Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.

Toward Fine-grained Image Retrieval with Adaptive Deep Learning for Cultural Heritage Image

Fine-grained image classification is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a specific category caused by different poses and scales.A cul-tural heritage image is one of thefine-grained images because each image has the same similarity in most cases.Using the classification technique,distinguishing cultural heritage architecture may be difficult.This study proposes a cultural heri-tage content retrieval method using adaptive deep learning forfine-grained image retrieval.The key contribution of this research was the creation of a retrieval mod-el that could handle incremental streams of new categories while maintaining its past performance in old categories and not losing the old categorization of a cul-tural heritage image.The goal of the proposed method is to perform a retrieval task for classes.Incremental learning for new classes was conducted to reduce the re-training process.In this step,the original class is not necessary for re-train-ing which we call an adaptive deep learning technique.Cultural heritage in the case of Thai archaeological site architecture was retrieved through machine learn-ing and image processing.We analyze the experimental results of incremental learning forfine-grained images with images of Thai archaeological site architec-ture from world heritage provinces in Thailand,which have a similar architecture.Using afine-grained image retrieval technique for this group of cultural heritage images in a database can solve the problem of a high degree of similarity among categories and a high degree of dissimilarity for a specific category.The proposed method for retrieving the correct image from a database can deliver an average accuracy of 85 percent.Adaptive deep learning forfine-grained image retrieval was used to retrieve cultural heritage content,and it outperformed state-of-the-art methods infine-grained image retrieval.

Towards frequency adaptation for delayed feedback deep brain stimulations

In neurodegenerative disorders such as Parkinson＇s disease （PD）, deep brain stimulation （DBS） is a desirable approach when the medication is less effective for treating the symptoms. DBS incorporates transferring electrical pulses to a specific tissue of the central nervous system, obtaining therapeutic results by modulating the neuronal activity of that region. DBS has certain advantages such as reversibility and adjustability features over medication, since the neuronal firing patterns can be recorded and used to alter the parameters of the DBS signal （Benabid et al., 2009）. One of the DBS indications is its ability to suppress the abnormal neuronal activity to treat symptoms like tremor, akinesia and dystonia.

Pseudo-chromosome–length genome assembly for a deep-sea eel Ilyophis brunneus sheds light on the deep-sea adaptation

High hydrostatic pressure,low temperature,and scarce food supply are the major factors that limit the survival of vertebrates in extreme deep-sea environments.Here,we constructed a high-quality genome of the deep-sea Muddy arrowtooth eel(MAE,Ilyophis brunneus,captured below a depth of 3,500 m)by using Illumina,Pac Bio,and Hi-C sequencing.We compare it against those of shallow-water eel and other outgroups to explore the genetic basis that underlies the adaptive evolution to deep-sea biomes.The MAE genome was estimated to be 1.47 Gb and assembled into 14 pseudo-chromosomes.Phylogenetic analyses indicated that MAE diverged from its closely related shallow-sea species,European eel,~111.9 Mya and experienced a rapid evolution.The genome evolutionary analyses primarily revealed the following:(i)under high hydrostatic pressure,the positively selected gene TUBGCP3 and the expanded family MLC1 may improve the cytoskeleton stability;ACOX1 may enhance the fluidity of cell membrane and maintain transport activity;the expansion of ABCC12 gene family may enhance the integrity of DNA;(ii)positively selected HARS likely maintain the transcription ability at low temperatures;and(iii)energy metabolism under a food-limited environment may be increased by expanded and positively selected genes in AMPK and m TOR signaling pathways.

Rice Bacterial Infection Detection Using Ensemble Technique on Unmanned Aerial Vehicles Images

Establishing a system for measuring plant health and bacterial infection is critical in agriculture.Previously,the farmers themselves,who observed them with their eyes and relied on their experience in analysis,which could have been incorrect.Plant inspection can determine which plants reflect the quantity of green light and near-infrared using infrared light,both visible and eye using a drone.The goal of this study was to create algorithms for assessing bacterial infections in rice using images from unmanned aerial vehicles(UAVs)with an ensemble classification technique.Convolution neural networks in unmanned aerial vehi-cles image were used.To convey this interest,the rice’s health and bacterial infec-tion inside the photo were detected.The project entailed using pictures to identify bacterial illnesses in rice.The shape and distinct characteristics of each infection were observed.Rice symptoms were defined using machine learning and image processing techniques.Two steps of a convolution neural network based on an image from a UAV were used in this study to determine whether this area will be affected by bacteria.The proposed algorithms can be utilized to classify the types of rice deceases with an accuracy rate of 89.84 percent.

Regression model for civil aero-engine gas path parameter deviation based on deep domainadaptation with Res-BP neural network

The variations in gas path parameter deviations can fully reflect the healthy state of aeroengine gas path components and units;therefore,airlines usually take them as key parameters for monitoring the aero-engine gas path performance state and conducting fault diagnosis.In the past,the airlines could not obtain deviations autonomously.At present,a data-driven method based on an aero-engine dataset with a large sample size can be utilized to obtain the deviations.However,it is still difficult to utilize aero-engine datasets with small sample sizes to establish regression models for deviations based on deep neural networks.To obtain monitoring autonomy of each aero-engine model,it is crucial to transfer and reuse the relevant knowledge of deviation modelling learned from different aero-engine models.This paper adopts the Residual-Back Propagation Neural Network(Res-BPNN)to deeply extract high-level features and stacks multi-layer Multi-Kernel Maximum Mean Discrepancy(MK-MMD)adaptation layers to map the extracted high-level features to the Reproduce Kernel Hilbert Space(RKHS)for discrepancy measurement.To further reduce the distribution discrepancy of each aero-engine model,the method of maximizing domain-confusion loss based on an adversarial mechanism is introduced to make the features learned from different domains as close as possible,and then the learned features can be confused.Through the above methods,domain-invariant features can be extracted,and the optimal adaptation effect can be achieved.Finally,the effectiveness of the proposed method is verified by using cruise data from different civil aero-engine models and compared with other transfer learning algorithms.

Adaptive Learning-Based Delay-Sensitive and Secure Edge-End Collaboration for Multi-Mode Low-Carbon Power IoT

Multi-mode power internet of things(PIoT)combines various communication media to provide spatio-temporal coverage for low-carbon operation in smart park.Edge-end collaboration is feasible to achieve the full utilization of heterogeneous resources and anti-eavesdropping.However,edge-end collaboration-based multi-mode PIoT faces challenges of mutual contradiction in communication and security quality of service(QoS)guarantee,inadaptability of resource management,and multi-mode access conflict.We propose an Adaptive learning based delAysensitive and seCure Edge-End Collaboration algorithm(ACE_(2))to optimize multi-mode channel selection and split device power into artificial noise(AN)transmission and data transmission for secure data delivery.ACE_(2) can achieve multi-attribute QoS guarantee,adaptive resource management and security enhancement,and access conflict elimination with the combined power of deep actor-critic(DAC),“win or learn fast(WoLF)”mechanism,and edge-end collaboration.Simulations demonstrate its superior performance in queuing delay,energy consumption,secrecy capacity,and adaptability to differentiated low-carbon services.

Modulation recognition network of multi-scale analysis with deep threshold noise elimination

To improve the accuracy of modulated signal recognition in variable environments and reduce the impact of factors such as lack of prior knowledge on recognition results,researchers have gradually adopted deep learning techniques to replace traditional modulated signal processing techniques.To address the problem of low recognition accuracy of the modulated signal at low signal-to-noise ratios,we have designed a novel modulation recognition network of multi-scale analysis with deep threshold noise elimination to recognize the actually collected modulated signals under a symmetric cross-entropy function of label smoothing.The network consists of a denoising encoder with deep adaptive threshold learning and a decoder with multi-scale feature fusion.The two modules are skip-connected to work together to improve the robustness of the overall network.Experimental results show that this method has better recognition accuracy at low signal-to-noise ratios than previous methods.The network demonstrates a flexible self-learning capability for different noise thresholds and the effectiveness of the designed feature fusion module in multi-scale feature acquisition for various modulation types.

Layer-wise domain correction for unsupervised domain adaptation

Deep neural networks have been successfully applied to numerous machine learning tasks because of their impressive feature abstraction capabilities.However,conventional deep networks assume that the training and test data are sampled from the same distribution,and this assumption is often violated in real-world scenarios.To address the domain shift or data bias problems,we introduce layer-wise domain correction(LDC),a new unsupervised domain adaptation algorithm which adapts an existing deep network through additive correction layers spaced throughout the network.Through the additive layers,the representations of source and target domains can be perfectly aligned.The corrections that are trained via maximum mean discrepancy,adapt to the target domain while increasing the representational capacity of the network.LDC requires no target labels,achieves state-of-the-art performance across several adaptation benchmarks,and requires significantly less training time than existing adaptation methods.