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.
