An Automated Classification Technique for COVID-19 Using Optimized Deep Learning Features

In 2020,COVID-19 started spreading throughout the world.This deadly infection was identified as a virus that may affect the lungs and,in severe cases,could be the cause of death.The polymerase chain reaction(PCR)test is commonly used to detect this virus through the nasal passage or throat.However,the PCR test exposes health workers to this deadly virus.To limit human exposure while detecting COVID-19,image processing techniques using deep learning have been successfully applied.In this paper,a strategy based on deep learning is employed to classify the COVID-19 virus.To extract features,two deep learning models have been used,the DenseNet201 and the SqueezeNet.Transfer learning is used in feature extraction,and models are fine-tuned.A publicly available computerized tomography(CT)scan dataset has been used in this study.The extracted features from the deep learning models are optimized using the Ant Colony Optimization algorithm.The proposed technique is validated through multiple evaluation parameters.Several classifiers have been employed to classify the optimized features.The cubic support vector machine(Cubic SVM)classifier shows superiority over other commonly used classifiers and attained an accuracy of 98.72%.The proposed technique achieves state-of-the-art accuracy,a sensitivity of 98.80%,and a specificity of 96.64%.

An Enhanced Data-Driven Model for Lithium-Ion Battery State-of-Health Estimation with Optimized Features and Prior Knowledge

In the long-term prediction of battery degradation,the data-driven method has great potential with historical data recorded by the battery management system.This paper proposes an enhanced data-driven model for Lithium-ion(Li-ion)battery state of health(SOH)estimation with a superior modeling procedure and optimized features.The Gaussian process regression(GPR)method is adopted to establish the data-driven estimator,which enables Li-ion battery SOH estimation with the uncertainty level.A novel kernel function,with the prior knowledge of Li-ion battery degradation,is then introduced to improve the mod-eling capability of the GPR.As for the features,a two-stage processing structure is proposed to find a suitable partial charging voltage profile with high efficiency.In the first stage,an optimal partial charging voltage is selected by the grid search;while in the second stage,the principal component analysis is conducted to increase both estimation accuracy and computing efficiency.Advantages of the proposed method are validated on two datasets from different Li-ion batteries:Compared with other methods,the proposed method can achieve the same accuracy level in the Oxford dataset;while in Maryland dataset,the mean absolute error,the root-mean-squared error,and the maximum error are at least improved by 16.36%,32.43%,and 45.46%,respectively.

Crops Leaf Diseases Recognition:A Framework of Optimum Deep Learning Features

Manual diagnosis of crops diseases is not an easy process;thus,a computerized method is widely used.Froma couple of years,advancements in the domain ofmachine learning,such as deep learning,have shown substantial success.However,they still faced some challenges such as similarity in disease symptoms and irrelevant features extraction.In this article,we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases recognition.The proposed architecture consists of five steps.In the first step,data augmentation is performed to increase the numbers of training samples.In the second step,pre-trained DarkNet19 deep model is opted and fine-tuned that later utilized for the training of fine-tuned model through transfer learning.Deep features are extracted from the global pooling layer in the next step that is refined using Improved Cuckoo search algorithm.The best selected features are finally classified using machine learning classifiers such as SVM,and named a few more for final classification results.The proposed architecture is tested using publicly available datasets–Cucumber National Dataset and Plant Village.The proposed architecture achieved an accuracy of 100.0%,92.9%,and 99.2%,respectively.Acomparison with recent techniques is also performed,revealing that the proposed method achieved improved accuracy while consuming less computational time.

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

Identifying fruit disease manually is time-consuming, expertrequired,and expensive;thus, a computer-based automated system is widelyrequired. Fruit diseases affect not only the quality but also the quantity.As a result, it is possible to detect the disease early on and cure the fruitsusing computer-based techniques. However, computer-based methods faceseveral challenges, including low contrast, a lack of dataset for training amodel, and inappropriate feature extraction for final classification. In thispaper, we proposed an automated framework for detecting apple fruit leafdiseases usingCNNand a hybrid optimization algorithm. Data augmentationis performed initially to balance the selected apple dataset. After that, twopre-trained deep models are fine-tuning and trained using transfer learning.Then, a fusion technique is proposed named Parallel Correlation Threshold(PCT). The fused feature vector is optimized in the next step using a hybridoptimization algorithm. The selected features are finally classified usingmachine learning algorithms. Four different experiments have been carriedout on the augmented Plant Village dataset and yielded the best accuracy of99.8%. The accuracy of the proposed framework is also compared to that ofseveral neural nets, and it outperforms them all.

Deep-Net:Fine-Tuned Deep Neural Network Multi-Features Fusion for Brain Tumor Recognition

Manual diagnosis of brain tumors usingmagnetic resonance images(MRI)is a hectic process and time-consuming.Also,it always requires an expert person for the diagnosis.Therefore,many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the literature.This paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization algorithm.NasNet-Mobile,a pre-trained deep learning model,has been fine-tuned and twoway trained on original and enhancedMRI images.The haze-convolutional neural network(haze-CNN)approach is developed and employed on the original images for contrast enhancement.Next,transfer learning(TL)is utilized for training two-way fine-tuned models and extracting feature vectors from the global average pooling layer.Then,using a multiset canonical correlation analysis(CCA)method,features of both deep learning models are fused into a single feature matrix—this technique aims to enhance the information in terms of features for better classification.Although the information was increased,computational time also jumped.This issue is resolved using a hybrid feature optimization algorithm that chooses the best classification features.The experiments were done on two publicly available datasets—BraTs2018 and BraTs2019—and yielded accuracy rates of 94.8%and 95.7%,respectively.The proposedmethod is comparedwith several recent studies andoutperformed inaccuracy.In addition,we analyze the performance of each middle step of the proposed approach and find the selection technique strengthens the proposed framework.

A Novel Efficient Patient Monitoring FER System Using Optimal DL-Features

Automated Facial Expression Recognition(FER)serves as the backbone of patient monitoring systems,security,and surveillance systems.Real-time FER is a challenging task,due to the uncontrolled nature of the environment and poor quality of input frames.In this paper,a novel FER framework has been proposed for patient monitoring.Preprocessing is performed using contrast-limited adaptive enhancement and the dataset is balanced using augmentation.Two lightweight efficient Convolution Neural Network(CNN)models MobileNetV2 and Neural search Architecture Network Mobile(NasNetMobile)are trained,and feature vectors are extracted.The Whale Optimization Algorithm(WOA)is utilized to remove irrelevant features from these vectors.Finally,the optimized features are serially fused to pass them to the classifier.A comprehensive set of experiments were carried out for the evaluation of real-time image datasets FER-2013,MMA,and CK+to report performance based on various metrics.Accuracy results show that the proposed model has achieved 82.5%accuracy and performed better in comparison to the state-of-the-art classification techniques in terms of accuracy.We would like to highlight that the proposed technique has achieved better accuracy by using 2.8 times lesser number of features.

Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like Pakistan.This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context.The research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate schedulers.In addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the tool.The findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation approach.Specifically,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test images.To validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD detection.This research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

Multiclass Stomach Diseases Classication Using Deep Learning Features Optimization

In the area of medical image processing,stomach cancer is one of the most important cancers which need to be diagnose at the early stage.In this paper,an optimized deep learning method is presented for multiple stomach disease classication.The proposed method work in few important steps—preprocessing using the fusion of ltering images along with Ant Colony Optimization(ACO),deep transfer learning-based features extraction,optimization of deep extracted features using nature-inspired algorithms,and nally fusion of optimal vectors and classication using Multi-Layered Perceptron Neural Network(MLNN).In the feature extraction step,pretrained Inception V3 is utilized and retrained on selected stomach infection classes using the deep transfer learning step.Later on,the activation function is applied to Global Average Pool(GAP)for feature extraction.However,the extracted features are optimized through two different nature-inspired algorithms—Particle Swarm Optimization(PSO)with dynamic tness function and Crow Search Algorithm(CSA).Hence,both methods’output is fused by a maximal value approach and classied the fused feature vector by MLNN.Two datasets are used to evaluate the proposed method—CUI WahStomach Diseases and Combined dataset and achieved an average accuracy of 99.5%.The comparison with existing techniques,it is shown that the proposed method shows signicant performance.

An Ensemble of Optimal Deep Learning Features for Brain Tumor Classification

Owing to technological developments,Medical image analysis has received considerable attention in the rapid detection and classification of diseases.The brain is an essential organ in humans.Brain tumors cause loss of memory,vision,and name.In 2020,approximately 18,020 deaths occurred due to brain tumors.These cases can be minimized if a brain tumor is diagnosed at a very early stage.Computer vision researchers have introduced several techniques for brain tumor detection and classification.However,owing to many factors,this is still a challenging task.These challenges relate to the tumor size,the shape of a tumor,location of the tumor,selection of important features,among others.In this study,we proposed a framework for multimodal brain tumor classification using an ensemble of optimal deep learning features.In the proposed framework,initially,a database is normalized in the form of high-grade glioma(HGG)and low-grade glioma(LGG)patients and then two pre-trained deep learning models(ResNet50 and Densenet201)are chosen.The deep learning models were modified and trained using transfer learning.Subsequently,the enhanced ant colony optimization algorithm is proposed for best feature selection from both deep models.The selected features are fused using a serial-based approach and classified using a cubic support vector machine.The experimental process was conducted on the BraTs2019 dataset and achieved accuracies of 87.8%and 84.6%for HGG and LGG,respectively.The comparison is performed using several classification methods,and it shows the significance of our proposed technique.

Malaria Blood Smear Classification Using Deep Learning and Best Features Selection

Malaria is a critical health condition that affects both sultry and frigid region worldwide,giving rise to millions of cases of disease and thousands of deaths over the years.Malaria is caused by parasites that enter the human red blood cells,grow there,and damage them over time.Therefore,it is diagnosed by a detailed examination of blood cells under the microscope.This is the most extensively used malaria diagnosis technique,but it yields limited and unreliable results due to the manual human involvement.In this work,an automated malaria blood smear classification model is proposed,which takes images of both infected and healthy cells and preprocesses themin the L^(*)a^(*)b^(*)color space by employing several contrast enhancement methods.Feature extraction is performed using two pretrained deep convolutional neural networks,DarkNet-53 and DenseNet-201.The features are subsequently agglutinated to be optimized through a nature-based feature reduction method called the whale optimization algorithm.Several classifiers are effectuated on the reduced features,and the achieved results excel in both accuracy and time compared to previously proposed methods.

Short-Term Mosques Load Forecast Using Machine Learning and Meteorological Data

The tendency toward achieving more sustainable and green buildings turned several passive buildings into more dynamic ones.Mosques are the type of buildings that have a unique energy usage pattern.Nevertheless,these types of buildings have minimal consideration in the ongoing energy efficiency applications.This is due to the unpredictability in the electrical consumption of the mosques affecting the stability of the distribution networks.Therefore,this study addresses this issue by developing a framework for a short-term electricity load forecast for a mosque load located in Riyadh,Saudi Arabia.In this study,and by harvesting the load consumption of the mosque and meteorological datasets,the performance of four forecasting algorithms is investigated,namely Artificial Neural Network and Support Vector Regression(SVR)based on three kernel functions:Radial Basis(RB),Polynomial,and Linear.In addition,this research work examines the impact of 13 different combinations of input attributes since selecting the optimal features has a major influence on yielding precise forecasting outcomes.For the mosque load,the(SVR-RB)with eleven features appeared to be the best forecasting model with the lowest forecasting errors metrics giving RMSE,nRMSE,MAE,and nMAE values of 4.207 kW,2.522%,2.938 kW,and 1.761%,respectively.

Two-Stream Deep Learning Architecture-Based Human Action Recognition

Human action recognition(HAR)based on Artificial intelligence reasoning is the most important research area in computer vision.Big breakthroughs in this field have been observed in the last few years;additionally,the interest in research in this field is evolving,such as understanding of actions and scenes,studying human joints,and human posture recognition.Many HAR techniques are introduced in the literature.Nonetheless,the challenge of redundant and irrelevant features reduces recognition accuracy.They also faced a few other challenges,such as differing perspectives,environmental conditions,and temporal variations,among others.In this work,a deep learning and improved whale optimization algorithm based framework is proposed for HAR.The proposed framework consists of a few core stages i.e.,frames initial preprocessing,fine-tuned pre-trained deep learning models through transfer learning(TL),features fusion using modified serial based approach,and improved whale optimization based best features selection for final classification.Two pre-trained deep learning models such as InceptionV3 and Resnet101 are fine-tuned and TL is employed to train on action recognition datasets.The fusion process increases the length of feature vectors;therefore,improved whale optimization algorithm is proposed and selects the best features.The best selected features are finally classified usingmachine learning(ML)classifiers.Four publicly accessible datasets such as Ut-interaction,Hollywood,Free Viewpoint Action Recognition usingMotion History Volumes(IXMAS),and centre of computer vision(UCF)Sports,are employed and achieved the testing accuracy of 100%,99.9%,99.1%,and 100%respectively.Comparison with state of the art techniques(SOTA),the proposed method showed the improved accuracy.

Landmarks-Driven Triplet Representation for Facial Expression Similarity

The facial landmarks can provide valuable information for expression-related tasks.However,most approaches only use landmarks for segmentation preprocessing or directly input them into the neural network for fully connection.Such simple combination not only fails to pass the spatial information to network,but also increases calculation amounts.The method proposed in this paper aims to integrate facial landmarks-driven representation into the triplet network.The spatial information provided by landmarks is introduced into the feature extraction process,so that the model can better capture the location relationship.In addition,coordinate information is also integrated into the triple loss calculation to further enhance similarity prediction.Specifically,for each image,the coordinates of 68 landmarks are detected,and then a region attention map based on these landmarks is generated.For the feature map output by the shallow convolutional layer,it will be multiplied with the attention map to correct the feature activation,so as to strengthen the key region and weaken the unimportant region.Finally,the optimized embedding output can be further used for downstream tasks.Three embeddings of three images output by the network can be regarded as a triplet representation for similarity computation.Through the CK+dataset,the effectiveness of such an optimized feature extraction is verified.After that,it is applied to facial expression similarity tasks.The results on the facial expression comparison(FEC)dataset show that the accuracy rate will be significantly improved after the landmark information is introduced.

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.

Accurate Machine Learning Predictions of Sci-Fi Film Performance

A groundbreaking method is introduced to leverage machine learn-ing algorithms to revolutionize the prediction of success rates for science fiction films.In the captivating world of the film industry,extensive research and accurate forecasting are vital to anticipating a movie’s triumph prior to its debut.Our study aims to harness the power of available data to estimate a film’s early success rate.With the vast resources offered by the internet,we can access a plethora of movie-related information,including actors,directors,critic reviews,user reviews,ratings,writers,budgets,genres,Facebook likes,YouTube views for movie trailers,and Twitter followers.The first few weeks of a film’s release are crucial in determining its fate,and online reviews and film evaluations profoundly impact its opening-week earnings.Hence,our research employs advanced supervised machine learning techniques to predict a film’s triumph.The Internet Movie Database(IMDb)is a comprehensive data repository for nearly all movies.A robust predictive classification approach is developed by employing various machine learning algorithms,such as fine,medium,coarse,cosine,cubic,and weighted KNN.To determine the best model,the performance of each feature was evaluated based on composite metrics.Moreover,the significant influences of social media platforms were recognized including Twitter,Instagram,and Facebook on shaping individuals’opinions.A hybrid success rating prediction model is obtained by integrating the proposed prediction models with sentiment analysis from available platforms.The findings of this study demonstrate that the chosen algorithms offer more precise estimations,faster execution times,and higher accuracy rates when compared to previous research.By integrating the features of existing prediction models and social media sentiment analysis models,our proposed approach provides a remarkably accurate prediction of a movie’s success.This breakthrough can help movie producers and marketers anticipate a film’s triumph before its release,allowing them to tailor their promotional activities accordingly.Furthermore,the adopted research lays the foundation for developing even more accurate prediction models,considering the ever-increasing significance of social media platforms in shaping individ-uals’opinions.In conclusion,this study showcases the immense potential of machine learning algorithms in predicting the success rate of science fiction films,opening new avenues for the film industry.

森林优化特征选择算法的增强与扩展

特征选择作为一种重要的数据预处理方法,不但能解决维数灾难问题,还能提高算法的泛化能力.各种各样的方法已被应用于解决特征选择问题,其中,基于演化计算的特征选择算法近年来获得了更多的关注并取得了一些成功.近期研究结果表明,森林优化特征选择算法具有更好的分类性能及维度缩减能力.然而,初始化阶段的随机性、全局播种阶段的人为参数设定,影响了该算法的准确率和维度缩减能力;同时,算法本身存在着高维数据处理能力不足的本质缺陷.从信息增益率的角度给出了一种初始化策略,在全局播种阶段,借用模拟退火控温函数的思想自动生成参数,并结合维度缩减率给出了适应度函数;同时,针对形成的优质森林采取贪心算法,形成一种特征选择算法EFSFOA(enhanced feature selection using forest optimization algorithm).此外,在面对高维数据的处理时,采用集成特征选择的方案形成了一个适用于EFSFOA的集成特征选择框架,使其能够有效处理高维数据特征选择问题.通过设计对比实验,验证了EFSFOA与FSFOA相比在分类准确率和维度缩减率上均有明显的提高,高维数据处理能力更是提高到了100 000维.将EFSFOA与近年来提出的比较高效的基于演化计算的特征选择方法进行对比,EFSFOA仍具有很强的竞争力.

Agricultural Ontology Based Feature Optimization for Agricultural Text Clustering

Feature optimization is important to agricultural text mining. Usually, the vector space model is used to represent text documents. However, this basic approach still suffers from two drawbacks： thecurse of dimension and the lack of semantic information. In this paper, a novel ontology-based feature optimization method for agricultural text was proposed. First, terms of vector space model were mapped into concepts of agricultural ontology, which concept frequency weights are computed statistically by term frequency weights; second, weights of concept similarity were assigned to the concept features according to the structure of the agricultural ontology. By combining feature frequency weights and feature similarity weights based on the agricultural ontology, the dimensionality of feature space can be reduced drastically. Moreover, the semantic information can be incorporated into this method. The results showed that this method yields a significant improvement on agricultural text clustering by the feature optimization.

Classification of Positive COVID-19 CT Scans Using Deep Learning

In medical imaging,computer vision researchers are faced with a variety of features for verifying the authenticity of classifiers for an accurate diagnosis.In response to the coronavirus 2019(COVID-19)pandemic,new testing procedures,medical treatments,and vaccines are being developed rapidly.One potential diagnostic tool is a reverse-transcription polymerase chain reaction(RT-PCR).RT-PCR,typically a time-consuming process,was less sensitive to COVID-19 recognition in the disease’s early stages.Here we introduce an optimized deep learning(DL)scheme to distinguish COVID-19-infected patients from normal patients according to computed tomography(CT)scans.In the proposed method,contrast enhancement is used to improve the quality of the original images.A pretrained DenseNet-201 DL model is then trained using transfer learning.Two fully connected layers and an average pool are used for feature extraction.The extracted deep features are then optimized with a Firefly algorithm to select the most optimal learning features.Fusing the selected features is important to improving the accuracy of the approach;however,it directly affects the computational cost of the technique.In the proposed method,a new parallel high index technique is used to fuse two optimal vectors;the outcome is then passed on to an extreme learning machine for final classification.Experiments were conducted on a collected database of patients using a 70:30 training:Testing ratio.Our results indicated an average classification accuracy of 94.76%with the proposed approach.A comparison of the outcomes to several other DL models demonstrated the effectiveness of our DL method for classifying COVID-19 based on CT scans.

Classication of COVID-19 CT Scans via Extreme Learning Machin

Here,we use multi-type feature fusion and selection to predict COVID-19 infections on chest computed tomography(CT)scans.The scheme operates in four steps.Initially,we prepared a database containing COVID-19 pneumonia and normal CT scans.These images were retrieved from the Radiopaedia COVID-19 website.The images were divided into training and test sets in a ratio of 70:30.Then,multiple features were extracted from the training data.We used canonical correlation analysis to fuse the features into single vectors;this enhanced the predictive capacity.We next implemented a genetic algorithm(GA)in which an Extreme Learning Machine(ELM)served to assess GA tness.Based on the ELM losses,the most discriminatory features were selected and saved as an ELM Model.Test images were sent to the model,and the best-selected features compared to those of the trained model to allow nal predictions.Validation employed the collected chest CT scans.The best predictive accuracy of the ELM classier was 93.9%;the scheme was effective.

Automatic Text Summarization Using Genetic Algorithm and Repetitive Patterns

Taking into account the increasing volume of text documents,automatic summarization is one of the important tools for quick and optimal utilization of such sources.Automatic summarization is a text compression process for producing a shorter document in order to quickly access the important goals and main features of the input document.In this study,a novel method is introduced for selective text summarization using the genetic algorithm and generation of repetitive patterns.One of the important features of the proposed summarization is to identify and extract the relationship between the main features of the input text and the creation of repetitive patterns in order to produce and optimize the vector of the main document features in the production of the summary document compared to other previous methods.In this study,attempts were made to encompass all the main parameters of the summary text including unambiguous summary with the highest precision,continuity and consistency.To investigate the efficiency of the proposed algorithm,the results of the study were evaluated with respect to the precision and recall criteria.The results of the study evaluation showed the optimization the dimensions of the features and generation of a sequence of summary document sentences having the most consistency with the main goals and features of the input document.