The use of an automatic histopathological image identification system is essential for expediting diagnoses and lowering mistake rates. Although it is of enormous clinical importance, computerized breast cancer multiclassification using histological pictures has rarely been investigated. A deep learning-based classification strategy is suggested to solve the challenge of automated categorization of breast cancer pathology pictures. The attention model that acts on the feature channel is the channel refinement model. The learned channel weight may be used to reduce superfluous features when implementing the feature channel. To increase classification accuracy, calibration is necessary. To increase the accuracy of channel recalibration findings, a multiscale channel recalibration model is provided, and the msSE-ResNet convolutional neural network is built. The multiscale properties flow through the network's highest pooling layer. The channel weights obtained at different scales are delivered into line fusion and used as input to the next channel recalibration model, which may improve the results of channel recalibration. The experimental findings reveal that the spatial recalibration model fares poorly on the job of classifying breast cancer pathology pictures when applied to the semantic segmentation of brain MRI images. The public BreakHis dataset is used to conduct the experiment. The network performs benign/malignant breast pathology picture classiﬁcation collected at various magnifications with a classification accuracy of 88.87 percent, according to experimental data. The diseased images are also more resilient. Experiments on pathological pictures at various magnifications show that msSE-ResNet34 is capable of performing well when used to classify pathological images at various magnifications.

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Breast Cancer Pathological Image Classification Based on the Multiscale CNN Squeeze Model

Internet of Vehicles (IoV) systems are vulnerable to a wide range of attacks because of the lack of security measures. IoV systems can be infiltrated by malicious and unauthorized nodes, which can cause the authenticity, accessibility, and privacy of shared information resources to be compromised. Indeed, the use of an access control system can help; as a result, it is unable to respond to such attacks on time. This paper introduces an artificial intelligence-enabled access control mechanism (AI-ACM) with vehicle nodes and roadside units (RSUs) to overcome these issues. Here, use vehicle nodes as lightweight nodes, while RSUs act as comprehensive and edge nodes to provide access control service. A generative adversarial network (GAN) is used in place of risk prediction (RP) due to the lack of training sets, resulting in a sequence generation rather than an accurate risk prediction. Afterward, the blockchain-based Internet of Vehicles (BIoV) approach is summarized for the security mechanisms of vehicles that are discussed from the aspects of access control and authentication to sustain the distributed processing architecture and solve security issues. The simulation results show that AI-ACM is more accurate than the previous GANs at predicting the future. In addition, the RP model’s access control accuracy can be improved as a result of this technique.

/pdf/risk-based-access-control-mechanism-for-internet-of-vehicles-3bjrnuz1.pdf

Risk-Based Access Control Mechanism for Internet of Vehicles Using Artificial Intelligence

The intelligent wireless system focuses on integrating with the advanced technologies like machine learning and related approaches in order to enhance the performance, productivity, and output. The implementation of machine learning approaches is mainly applied in order to enhance the efficient communication system, enable creation of variable node locations, support collection of data and information, analyze the pattern, and forecast so as to provide better services to the end users. The efficiency of using these technologies tend to lower the cost and support in deploying the resources effectively. The wireless network system tends to enhance the bandwidth, and the application of novel machine learning approaches supports detection of unrelated data and information and enables analysis of latency at each part of the communication channel. The study involves critically analyzing the key determinants of machine learning approaches in supporting enhanced intelligent network communication in the industries. The researchers are aimed at gathering both primary data and secondary data for the study. The respondents are chosen in the industry so that they can provide better inputs and insights related to the area of research. The key determinants considered for the study are machine learning-influenced management of hotspots, identification of critical congestion points, spectrum availability, and management. The analysis is made using SPSS data analysis package based on which it is noted that all the factors make major influences towards the intelligent communication, and hence machine learning supports critically in enhancing the user experience effectively.

/pdf/machine-learning-based-intelligent-wireless-communication-i6qtzsg2.pdf

Machine Learning-Based Intelligent Wireless Communication System for Solving Real-World Security Issues

High-speed rail (HSR) has greatly promoted cross-regional economic activities, but few studies have been conducted from the perspective of urban network externalities (UNEs). Based on data from China from 2008 to 2019, this paper constructs an HSR operation network model to study the impact of the network position (NP) on urban agglomeration economies (UAEs) by jointly employing network and econometric analysis methods. The findings suggest the following: (1) Improvements to NP significantly enhance UAEs. Improving the comprehensive NP by 1% will increase urban output per capita by 193.5–226.8 RMB. The findings hold under several robustness tests. (2) The NP's facilitation of growth in UAEs is reflected mainly in national core cities, while the impact of peripheral cities reflects a “∽-shaped” curve as their distance from each nearest neighboring core city changes. (3) From the temporal-dynamic perspective, the NP's impact on UAEs appeared one year before the HSR line was opened and was fully realized three years after the opening of the HSR. (4) Investment agglomeration and expansions in borrowed size are the main mediating mechanisms whereby NP facilitates UAEs. 

High-speed rail network and urban agglomeration economies: Research from the perspective of urban network externalities

Current research has generally concentrated on the motivations of environmental policies on local green innovation while ignoring the effect they may have on green innovation in neighboring places. To obtain a thorough understanding and explanation of the influencing mechanism of environmental regulation (ER) on green innovation efficiency (GIE), the super-slack based measure-data envelopment analysis (Super-SBM-DEA) method was applied to evaluate Chinese provinces’ GIE, a spatial Durbin model was developed to evaluate the effect of ER on GIE from the perspective of the “local neighborhood” effect, and a mediating effect model was built to analyze the transmission mechanism of the neighborhood effect of ER on GIE. The study indicated that China’s regional GIE is high in the east and low in the west, with large spatial variability and significant positive spatial clustering characteristics. The effect of ER on local GIE is “U” shaped, while the influence on green innovation efficiency in neighboring areas is an inverted “U” shape. The influence of environmental regulation on GIE in neighboring areas is mainly achieved through the transfer of local polluting industries to neighboring areas. Based on the results, policy recommendations from the perspectives of choosing environmental regulation tools and transferring polluting industries are made to promote and realize the coordinated development of ER and green innovation.

/pdf/the-local-neighborhood-effect-of-environmental-regulation-on-ce9h6146.pdf

The “Local Neighborhood” Effect of Environmental Regulation on Green Innovation Efficiency: Evidence from China

The platform safety gate is important safety protection equipment in urban rail transit, which makes the rail area relatively independent from the platform waiting area, ensures the safety of passengers, reduces the noise pollution brought by the subway train to the platform, and provides a comfortable waiting environment for passengers. In order to solve the problems of low intelligent degree and single debugging method of railway station safety door equipment data monitoring, a correction algorithm using machine learning based on image grid is proposed. Firstly, based on virtual instrument technology, a set of acoustic signal acquisition and processing systems for sound field visualization is designed and implemented. Then, based on the analysis of requirements, the hardware configuration and system software design are carried out. Finally, the extraction technology of image feature information is adopted, which can reduce the operation time of image target recognition and make the security door control system have real time. The experimental results show that the calibration algorithm is used to calculate the coordinate values of the actual road by using the third-order fitting method. Compared with the coordinate values of the standard grid, the average error of X is 0.0662%, and the average error of Y is 0.0011%. It can not only improve the accuracy of judgment, but also meet the real-time requirements of video monitoring. The system can realize wireless monitoring on the status of platform safety door equipment using machine learning, improve the efficiency of subway operation and the flexibility of station staff maintenance and protection, and ensure the safety and reliability of the platform safety door system.

Design and implementation of intelligent monitoring system for platform security gate based on wireless communication technology using ML

Does the opening of China railway express promote urban total factor productivity? New evidence based on SDID and SDDD model

Based on the panel data of 285 prefecture-level cities in China from 2010 to 2020, the policy net effect of the new urbanization strategy on industrial transformation and upgrading is evaluated by using PSM-DID. The research results show that the new urbanization strategy has a significant and sustained positive effect on industrial transformation and upgrading. The mechanism test results show that the new urbanization strategy promotes the industrial transformation and upgrading by influencing the regional financial development.

Chunying Li

Papers

Design and implementation of intelligent monitoring system for platform security gate based on wireless communication technology using ML

Does the opening of China railway express promote urban total factor productivity? New evidence based on SDID and SDDD model

Research on the Impact of New Urbanization on Industrial Transformation and Upgrading