With the advancements of the technologies, healthcare industry has become more digitized, data driven, patient-centric, innovative as well as collaborative. It has become possible to access and share patient information globally irrespective of time and locations. The traditional healthcare facilities have proved inadequate in facilitating better patient treatment and services as per the patient requirements. The modern healthcare provisioning involves the use of digital technologies and operations and manifest digitized healthcare facilities. The digital technologies such as cloud computing, Internet of Things and many other supports the facilitation of smart healthcare utilities and services. This paper describes how cloud computing and IoT deliver healthcare-as-a-service. Further, the various proposed healthcare frameworks integrated with healthcare field have been discussed such as based on technologies implemented, offered services, and focused disease(s). Also, this survey enlightens the comparisons of existing frameworks based on technologies used and based on the focused methodologies.
In order to solve the problems of dimensionality reduction and color display of spectral data after redundancy reduction in interactive display in environmental art design, the author proposes a research on an interactive display system that integrates information technology in environmental art design. The author first performs principal component transformation on the spectral data cube, assigning the first three components to the black and white channels, red and green channels, and yellow and blue channels in the color space. Then, after spatial transformation to sRGB space, the data is segmented and translated to the range of 0-1, mapped to 8-bit RGB, and single item evaluations of standard deviation, entropy, and average gradient are performed on each translated image. After all translations are completed, a comprehensive evaluation is performed on all evaluation values, and the interval with the highest comprehensive evaluation value is selected to output the mapping. The experimental results show that although this algorithm has a slightly longer time overhead, it is still far less than the acquisition time of 6.63 seconds for one spectral data cube, and is suitable for real-time detection applications of hyperspectral data. The fusion of images can maximize the energy, information, and clarity of the images, which is beneficial for the rapid recognition and judgment of the human eye.
In the ever-changing automobile industry, sustainable practices are of utmost importance. It is essential to perform preventative maintenance to accomplish sustainability objectives. Vehicles will have fewer unanticipated breakdowns and last longer as a result. This study introduces a machine learning model that has been optimized to enhance preventive maintenance operations in the automotive sector. To improve the efficacy of predictive maintenance in intelligent manufacturing systems, this paper presents an improved AdaBoost algorithm linked with big data analytics. First, in the proposed framework, massive datasets are gathered and preprocessed from sensors, IoT devices, and other sources in the industrial setting. Then, the meta-algorithm AdaBoost is used to improve the efficiency of subpar learners, allowing for reliable failure and deterioration prediction in machinery. Adjusting hyperparameters like the number of iterations and the learning rate is part of the algorithmic optimization process to strike a good balance between model accuracy and computational efficiency. The proposed model gains an accuracy level of 0.972 value, Precision level of 0.977 value, Recall level of 0.972 value and F1-score level of 0.974 value. By analyzing historical data, our algorithm can predict when problems will occur, enabling us to take quick action and minimize downtime. Improved maintenance scheduling and reduced environmental effects are outcomes of the proposed model’s use of cutting-edge optimization techniques, which boost the model’s predictive capabilities. The model achieves better results than the state-of-the-art methods in extensive trials conducted on a dataset from a leading automaker. It achieves significant improvements in maintenance efficiency and prediction accuracy. Sustainability in the automobile sector is a wider purpose of this study, which proposes a data-driven plan for maintenance that is strong and in line with economic and environmental aims.
The popularity of smart home systems has greatly increased over the past several years, which provide convenience, automation, and control over various areas of daily life at home. However, these technologies still have issues with scalable user experience, mainly because there aren’t any engaging and intuitive user interfaces. Incorporating pervasive virtual reality (VR) interfaces into smart home systems is the unique strategy proposed in this study to improve scalable user experience. The study’s goal is to use VR technology to develop engaging and simple user interfaces that seamlessly integrate with the actual surroundings of a smart home. Users can engage with their smart home appliances and services through a mixed-reality experience in which virtual items and information are seamlessly incorporated into their environment by donning portable VR headsets. This application considers various difficulties related to developing Ambient Assisted Living (AAL) solutions, including unique characteristics of each end user, appliance, technology, deployment, and data-sharing problems. The Smart Home Systems take advantage of Semantic Web technologies integration abilities and their capacity facing represent significant information into legal models. A virtual reality application called Smart Home Systems allows for residential settings’ setup and customization in AAL systems. Additionally, it effectively uses VR technology to streamline the creation of specialized AAL settings. The application and underlying framework were evaluated for each through two scenarios: designing a home setting specifically for specific scalable user categories.
In order to solve the problem of selecting transportation routes and transfer nodes reasonably in the process of multimodal logistics distribution, the author proposes a coal transportation multimodal transportation path selection based on genetic algorithm. Firstly, this paper establishes an object function for routing according to the features of multi-modal transport, which has the minimum transport time, the minimum transport length and the minimum transport cost. Secondly, we design appropriate GA components, and get a multiobjective route optimal model for multimodal transport by using GA. Taking into account the high transportation costs of coal as a bulk commodity, a coal transportation multimodal transport path optimization model was constructed with the total transportation cost as the objective function of the model, and the minimum economic cost as the objective. At last, this paper applies GA and MATLAB to resolve the case. Experiments showed that the starting population was 90, with a cross rate of 0.6 and a mutation rate of 0.02. After 100 iterations, it was found that the fitness change between adjacent generations was less than 0.01, indicating that the population mean of the running results had stabilized. At this point, it can be considered that the results have converged. This method validates the practicality of the established model and provides a reference for logistics enterprises to carry out multimodal transportation.
In order to solve the problem of measurement errors being easily affected by various factors and poor stability of all fiber current transformers, the author proposes an online evaluation of the error status of current transformers based on data analysis. The author proposes a method for evaluating the error status of all fiber current transformers based on correlation analysis: collecting measurement data of three all fiber current transformers at the same measurement point in the converter station, under the constraint of electrical physical correlation, principal component fractal is applied to the measurement data of all fiber current transformers, and error evaluation is mapped to the analysis of changes in Q-statistics. The experimental results indicate that: The method proposed by the author can achieve real-time evaluation of measurement errors in all fiber current transformers, with an evaluation accuracy of up to 0.2 levels. This method greatly improves the evaluation efficiency of measurement errors in all fiber current transformers, reduces the effective power outage time of the power grid, and provides data support for the reliable operation, state prediction, and related technology improvement of all fiber current transformers.
Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results.
In order to improve the safety management level of construction sites, prevent and reduce the occurrence of building safety accidents, this article uses deep learning methods to study these unsafe behavior recognition and detection techniques. The most typical hazardous behavior is not wearing a safety helmet. However, on-site personnel often neglect to wear helmets due to various reasons. In this study, the target detection algorithm is applied to monitor helmet-wearing. The YOLOX algorithm is selected as the basic detection model and improved by combining the construction site environment and helmet detection characteristics, meeting the real-time monitoring needs of helmet-wearing. Comparison experiments before and after improvement were conducted on the self-constructed helmet dataset, verifying the performance of the improved YOLOX network model. The results show that the average accuracy of the enhanced network model on the helmet-wearing dataset increased to 89.12%, showing a better detection effect.
In order to solve the problem of vehicle path scheduling management more reasonably, the author proposes a logistics path planning research based on improved particle swarm optimization algorithm. The author introduces a particle swarm optimization algorithm that incorporates a dynamic monkey jumping mechanism. Initially, dynamic population grouping is used to assign varying dynamic inertia weights, enhancing the algorithm’s speed. Subsequently, the monkey jumping mechanism is added to ensure global convergence. This enhanced algorithm was then tested on two logistics distribution path optimization scenarios. In a consistent environment, the improved algorithm outperformed the standard particle swarm optimization algorithm by achieving a better optimal path fitness value, shorter average operation time, and a higher number of successful attempts to find the optimal solution. The experimental results show that out of 10 instances solved using the improved algorithm, 5 times obtained the optimal solution of 67.1km, and the optimal delivery path corresponding to the optimal solution was 0-4-7-6-0; 0-2-8-5-3-1-0, with an average calculation time of 1.26s, indicating high computational efficiency. The total delivery distance and average calculation time of the particle swarm algorithm, as well as the number of times to obtain the optimal solution, are 69.01, 2.7, and 3, respectively. It is evident that the enhanced particle swarm optimization algorithm significantly outperforms the conventional particle swarm algorithm. The improvements not only accelerate the optimization process but also enhance the algorithm’s convergence, ensuring high-quality optimization results. Consequently, this improved algorithm holds substantial application value.
This paper proposes a precision medical service system driven by big data. The PCA-GRA-BK algorithm, which combines principal component analysis (PCA), grey association analysis (GRA) and Bayesian classifier (BK), is adopted. The algorithm extracts critical information from massive medical data, identifies patient characteristics, predicts disease risk, and provides personalized treatment plans. First, the system uses PCA technology to reduce the dimensionality of the original medical data and extract the most representative principal components to reduce data redundancy and retain critical information. Then GRA method was used to analyze the correlation between different medical indicators to determine the main factors affecting health status. Finally, the BK algorithm updates the probability model based on prior knowledge and current data to predict patients’ disease risk accurately. A simulation modeling environment is constructed and the PCA-GRA-BK algorithm is tested in this environment to verify the effectiveness of the system. The experimental results show that the algorithm has excellent performance in the accuracy of disease prediction and personalized treatment recommendation. Compared with traditional medical decision support systems, this system has shown significant advantages in extensive data processing capabilities and precision medical services.
With the rapid development of the digital information age on the Internet, information data on the Internet grows exponentially every day. In today’s online learning environment, fast retrieval of English sentences plays a crucial role in the teaching and learning of modern English. The current case-based Machine translation methods can perform in-depth Parsing on sentences, and only use similar instances in the original corpus for matching and replacement processing. However, there are still certain limitations in terms of retrieval speed and similarity calculation. The study proposes an improved Simhash algorithm, which introduces substitution cost for synonym replacement and combines Term Frequency-Inverse Document Frequency (TFIDF) weights with lexical weights for sentence-to-sentence similarity calculation. The results showed that the performance of the improved Simhash algorithm reached a maximum RI of 98.9%, an improvement of 1.4% compared to the traditional Simhash algorithm. The minimum misclassification rate of the improved algorithm was only 1.1%, a reduction of 1.4% compared to the traditional algorithm. The runtime of the improved Simhash algorithm was only 0.71s per sentence without processing synonyms and 1.82s with processing synonyms, while the runtime of the TF-IDF method alone was 71.82s and 98.11s in these two cases respectively. The improved Simhash algorithm, which combines TF-IDF weight, part of speech weight, and replacement cost, achieved an average accuracy of 92.87%, a recall rate of 88.7%, and an F1 Score of 92.87% in two calculations. This shows that the improved Simhash algorithm has high retrieval accuracy for fast retrieval of English sentences and shows excellent performance, providing a reliable technical support for the current English learning field.
Reduced energy consumption is an important goal for virtualized cloud computing systems since it has the potential to improve system efficiency, save operating costs, and lessen environmental impact. These objectives can be achieved by using an energy-efficient approach to job scheduling. The huge challenge lies in coordinating user demands with available cloud resources in a way that maximizes performance while reducing energy usage, all within the time frame that the user specifies. This article suggests a novel method called Energy Efficient Task Re-scheduling (EETRS) for a heterogeneous virtualized cloud environment as a solution to the problem of energy usage. The first step of the suggested approach assigns jobs strictly according to due dates, ignoring energy consumption. Task reassignment scheduling determines the optimal execution location within the deadline constraints while minimizing energy consumption in the second stage of the proposed method, which speeds up execution and meets deadlines. According to the simulation results, the suggested technique helps to significantly reduce energy use and boost performance by 5% while satisfying deadline constraints, in comparison to the current energy-efficient scheduling methods of EPETS, AMTS, and EPAGA. The proposed method outperforms the existing one with less than 1% total execution time, a reduction of 14% in total execution cost, a 3% decrease in energy consumption, and a 3% reduction in average resource utilization.
In order to address the increasing demand for vital sign detection, the author proposes a multi-target vital sign detection research that combines biological radar and convolutional neural network. Based on the fundamental architecture of convolutional neural networks (CNNs), the author combines classification-based CNN object detection techniques to develop a biological radar multi-target vital sign detection platform. The feasibility of this approach is confirmed through experiments, demonstrating the integration of biological radar and CNNs for multi-target vital sign detection. The experimental results indicate that the biological radar achieves a recognition accuracy of 96.1%, proving the effectiveness of the biological radar detection algorithm. The research on multi-target vital sign detection based on the fusion of biological radar and convolutional neural network is an effective auxiliary method that can provide reference for relevant researchers.
The crucial duty of developing translation skills for China's modernization falls on higher education institutions that teach translation. Information and intelligent technology are becoming increasingly ingrained in people's lives as civilization grows and develops. In this work, we use natural language processing and communication technologies to build a new type of university English translation classroom. To address the challenge of inferring semantic implication linkages in natural language processing, we put forth a deep learning model based on semantic rounding and semantic fusing. The technique can be applied to university translation classes to help basic translation tasks with effective reading comprehension. Furthermore, we developed a wireless classroom interaction system that enables effective interoperability between teachers and students in the classroom by embedding a natural language processing model in real time. Our natural language processing model performs exceptionally well and is capable of making predictions in real time, according to experimental results. The entire solution gives universities English translation classes a whole new experience.
High processing needs and latency make it difficult to simulate motor drive systems in distributed environments, which affect accuracy and real-time performance. Optimizing motor control and cutting energy use require effective modeling. Conventional simulation techniques have trouble scaling up and down, frequently demanding large amounts of resources and being unable to adjust to changing load circumstances, which leads to sluggish or imprecise simulations. This method improves scalability and lowers latency by dynamically adjusting computing loads in a distributed system through the use of adaptive algorithms. It improves the accuracy and efficiency of simulation by utilizing real-time adaption and parallel processing. The purpose of this work was to suggest distributed systems for motor drive system simulation analysis utilizing adaptive algorithms. Initially, the dataset was gathered from a test bench-mounted momentum permanent magnet synchronous motor (PMSM) in a three-phase system motor vehicle. The exponentially weighted moving standard deviation (EWMS) utilized in standardized data process representations for training. We proposed the Adaptive Controller with dynamic fuzzy system ensemble (AC-DMFSE) for distributed systems for simulation analysis of motor drive systems. To optimize motor performance in dynamic situations, adaptive techniques are used, such as fuzzy logic-based optimization and model predictive control. Our test findings show that the suggested distributed technique reduced simulation times and MSE while enhancing the accuracy of system performance evaluation. The foundation for scalable and effective motor drive system simulations is laid by this work, which also offers insightful information for improving the systems’ performance in practical applications.
In order to overcome some of the problems that have been encountered in the past, for example, due to their reliance on manual feature extraction and limitation of model generalization, this paper proposes a new method to identify people’s behavior in complicated situations. Based on Convolutional Neural Networks (CNN), this method has been proposed for the automatic extraction of a large number of datasets. In addition, the Long Short Term Memory (LSTM) network is used to capture the long-run dependence in time order. Lastly, we use the soft max classifier to classify the various actions of people. Experiments show that the CLT network is able to achieve a high performance of 97.5% over 13 different types of people, outperforming CNN alone, LSTM, and BP models on the DaLiAc dataset, demonstrating superior performance in human behavior recognition and classification. The accuracy, recall, and F1 score evaluation indicators of the CLT net model are the highest, while all indicators of the BP model are the lowest, indicating that the CLT net model has good stability and reliability in recognizing and classifying different human behaviors.
To address the challenge of real-time data collection and analysis in human motion data mining, the author proposes a system that integrates intelligent wearable devices into physical education. Initially, motion data is gathered through these smart devices, then transformed into binary format. This data undergoes cleaning and supplementation processes before being clustered. The Firefly Algorithm is employed to enhance the K-means clustering technique, which is then applied to the processed data. Experimental results indicate that this refined approach achieves an average recall rate of 97.12% and an average data mining accuracy of 98.42%, thus offering a valuable foundation for the real-time monitoring and assessment of students’ physiological metrics. This algorithm can be applied in student physical condition assessment, sports injury and fatigue monitoring, sports posture and movement assessment, personalized training and rehabilitation program development, scientific decision-making and management, and other aspects.
Color functions as a distinct type of ideographic symbol in animation for film and television, playing a crucial role in enhancing visual narratives and conveying emotions. In different types of animation, such as fantasy, horror, or children’s genres, color language influences audience perception and can convey meanings beyond the capabilities of image language alone. For instance, bright colors may symbolize innocence in children’s animation, while darker shades may evoke tension or fear in horror. However, current approaches to representing color in animation often fail to capture its full semantic richness and ideographic potential. Existing methods primarily focus on image-based analysis, overlooking the deeper layers of meaning encoded in color language. In this paper, we address these gaps by utilizing semantic segmentation techniques to combine the three modalities of color, content, and text to establish a consistent representation of color language in animation. We propose a method for semantic segmentation of color-depth (RGB-D) images using two-stream weighted Gabor convolutional network fusion. A weighted Gabor orientation filter builds a deep convolutional network (DCN) capable of extracting feature information adaptive to changes in orientation and scale, allowing for orientation- and scale-invariant features. Dual-stream picture features - color and depth - are extracted using a broad residual-weighted Gabor convolutional network and then combined into a lightweight feature extraction network. To evaluate the ideographic functions of color language quantitatively, we conducted extensive experiments using open databases. Our proposed method outperforms existing RGB-D picture semantic segmentation algorithms, demonstrating its effectiveness in representing color language in animation.
To tackle the challenge of sluggish resource scheduling in smart city management, the author introduces a study focused on smart city development leveraging artificial intelligence and big data governance strategies. The research uses the Cloud Management Module to monitor the various hardware equipment, and establishes the target functionality of the Cloud Computing Resource Scheduling. Through the application of PSO to this goal function, a optimal scheme is created for the Cloud Resource Scheduling. Experimental results demonstrate that the system achieves optimal resource utilization, nearing 100%, whereas the other two systems have lower utilization rates, both under 90%. This indicates that, compared to similar systems, this approach offers superior overall utilization. The system is capable of efficiently managing smart cities and providing real-time monitoring of urban conditions. When scheduling resources, it achieves shorter task completion times and higher system efficiency, ensuring maximum resource utilization.
The popularity of Internet of Things (IoT) devices has surged due to their applications in diverse areas such as e-Health, smart vehicles, and smart cities. However, the rapid deployment of these devices has led to an exponential increase in security attacks targeting IoT systems, making security a prime concern for the community. Securing IoT-based systems is challenging because the devices involved are often resource-constrained. Providing security to these systems requires a thorough understanding of their specific security needs, along with a systematic security engineering approach. Previous research lacks a systematic methodology for identifying and implementing security requirements. Therefore, there is a growing demand for a structured approach to identify security requirements, select appropriate algorithms, and ensure their effective implementation. While existing studies have extensively explored IoT security threats, they fall short of offering a structured method to comprehensively address these threats. This paper proposes a comprehensive security engineering framework that systematically identifies security threats by analyzing assets present over various layers of IoT system, considering their diverse roles. It includes creating repositories to identify potential vulnerabilities and applicable threats. Once threats are identified, they are evaluated for their severity level based on risk analysis. Following this, the framework focuses on designing the security solutions, where we proposed to add two new security services namely trust and data freshness besides the existing security services, algorithms are selected to mitigate threats by considering the domain and constraints of the devices involved. Ultimately, the security of the entire system is validated to ensure robustness. Throughout this process, we have developed comprehensive repositories for asset management, vulnerability-threat mapping, and algorithm-threat matching to help identify and analyze security needs and recommend algorithms for implementation.
