Robust and secure zero-watermarking algorithm for medical images based on Harris-SURF-DCT and chaotic map

To protect the patient information in medical images, this article proposes a robust watermarking algorithm for medical images based on Harris-SURF-DCT. First, the corners of the medical image are extracted using the Harris corner detection algorithm, and then, the previously extracted corners are described using the method of describing feature points in the SURF algorithm to generate the feature descriptor matrix. *en, the feature descriptor matrix is processed through the perceptual hash algorithm to obtain the feature vector of the medical image, which is a binary feature vector with a size of 32 bits. Secondly, to enhance the security of the watermark information, the logistic map algorithm is used to encrypt the watermark before embedding the watermark. Finally, with the help of cryptography knowledge, third party, and zero-watermarking technology, the algorithm can embed the watermark without modifying the medical image. When extracting the watermark, the algorithm can extract the watermark from the test image without the original image. In addition, the algorithm has strong robustness to conventional attacks and geometric attacks. Especially under geometric attacks, the algorithm performs better

Robust zero watermarking algorithm for medical images based on Zernike-DCT

Digital medical system not only facilitates the storage and transmission of medical information but also brings information security problems. Aiming at the security of medical images, a robust zero watermarking algorithm for medical images based on Zernike-DCT is proposed. *e algorithm first uses a chaotic logic sequence to preprocess and encrypt the watermark, then performs edge detection and Zernike moment processing on the original medical image to get the accurate edge points, and then performs discrete cosine transform (DCT) on them to get the feature vector. Finally, it combines perceptual Hash and zero watermark technology to generate the key to complete the watermark embedding and extraction. *e algorithm has good robustness to conventional and geometric attacks, strong antinoise ability, high positioning accuracy, and processing efficiency and is superior to the classical edge detection algorithm in extraction effect. It is a stable and reliable image edge detection algorithm

Time Series Analysis and Forecasting of Air Pollutants Based on Prophet Forecasting Model in Jiangsu Province, China

Due to recent developments in the global economy, transportation, and industrialization, air pollution is one of main environmental issues in the 21st century. The current study aimed to predict both short-term and long-term air pollution in Jiangsu Province, China, based on the Prophet forecasting model (PFM). We collected data from 72 air quality monitoring stations to forecast six air pollutants: PM10, PM2.5, SO2, NO2, CO, and O3. To determine the accuracy of the model and to compare its results with predicted and actual values, we used the correlation coefficient (R), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). The results show that PFM predicted PM10 and PM2.5 with R values of 0.40 and 0.52, RMSE values of 16.37 and 12.07 μg/m3, and MAE values of 11.74 and 8.22 μg/m3, respectively. Among other pollutants, PFM also predicted SO2, NO2, CO, and O3 with R values are between 5 μg/m3 to 12 μg/m3; and MAE values between 2 μg/m3 to 11 μg/m3. PFM has extensive power to accurately predict the concentrations of air pollutants and can be used to forecast air pollution in other regions. The results of this research will be helpful for local authorities and policymakers to control air pollution and plan accordingly in upcoming years

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Safe Decision Controller for Autonomous DrivingBased on Deep Reinforcement Learning inNondeterministic Environment

Autonomous driving systems are crucial complicated cyber–physical systems that combine physical environment awareness with cognitive computing. Deep reinforcement learning is currently commonly used in the decision-making of such systems. However, black-box-based deep reinforcement learning systems do not guarantee system safety and the interpretability of the reward-function settings in the face of complex environments and the influence of uncontrolled uncertainties. Therefore, a formal security reinforcement learning method is proposed. First, we propose an environmental modeling approach based on the influence of nondeterministic environmental factors, which enables the precise quantification of environmental issues. Second, we use the environment model to formalize the reward machine’s structure, which is used to guide the reward-function setting in reinforcement learning. Third, we generate a control barrier function to ensure a safer state behavior policy for reinforcement learning. Finally, we verify the method’s effectiveness in intelligent driving using overtaking and lane-changing scenarios

Interpretable Machine Learning for Personalized Medical Recommendations: A LIME-Based Approach

Chronic diseases are increasingly major threats to older persons, seriously affecting their physical health and well-being. Hospitals have accumulated a wealth of health-related data, including patients’ test reports, treatment histories, and diagnostic records, to better understand patients’ health, safety, and disease progression. Extracting relevant information from this data enables physicians to provide personalized patient-treatment recommendations. While collaborative filtering techniques and classical algorithms such as naive Bayes, logistic regression, and decision trees have had notable success in health-recommendation systems, most current systems primarily inform users of their likely preferences without providing explanations. This paper proposes an approach of deep learning with a local interpretable model–agnostic explanations (LIME)-based interpretable recommendation system to solve this problem. Specifically, we apply the proposed approach to two chronic diseases common in older adults: heart disease and diabetes. After data preprocessing, we use six deep-learning algorithms to form interpretations. In the heart-disease data set, the actual model recommendation of multi-layer perceptron and gradient-boosting algorithm differs from the local model’s recommendation of LIME, which can be used as its approximate prediction. From the feature importance of these two algorithms, it can be seen that the CholCheck, GenHith, and HighBP features are the most important for predicting heart disease. In the diabetes data set, the actual model predictions of the multi-layer perceptron and logistic-regression algorithm were little different from the local model’s prediction of LIME, which can be used as its approximate recommendation. Moreover, from the feature importance of the two algorithms, it can be seen that the three features of glucose, BMI, and age were the most important for predicting heart disease. Next, LIME is used to determine the importance of each feature that affected the results of the calculated model. Subsequently, we present the contribution coefficients of these features to the final recommendation. By analyzing the impact of different patient characteristics on the recommendations, our proposed system elucidates the underlying reasons behind these recommendations and enhances patient trust. This approach has important implications for medical recommendation systems and encourages informed decision-making in healthcare

An Extended-Tag-Induced Matrix Factorization Technique for Recommender Systems

Social tag information has been used by recommender systems to handle the problem of data sparsity. Recently, the relationships between users/items and tags are considered by most tag-induced recommendation methods. However, sparse tag information is challenging to most existing methods. In this paper, we propose an Extended-Tag-Induced Matrix Factorization technique for recommender systems, which exploits correlations among tags derived by co-occurrence of tags to improve the performance of recommender systems, even in the case of sparse tag information. The proposed method integrates coupled similarity between tags, which is calculated by the co-occurrences of tags in the same items, to extend each item’s tags. Finally, item similarity based on extended tags is utilized as an item relationship regularization term to constrain the process of matrix factorization. MovieLens dataset and Book-Crossing dataset are adopted to evaluate the performance of the proposed algorithm. The results of experiments show that the proposed method can alleviate the impact of tag sparsity and improve the performance of recommender systems

Advance hybrid medical watermarking algorithm using speeded up robust features and discrete cosine transform.

In the continuous development of computer network technology, multimedia technology and information technology, digitization has become the main means of displaying information, thus facilitating the storage, copying and dissemination of digital multimedia information. In this context, there are no restrictions on arbitrary editing, copying, modification, and dissemination of digital images, music, etc., which leads to various social problems such as information security, copyright disputes, and piracy. With the advancement of networks and multimedia, digital watermarking technology has received worldwide attention as an effective method of copyright protection. Improving the anti-geometric attack ability of digital watermarking algorithms using image feature-based algorithms have received extensive attention. This paper proposes a novel robust watermarking algorithm based on SURF-DCT perceptual hashing (Speeded Up Robust Features and Discrete Cosine Transform), namely blind watermarking. The algorithm firstly uses the affine transformation with a feature matrix and chaotic encryption technology to preprocess the watermark image, enhance the confidentiality of the watermark, and perform block and DCT coefficients extraction on the carrier image, and then uses the positive and negative quantization rules to modify the DCT coefficients. The embedding of the watermark is completed, and the blind extraction of the watermark realized. Experiments show that the algorithm has good invisibility and strong robustness against conventional and geometric attacks and can effectively protect the security of images with NC value more than 90%

Strategy for assessment of disaster risk using typhoon hazards modeling based on chlorophyll-a content of seawater

Abstract With deterioration of the global climate situation, the frequency and uncertainty of typhoons are the major causes of their hazards in tropical coastal regions, both in terms of loss of life and economic damage. Heavy rainfall triggers massive landslides and devastating flash floods, which can entail tremendous damage. In typhoon-affected areas, the key issue is to estimate the disaster zone and to help ships avoid disaster areas in the sea. Existing studies on typhoon disasters are mainly based on the overall wind assessment or the route prediction of the typhoon, with less attention to the detailed impact in different regions along the route. We propose in this paper a new framework to assess regional disaster risk based on chlorophyll-a concentration inversion in seawater. To calculate the concentration of chlorophyll-a, we analyze Landsat-8 satellite images in typhoon weather and normal weather in the same area. The experiments on realistic samples show that our approach has major potential to improve safety via assessing the impact of a typhoon in different regions based on the level of chlorophyll-a concentration