Noise Assisted Image Encryption and Decryption using 2-D Chaotic System

In this paper, a new two-dimensional (2-D) chaos-based color image encryption and decryption scheme is proposed in which the noise signal is selected randomly to set the initial values for a chaotic system which also enhances the security of the system. The 256-bit hash value of noise is transformed into one-time initial values for the state variables of this proposed chaotic system. XOR operation is further carried out to diffuse the pixels. Finally, statistical and security analyses are performed for understanding the effectiveness of the proposed system. Experimental results confirm that the proposed chaos-based cryptosystem is efficient and suitable for information (image) transmission in a highly secured way.

Powerful and Novel Tumour Detection in Brain MRI Images Employing Hybrid Computational Techniques

Brain tumour detection is an evergreen topic to attract attention in the examination field of Information Technology innovation with biomedical designing, in view of the gigantic need of proficient and viable strategy for assessment of enormous measure of information. Image segmentation is considered as one of the most vital systems for visualizing tissues in an individual. To robotize image segmentation, we have proposed a calculation to get global optimal thresholding esteem for a specific brain MRI image, utilizing OTSU+Sauvola binarization strategy. The fundamental reason for feature collection is to diminish the quantity of structures utilized in classification while keeping up satisfactory classification exactness. One of the most extra-customary procedures applied for feature extraction is Discrete Wavelet Transform (DWT). Adequately it anticipates the estimation space on a plane to such an extent that the fluctuation of the information is ideally protected. We propose a justifiable model for brain tumours discovery and classification i.e., to classify whether the tumour is benign or malignant, utilizing SVM classification. SVM utilized here deals with basic hazard minimization to group the images for the tumour extraction, and a Graphical User Interface is created for the tumour classification operation, using the MATLAB platform.

Automatic Translation of Medical Information Based on Text Feature Recognition

In order to improve the effect of the medical information automatic translation system, based on the text feature recognition technology of medical image, this paper constructs an automatic translation system that can recognize medical images. On the basis of the medical information image retrieval based on the combination of text information and visual information, this paper uses automatic image annotation technology and semantic similarity calculation method to extract the text and semantic features of medical information images. Then, this paper uses the inherent multi-information fusion capability of the Bayesian inference network to fuse the text features of medical information images and the semantic features of image content together to realize medical information image retrieval. Finally, this paper designs experiments to test the performance of the medical information automatic translation system. The research shows that the system constructed in this paper has certain effects.

Research on Mathematical Model of Smart Service for the Elderly in Small- and Medium-Sized Cities Based on Image Processing

Image processing technology is to use computer, camera, and other technologies to calculate and process images and make the image clearer and convenient for quick extraction of information. Image processing technology has entered an all-round development stage. It also plays a great role in the components of the intelligent service model for the aged. Now many countries in the world have entered the aging stage, but old-age equipment is relatively backward and personnel management is not standardized. Based on these problems, this paper studies the intelligent model of old-age care in small- and medium-sized cities by using the image recognition method. Based on the analysis of the present situation of intelligent old-age care, an intelligent system is proposed, which solves the problems of defects in old-age care facilities and insufficient comprehensive management of medical staff in some small- and medium-sized cities. This system has RTID positioning system and APP client, which can ensure the privacy of the elderly. Through real-time identification of images in the elderly service, the rationality and layout optimization of existing old-age facilities are analyzed. The mathematical model is used to detect the regularity of participants’ daily activities. The image experiment results show that the prediction accuracy is over 90%, and the optimal prediction effect is obtained. In addition, a questionnaire survey was conducted among many elderly people over 50 years old to investigate their willingness to use smart old-age products.

Feature Extraction Model with Group-Based Classifier for Content Extraction from Video Data

In today's PC illustration, numerous object locations of videos are quite critical duties to accomplish. Swiftly and reliably recognising and distinguishing the multiple aspects of a video is a crucial attribute for collaborating with one's condition (object). The core issue is that in theory, to ensure that no significant aspect is missing; all aspects of a content in a video must be scanned for elements on various different scales. It requires some investment and effort anyway, to really arrange the substance of a given content region and both time and computational limits that an operator can spend on classification are constrained. Two presumption procedures for accelerating the standard identifier are performed by the proposed method and demonstrate their capability by performing both identification efficiency and velocity. The main enhancement of our group-based classifier focuses on accelerating the grouping of sub features by planning the problem as a selection procedure for consecutive features. The subsequent improvement gives better multiscale features to distinguish objects of all sizes without rescaling the information image from a video. Extracting contents from video is an assortment of successive images with a steady time interim. So video can give more data about contents in it when situations are changing regarding time. Along these lines, physically taking care of contents with features are very unimaginable. In the proposed work, it is suggested that a Group-based Video Content Extraction Classifier (GbCCE) extracts content from a video by extracting relevant features using a group-based classifier. The proposed method is distinct from conventional approaches and the findings indicate that better output is demonstrated by the proposed method.

Application of B-ultrasound information image in Renal Puncture Biopsy treatment and Nursing

Objectives: This study was to explore the application value of B-ultrasound in guiding puncture biopsy of chronic kidney disease (CKD) and the clinical nursing effects under the guidance of B-ultrasound. Methods: Pathological examination of kidney biopsy was performed on 94 patients with CKD under the guidance of ultrasound from August 2020 to December 2020.; patients were observed for symptoms such as low back pain, backache, hematuria, and subcapsular hematoma. Color Doppler ultrasonography was performed on the punctured patients on day 1, 2, and 3 to observe whether there was subrental hematoma. The pathological results were analyzed and the success rate of percutaneous renal biopsy under ultrasound guidance was analyzed. Before the patient was discharged, investigate the satisfaction with the nursing work. Results: (1) After the puncture, 45 patients developed low back pain and low back pain symptoms, 12 cases developed subcapsular hematoma; 8 cases showed gross hematuria, 62 cases showed microscopic hematuria, and the rest had no obvious symptoms; (2) the nursing satisfaction rate of 94 cases was as high as 95.7%. Conclusion: US-PRB is a safe and effective auxiliary examination method, which can improve the success rate of puncture and reduce postoperative complications. Effective nursing can reduce the incidence of postoperative complications and improve patient satisfaction. doi: https://doi.org/10.12669/pjms.37.6-WIT.4831 How to cite this:Dong L, Li J, Zhao M, Ren J. Application of B-ultrasound information image in Renal Puncture Biopsy treatment and Nursing. Pak J Med Sci. 2021;37(6):1564-1568. doi: https://doi.org/10.12669/pjms.37.6-WIT.4831 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

VISUALIZATION OF THE IMAGE OF THE KETS AS A MODERN CULTURAL PRACTICE

Modern ethnocultural processes taking place among the indigenous peoples of the northern and Arctic territories, the current state and issues of broadcasting and preserving their ethnic identity are one of the topical research topics of the world scientific community. The attention to the Ket ethnocultural group is justified by its uniqueness as the last representatives of the Yenisei group of peoples. Research immersion into the foundations of the formation of the modern ethnocultural image of the Kets, including in the information and media space, reveals the need to refer to the cultural memory of the Kets, the basic foundations of their ethnocultural identification. Thus, the study of the representation of Kets in the media space makes it possible to fix the modern information image and visualization features of the Ket ethnocultural group.

Artificial intelligence data-driven 3D model for AIS

Scoliosis is a 3D deformation of the spinal column, characterized by a lateral deviation of the spine, accompanied by axial rotation of the vertebrae. Adolescent Idiopathic Scoliosis (AIS), is the most common type, affecting children between ages 8 to 18 when bone growth is at its maximum rate. The selection of the most appropriate treatment options is based on the surgeon’s experience. So, developing a clinically validated patient-specific model of the spine would aid surgeons in understanding AIS in early stages and propose an efficient method of treatment for the individual patient. This project steps include: Developing a clinically validated patient-specific Reduced Order Finite Element Model (ROFEM) of the spine, predicting AIS progression using data mining and proposing a method of treatment. First we implement FE synergistically with bio-mechanical information, image processing and data science techniques to improve predictive ability. Initial geometry of the spine will be extracted from the x-ray images from different planes and imported to FEM software to generate the spine model and perform analysis. A RO model is developed based on the detailed spinal FEM. Next, a neural network is used to predict the spinal curvature. The ability to predict the severity of AIS will have an immense impact on the treatment of AIS-affected children. Access to a predictive and patient-specific model will enable the physicians to have a better understanding of spinal curvature progression. Consequently, the physicians will be able to educate families, choose the most appropriate treatment option and asses for surgical intervention.

Toward Improving Image Retrieval via Global Saliency Weighted Feature

For full description of images’ semantic information, image retrieval tasks are increasingly using deep convolution features trained by neural networks. However, to form a compact feature representation, the obtained convolutional features must be further aggregated in image retrieval. The quality of aggregation affects retrieval performance. In order to obtain better image descriptors for image retrieval, we propose two modules in our method. The first module is named generalized regional maximum activation of convolutions (GR-MAC), which pays more attention to global information at multiple scales. The second module is called saliency joint weighting, which uses nonparametric saliency weighting and channel weighting to focus feature maps more on the salient region without discarding overall information. Finally, we fuse the two modules to obtain more representative image feature descriptors that not only consider the global information of the feature map but also highlight the salient region. We conducted experiments on multiple widely used retrieval data sets such as roxford5k to verify the effectiveness of our method. The experimental results prove that our method is more accurate than the state-of-the-art methods.

2. Simondon as a Neo-Scholastic?

Johan Fredrikzon spent one and a half years as a visiting research assistant at the Film and Media Studies Program at Yale University 2018/2019. Some months before he arrived, a two-day workshop on Simondon was held by the Yale-Düsseldorf Working Group on Philosophy and Media, titled Modes of Technical Objects, with scholars from the US and Germany. Fredrikzon decided to engage a few of the workshop participants for this special issue of Sensorium, with the purpose to discuss perspectives on Simondon as a theoretical instrument for thinking technology, how the French philosopher matters in their work, and why there seems to be a revival in the interest in the writing of Simondon these days. On behalf of the Sensorium journal, the interviewer would like to thank the three interviewees for their generous participation. About John Durham Peters: John Durham Peters is María Rosa Menocal Professor of English and of Film & Media Studies at Yale University. Peters has been a creative force in media studies for many years and his thinking continues to influence academic environments throughout the world. His book The Marvelous Clouds: Toward a Philosophy of Elemental Media (Chicago, 2015) was an attempt to rethink the concept of media by including weather, dolphins and fire to the infrastructural landscape of digital communications and climate change. His new book, in cooperation with Kenneth Cmiel, is called Promiscuous Knowledge: Information, Image, and Other Truth Games in History (Chicago, 2020).