaginellaceae is proposed and the currently defined Selaginella s.l.is split into seven subfamilies(corresponding to the current six subgenera t the Sinensis group)and 19 genera(the major diagnosable clades)with nine new species-poor genera.We support the conservation of Selaginella with a new type,S.flabellata,to minimize nomenclatural instability.We provide a key to subfamilies and genera,images illustrating their morphology,their morphological and geographical synopses,a list of constituent species,and necessary new combinations.This new classification will hopefully facilitate communication,promote further studies,and help conservation.

Hyperspectral Remote Sensing Image Classification Using Improved Metaheuristic with Deep Learning

Remote sensing image(RSI)classifier roles a vital play in earth observation technology utilizing Remote sensing(RS)data are extremely exploited from both military and civil fields.More recently,as novel DL approaches develop,techniques for RSI classifiers with DL have attained important breakthroughs,providing a new opportunity for the research and development of RSI classifiers.This study introduces an Improved Slime Mould Optimization with a graph convolutional network for the hyperspectral remote sensing image classification(ISMOGCN-HRSC)model.The ISMOGCN-HRSC model majorly concentrates on identifying and classifying distinct kinds of RSIs.In the presented ISMOGCN-HRSC model,the synergic deep learning(SDL)model is exploited to produce feature vectors.The GCN model is utilized for image classification purposes to identify the proper class labels of the RSIs.The ISMO algorithm is used to enhance the classification efficiency of the GCN method,which is derived by integrating chaotic concepts into the SMO algorithm.The experimental assessment of the ISMOGCN-HRSC method is tested using a benchmark dataset.

Dynamics and adaptive control of a dual-arm space robot with closed-loop constraints and uncertain inertial parameters

A dynamics-based adaptive control approach is proposed for a planar dual-arm space robot in the presence of closed-loop constraints and uncertain inertial parameters of the payload. The controller is capable of controlling the po- sition and attitude of both the satellite base and the payload grasped by the manipulator end effectors. The equations of motion in reduced-order form for the constrained system are derived by incorporating the constraint equations in terms of accelerations into Kane＇s equations of the unconstrained system. Model analysis shows that the resulting equations perfectly meet the requirement of adaptive controller design. Consequently, by using an indirect approach, an adaptive control scheme is proposed to accomplish position/attitude trajectory tracking control with the uncertain parameters be- ing estimated on-line. The actuator redundancy due to the closed-loop constraints is utilized to minimize a weighted norm of the joint torques. Global asymptotic stability is proven by using Lyapunov＇s method, and simulation results are also presented to demonstrate the effectiveness of the proposed approach.

Soil Parameter Identification for Wheel-terrain Interaction Dynamics and Traversability Prediction

This paper presents a novel technique for identifying soil parameters for a wheeled vehicle traversing unknown terrain. The identified soil parameters are required for predicting vehicle drawbar pull and wheel drive torque, which in turn can be used for traversability prediction, traction control, and performance optimization of a wheeled vehicle on unknown terrain. The proposed technique is based on the Newton Raphson method. An approximated form of a wheel-soil interaction model based on Composite Simpson＇s Rule is employed for this purpose. The key soil parameters to be identified are internal friction angle, shear deformation modulus, and lumped pressure-sinkage coefficient. The fourth parameter, cohesion, is not too relevant to vehicle drawbar pull, and is assigned an average value during the identification process. Identified parameters are compared with known values, and shown to be in agreement. The identification method is relatively fast and robust. The identified soil parameters can effectively be used to predict drawbar pull and wheel drive torque with good accuracy. The use of identified soil parameters to design a traversability criterion for wheeled vehicles traversing unknown terrain is presented.

Leveraging Gradient-Based Optimizer and Deep Learning for Automated Soil Classification Model

Soil classification is one of the emanating topics and major concerns in many countries.As the population has been increasing at a rapid pace,the demand for food also increases dynamically.Common approaches used by agriculturalists are inadequate to satisfy the rising demand,and thus they have hindered soil cultivation.There comes a demand for computer-related soil classification methods to support agriculturalists.This study introduces a Gradient-Based Optimizer and Deep Learning(DL)for Automated Soil Clas-sification(GBODL-ASC)technique.The presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision approaches.In the presented GBODL-ASC technique,three major processes are involved.At the initial stage,the presented GBODL-ASC technique applies the GBO algorithm with the EfficientNet prototype to generate feature vectors.For soil categorization,the GBODL-ASC procedure uses an arithmetic optimization algorithm(AOA)with a Back Propagation Neural Network(BPNN)model.The design of GBO and AOA algorithms assist in the proper selection of parameter values for the EfficientNet and BPNN models,respectively.To demonstrate the significant soil classification outcomes of the GBODL-ASC methodology,a wide-ranging simulation analysis is performed on a soil dataset comprising 156 images and five classes.The simulation values show the betterment of the GBODL-ASC model through other models with maximum precision of 95.64%.

Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images.Hyperspectral remote sensing contains acquisition of digital images from several narrow,contiguous spectral bands throughout the visible,Thermal Infrared(TIR),Near Infrared(NIR),and Mid-Infrared(MIR)regions of the electromagnetic spectrum.In order to the application of agricultural regions,remote sensing approaches are studied and executed to their benefit of continuous and quantitativemonitoring.Particularly,hyperspectral images(HSI)are considered the precise for agriculture as they can offer chemical and physical data on vegetation.With this motivation,this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification(HOADTL-CC)model onHyperspectralRemote Sensing Images.The presentedHOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images.To accomplish this,the presentedHOADTL-CC model involves the design ofHOAwith capsule network(CapsNet)model for generating a set of useful feature vectors.Besides,Elman neural network(ENN)model is applied to allot proper class labels into the input HSI.Finally,glowworm swarm optimization(GSO)algorithm is exploited to fine tune the ENNparameters involved in this article.The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects.Extensive comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.

An Efficient 3D CNN Framework with Attention Mechanisms for Alzheimer’s Disease Classification

Neurodegeneration is the gradual deterioration and eventual death of brain cells,leading to progressive loss of structure and function of neurons in the brain and nervous system.Neurodegenerative disorders,such as Alzheimer’s,Huntington’s,Parkinson’s,amyotrophic lateral sclerosis,multiple system atrophy,and multiple sclerosis,are characterized by progressive deterioration of brain function,resulting in symptoms such as memory impairment,movement difficulties,and cognitive decline.Early diagnosis of these conditions is crucial to slowing down cell degeneration and reducing the severity of the diseases.Magnetic resonance imaging(MRI)is widely used by neurologists for diagnosing brain abnormalities.The majority of the research in this field focuses on processing the 2D images extracted from the 3D MRI volumetric scans for disease diagnosis.This might result in losing the volumetric information obtained from the whole brain MRI.To address this problem,a novel 3D-CNN architecture with an attention mechanism is proposed to classify whole-brain MRI images for Alzheimer’s disease(AD)detection.The 3D-CNN model uses channel and spatial attention mechanisms to extract relevant features and improve accuracy in identifying brain dysfunctions by focusing on specific regions of the brain.The pipeline takes pre-processed MRI volumetric scans as input,and the 3D-CNN model leverages both channel and spatial attention mechanisms to extract precise feature representations of the input MRI volume for accurate classification.The present study utilizes the publicly available Alzheimer’s disease Neuroimaging Initiative(ADNI)dataset,which has three image classes:Mild Cognitive Impairment(MCI),Cognitive Normal(CN),and AD affected.The proposed approach achieves an overall accuracy of 79%when classifying three classes and an average accuracy of 87%when identifying AD and the other two classes.The findings reveal that 3D-CNN models with an attention mechanism exhibit significantly higher classification performance compared to other models,highlighting the potential of deep learning algorithms to aid in the early detection and prediction of AD.

Parkinson’s Disease Classification Using Random Forest Kerb Feature Selection

Parkinson’s disease(PD)is a neurodegenerative disease cause by a deficiency of dopamine.Investigators have identified the voice as the underlying symptom of PD.Advanced vocal disorder studies provide adequate treatment and support for accurate PD detection.Machine learning(ML)models have recently helped to solve problems in the classification of chronic diseases.This work aims to analyze the effect of selecting features on ML efficiency on a voice-based PD detection system.It includes PD classification models of Random forest,decision Tree,neural network,logistic regression and support vector machine.The feature selection is made by RF mean-decrease in accuracy and mean-decrease in Gini techniques.Random forest kerb feature selection(RFKFS)selects only 17 features from 754 attributes.The proposed technique uses validation metrics to assess the performance of ML models.The results of the RF model with feature selection performed well among all other models with high accuracy score of 96.56%and a precision of 88.02%,a sensitivity of 98.26%,a specificity of 96.06%.The respective validation score has an Non polynomial vector(NPV)of 99.47%,a Geometric Mean(GM)of 97.15%,a Youden’s index(YI)of 94.32%,and a Matthews’s correlation method(MCC)90.84%.The proposed model is also more robust than other models.It was also realised that using the RFKFS approach in the PD results in an effective and high-performing medical classifier.

Investigation of ductile iron casting process parameters using Taguchi approach and response surface methodology

To find the optimized levels of various casting parameters in the ductile iron casting, various casting defects and the rejection rate were observed from a medium scale foundry. The controlled values of different casting parameters such as pouring temperature, inoculation, carbon equivalent, moisture content, green compression strength, permeability and mould hardness were selected. Three different melts of metal with 0.4wt.%, 0.6wt.%, and 0.8wt.% inoculation （Fe-Si-Mg alloy and post inoculant） were produced using a 1-ton capacity coreless medium frequency induction furnace. L-27 orthogonal array with 3-level settings were chosen for the analysis. Responses for each run were observed. The signal-to-noise （S/N） ratio for each run was calculated using the Taguchi approach, and the optimized levels of different casting parameters were identified based on the SIN ratio. The analysis of variance for the casting acceptance percentage concludes that inoculation is the most significant factor affecting the castings＇ quality with a contribution percentage of 44%; an increase in inoculation results in a significant improvement in acceptance percentage of ductile iron castings. The experiment results showed that with the optimized parameters, the rejection rate was reduced from 16.98% to 6.07%.