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Purpose: Accurate foreign tourist arrivals forecasting can help public and private sectors to formulate scientific tourism planning and improve the allocation efficiency of tourism resources. This paper aims to address the problem of low prediction accuracy of Chinese inbound tourism demand caused by the lack of valid historical data. Design/methodology/approach: A novel hybrid Chinese inbound tourism demand forecasting model combining fractional non-homogenous discrete grey model and firefly algorithm is constructed. In the proposed model, all adjustable parameters of the fractional non-homogenous discrete grey model are optimized simultaneously by the firefly algorithm. Findings: The data sets of annual foreign tourist arrivals to China are used to verify the validity of the proposed model. Experimental results show that the proposed method is effective and can be used as a useful predictor for the prediction of Chinese inbound tourism demand. Originality/value: The method proposed in this paper is effective and can be used as a feasible approach for forecasting the development trend of Chinese inbound tourism. [ABSTRACT FROM AUTHOR]

A researcher from Renmin Hospital of Wuhan University in China has published new data on the use of machine learning and natural language processing algorithms in the remote mobile medical diagnosis system of internet hospitals. The study aimed to integrate internet technology into the medical field and improve the accuracy of medical diagnoses. The research found that the remote mobile medical diagnosis system based on machine learning achieved an accuracy of 80% or higher compared to traditional systems. The study suggests that this system has a broad application prospect and can accommodate more than 10,000 people simultaneously. [Extracted from the article]

A study conducted by researchers at Guangxi University for Nationalities in Nanning, China, proposes a new method for diagnosing Parkinson's disease based on speech signals. The researchers developed a Binary Hybrid Artificial Hummingbird and Flower Pollination Algorithm (BFAHA) to selectively extract disease-specific information from speech signals, improving accuracy and reducing the number of features used. The algorithm was tested on high-dimensional datasets and showed competitive performance compared to other algorithms. This research has been peer-reviewed and published in the Journal of Bionic Engineering. [Extracted from the article]

A recent study conducted by researchers at the First Affiliated Hospital of Wenzhou Medical University in China explores the use of deep learning algorithms in medical image analysis. The study highlights the importance of employing advanced deep learning techniques in this field, as they have shown impressive results in various areas of healthcare. The researchers categorized and assessed different deep learning techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), based on their principles, advantages, limitations, methodologies, and datasets. The study also addresses the challenges that hinder the widespread implementation of deep learning in medical image analysis. The findings of this research provide valuable insights for future studies and advancements in image analysis in medical healthcare research. [Extracted from the article]

Researchers from the University of Electronic Science and Technology of China have developed a noise reduction method for the vibration signal of the reactor control rod drive mechanism (CRDM). The CRDM is a crucial component for the normal operation of a reactor. The proposed method, called complete ensemble empirical mode decomposition with adaptive amplitude correction noise and spectral kurtosis (CEEMDAACN-SK), effectively reduces the vibration signal of the CRDM by decomposing it into multiple intrinsic mode functions and analyzing the spectral kurtosis of each component. Experimental analysis showed that the CEEMDAACN-SK algorithm outperformed existing methods. This method has potential applications in other fields such as nuclear power and the chemical industry. [Extracted from the article]

Researchers from Jiangnan University in China have developed a new algorithm called MFM-SFCM for segmenting brain infarction lesions in diffusion-weighted imaging (DWI) images. The algorithm addresses the issue of weakened constraints and insufficient influence of labeled samples on the clustering process in semi-supervised fuzzy C-means clustering (SFCM). By using a new membership fusion mechanism, MFM-SFCM improves the accuracy of clustering results and speeds up convergence. The algorithm was tested on a real-world dataset of DWI brain images and demonstrated effective results. [Extracted from the article]

A new report from Zhengzhou Normal University in China discusses the use of federated learning in connected and autonomous vehicles (CAVs). Federated learning is a distributed learning approach where models are trained locally and only model parameters are exchanged to create a global model. However, the distributed nature of federated learning makes it vulnerable to poisoning attacks, which can compromise the integrity of the trained model. The report proposes two novel optimization-based attacking methods using particle swarm optimization algorithms, which significantly downgrade the prediction accuracy of the global model even with a small portion of poisoned data. The research focuses on traffic sign recognition systems in CAVs. [Extracted from the article]

A recent study conducted by researchers from Sichuan University in China explores the use of machine learning and deep learning algorithms in the diagnosis of skin diseases. The study reviews existing research on the topic, focusing on recent advancements in deep learning methods. The findings suggest that deep learning techniques are more effective than traditional machine learning algorithms in dermatological diagnosis, but there is still room for improvement in terms of algorithm accuracy. The study also highlights the need for more diverse datasets and a broader focus on pigmented dermatology research in future studies. [Extracted from the article]

A new report discusses research conducted at the Sichuan Cancer Hospital & Institute in China on lung cancer. The researchers aimed to develop a mapping algorithm to generate the Short Form Six-Dimension (SF-6D) utility score based on the Functional Assessment of Cancer Therapy-Lung (FACT-L) of lung cancer patients. Data was collected from 625 lung cancer patients in mainland China, and various statistical techniques were used to derive mapping algorithms. The research concluded that the algorithm allows for the calculation of quality-adjusted life years for cost-utility analyses of lung cancer. [Extracted from the article]

A report from researchers at Taiyuan University of Technology in Jinzhong, China, discusses the importance of protecting sensitive medical images during transmission, particularly in light of the COVID-19 pandemic. The researchers propose a zero-watermarking encryption scheme with an authentication function based on multimodal medical image fusion. They also introduce an improved three-dimensional Chebyshev and ICMIC closed-loop coupling mapping (3D-CICCM) for medical image encryption. Experimental results support the effectiveness of this scheme. The research has been peer-reviewed and can be found in The Journal of Supercomputing. [Extracted from the article]

Researchers at Yancheng Teachers University in China have developed a new color image-encryption algorithm to enhance the security of digital images. The algorithm utilizes extended DNA coding, Zig-Zag transform, and a fractional-order laser system to encrypt the images. The researchers analyzed the chaotic sequences generated by the laser system and used them to design the encryption algorithm. The algorithm was found to have strong encryption capabilities and can withstand multiple decryption methods. This research contributes to the field of information and data encoding and encryption, ensuring the integrity and secure communication of digital images. [Extracted from the article]

Keywords: Algorithms; Computed Tomography; Imaging Technology; Life Sciences; Mathematical Biosciences and Engineering; Technology EN Algorithms Computed Tomography Imaging Technology Life Sciences Mathematical Biosciences and Engineering Technology 2023 FEB 18 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- New research on mathematical biosciences and engineering is the subject of a new report. Algorithms, Computed Tomography, Imaging Technology, Life Sciences, Mathematical Biosciences and Engineering, Technology. [Extracted from the article]

The article acknowledges ByteDance, an Internet technology firm based in China, for the development of artificial intelligence-powered algorithms responsible for the mechanics of its video app TikTok. Topics discussed include popularity of TikTok among the Generation Z pre-teenagers, impact on the operations of ByteDance in the field of AI, and AI developments under the administration of its founder, former Microsoft engineer Zhang Yiming.