Narrow Results

For the long-term continuous monitoring of bridge-related indicators, it is necessary to arrange relatively perfect acquisition equipment on the bridge, which can feedback various information parameters of the bridge. However, there are many parameters to feedback the bridge information, which leads to the complex and overstaffed structure of the monitoring system. Furthermore, the huge amount of data collected and the complex calculation process also increase the difficulty of the operation of the monitoring system. In this regard, we should choose more scientific and reasonable indicators, lightweight data structure, stable data transmission, and analysis programs to improve the accuracy of continuous monitoring. To establish a stable and efficient bridge monitoring system, we use the distance coefficient-effective independent algorithm to optimize. Then, we calculate the relevant information of the strain environment with the help of a neural network model, strengthen the training of deep learning through the YOLOv5s model, and improve the task scheduling strategy of attention concentration. Through that, we solve the problem of embedded systems with relatively low computing power. Different weights are assigned to each fused feature map, and the nodes at the highest level and the lowest level are deleted so that a concise and efficient lightweight network model is constructed. Multiple iterations are performed to achieve deeper feature fusion. Therefore, the complexity of the model is effectively reduced, and the monitoring performance can be effectively improved. Finally, through the experimental analysis, it is proved that compared with the traditional fusion model, the number of parameters of the improved fusion network structure in bridge health monitoring is reduced by 7.37%. The detection speed is increased by 18.2%. The amount of computation is reduced by 42.92%, and the average detection accuracy is required to reach 95.33%. It is verified that the proposed method can effectively improve the accuracy and risk control ability of the detection data by learning from the samples with small labels. It also has great practical significance and market value for the design and optimization of the bridge health monitoring system, which is suitable for the monitoring data of large-scale construction projects.

A smart sound and light anomaly monitoring system for highway tunnels based on Internet of Things technology was studied to address the issues of highway tunnel lighting systems. By utilizing Internet of Things technology, the tunnel lighting system is combined with abnormal sound recognition. Through the design of algorithm models, the recognition of abnormal sound inside the tunnel and the intelligent control of the lighting system is achieved. By pruning and validating the hidden layer nodes of the model, a more streamlined abnormal sound recognition model is obtained. Through experimental verification, this model has the highest recognition accuracy among all models, with a recognition rate of 91.75% at a compression rate of 20%. Compared with Average Percentage of Zeros (APoZ), Random Pruning and Mean Activation, the recognition rate is increased by 2.64%, 1.47% and 1.40%, respectively. In the design of tunnel lighting, fuzzy control is applied to the lighting inside the tunnel to improve the driving safety of drivers and further reduce the power consumption of excessive lighting in the tunnel. Through experiments, it has been proven that the system can work well, saving up to 727${}^{\circ }$ of energy per day.

Rural revitalization refers to systematically promoting products and services to individuals residing in rural regions for marketing. It is a branch of marketing that specifically targets those living in more remote places. An organization’s data-driven insights, collaborative culture, adaptive systems, and developing technology may all be a part of a digital fusion strategy, which is an all-encompassing approach. A digital strategy outlines the company’s steps to adopt and use digital technologies to achieve its objectives. Data Fusion Marketing leverages data from several sources depending on the campaign goal. The challenging characteristics are water pollution caused by agricultural production, animal manure affecting the quality of rural water resources in rural revitalization and lack of collaboration among different organizations related to planning for development strategy in marketing. Hence, in this research, the Digital Fusion-enabled Internet of Things (DF-IoT) has been designed to help the marketing of development strategy in driving rural revitalization through the information age. Global economic integration cannot exist apart from national economic development integration, and developing marketing in economic integration cannot live apart from the composition of the rural revitalization unit. Economic integration and development in DF-IoT must be improved to prioritize rural regeneration and push for multi-cell integration of regional growth in marketing and domains of development strategy, with the expectation that they would serve as models for top-notch growth and suggestions for future strategic routes for rural revitalization. The experimental analysis of DF-IoT outperforms the development strategy for rural revitalization in terms of performance, accuracy, error rate, classification error and efficiency.

Autism Spectrum Disorder (ASD) delivers unique challenges for children in their communication, social interaction, and learning abilities. To address these challenges and empower children with ASD, this work introduces an innovative AI-powered education tool that harnesses the potential of the Internet of Things (IoT) and Emotional Intelligence (EI). The proposed tool utilizes cutting-edge Artificial Intelligence (AI) algorithms, such as Haar-cascade Python libraries, Convolution Neural Network (CNN) for accurate Facial Expression Recognition (FER). By capturing real-time facial expressions, the system aims to better understand and respond to the emotional states of children with ASD, enhancing their social engagement and interaction skills. To further support the emotional well-being of children with ASD, the system integrates a sweat conductance detection sensor based on Galvanic Skin Response (GSR). The GSR sensor enables the real-time monitoring of stress levels, providing valuable insights into the child’s emotional states and facilitating timely interventions when emotions become unstable. The power of the Internet of Things (IoT) is leveraged through the use of NodeMCU (ESP8266–12E Microcontroller unit), enabling seamless communication and data transmission for remote monitoring and analysis. This allows parents, caregivers, and educators to access valuable information regarding the child’s emotional responses and progress in real-time, facilitating personalized and effective support. Through the AI-powered education tool’s interactive interface, children with ASD are engaged in stimulating and educational activities, fostering their cognitive and emotional growth. The system offers a range of interactive learning experiences, including rhymes audio, promoting self-expression and learning in an inclusive environment.

This paper evaluates the application of the Internet of Things (IoT) in designing smart teaching systems by optimizing ideological and political education (IPE) resources using machine learning (ML). To increase educational reform’s efficacy, we also examine the issues with conventional IPE instruction. This study further develops a cutting-edge, smart framework for teaching ideological and political concepts in the educational setting and changes teaching according to real-life scenarios. Comprising the perception, network, and application stages, the IPE platform is a three-layer IoT architecture. The IoT and internet connections are used by the technology to receive effective IPE instructional activity information in real time. After that, the data are sent over the internet to the data center, where it is used as the initial information for applications, analysis of information, and modeling training. We are able to collect and acquire IPE teaching activity data sets on instructive and educational developments in courses by using IoT devices. The principal component analysis (PCA) approach is used to remove interface characteristics, date, and time elements from the data to increase the evaluation’s correctness. To remove noisy information from the data, the z-score normalization method is used. In addition, this paper suggests a novel efficient Cat Boost method inspired by hunter–prey optimization (AHPO-ECB) for evaluating IPE performance. In this case, the AHPO approach is employed to reduce the misprediction rate of the CB. The suggested model is then used to analyze the predicted performance using a Python program. The results of the experiments indicate that, in comparison to other models currently in use, the suggested AHPO-ECB model performs at the highest level when assessing IPE teaching performance.

Exercise rehabilitation refers to a rehabilitation method that restores or improves human motor function through scientific, systematic, and regular exercise training, and it has a very wide range of applications. This paper explored the construction of an intelligent medical monitoring system network for sports rehabilitation based on the Internet of Things (IoT) technology. The system consists of a wireless sensor acquisition module for collecting sports rehabilitation data, a data storage and analysis module for cloud management and deep learning analysis, and a medical monitoring module for remote supervision and patient interaction. The system aimed to achieve personalized exercise rehabilitation plans through real-time monitoring and data collection of patients, and provide remote medical monitoring services to improve the rehabilitation effect and quality of life of patients. The system architecture adopted the IoT technology and cloud storage technology, and combined the deep learning convolutional neural network (CNN) model to achieve remote monitoring and data analysis. The monitoring center of the system can monitor patients’ physiological indicators, exercise status, and rehabilitation effects in real time through IoT wireless sensors, and upload the data to the cloud platform for storage and analysis. Doctors and patients can access data through mobile phones or computers and communicate online. The data showed that when measuring a patient’s heart rate using wireless sensors, the measurement accuracy mostly exceeded 99.8%, and some samples had detection accuracy up to 100%. In addition, traditional sensors can detect heart rates up to 5.1s, while wireless sensors can detect heart rates up to 1.28s. The CNN had a maximum recall rate of 99.51%, a precision of 99.68%, and an accuracy rate of 99.8% in the classification of psychological data of rehabilitation patients. The experimental results indicated that the system can achieve comprehensive monitoring and effective management of the patient’s rehabilitation process, and its operation is stable and has good practical value.

The social and ideology education, focus instruction, hands-on learning, and a variety of means, students are cultivated in their political quality, moral character, and sense of social responsibility while it is being guided in understanding the nation’s important policies and social current affairs. Ideological and political disciplines are implementing the problem of transformation within the framework of the major criteria of the new curriculum reform. In the conventional teaching process, students face several challenges in achieving the essential quality of ideological and political education. The artificial intelligence-assisted interactive modeling on the Internet of Things platform (AI-IM-IoT) is able to generate a smart system for ideological and political education, addressing various problems in the current situation. Performance in conventional ideological and political learning platforms, an investigation on the construction of intelligent media ideological and political learning platforms are based on AI technology. Hence AI-IM-IoT has been the Ideology of ideas and beliefs that influence social behavior and can possess an effect on educational achievements. Ideological viewpoints on education can influence educational institutions’ aims, values, and priorities, as well as how resources and opportunities are allocated. Students are encouraged to develop an understanding of social, economic, and political issues via ideological and political education programs.

The existing data security technology and privacy protection mechanism are not perfect, and it is difficult to effectively deal with the complex security challenges in the Internet of Things (IoTs) environment. Therefore, a method of security sharing of ideological and political education resources in universities based on the IoTs is proposed. In combination with IoTs technology and blockchain technology, we design data request access for educational resources, introduce dual encryption algorithm to encrypt ideological and political resources of online colleges and universities, establish ideological and political teaching database and cloud computing resource sharing platform, build data security sharing model based on master–slave blockchain structure, introduce slave chain access strategy and based on data encryption and decryption processing and sharing transmission methods. Introduce the idea of integration of production and education to realize the sharing. The experimental results show that the design method can process more than 700 concurrent requests per second, the fastest transmission speed is 23.39 MB/s, the lowest packet loss rate is only 0.5% and the highest data encryption strength can reach about 0.88.

This paper takes the enterprise economic data of new energy vehicles (NEVs) in 2018–2023 as the research background, combined with the performance evaluation theory of the NEV industry, and systematically discusses how to make the high-quality innovative development of NEVs as the research purpose. We use the new dynamic network data envelope analysis (DEA) model, Marmquist Index and other and the Internet of Things internationally recognized, scientific and effective data models as research methods to make an in-depth analysis of the business performance of five representative enterprises on multi-quantitative input-output issues. The research results show that the NEV industry has a good development prospect and economic foundation on the whole, but it still faces challenges such as limited space for core technology progress, insufficient carbon emission reduction and environmental protection planning. At the end, this paper puts forward innovation suggestions such as accelerating technological innovation, optimizing production process and responding to preferential policies and exploring international market.

Due to the continuous development of Internet of Things (IoT) technology and the increasingly stringent teaching standards in universities, educational institutions are increasingly using multimedia in their lesson plans. In today’s classrooms, however, there have been no major changes in curriculum content and teachers’ teaching practices have not eliminated traditional models of skill development. In this environment, intelligent multimedia English classroom teaching using the Internet of Things came into being. Therefore, the Multimedia and IoT-Assisted Intelligent English Teaching Framework (MIoT-IETF) is proposed in this study to improve the stability of the system and enhance the personalized experience of students. Optimizing college English classrooms using intelligent multimedia and IoT is the main focus of this study, and the results of student test scores demonstrate the success of this strategy. The online system based on browser and server (B/S) architecture is the foundation of the system’s English teaching management platform. English teachers can easily access the platform’s monitoring data using the system’s diverse interface. One of the main roles of the English teaching management platform is to receive the data sent by the monitoring terminal based on the Internet of Things, process it, and then display the processing result page to the teacher. The experimental results show that the proposed model can find a reliable research idea for the current intelligent teaching in colleges and universities, and can greatly improve students’ learning performance and narrow the gap in classroom learning performance.

This paper aims to explore the application effect of intelligent robots enabled by the Internet of Things (IoT) in basketball teaching, to improve the quality and efficiency of basketball teaching. By choosing the basketball courses of two schools as practical cases, the paper deploys an intelligent robot teaching system and carries out a three-month teaching experiment. A variety of evaluation methods such as questionnaire survey, skill test, learning performance comparison and teacher feedback are used to evaluate the effect of an intelligent robot teaching system. The level of students’ basketball skills is improved, and their satisfaction with the intelligent robot teaching system is also high. Teachers believe that the system reduces the teaching burden and improves teaching efficiency. Therefore, this paper believes that intelligent robots enabled by IoT have significant value in basketball teaching, which can improve students’ skill levels and improve teaching efficiency.

In the era of the 4th Industrial Revolution, the concept of ownership is decreasing and the concept of sharing is increasing. Therefore, the concepts of sharing economy are being used in various ways around the world. A sharing economy refers to an economic system in which products and services are shared and used by several people. A representative business model using a sharing economy is a sharing office. Therefore, in this study, the spatial composition of the shared office was analyzed based on the example of the globally expanding shared office. Based on the analyzed data, I would like to present an appropriate method of designing and operating a shared office. The research method of this study first conducted the literature study to analyze the characteristics and utility of the shared office. After conducting these literature studies, the researcher visited a shared office company and conducted a participant observation method. The participant observation method was based on James Spradley’s theory. The subjects of this study are WeWork in the United States and Fastfive in Korea, which are famous for shared offices.The reason for selecting these two companies is that they are representatively successful shared office companies. This study conducted in-depth interviews with people using shared offices and operators of shared offices. Based on the research results, I would like to present the future development direction of the shared office. These research results can be used as theoretical basic data when designing a shared office.

Majority of individuals are experiencing recurrent issues due to CAD, necessitating the development of an algorithm to accurately predict cardiac arrest onset. So, this paper proposes a heart disease prediction (HDP) model utilizing a deep learning modified neural network (EPHP-DLMNN). The solution makes use of the universal, accessible heart disease dataset, which contains patient-provided data on heart disease obtained via IoT sensors. In the preprocessing phase, adaptive median filter (SAMF) and fixed weighted mean (FWM) filtering are applied to the input image. Feature extraction is performed using methods like local binary pattern (LBP), gray level co-occurrence matrix (GLCM), gray level run length method (GLRLM), and Haralick features. The optimal features are selected using a hybrid African vulture with egret swarm optimization (HAVESO), combining African vulture optimization (AVO) and ESO. The selected features are classified by a modified deep bi-gated recurrent neural network (MDBi-GRNN), which is enhanced using a Bernoulli distribution function. The output is refined with the improved Beluga whale optimizer (IBWO) optimized by the butterfly optimization algorithm (BOA). The MDBi-GRNN model, implemented in Python, achieves superior performance with an accuracy of 0.984 and precision of 0.976 across various metrics including specificity, sensitivity, F-score, kappa value, and execution time.

Cardiovascular disease (CVD) is the leading cause of global mortality in the modern world. This situation is difficult to predict and requires a combination of advanced techniques and specialist knowledge. Healthcare systems have recently adopted the Internet of Things (IoT) to collect critical sensor data to diagnose and predict CVD. Predictive models can be made more accurate and effective through such integration, which could radically change how we manage cardiovascular health. This study presents an improved squirrel search optimization algorithm for searching vital indications of CVD. To address the issue of low-cardiac diagnostic accuracy, the proposed IoT system uses enhanced squirrel search optimization with deep convolutional neural networks (SSO-DCNN). This new approach uses data from smartwatches and cardiac devices, which monitor patients’ electrocardiogram (ECG) and blood pressure readings. The proposed SSO-DCNN performs well compared to well-known deep learning networks such as logistic regression. The findings show an accuracy of 99.1% over current classifiers, suggesting effectiveness in the CVD prediction.

The Internet of Things (IoT) refers to the interconnected network of objects and devices that seamlessly communicate and share information. The need for robust cybersecurity measures becomes paramount with the increase of IoT devices, ranging from smart home devices to industrial sensors. The inherent vulnerability of the IoT ecosystem to cyber threats necessitates cutting-edge security protocols to ensure the integrity of connected systems and safeguard sensitive information. IoT security is crucial to protect against potential manipulation of connected devices, unauthorized access, and data breaches. An essential facet of IoT cybersecurity, Anomaly detection, includes the detection of unusual behaviors or patterns in device activity or network traffic in many complex systems that may indicate security breaches. Deep learning (DL), with its ability to analyze complex and vast datasets, has improved anomaly detection in IoT environments. By leveraging DL techniques, IoT systems can better adapt to evolving cyber threats, which offer a proactive defense system against complex cyber threats in various complex systems. In essence, incorporating anomaly detection and DL within the IoT cybersecurity framework is crucial to ensure the entire IoT ecosystem’s trustworthiness and fortify interconnected devices’ resilience. This study presents an anomaly recognition using fractals thermal exchange optimization with deep learning (ARA-TEODL) technique for cybersecurity on IoT Networks. The ARA-TEODL technique focuses on identifying anomalous behavior in the IoT network to achieve cybersecurity. In the ARA-TEODL technique, Z-score normalization is primarily used to scale the input networking data. Besides, the selection of features takes place utilizing the chimp fractals optimization algorithm (ChOA). Moreover, a modified Mogrifier long short-term memory (MM-LSTM) model is used to identify anomalies in the network. Finally, the hyperparameter tuning process takes place using the TEO algorithm. The experimental evaluation of the ARA-TEODL technique takes place using a benchmark dataset. The experimental results stated that the ARA-TEODL technique reaches optimal cybersecurity in the IoT networks.

The security of IoT networks has become a major concern with the ubiquity of Internet of Things (IoT) technology. The concept of intrusion detection system (IDS) is a complex system to discover an intruder in the IoT platform, where the intruder can be a host that tries to gain some other nodes without authorization. Due to the complexity and resource constraints, classical IDS have their limitations in the context of IoT networks. There has been significant research in integrating both IDS and blockchain (BC) to detect existing and emerging cyberattacks and improve data privacy, correspondingly. In these approaches, learning-based ensemble algorithms can concurrently ensure data privacy and facilitate the detection of sophisticated malicious events. This study introduces a BC with fractal chaotic oppositional barnacles mating optimizer-based deep learning (BCOBMO-DL) Model for Secure IoT environment. The BCOBMO-DL technique exploits BC technology with DL-based intrusion detection to protect the IoT environment. In the BCOBMO-DL technique, the linear scaling normalization (LSN) approach can be used to scale the input data into a uniform format. In addition, the BCOBMO-DL technique designs the COBMO algorithm for electing the optimal subset of features. For intrusion detection, the self-attention bidirectional gated recurrent unit (SA-BiGRU) model is applied with fractal theory. Finally, the reptile search algorithm (RSA)-based hyperparameter tuning process is utilized for tuning the parameters based on the SA-BiGRU model. Furthermore, the BC technology is useful for achieving security in the IoT network. The experimental evaluation of the BCOBMO-DL method takes place on the benchmark NSLKDD dataset. The widespread experimental outcomes highlighted the enhanced detection outcomes of the BCOBMO-DL algorithm over other models.

Internet of Things (IoT)-assisted consumer electronics refer to common devices that are improved with IoT technology, allowing them to attach to the internet and convey with other devices. These smart devices contain smart home systems, smartphones, wearables, and appliances, which can be monitored remotely, gather, and share data, and deliver advanced functionalities like monitoring, automation, and real-time upgrades. Safety in IoT-assisted consumer electronics signifies a cutting-edge technique to improve device safety and user authentication. Iris recognition (IR) is a biometric authentication technique that employs the exclusive patterns of the iris (the colored part of the eye that surrounds the pupil) to recognize individuals. This method has gained high popularity owing to the uniqueness and stability of iris patterns in finance, healthcare, industries, complex systems, and government applications. With no dual irises being equal and small changes through an individual’s lifetime, IR is considered to be more trustworthy and less susceptible to exterior factors than other biometric detection models. Different classical machine learning (ML)-based IR techniques, the deep learning (DL) approach could not depend on feature engineering and claims outstanding performance. In this paper, we propose an enhanced IR using the Remora fractals optimization algorithm with deep learning (EIR-ROADL) technique for biometric authentication. The main intention of the EIR-ROADL model is to project a hyperparameter-tuned DL technique for automated and accurate IR. For securing consumer electronics, blockchain (BC) technology can be used. In the EIR-ROADL technique, the EIR-ROADL approach uses the Inception v3 method for the feature extraction procedures and its hyperparameter selection process takes place using ROA. For the detection and classification of iris images, the EIR-ROADL technique applies the variational autoencoder (VAE) model. The experimental assessment of the EIR-ROADL algorithm can be executed on benchmark iris datasets. The experimentation outcomes indicated better IR outcomes of the EIR-ROADL methodology with other current approaches and ensured better biometric authentication results.

The development of any country is influenced by the growth in the agriculture sector. The prevalence of pests and diseases in plants affects the productivity of any agricultural product. Early diagnosis of the disease can substantially decrease the effort and the fund required for disease management. The Internet of Things (IoT) provides a framework for offering solutions for automatic farming. This paper devises an automated detection technique for foliar disease classification in apple trees using an IoT network. Here, classification is performed using a hybrid classifier, which utilizes the Deep Residual Network (DRN) and Deep $Q$ Network (DQN). A new Adaptive Tunicate Swarm Sine–Cosine Algorithm (TSSCA) is used for modifying the learning parameters as well as the weights of the proposed hybrid classifier. The TSSCA is developed by adaptively changing the navigation foraging behavior of the tunicates obtained from the Tunicate Swarm Algorithm (TSA) in accordance with the Sine–Cosine Algorithm  (SCA). The outputs obtained from the Adaptive TSSCA-based DRN and Adaptive TSSCA-based DQN are merged using cosine similarity measure for detecting the foliar disease. The Plant Pathology 2020 — FGVC7 dataset is utilized for the experimental process to determine accuracy, sensitivity, specificity and energy and we achieved the values of 98.36%, 98.58%, 96.32% and 0.413 J, respectively.

To help Latin dancers feel the rhythm of the sports dance, it is necessary to standardize the posture action during basic training. Therefore, it is important to study the method of correcting Latin dance posture action. The traditional Latin dance posture correction methods have some problems, such as the included angle error of head motion, the angle error of spine transformation, the error of fit between foot and ground and so on. In this paper, a Latin dance posture correction method using improved deep reinforcement learning in the Internet of things (IoT) is proposed. First, the Latin dance posture image acquisition architecture is constructed using IoT and binocular stereo vision to acquire Latin dance posture images and extract Latin dance posture features. Second, the channel attention module in the deep learning network is improved, and the Latin dance posture diagnosis model is constructed based on the action feature extraction results using the improved deep robust chemical network. Finally, the action correction coefficients are calculated according to the Latin dance posture diagnosis results to realize the Latin dance posture correction. The results showed that after the application of the proposed correction method, including angle error of head movement, the spine transformation angle error and fit between foot and ground error of the participants’ motions were kept below 1${}^{\circ }$, and the frame position offset was 1.3cm. It indicates that the proposed method can effectively improve the degree of Latin dance posture specification.

With the increasing demand for automated network systems in the Internet of Things (IoT), the models are becoming more complex and undergoing a tremendous change. Since the gadgets broadcast data wirelessly, they are easily targeted for attacks. Every day, thousands of attacks arise as a result of the addition of new protocols to the Internet of Things. This frequently makes the computing process more unreliable, ineffective and worse. The majority of these assaults are scaled-down versions of recognized cyberattacks from the past. This suggests that over time, even sophisticated systems like conventional systems will have trouble identifying even minute variations in attacks. However, Deep Learning (DL) has shown tremendous promise among attack detection techniques because of its early detection capability. Nevertheless, the efficacy of these DL methods is contingent upon the ability to gather vast amounts of labeled data from IoT sensors, requires more training time, and suffers from inaccuracies in detection. Hence, this research presents a modified activation function-based deep bidirectional long-short-term memory (Deep BiLSTM) model, which effectively captures the temporal dependencies and detect attacks effectively. Here, the modified activation function solves the vanishing gradient problem and high computational requirements. Specifically, the efficient features are extracted through the Ant-Chase optimization (AnChO), which assists in optimizing the BiLSTM model by tuning its parameters for attaining the best solution as well as to detect the attack in a precise manner with less computational time. Therefore, the accuracy, specificity, precision and recall of the proposed attack detection model attain the values of 96.46%, 97.40%, 97.91%, 95.05% and 97.465% correspondingly and the proposed system enhances IoT security by effectively detecting attacks.