With the change of people's consumption mode, credit consumption has gradually become a new consumption trend. Frequent loan defaults give default prediction more and more attention. This paper... Show More

With the change of people's consumption mode, credit consumption has gradually become a new consumption trend. Frequent loan defaults give default prediction more and more attention. This paper proposes a new comprehensive prediction method of loan default. This method combines convolutional neural network and LightGBM algorithm to establish a prediction model. Firstly, the excellent feature extraction ability of convolutional neural network is used to extract features from the original loan data and generate a new feature matrix. Secondly, the new feature matrix is used as input data, and the parameters of LightGBM algorithm are adjusted through grid search so as to build the LightGBM model. Finally, the LightGBM model is trained based on the new feature matrix, and the CNN-LightGBM loan default prediction model is obtained. To verify the effectiveness and superiority of our model, a series of experiments were conducted to compare the proposed prediction model with four classical models. The results show that CNN-LightGBM model is superior to other models in all evaluation indexes.

It is essential to have a fast, reliable, and energy-efficient connection between wireless sensor networks (WSNs). Control specifications, networking layers, media access control, and physical... Show More

It is essential to have a fast, reliable, and energy-efficient connection between wireless sensor networks (WSNs). Control specifications, networking layers, media access control, and physical layers should be optimised or co-designed. Health insurance will become more expensive for individuals with lower incomes. There are privacy and cyber security issues, an increased risk of malpractice lawsuits, and more costs in terms of both time and money for doctors and patients. In this paper, personal health biomedical clothing based on wireless sensor networks (PH-BC-WSN) was used to enhance access to quality health care, boost food production through precision agriculture, and improve the quality of human resources. The internet of things enables the creation of healthcare and medical asset monitoring systems that are more efficient. There was extensive discussion of medical data eavesdropping, manipulation, fabrication of warnings, denial of services, position and tracker of users, physical interference with devices, and electromagnetic attacks.

Aspect-based sentiment analysis (ABSA) aims to classify the sentiment polarity of a given aspect in a sentence or document, which is a fine-grained task of natural language processing. Recent ABSA... Show More

Aspect-based sentiment analysis (ABSA) aims to classify the sentiment polarity of a given aspect in a sentence or document, which is a fine-grained task of natural language processing. Recent ABSA methods mainly focus on exploiting the syntactic information, the semantic information and both. Research on cognition theory reveals that the syntax an*/874d the semantics have effects on each other. In this work, a graph convolutional network-based model that fuses the syntactic information and semantic information in line with the cognitive practice is proposed. To start with, the GCN is taken to extract syntactic information on the syntax dependency tree. Then, the semantic graph is constructed via a multi-head self-attention mechanism and encoded by GCN. Furthermore, a parameter-sharing GCN is developed to capture the common information between the semantics and the syntax. Experiments conducted on three benchmark datasets (Laptop14, Restaurant14 and Twitter) validate that the proposed model achieves compelling performance comparing with the state-of-the-art models.

Non-negative matrix factorization (NMF) has gained sustaining attention due to its compact leaning ability. Cancer subtyping is important for cancer prognosis analysis and clinical precision... Show More

Non-negative matrix factorization (NMF) has gained sustaining attention due to its compact leaning ability. Cancer subtyping is important for cancer prognosis analysis and clinical precision treatment. Integrating multi-omics data for cancer subtyping is beneficial to uncover the characteristics of cancer at the system-level. A unified multi-view clustering method was developed via adaptive graph and sparsity regularized non-negative matrix factorization (multi-GSNMF) for cancer subtyping. The local geometrical structures of each omics data were incorporated into the procedures of common consensus matrix learning, and the sparsity constraints were used to reduce the effect of noise and outliers in bioinformatics datasets. The performances of multi-GSNMF were evaluated on ten cancer datasets. Compared with 10 state-of-the-art multi-view clustering algorithms, multi-GSNMF performed better by providing significantly different survival in 7 out of 10 cancer datasets, the highest among all the compared methods.

The main task of aspect-based sentiment analysis is to determine the sentiment polarity of a given aspect in the sentence. A major issue lies in identifying the aspect sentiment is to establish the... Show More

The main task of aspect-based sentiment analysis is to determine the sentiment polarity of a given aspect in the sentence. A major issue lies in identifying the aspect sentiment is to establish the relationship between the aspect and its opinion words. The application of syntactic dependency trees is one such resolution. However, the widely-used dependency parsers still have challenges in obtaining a solid sentiment classification result. In this work, an information propagation graph convolutional network based on syntactic structure optimization is proposed on the task of ABSA. To further complement the syntactic information, the semantic information is incorporated to learn the representations using graph information propagation mechanism. In addition, the effects of syntactic and semantic information are adapted via feature separation. Experimental results on three benchmark datasets show that the proposed model achieves satisfying performance against the state-of-the-art methods, indicating that the model can precisely build the relation between aspect and its context words.

Identifying the places an infected person has visited during a virus incubation time in order to conduct contact tracing is currently done using manual interviews since proximity-based contact... Show More

Identifying the places an infected person has visited during a virus incubation time in order to conduct contact tracing is currently done using manual interviews since proximity-based contact tracing methods do not store geolocation information due to privacy concerns. During the incubation time, an infected person might visit several locations and either forget where they went or are reluctant to disclose their trip details. To minimize manual location tracing while preserving the user's privacy, the authors propose a mesh block sequence method where the trajectories are transformed into a mesh block sequence before being shared with health authorities. These simulations show that this a useful method by which to protect user privacy by concealing specific details related to a trajectory. While this simulation uses an Australian administrative region structure, this method is applicable in countries which implement similar administrative hierarchical building blocks.

Skin cancer is affected by the uncommon evolution of skin cells and is a deadly type of cancer. In addition, skin lesion is affected by numerous factors, such as exposure to the sun, infections... Show More

Skin cancer is affected by the uncommon evolution of skin cells and is a deadly type of cancer. In addition, skin lesion is affected by numerous factors, such as exposure to the sun, infections, allergies, etc. These skin illnesses have become a challenge in therapeutic diagnosis because of virtual resemblances, where image classification is vital to sufficiently diagnose dissimilar lesions. Therefore, early diagnosis is significant and can avert skin cancers like focal cell carcinoma and melanoma. A deep learning-based computer analyzing model can be an automatic solution in medical evaluations to overcome this issue. Hence, this paper suggests an improved chameleon swarm algorithm and convolutional neural networks (ICSA-CNN) for effective skin cancer identification and classification. The data are collected from the Kaggle dataset for classifying skin cancer. Chameleon swarm algorithm is a clustering technique utilized in data mining to the cluster dataset utilizing dynamic systems, and it can resolve constrained and global numerical optimization issues in skin cancer detection.

Anomaly detection on sequence dataset typically focuses on the detection of collective anomalies, aiming to find anomalous patterns consisting of sequences of data with specific relationships rather... Show More

Anomaly detection on sequence dataset typically focuses on the detection of collective anomalies, aiming to find anomalous patterns consisting of sequences of data with specific relationships rather than individual observations. In this survey, existing studies are summarized to align with temporal sequence dataset and spatial sequence dataset. For the first category, the detection can be subdivided into symbolic dataset based and time series dataset based, which include similarity, probabilistic, and trend approaches. For the second category, it can be subdivided into homogeneous datasets based heterogeneous datasets based, which include multi-dataset fusion and joint approaches. Compared to the state-of-the-art survey papers, the contribution of this paper lies in providing a deep analysis of various representations of collective anomaly in different application field and their corresponding detection methods, representative techniques. As a result, practitioners can receive some guidance for selecting the most suitable methods for their particular case.

Spatiotemporal data prediction is of great significance in the fields of smart cities and smart manufacturing. Current spatiotemporal data prediction models heavily rely on traditional spatial views... Show More

Spatiotemporal data prediction is of great significance in the fields of smart cities and smart manufacturing. Current spatiotemporal data prediction models heavily rely on traditional spatial views or single temporal granularity, which suffer from missing knowledge, including dynamic spatial correlations, periodicity, and mutability. This paper addresses these challenges by proposing a multi-layer attention-based predictive model. The key idea of this paper is to use a multi-layer attention mechanism to model the dynamic spatial correlation of different features. Then, multi-granularity historical features are fused to predict future spatiotemporal data. Experiments on real-world data show that the proposed model outperforms six state-of-the-art benchmark methods.

Clustering is a basic primer of exploratory tasks. In order to obtain valuable results, the parameters in the clustering algorithm, the number of clusters must be set appropriately. Existing methods... Show More

Clustering is a basic primer of exploratory tasks. In order to obtain valuable results, the parameters in the clustering algorithm, the number of clusters must be set appropriately. Existing methods for determining the number of clusters perform well on low-dimensional small datasets, but how to effectively determine the optimal number of clusters on large high-dimensional datasets is still a challenging problem. In this paper, the authors design a method for effectively estimating the optimal number of clusters on large-scale high-dimensional datasets that can overcome the shortcomings of existing estimation methods and accurately and quickly estimate the optimal number of clusters on large-scale high-dimensional datasets. Extensive experiments show that it (1) outperforms existing estimation methods in accuracy and efficiency, (2) generalizes across different datasets, and (3) is suitable for high-dimensional large datasets.

In this paper, a top-k pseudo labeling method for semi-supervised self-learning is proposed. Pseudo labeling is a key technology in semi-supervised self-learning. Briefly, the quality of the pseudo... Show More

In this paper, a top-k pseudo labeling method for semi-supervised self-learning is proposed. Pseudo labeling is a key technology in semi-supervised self-learning. Briefly, the quality of the pseudo label generated largely determined the convergence of the neural network and the accuracy obtained. In this paper, the authors use a method called top-k pseudo labeling to generate pseudo label during the training of semi-supervised neural network model. The proposed labeling method helps a lot in learning features from unlabeled data. The proposed method is easy to implement and only relies on the neural network prediction and hyper-parameter k. The experiment results show that the proposed method works well with semi-supervised learning on CIFAR-10 and CIFAR-100 datasets. Also, a variant of top-k labeling for supervised learning named top-k regulation is proposed. The experiment results show that various models can achieve higher accuracy on test set when trained with top-k regulation.

Spatial keyword query has attracted the attention of many researchers. Most of the existing spatial keyword indexes do not consider the differences in keyword distribution, so their efficiencies are... Show More

Spatial keyword query has attracted the attention of many researchers. Most of the existing spatial keyword indexes do not consider the differences in keyword distribution, so their efficiencies are not high when data are skewed. To this end, this paper proposes a novel association rule mining based spatial keyword index, ARM-SQ, whose inverted lists are materialized by the frequent item sets mined by association rules; thus, intersections of long lists can be avoided. To prevent excessive space costs caused by materialization, a depth-based materialization strategy is introduced, which maintains a good balance between query and space costs. To select the right frequent item sets for answering a query, the authors further implement a benefit-based greedy frequent item set selection algorithm, BGF-Selection. The experimental results show that this algorithm significantly outperforms the existing algorithms, and its efficiency can be an order of magnitude higher than SFC-Quad.

Due to the rapid advancement of wireless sensor and location technologies, a large amount of mobile agent trajectory data has become available. Intelligent city systems and video surveillance all... Show More

Due to the rapid advancement of wireless sensor and location technologies, a large amount of mobile agent trajectory data has become available. Intelligent city systems and video surveillance all benefit from trajectory anomaly detection. The authors propose an unsupervised reconstruction error-based trajectory anomaly detection (RETAD) method for vehicles to address the issues of conventional anomaly detection, which include difficulty extracting features, are susceptible to overfitting, and have a poor anomaly detection effect. RETAD reconstructs the original vehicle trajectories through an autoencoder based on recurrent neural networks. The model obtains moving patterns of normal trajectories by eliminating the gap between the reconstruction results and the initial inputs. Anomalous trajectories are defined as those with a reconstruction error larger than anomaly threshold. Experimental results demonstrate that the effectiveness of RETAD in detecting anomalies is superior to traditional distance-based, density-based, and machine learning classification algorithms on multiple metrics.

Outlier detection is an important field in data mining, which can be used in fraud detection, fault detection, and other fields. This article focuses on the problem where the density peak clustering... Show More

Outlier detection is an important field in data mining, which can be used in fraud detection, fault detection, and other fields. This article focuses on the problem where the density peak clustering algorithm needs a manual parameter setting and time complexity is high; the first is to use the k nearest neighbors clustering algorithm to replace the density peak of the density estimate, which adopts the KD-Tree index data structure calculation of data objects k close neighbors. Then it adopts the method of the product of density and distance automatic selection of clustering centers. In addition, the central relative distance and fast density peak clustering outliers were defined to characterize the degree of outliers of data objects. Then, based on fast density peak clustering outliers, an outlier detection algorithm was devised. Experiments on artificial and real data sets are performed to validate the algorithm, and the validity and time efficiency of the proposed algorithm are validated when compared to several conventional and innovative algorithms.

It has been widely known that long non-coding RNA (lncRNA) plays an important role in gene expression and regulation. However, due to a few characteristics of lncRNA (e.g., huge amounts of data... Show More

It has been widely known that long non-coding RNA (lncRNA) plays an important role in gene expression and regulation. However, due to a few characteristics of lncRNA (e.g., huge amounts of data, high dimension, lack of noted samples, etc.), identifying key lncRNA closely related to specific disease is nearly impossible. In this paper, the authors propose a computational method to predict key lncRNA closely related to its corresponding disease. The proposed solution implements a BPSO based intelligent algorithm to select possible optimal lncRNA subset, and then uses ML-ELM based deep learning model to evaluate each lncRNA subset. After that, wrapper feature extraction method is used to select lncRNAs, which are closely related to the pathophysiology of disease from massive data. Experimentation on three typical open datasets proves the feasibility and efficiency of our proposed solution. This proposed solution achieves above 93% accuracy, the best ever.

Network representation learning is one of the important works of analyzing network information. Its purpose is to learn a vector for each node in the network and map it into the vector space, and... Show More

Network representation learning is one of the important works of analyzing network information. Its purpose is to learn a vector for each node in the network and map it into the vector space, and the resulting number of node dimensions is much smaller than the number of nodes in the network. Most of the current work only considers local features and ignores other features in the network, such as attribute features. Aiming at such problems, this paper proposes novel mechanisms of combining network topology, which models node text information and node clustering information on the basis of network structure and then constrains the learning process of network representation to obtain the optimal network node vector. The method is experimentally verified on three datasets: Citeseer (M10), DBLP (V4), and SDBLP. Experimental results show that the proposed method is better than the algorithm based on network topology and text feature. Good experimental results are obtained, which verifies the feasibility of the algorithm and achieves the expected experimental results.

The higher-order and temporal characteristics of tweet sequences are often ignored in the field of rumor detection. In this paper, a new rumor detection method (T-BiGAT) is proposed to capture the... Show More

The higher-order and temporal characteristics of tweet sequences are often ignored in the field of rumor detection. In this paper, a new rumor detection method (T-BiGAT) is proposed to capture the temporal features between tweets by combining a graph attention network (GAT) and gated recurrent neural network (GRU). First, timestamps are calculated for each tweet within the same event. On the premise of the same timestamp, two different propagation subgraphs are constructed according to the response relationship between tweets. Then, GRU is used to capture intralayer dependencies between sibling nodes in the subtree; global features of each subtree are extracted using an improved GAT. Furthermore, GRU is reused to capture the temporal dependencies of individual subgraphs at different timestamps. Finally, weights are assigned to the global feature vectors of different timestamp subtrees for aggregation, and a mapping function is used to classify the aggregated vectors.

Efficient and convenient vulnerability detection for smart contracts is a key issue in the field of smart contracts. The earlier vulnerability detection for smart contracts mainly relies on static... Show More

Efficient and convenient vulnerability detection for smart contracts is a key issue in the field of smart contracts. The earlier vulnerability detection for smart contracts mainly relies on static symbol analysis, which has high accuracy but low efficiency and is prone to path explosion. In this paper, the authors propose a static method for vulnerability detection based on deep learning. It first disassembles Ethereum smart contracts into opcode sequences and then converts the vulnerability detection problem into a natural language text classification problem. The word vector method is employed to map each opcode to a uniform vector space, and the opcode sequence matrix is trained by the TextCNN method to detect vulnerabilities. Furthermore, a code obfuscation method is given to enhance and balance the dataset, while three different opcode sequence generation methods are proposed to construct features. The experimental results verify that the average prediction accuracy of each smart contract exceeds 96%, and the average detection time is less than 0.1 s.

The COVID-19 pandemic is one of the current universal threats to humanity. The entire world is cooperating persistently to find some ways to decrease its effect. The time series is one of the basic... Show More

The COVID-19 pandemic is one of the current universal threats to humanity. The entire world is cooperating persistently to find some ways to decrease its effect. The time series is one of the basic criteria that play a fundamental part in developing an accurate prediction model for future estimations regarding the expansion of this virus with its infective nature. The authors discuss in this paper the goals of the study, problems, definitions, and previous studies. Also they deal with the theoretical aspect of multi-time series clusters using both the K-means and the time series cluster. In the end, they apply the topics, and ARIMA is used to introduce a prototype to give specific predictions about the impact of the COVID-19 pandemic from 90 to 140 days. The modeling and prediction process is done using the available data set from the Saudi Ministry of Health for Riyadh, Jeddah, Makkah, and Dammam during the previous four months, and the model is evaluated using the Python program. Based on this proposed method, the authors address the conclusions.

The electronic health record (EHR) is a patient care database, which helps doctors or nurses to analyse comprehensive patient healthcare through health-cart (h-cart) assistance. Electronic health... Show More

The electronic health record (EHR) is a patient care database, which helps doctors or nurses to analyse comprehensive patient healthcare through health-cart (h-cart) assistance. Electronic health (e-Health) services offer efficient sharing of the patient's information based on geo-location in which nurses, doctors, or health care practitioners access the patients, promptly and without time delay in case of emergency. In e-Health services, nurses are considered as the data holder who can store and maintain patient's health records in the cloud h-cart platform to analyses patient's data effectively. Therefore, nurses need to safely share and manage access to data in the healthcare system; this need required prominent solutions. However, data authenticity and response time are considered as challenging characteristics in the e-health care system. Hence, in this paper, an improved e-health service model (IeHSM) has been proposed based on cloud computing technology to improve the data authenticity, reliability, and accessibility time of the healthcare information.

Artificial intelligence (AI) is the primary tool used by businesses to enhance economic advantages. Family company management is undergoing information and the digital industrial revolution because... Show More

Artificial intelligence (AI) is the primary tool used by businesses to enhance economic advantages. Family company management is undergoing information and the digital industrial revolution because of artificial intelligence. With the fast development of new technologies, the family company management style has a significant effect and previously unimaginable impact. For most enterprises, intergenerational succession is either taking place or will occur shortly. Here intergenerational inheritance significance for family-owned businesses (IISF-OB) has been proposed to solve family company performance and the internal action mechanisms. Intergenerational succession pathways deserve consideration, and research for the family businesses must utilize artificial intelligence technology to enhance related work. The proposed method investigates how family company management modes can be innovated to fulfill societal development requirements methods in artificial intelligence. It will use case studies, comparative analyses, and develop a new solution.

Accounting information systems (AIS) gather, store, and analyze data in providing information to business leaders. Information technology resources and a computer-based accounting system are often... Show More

Accounting information systems (AIS) gather, store, and analyze data in providing information to business leaders. Information technology resources and a computer-based accounting system are often used to monitor accounting activities in an accounting information system. Supply chain management strategies, planning, and implementation are increasingly dependent on the expertise of accountants with globalization. The accountant's job is to assist the supply chain design, development, and implementation group. Top management commitment, the kind of accounting information systems used, and input controls are all factors that affect accounting information systems' data quality. They can trace any form of theft or misappropriation using the blockchain (BC), which maintains asset transfers. In hopes of avoiding fraud, agreements loaded with economics and finance principles might be used to govern corporate operations. Using internet of things (IoT) data, banks can better understand their customers' business demands and value chain.

The data under the smart city spatio-temporal big data platform is very diverse, and there are many modern spatial and spatial databases in the archives management system related to natural... Show More

The data under the smart city spatio-temporal big data platform is very diverse, and there are many modern spatial and spatial databases in the archives management system related to natural resources. Big data can effectively improve the quality and classification of natural resource archives management (referred to as NRAM for convenience of description). However, the traditional NRAM method and informatization level can no longer meet the needs of the current NRAM, so people must continue to make efforts to digitize the natural resource archives. To this end, this paper analyzed the characteristics and problems of NRAM and then used the big data platform to make corresponding management adjustments to promote the development of NRAM. Under big data, the degree of management improvement and management efficiency were better than the original NRAM, and the degree of management improvement was 14% higher than the original NRAM. In short, both big data and artificial intelligence can improve the integrated management of natural resource archives.

With the innovation of information technology, digital stadiums and gymnasiums are based on independent innovation, information management, and intelligent cultivation and development, which... Show More

With the innovation of information technology, digital stadiums and gymnasiums are based on independent innovation, information management, and intelligent cultivation and development, which promotes the construction of a new ecology of intelligent sports and also determines the development situation and development mode of traditional sports. In the construction of intelligent service mode of digital stadiums and gymnasiums in the context of smart cities, the management information system, as the application support of stadium information technology, is the key channel to improve the intelligent level of stadiums and gymnasiums. The establishment and promotion of a smart management information system is an inevitable trend for smart stadiums and gymnasiums to complete key technologies, consumer experience, and efficient functional innovation of management methods, which is also an important content of science and technology-enabling stadiums and gymnasiums. This paper proposed the research on intelligent service mode of digital stadiums and gymnasiums under the background of smart cities.

With the acceleration of the urbanization process, the traditional urban management has become increasingly unable to meet the needs of urban management and development. At the same time, with the... Show More

With the acceleration of the urbanization process, the traditional urban management has become increasingly unable to meet the needs of urban management and development. At the same time, with the rapid development of artificial intelligence (AI) and big data (BD), the use of AI and BD to analyze cities has been gradually emerging. Therefore, this paper used AI and BD to study the optimization method of sustainable development of smart city public management. The research showed that the respondents in N, Z, and S cities were 60.67%, 60.07%, and 60.31% satisfied with the handling of events by urban public management subjects, respectively. The experts' evaluation scores on the feasibility and effectiveness of urban public management optimization strategies were 88.79 and 92.82, respectively. The public's satisfaction with the smart city public management subject's handling of events was still not high enough. The optimization strategy for sustainable development of smart city public management proposed in this paper with BD had certain practical value.

Blockchain and distributed ledger technologies have attracted massive attention from both legal communities and businesses. Asset securitization is the procedure in which an issuer designs a... Show More

Blockchain and distributed ledger technologies have attracted massive attention from both legal communities and businesses. Asset securitization is the procedure in which an issuer designs a financial instrument that is marketable by combining or merging different financial assets into one group. However, most securitization occurs with loans and other assets that generate receivables, such as consumer or business debt of various types. This article discusses the possible benefits of blockchain during the securitization process using the deep learning-based adaptive online intelligent framework (DLAOIF). The benefits can be significant, from reduced costs, time, and fraud risks to increased safety, trust, and accuracy. Tracking financial assets on a blockchain can reduce dependence on credit rating organizations and allow investors to monitor asset performance and the associated risk more carefully. It should improve investor confidence and increase secondary market interest.

Since its birth, supply chain finance (SCF) has made contributions to the development of small and medium-sized enterprises, but it also faces many challenges in the development process. With the... Show More

Since its birth, supply chain finance (SCF) has made contributions to the development of small and medium-sized enterprises, but it also faces many challenges in the development process. With the development and continuous progress of the internet and information technology, it has also opened up new ways for urban development and innovation. This article introduced the background of intelligent business model, conducted academic research and summary on the keywords of SCF and the internet of things (IOT), and then summarized urban analysis by combining AI and big data. Then it put forward the business model factor analysis of SCF and modern logistics enterprises. At the end of the article, the simulation experiment was carried out, and the experiment was summarized and discussed. The experimental results showed that the average transaction cost of the new business model was 3.5 lower than that of the traditional business model. With the continuous development of artificial intelligence technology and big data technology, urban planning is also facing new opportunities and challenges.

In this study, the authors devised a big data-driven evaluation model to measure the effect of college music education, aiming at filling the gaps of poor accuracy and time-consuming results of... Show More

In this study, the authors devised a big data-driven evaluation model to measure the effect of college music education, aiming at filling the gaps of poor accuracy and time-consuming results of music education effect evaluation. Firstly, the authors determined the effect of an evaluation system of music for learning, and the evaluation of this effect. Then, they carried out a simulation experiment. The literature review evidenced that few domestic research reports considered college students' communication fear. Thus, combining the characteristics of current college students' psychological counseling and the theory of communication fear, the authors tried to apply the music system desensitization therapy to address college students' communication fear, from the intervention effect, feasibility, and therapy as a psychological counseling method. The results showed that music system desensitization therapy eliminates college students' fear of communication, reduces speech anxiety, reduces shyness, and improves interpersonal communication skills.

In the modern era, nursing intervention is an increased commitment to patient quality and protection that allows nurses to make evidence-based healthcare decisions. The challenging characteristic of... Show More

In the modern era, nursing intervention is an increased commitment to patient quality and protection that allows nurses to make evidence-based healthcare decisions. The challenging characteristic of patients such as high deep venous thrombosis (DVT) and respiratory embolisms (RE) are significant health conditions that lead to post-operative severe injury and death. In this article, hybrid machine learning (HML) is used for senile patients with lower extremity fractures during the perioperative time and the clinical effectiveness of early stages nursing protocol for deep venous thrombosis of patients and nurses. A three-dimensional shape model of the user interface is shown the examined vessels, which have compression measurements mapped to the surface as colors and virtual image plane representation of DVT. The measures of comprehension have been validated using HML model segmentation experts and contrasted with paired f-tests to reduce the incidence of lower extremity deep venous thrombosis in patients and nurses.

Health budget allocation choices are increasingly aided by medical technology's economic evaluation (EE). With thousands of new items launched each year, the medical device (MD) business is one of... Show More

Health budget allocation choices are increasingly aided by medical technology's economic evaluation (EE). With thousands of new items launched each year, the medical device (MD) business is one of the most active domains of medical advancement among providers of innovation. Some of these considerations have to do with the specifics of how the gadget works. The paper examines the investment assessment of new medical devices from an industrial viewpoint. The strategy EE-MD presented should lead to more inventive and cost-effective surgical supplies for the medical industry. The study's purpose is to better the decision-making process for medical device development. Small and medium-sized firms are a crucial source of innovation for the future, and the research focuses mostly on them. Design economy and professional engineering literature are linked in the article. The proposed multi-attribute and team method to construct selection discusses the financial factor and provides a strong foundation for ongoing program management by identifying project-specific risks and strategic alliances.

Big data brings new opportunities to discover the new value of healthcare industry, since it can help us understand the hidden value of data deeply. This also brings new challenges: how to... Show More

Big data brings new opportunities to discover the new value of healthcare industry, since it can help us understand the hidden value of data deeply. This also brings new challenges: how to effectively manage and organize these datasets. Throughout the whole life cycle of publishing, storing, mining, and using big data in health care, different users are involved, so there are corresponding privacy protection methods and technologies for different life cycles. Data usage is the last and most important part of the whole life cycle. Therefore, this article proposes a privacy protection method for large medical data: an access control based on credibility of the requesting user. This model evaluates and quantifies doctors' credibility from the perspective of behavioral trust. Comparative experiments show that under the background of linear, geometric and exponential distribution trends and mixed trends, the regression model in this article is better than the existing methods in predicting trust accuracy and trust trends.

Investors can learn a lot about the health of a firm by looking at its FP (financial performance). For investors, it offers a glimpse into the company's financial health and performance, as well as... Show More

Investors can learn a lot about the health of a firm by looking at its FP (financial performance). For investors, it offers a glimpse into the company's financial health and performance, as well as a forecast for the stock's performance in the future. Certain criteria, including liquidity, ownership, maturity, and size, have been linked to financial success. Blockchain provides several benefits in the logistics business, including increased trust in the system owing to improved transparency and traceability and cost savings by removing manual and paper-based administration. The study uses the FP-BCT technique, a new approach to measuring company performance. However, e-business helps expand data exchange, aspects, and data quantity. Improving processing capabilities impacts the macroeconomic and financial environments, reducing economic activity, ensuring timely implementation of information, and decreasing costs.

E-businesses (EBEs) may commit legal offenses due to perpetrating cybercrime while doing the commercial activity. According to the findings, various obstacles might deter cybercrime throughout... Show More

E-businesses (EBEs) may commit legal offenses due to perpetrating cybercrime while doing the commercial activity. According to the findings, various obstacles might deter cybercrime throughout accounting. The study examined the present laws for accounting policy elements and determined those aspects that should be included in the administrative document for e-business enterprise accounting policies. E-businesses must avoid cyber-crime (CC), which has a detrimental influence on the company's brand and diminishes client loyalty to ensure their success. According to the study's findings, the use of information and control functions of accounting can help prevent cyber-crime in the bookkeeping system by increasing the content of individual internal rules. The authors intended to make online payments for EBE-CC as safe, easy, and fast as possible. However, the internet is known for making its users feel anonymous. E-commerce (EC) transactions are vulnerable to cybercrime, resulting in considerable money and personal information losses.