Recognition of Human Actions through Speech or Voice Using Machine Learning Techniques

The development of artificial intelligence(AI)and smart home technologies has driven the need for speech recognition-based solutions.This demand stems from the quest for more intuitive and natural interaction between users and smart devices in their homes.Speech recognition allows users to control devices and perform everyday actions through spoken commands,eliminating the need for physical interfaces or touch screens and enabling specific tasks such as turning on or off the light,heating,or lowering the blinds.The purpose of this study is to develop a speech-based classification model for recognizing human actions in the smart home.It seeks to demonstrate the effectiveness and feasibility of using machine learning techniques in predicting categories,subcategories,and actions from sentences.A dataset labeled with relevant information about categories,subcategories,and actions related to human actions in the smart home is used.The methodology uses machine learning techniques implemented in Python,extracting features using CountVectorizer to convert sentences into numerical representations.The results show that the classification model is able to accurately predict categories,subcategories,and actions based on sentences,with 82.99%accuracy for category,76.19%accuracy for subcategory,and 90.28%accuracy for action.The study concludes that using machine learning techniques is effective for recognizing and classifying human actions in the smart home,supporting its feasibility in various scenarios and opening new possibilities for advanced natural language processing systems in the field of AI and smart homes.

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.

Intelligent 3D garment system of the human body based on deep spiking neural network

Background Intelligent garments,a burgeoning class of wearable devices,have extensive applications in domains such as sports training and medical rehabilitation.Nonetheless,existing research in the smart wearables domain predominantly emphasizes sensor functionality and quantity,often skipping crucial aspects related to user experience and interaction.Methods To address this gap,this study introduces a novel real-time 3D interactive system based on intelligent garments.The system utilizes lightweight sensor modules to collect human motion data and introduces a dual-stream fusion network based on pulsed neural units to classify and recognize human movements,thereby achieving real-time interaction between users and sensors.Additionally,the system incorporates 3D human visualization functionality,which visualizes sensor data and recognizes human actions as 3D models in real time,providing accurate and comprehensive visual feedback to help users better understand and analyze the details and features of human motion.This system has significant potential for applications in motion detection,medical monitoring,virtual reality,and other fields.The accurate classification of human actions contributes to the development of personalized training plans and injury prevention strategies.Conclusions This study has substantial implications in the domains of intelligent garments,human motion monitoring,and digital twin visualization.The advancement of this system is expected to propel the progress of wearable technology and foster a deeper comprehension of human motion.

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.

SlowFast Based Real-Time Human Motion Recognition with Action Localization

Artificial intelligence is increasingly being applied in the field of video analysis,particularly in the area of public safety where video surveillance equipment such as closed-circuit television(CCTV)is used and automated analysis of video information is required.However,various issues such as data size limitations and low processing speeds make real-time extraction of video data challenging.Video analysis technology applies object classification,detection,and relationship analysis to continuous 2D frame data,and the various meanings within the video are thus analyzed based on the extracted basic data.Motion recognition is key in this analysis.Motion recognition is a challenging field that analyzes human body movements,requiring the interpretation of complex movements of human joints and the relationships between various objects.The deep learning-based human skeleton detection algorithm is a representative motion recognition algorithm.Recently,motion analysis models such as the SlowFast network algorithm,have also been developed with excellent performance.However,these models do not operate properly in most wide-angle video environments outdoors,displaying low response speed,as expected from motion classification extraction in environments associated with high-resolution images.The proposed method achieves high level of extraction and accuracy by improving SlowFast’s input data preprocessing and data structure methods.The input data are preprocessed through object tracking and background removal using YOLO and DeepSORT.A higher performance than that of a single model is achieved by improving the existing SlowFast’s data structure into a frame unit structure.Based on the confusion matrix,accuracies of 70.16%and 70.74%were obtained for the existing SlowFast and proposed model,respectively,indicating a 0.58%increase in accuracy.Comparing detection,based on behavioral classification,the existing SlowFast detected 2,341,164 cases,whereas the proposed model detected 3,119,323 cases,which is an increase of 33.23%.

China's Human Rights Actions under the Impact of the COVID-19 Pandemic

Only on the premise of the safety of life and good health can human beings have the opportunity to fully enjoy and develop various rights,achieve a state of free and comprehensive development and pursue the highest value of human rights.In 2020,the COVID-19 pandemic spread globally.Under the guidance of the“people-centered”human rights concept,China has put people’s life and health in the first place,and safeguards the people’s right to life and health as its primary task and important mission.Facts have proven that under the strong leadership of the CPC Central Committee with General Secretary Xi Jinping at the core,the people’s right to life and health has been guaranteed,which fully demonstrates the value in the“people-centered”human rights concept that people’s interests are above all else.

National Human Rights Action Plan of China (2009-2010)

Introduction The realization of human rights in the broadest sense has been a long-cherished ideal of mankind and also a longpursued goal of the Chinese government and people.

National Human Rights Action Plan of China(2016-2020)

Introduction The period from 2016 to 2020 is a decisive stage for China in the building of a moderately prosperous society in an all-round way as well as a major stage for realizing the orderly,steady and sustainable development of human rights in China.

National Human Rights Action Plan of China (2012-2015)

The formulation of the National Human Rights Action Plan is an impor- tant measure taken by theChinese government to ensure the implementation of the constitutional principle of respecting and safeguarding human rights. It is of great significance to promoting scientific development and social harmony, and to achieving the great objective of building a moderately prosperous society in an all-round way.

Smart Deep Learning Based Human Behaviour Classification for Video Surveillance

Real-time video surveillance system is commonly employed to aid security professionals in preventing crimes.The use of deep learning(DL)technologies has transformed real-time video surveillance into smart video surveillance systems that automate human behavior classification.The recognition of events in the surveillance videos is considered a hot research topic in the field of computer science and it is gaining significant attention.Human action recognition(HAR)is treated as a crucial issue in several applications areas and smart video surveillance to improve the security level.The advancements of the DL models help to accomplish improved recognition performance.In this view,this paper presents a smart deep-based human behavior classification(SDL-HBC)model for real-time video surveillance.The proposed SDL-HBC model majorly aims to employ an adaptive median filtering(AMF)based pre-processing to reduce the noise content.Also,the capsule network(CapsNet)model is utilized for the extraction of feature vectors and the hyperparameter tuning of the CapsNet model takes place utilizing the Adam optimizer.Finally,the differential evolution(DE)with stacked autoencoder(SAE)model is applied for the classification of human activities in the intelligent video surveillance system.The performance validation of the SDL-HBC technique takes place using two benchmark datasets such as the KTH dataset.The experimental outcomes reported the enhanced recognition performance of the SDL-HBC technique over the recent state of art approaches with maximum accuracy of 0.9922.

National Human Rights Action Plan as a Milestone

Since ＂the state respects and protects human rights＂ was written into the Constitution in 2004, the Chinese government has issued many white papers during a short period of a few years and has included ＂respect and protect human rights＂ in the 11 th Five Year Plan of National Economic and Social Development.

Pursuit of Excellence:The Past Development and New Progress of the Human Rights Action Plans of China

Since the Vienna Declaration and programme of Action in 1993 recommended that countries formulate national human rights action plans,many countries have carried out relevant explorations.Since 2009,China has formulated four series of Human Rights Action plan of China,which is significant for promoting the development of human rights,enhancing the say in international human rights,reducing social risks and protecting individual rights.The formulation of the plan adheres to the principles of being laws and policies-based and human rights-oriented,and taking into account both the country and society.The first three series of the Action plans have undergone such evolution as upgrade of guiding principles and goals,refinement of rights content and measures,diversification of responsible subjects,increasingly reasonable framework structure,and more human rights consideration in discourse expression.The fourth series of the Action plan pays more attention to expanding public participation and the content,improving the supervision mechanism,and further promoting the formulation and implementation of the Action plan.

Four Mechanisms for Safeguarding the Effectiveness of the Human Rights Action Plan: Based on African Experience

Since the 1993 World Conference on human Rights, nine African countries have implemented ten human rights action plans. An analysis of the texts and related implementation of these plans reveals that there are four mechanisms that play a key role in improving the effectiveness of the implementation of the national human Rights Action Plan, namely, the positioning and focusing mechanism for the country’s core human rights issues, the integration mechanism between the action plans and the countries’ development strategies, domestic economic growth and related resources utilization mechanism, and effective governance of domestic public conflicts and public order guarantee mechanism. defining and coordinating these mechanisms is of great practical significance for improving the effectiveness of human rights action plans in developing countries.

Three Categories of International Comments on National Human Rights Action Plans and Their Implications——An Analysis Based on UPR Reports

At least 57 countries have formulated and implemented 78 national human rights action plans, and the international assessment of them has had direct influence on their international human rights images of their issuers and the focuses of future planning According to related reports from the universal periodic review by the united nations Human rights Council, three categories of comments in a rough quantitative proportion of 1:4:2 have been made by the international community on these plans, which can be categorized as: Attention, Laudatory and expectation, representing objective attention, appreciation or encouragement and anticipation of further implementation or improvement, respectively In terms of regions, Asian countries have received the most Attention Comments, europe and Africa fewer, and America the least The marked achievements in the formulation and implementation of human rights action plans in China have attracted widespread attention and recognition, and further efforts should be made to implement steady and consistent human rights policies, improve the implementation mechanisms and integrate the external and internal functions of human rights action plans so as to promote the sustainable development of China’s human rights cause.

Law School of Nankai University Holds International Seminar on National Human Rights Action Plan

The Information Office of the State council issued the first National Human Rights Action Plan of China （NHRAP） （2009-2010） on April 13, 2009. The Nankai University participated in the drafting of this significant national document on human rights, with three teachers invited one after another to work at the panel of experts under the drafting committee. In cooperation with the Raoul Wallenberg Institute of Human Rights and Humanitarian Law （RWlHRHL） of Sweden, the Research Center for Human Rights under Law School of the university held an international seminar titled ＂Formulation and Implementation of NHRAP- Swedish Experience.＂

Action Plan to Promote Human Rights in China

The State Council Information Office un- veiled the National Human Rights Action Plan （2012-2015） （hereinafter referred to as the Action Plan） on June 11, drawing wide attention, both domestically and internationally. Wang Chen, minis- ter of the State Council Information Office, answered questions concern- ing China＇s formulation of the Action Plan in an exclusive interview with the Xinhua News Agency. The fol- lowing is the translated version of the full text of the interview：

China Attains Targets in National Human Rights Action Plan

In April 2009, after receiving ap- proval from the State Council, the Information Office of the State Council published the NationalHuman Rights Action Plan of China （2009-2010）. It is China＇s first national plan on the theme of human rights, and serves as a policy document of the current stage for advancing China＇s human rights in a comprehensive way. It is an important move to implement the constitutional principle of respect- ing and safeguarding human rights, and to promote sustainable development and social harmony. It is also a solemn commitment to the world made by the Chinese government on human rights.

Deep Learning-Based Action Classification Using One-Shot Object Detection

Deep learning-based action classification technology has been applied to various fields,such as social safety,medical services,and sports.Analyzing an action on a practical level requires tracking multiple human bodies in an image in real-time and simultaneously classifying their actions.There are various related studies on the real-time classification of actions in an image.However,existing deep learning-based action classification models have prolonged response speeds,so there is a limit to real-time analysis.In addition,it has low accuracy of action of each object ifmultiple objects appear in the image.Also,it needs to be improved since it has a memory overhead in processing image data.Deep learning-based action classification using one-shot object detection is proposed to overcome the limitations of multiframe-based analysis technology.The proposed method uses a one-shot object detection model and a multi-object tracking algorithm to detect and track multiple objects in the image.Then,a deep learning-based pattern classification model is used to classify the body action of the object in the image by reducing the data for each object to an action vector.Compared to the existing studies,the constructed model shows higher accuracy of 74.95%,and in terms of speed,it offered better performance than the current studies at 0.234 s per frame.The proposed model makes it possible to classify some actions only through action vector learning without additional image learning because of the vector learning feature of the posterior neural network.Therefore,it is expected to contribute significantly to commercializing realistic streaming data analysis technologies,such as CCTV.

What Was It Like to Be Caesar Crossing Rubicon 49 B.C.?

The mind-body problem receives new attention,partly under the inspiration from the success of quantum mechanics.Here I will discuss reductionism.The mind-body problem remains central to modern philosophy,no doubt stimulated by the progress in brain research.Following Thomas Nagel’s article on bats,one may similarly examine one of history’s champions.I will also deny reduction or physicalism,though my argument is very much simpler than Chalmers(1996).

Climate Change and Ecological Projects Jointly Promote Vegetation Restoration in Three-River Source Region of China

As the source of the Yellow River,Yangtze River,and Lancang River,the Three-River Source Region(TRSR)in China is very important to China’s ecological security.In recent decades,TRSR’s ecosystem has degraded because of climate change and human disturbances.Therefore,a range of ecological projects were initiated by Chinese government around 2000 to curb further degradation.Current research shows that the vegetation of the TRSR has been initially restored over the past two decades,but the respective contribution of ecological projects and climate change in vegetation restoration has not been clarified.Here,we used the Moderate Resolution Imaging Spectroradiometer(MODIS)Enhanced Vegetation Index(EVI)to assess the spatial-temporal variations in vegetation and explore the impact of climate and human actions on vegetation in TRSR during 2001–2018.The results showed that about 26.02%of the TRSR had a significant increase in EVI over the 18 yr,with an increasing rate of 0.010/10 yr(P<0.05),and EVI significantly decreased in only 3.23%of the TRSR.Residual trend analysis indicated vegetation restoration was jointly promoted by climate and human actions,and the promotion of human actions was greater compared with that of climate,with relative contributions of 59.07%and40.93%,respectively.However,the degradation of vegetation was mainly caused by human actions,with a relative contribution of71.19%.Partial correlation analysis showed that vegetation was greatly affected by temperature(r=0.62,P<0.05)due to the relatively sufficient moisture but lower temperature in TRSR.Furthermore,the establishment of nature reserves and the implementation of the Ecological Protection and Restoration Program(EPRP)improved vegetation,and the first stage EPRP had a better effect on vegetation restoration than the second stage.Our findings identify the driving factors of vegetation change and lay the foundation for subsequent effective management.