Recognition of Film Type Using HSV Features on Deep-Learning Neural Networks

The number of films is numerous and the film contents are complex over the Internet and multimedia sources. It is time consuming for a viewer to select a favorite film. This paper presents an automatic recognition system of film types. Initially, a film is firstly sampled as frame sequences. The color space, including hue, saturation,and brightness value(HSV), is analyzed for each sampled frame by computing the deviation and mean of HSV for each film. These features are utilized as inputs to a deep-learning neural network(DNN) for the recognition of film types. One hundred films are utilized to train and validate the model parameters of DNN. In the testing phase, a film is recognized as one of the five categories, including action, comedy, horror thriller, romance, and science fiction, by the trained DNN. The experimental results reveal that the film types can be effectively recognized by the proposed approach, enabling the viewer to select an interesting film accurately and quickly.

Deep-learning architecture for PM_(2.5) concentration prediction: A review

Accurately predicting the concentration of fine particulate matter(PM_(2.5))is crucial for evaluating air pollution levels and public exposure.Recent advancements have seen a significant rise in using deep learning(DL)models for forecasting PM_(2.5) concentrations.Nonetheless,there is a lack of unified and standardized frameworks for assessing the performance of DL-based PM_(2.5) prediction models.Here we extensively reviewed those DL-based hybrid models for forecasting PM_(2.5) levels according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines.We examined the similarities and differences among various DL models in predicting PM_(2.5) by comparing their complexity and effectiveness.We categorized PM_(2.5) DL methodologies into seven types based on performance and application conditions,including four types of DL-based models and three types of hybrid learning models.Our research indicates that established deep learning architectures are commonly used and respected for their efficiency.However,many of these models often fall short in terms of innovation and interpretability.Conversely,models hybrid with traditional approaches,like deterministic and statistical models,exhibit high interpretability but compromise on accuracy and speed.Besides,hybrid DL models,representing the pinnacle of innovation among the studied models,encounter issues with interpretability.We introduce a novel three-dimensional evaluation framework,i.e.,Dataset-MethodExperiment Standard(DMES)to unify and standardize the evaluation for PM_(2.5) predictions using DL models.This review provides a framework for future evaluations of DL-based models,which could inspire researchers to standardize DL model usage in PM_(2.5) prediction and improve the quality of related studies.

Universal materials model of deep-learning density functional theory Hamiltonian

Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence,but how to achieve this fantastic and challenging objective remains elusive.Here,we propose a feasible pathway to address this paramount pursuit by developing universal materials models of deep-learning density functional theory Hamiltonian(Deep H),enabling computational modeling of the complicated structure-property relationship of materials in general.By constructing a large materials database and substantially improving the Deep H method,we obtain a universal materials model of Deep H capable of handling diverse elemental compositions and material structures,achieving remarkable accuracy in predicting material properties.We further showcase a promising application of fine-tuning universal materials models for enhancing specific materials models.This work not only demonstrates the concept of Deep H's universal materials model but also lays the groundwork for developing large materials models,opening up significant opportunities for advancing artificial intelligencedriven materials discovery.

A deep-learning method for evaluating shaft resistance of the cast-in-site pile on reclaimed ground using field data

This study proposes a deep learning-based approach for shaft resistance evaluation of cast-in-site piles on reclaimed ground,independent of theoretical hypotheses and engineering experience.A series of field tests was first performed to investigate the characteristics of the shaft resistance of cast-in-site piles on reclaimed ground.Then,an intelligent approach based on the long short term memory deep-learning technique was proposed to calculate the shaft resistance of the cast-in-site pile.The proposed method allows accurate estimation of the shaft resistance of cast-in-site piles,not only under the ultimate load but also under the working load.Comparisons with empirical methods confirmed the effectiveness of the proposed method for the shaft resistance estimation of cast-in-site piles on reclaimed ground in offshore areas.

A unified deep-learning network to accurately segment insulin granules of different animal models imaged under different electron microscopy methodologies

Insulin is important for body metabolism regulation and glucose homeostasis,and its dysregulation often leads to metabolic syndrome(MS)and diabetes.Insulin is normally stored in large dense-core vesicles(LDCVs)in pancreatic beta cells,and significant reductions in the number,size,gray level and density of insulin granules confer diabetes both in mice(Xue et al.,2012)and humans(Masini et al.,2012).

Network Security Enhanced with Deep Neural Network-Based Intrusion Detection System

This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful activity.The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS research.The model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation accuracy.Thus,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in validation.Furthermore,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 scores.The macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are 0.942.Furthermore,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies precisely.The research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network security.The study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this field.Upcoming research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.

Spatial Attention Integrated EfficientNet Architecture for Breast Cancer Classification with Explainable AI

Breast cancer is a type of cancer responsible for higher mortality rates among women.The cruelty of breast cancer always requires a promising approach for its earlier detection.In light of this,the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast tumors.In addition,the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input mammogram.Accordingly,the work proposed an EfficientNet-B0 having a Spatial Attention Layer with XGBoost(ESA-XGBNet)for binary classification of mammograms.For this,the work is trained,tested,and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSM,INbreast,and MIAS databases.Maximumclassification accuracy of 97.585%(CBISDDSM),98.255%(INbreast),and 98.91%(MIAS)is obtained using the proposed ESA-XGBNet architecture as compared with the existing models.Furthermore,the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based Explainable AI technique.

Hybrid pedestrian positioning system using wearable inertial sensors and ultrasonic ranging

Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,etc.The performance of traditional PPS is limited by the cumulative error of inertial sensors,complex motion modes of pedestrians,and the low robustness of the multi-sensor collaboration structure.This paper presents a hybrid pedestrian positioning system using the combination of wearable inertial sensors and ultrasonic ranging(H-PPS).A robust two nodes integration structure is developed to adaptively combine the motion data acquired from the single waist-mounted and foot-mounted node,and enhanced by a novel ellipsoid constraint model.In addition,a deep-learning-based walking speed estimator is proposed by considering all the motion features provided by different nodes,which effectively reduces the cumulative error originating from inertial sensors.Finally,a comprehensive data and model dual-driven model is presented to effectively combine the motion data provided by different sensor nodes and walking speed estimator,and multi-level constraints are extracted to further improve the performance of the overall system.Experimental results indicate that the proposed H-PPS significantly improves the performance of the single PPS and outperforms existing algorithms in accuracy index under complex indoor scenarios.

CNN Channel Attention Intrusion Detection SystemUsing NSL-KDD Dataset

Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online threats.The goal of this research is to meet the urgent need for small-footprint,highly-adaptable Network Intrusion Detection Systems(NIDS)that can identify anomalies.The NSL-KDD dataset is used in the study;it is a sizable collection comprising 43 variables with the label’s“attack”and“level.”It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks(CNN).Furthermore,this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection strategy.Furthermore,maintaining operating efficiency while improving detection accuracy is the primary goal of this work.Moreover,typical NIDS examines both risky and typical behavior using a variety of techniques.On the NSL-KDD dataset,our CNN-based approach achieves an astounding 99.728%accuracy rate when paired with channel attention.Compared to previous approaches such as ensemble learning,CNN,RBM(Boltzmann machine),ANN,hybrid auto-encoders with CNN,MCNN,and ANN,and adaptive algorithms,our solution significantly improves intrusion detection performance.Moreover,the results highlight the effectiveness of our suggested method in improving intrusion detection precision,signifying a noteworthy advancement in this field.Subsequent efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.

HIPPO artificial intelligence:Correlating automated radiographic femoroacetabular measurements with patient-reported outcomes in developmental hip dysplasia

BACKGROUND Hip dysplasia(HD)is characterized by insufficient acetabular coverage of the femoral head,leading to a predisposition for osteoarthritis.While radiographic measurements such as the lateral center edge angle(LCEA)and Tönnis angle are essential in evaluating HD severity,patient-reported outcome measures(PROMs)offer insights into the subjective health impact on patients.AIM To investigate the correlations between machine-learning automated and manual radiographic measurements of HD and PROMs with the hypothesis that artificial intelligence(AI)-generated HD measurements indicating less severe dysplasia correlate with better PROMs.METHODS Retrospective study evaluating 256 hips from 130 HD patients from a hip preservation clinic database.Manual and AI-derived radiographic measurements were collected and PROMs such as the Harris hip score(HHS),international hip outcome tool(iHOT-12),short form(SF)12(SF-12),and Visual Analogue Scale of the European Quality of Life Group survey were correlated using Spearman's rank-order correlation.RESULTS The median patient age was 28.6 years(range 15.7-62.3 years)with 82.3%of patients being women and 17.7%being men.The median interpretation time for manual readers and AI ranged between 4-12 minutes per patient and 31 seconds,respectively.Manual measurements exhibited weak correlations with HHS,including LCEA(r=0.18)and Tönnis angle(r=-0.24).AI-derived metrics showed similar weak correlations,with the most significant being Caput-Collum-Diaphyseal(CCD)with iHOT-12 at r=-0.25(P=0.042)and CCD with SF-12 at r=0.25(P=0.048).Other measured correlations were not significant(P>0.05).CONCLUSION This study suggests AI can aid in HD assessment,but weak PROM correlations highlight their continued importance in predicting subjective health and outcomes,complementing AI-derived measurements in HD management.

Intrusion Detection System with Customized Machine Learning Techniques for NSL-KDD Dataset

Modern networks are at risk from a variety of threats as a result of the enormous growth in internet-based traffic.By consuming time and resources,intrusive traffic hampers the efficient operation of network infrastructure.An effective strategy for preventing,detecting,and mitigating intrusion incidents will increase productivity.A crucial element of secure network traffic is Intrusion Detection System(IDS).An IDS system may be host-based or network-based to monitor intrusive network activity.Finding unusual internet traffic has become a severe security risk for intelligent devices.These systems are negatively impacted by several attacks,which are slowing computation.In addition,networked communication anomalies and breaches must be detected using Machine Learning(ML).This paper uses the NSL-KDD data set to propose a novel IDS based on Artificial Neural Networks(ANNs).As a result,the ML model generalizes sufficiently to perform well on untried data.The NSL-KDD dataset shall be utilized for both training and testing.In this paper,we present a custom ANN model architecture using the Keras open-source software package.The specific arrangement of nodes and layers,along with the activation functions,enhances the model’s ability to capture intricate patterns in network data.The performance of the ANN is carefully tested and evaluated,resulting in the identification of a maximum detection accuracy of 97.5%.We thoroughly compared our suggested model to industry-recognized benchmark methods,such as decision classifier combinations and ML classifiers like k-Nearest Neighbors(KNN),Deep Learning(DL),Support Vector Machine(SVM),Long Short-Term Memory(LSTM),Deep Neural Network(DNN),and ANN.It is encouraging to see that our model consistently outperformed each of these tried-and-true techniques in all evaluations.This result underlines the effectiveness of the suggested methodology by demonstrating the ANN’s capacity to accurately assess the effectiveness of the developed strategy in identifying and categorizing instances of network intrusion.

Deep learning method for cell count from transmitted-light microscope

Automatic cell counting provides an effective tool for medical research and diagnosis.Currently,cell counting can be completed by transmitted-light microscope,however,it requires expert knowledge and the counting accuracy which is unsatisfied for overlapped cells.Further,the image-translation-based detection method has been proposed and the potential has been shown to accomplish cell counting from transmitted-light microscope,automatically and effectively.In this work,a new deep-learning(DL)-based two-stage detection method(cGAN-YOLO)is designed to further enhance the performance of cell counting,which is achieved by combining a DL-based fluorescent image translation model and a DL-based cell detection model.The various results show that cGAN-YOLO can effectively detect and count some different types of cells from the acquired transmitted-light microscope images.Compared with the previously reported YOLO-based one-stage detection method,high recognition accuracy(RA)is achieved by the cGAN-YOLO method,with an improvement of 29.80%.Furthermore,we can also observe that cGAN-YOLO obtains an improvement of 12.11%in RA compared with the previously reported image-translation-based detection method.In a word,cGAN-YOLO makes it possible to implement cell counting directly from the experimental acquired transmitted-light microscopy images with high flexibility and performance,which extends the applicability in clinical research.

Robust Vehicle Detection Based on Improved You Look Only Once

Vehicle detection is still challenging for intelligent transportation systems(ITS)to achieve satisfactory performance.The existing methods based on one stage and two-stage have intrinsic weakness in obtaining high vehicle detection performance.Due to advancements in detection technology,deep learning-based methods for vehicle detection have become more popular because of their higher detection accuracy and speed than the existing algorithms.This paper presents a robust vehicle detection technique based on Improved You Look Only Once(RVD-YOLOv5)to enhance vehicle detection accuracy.The proposed method works in three phases;in the first phase,the K-means algorithm performs data clustering on datasets to generate the classes of the objects.Subsequently,in the second phase,the YOLOv5 is applied to create the bounding box,and the Non-Maximum Suppression(NMS)technique is used to eliminate the overlapping of the bounding boxes of the vehicle.Then,the loss function CIoU is employed to obtain the accurate regression bounding box of the vehicle in the third phase.The simulation results show that the proposed method achieves better results when compared with other state-of-art techniques,namely LightweightDilated Convolutional Neural Network(LD-CNN),Single Shot Detector(SSD),YOLOv3 and YOLOv4 on the performance metric like precision,recall,mAP and F1-Score.The simulation and analysis are carried out on PASCAL VOC 2007,2012 and MS COCO 2017 datasets to obtain better performance for vehicle detection.Finally,the RVD-YOLOv5 obtains the results with an mAP of 98.6%and Precision,Recall,and F1-Score are 98%,96.2%and 97.09%,respectively.

Application of artificial intelligence in gastroenterology

Artificial intelligence(AI) using deep-learning(DL) has emerged as a breakthrough computer technology. By the era of big data, the accumulation of an enormous number of digital images and medical records drove the need for the utilization of AI to efficiently deal with these data, which have become fundamental resources for a machine to learn by itself. Among several DL models, the convolutional neural network showed outstanding performance in image analysis. In the field of gastroenterology, physicians handle large amounts of clinical data and various kinds of image devices such as endoscopy and ultrasound. AI has been applied in gastroenterology in terms of diagnosis,prognosis, and image analysis. However, potential inherent selection bias cannot be excluded in the form of retrospective study. Because overfitting and spectrum bias(class imbalance) have the possibility of overestimating the accuracy,external validation using unused datasets for model development, collected in a way that minimizes the spectrum bias, is mandatory. For robust verification,prospective studies with adequate inclusion/exclusion criteria, which represent the target populations, are needed. DL has its own lack of interpretability.Because interpretability is important in that it can provide safety measures, help to detect bias, and create social acceptance, further investigations should be performed.

Predicting oral disintegrating tablet formulations by neural network techniques

Oral disintegrating tablets(ODTs) are a novel dosage form that can be dissolved on thetongue within 3 min or less especially for geriatric and pediatric patients. Current ODT for-mulation studies usually rely on the personal experience of pharmaceutical experts andtrial-and-error in the laboratory, which is inefficient and time-consuming. The aim of cur-rent research was to establish the prediction model of ODT formulations with direct com-pression process by artificial neural network(ANN) and deep neural network(DNN) tech-niques. 145 formulation data were extracted from Web of Science. All datasets were dividedinto three parts: training set(105 data), validation set(20) and testing set(20). ANN andDNN were compared for the prediction of the disintegrating time. The accuracy of the ANNmodel have reached 85.60%, 80.00% and 75.00% on the training set, validation set and testingset respectively, whereas that of the DNN model were 85.60%, 85.00% and 80.00%, respec-tively. Compared with the ANN, DNN showed the better prediction for ODT formulations.It is the first time that deep neural network with the improved dataset selection algorithmis applied to formulation prediction on small data. The proposed predictive approach couldevaluate the critical parameters about quality control of formulation, and guide researchand process development. The implementation of this prediction model could effectivelyreduce drug product development timeline and material usage, and proactively facilitatethe development of a robust drug product.

Automated classification of dual channel dental imaging of auto-fluorescence and white lightby convolutional neural networks

Prevention is the most effective way to reduce dental caries.In order to provide a simple way to achieve oral healthcare direction in daily life,dual Channel,portable dental Imaging system that combine white light with autofluorescence techniques was established,and then,a group of volunteers were recruited,7200 tooth pictures of different dental caries stage and dental plaque were taken and collected.In this work,a customized Convolutional Neural Networks(CNNs)have been designed to classify dental image with early stage caries and dental plaque.Eighty percentage(n=6000)of the pictures taken were used to supervised training of the CNNs based on the experienced dentists'advice and the rest 20%(n=1200)were used to a test dataset to test the trained CNNs.The accuracy,sensitivity and specificity were calculated to evaluate perfor-mance of the CNNs.The accuracy for the early stage caries and dental plaque were 95.3%and 95.9%,respectively.These results shown that the designed image system combined the cus-tomized CNNs that could automatically and efficiently find early caries and dental plaque on occlusal,lingual and buccal surfaces.Therefore,this will provide a novel approach to dental caries prevention for everyone in daily life.

An Innovative Bias-Correction Approach to CMA-GD Hourly Quantitative Precipitation Forecasts

This paper proposes a simple and powerful optimal integration(OPI)method for improving hourly quantitative precipitation forecasts(QPFs,0-24 h)of a single-model by integrating the benefits of different biascorrected methods using the high-resolution CMA-GD model from the Guangzhou Institute of Tropical and Marine Meteorology of China Meteorological Administration(CMA).Three techniques are used to generate multi-method calibrated members for OPI:deep neural network(DNN),frequency-matching(FM),and optimal threat score(OTS).The results are as follows:(1)The QPF using DNN follows the basic physical patterns of CMA-GD.Despite providing superior improvements for clear-rainy and weak precipitation,DNN cannot improve the predictions for severe precipitation,while OTS can significantly strengthen these predictions.As a result,DNN and OTS are the optimal members to be incorporated into OPI.(2)Our new approach achieves state-of-the-art performances on a single model for all magnitudes of precipitation.Compared with the CMA-GD,OPI improves the TS by 2.5%,5.4%,7.8%,8.3%,and 6.1%for QPFs from clear-rainy to rainstorms in the verification dataset.Moreover,OPI shows good stability in the test dataset.(3)It is also noted that the rainstorm pattern of OPI relies heavily on the original model and that OPI cannot correct for deviations in the location of severe precipitation.Therefore,improvements in predicting severe precipitation using this method should be further realized by improving the numerical model's forecasting capability.

Multi-Person Device-Free Gesture Recognition Using mmWave Signals

Device-free gesture recognition is an emerging wireless sensing technique which could recognize gestures by analyzing its influence on surrounding wireless signals,it may empower wireless networks with the augmented sensing ability.Researchers have made great achievements for singleperson device-free gesture recognition.However,when multiple persons conduct gestures simultaneously,the received signals will be mixed together,and thus traditional methods would not work well anymore.Moreover,the anonymity of persons and the change in the surrounding environment would cause feature shift and mismatch,and thus the recognition accuracy would degrade remarkably.To address these problems,we explore and exploit the diversity of spatial information and propose a multidimensional analysis method to separate the gesture feature of each person using a focusing sensing strategy.Meanwhile,we also present a deep-learning based robust device free gesture recognition framework,which leverages an adversarial approach to extract robust gesture feature that is insensitive to the change of persons and environment.Furthermore,we also develop a 77GHz mmWave prototype system and evaluate the proposed methods extensively.Experimental results reveal that the proposed system can achieve average accuracies of 93%and 84%when 10 gestures are conducted in Received:Jun.18,2020 Revised:Aug.06,2020 Editor:Ning Ge different environments by two and four persons simultaneously,respectively.

Deep Learning-based Wireless Signal Classification in the IoT Environment

With the development of the Internet of Things(IoT),diverse wireless devices are increasing rapidly.Those devices have different wireless interfaces that generate incompatible wireless signals.Each signal has its own physical characteristics with signal modulation and demodulation scheme.When there exist different wireless devices,they can suffer from severe Cross-Technology Interferences(CTI).To reduce the communication overhead due to the CTI in the real IoT environment,a central coordinator can be able to detect and identify wireless signals existing in the same communication areas.This paper investigates how to classify various radio signals using Convolutional Neural Networks(CNN),Long Short-TermMemory(LSTM)and attention mechanism.CNN can reduce the amount of computation by reducing weights by using convolution,and LSTM belonging to RNNmodels can alleviate the long-term dependence problem.Furthermore,attention mechanism can reduce the short-term memory problem of RNNs by reexamining the data output from the decoder and the entire data entered into the encoder at every point in time.To accurately classify radio signals according to their weights,we design a model based on CNN,LSTM,and attention mechanism.As a result,we propose a model CLARINet that can classify original data by minimizing the loss and detects changes in sequences.In a case of the real IoT environment with Wi-Fi,Bluetooth and ZigBee devices,we can normally obtain wireless signals from 10 to 20 dB.The accuracy of CLARINet’s radio signal classification with CNN-LSTM and attention mechanism can be seen that signal-to-noise ratio(SNR)exhibits high accuracy at 16 dB to about 92.03%.

Shadow Detection and Removal From Photo-Realistic Synthetic Urban Image Using Deep Learning

Recently,virtual reality technology that can interact with various data is used for urban design and analysis.Reality,one of the most important elements in virtual reality technology,means visual expression so that a person can experience three-dimensional space like reality.To obtain this realism,real-world data are used in the various fields.For example,in order to increase the realism of 3D modeled building textures real aerial images are utilized in 3D modelling.However,the aerial image captured during the day can be shadowed by the sun and it can cause the distortion or deterioration of image.To resolve this problem,researches on detecting and removing shadows have been conducted,but the detecting and removing shadow is still considered as a challenging problem.In this paper,we propose a novel method for detecting and removing shadows using deep learning.For this work,we first a build a new dataset of photo-realistic synthetic urban data based on the virtual environment using 3D spatial information provided by VWORLD.For detecting and removing shadow from the dataset,firstly,the 1-channel shadow mask image is inferred from the 3-channel shadow image through the CNN.Then,to generate a shadow-free image,a 3-channel shadow image and a detected 1-channel shadow mask into the GAN is executed.From the experiments,we can prove that the proposed method outperforms the existing methods in detecting and removing shadow.