Expressive Color Visual Secret Sharing with Color to Gray & Back and Cosine Transform

Color Visual Secret Sharing (VSS) is an essential form of VSS. It is so because nowadays, most people like to share visual data as a color image. There are color VSS schemes capable of dealing with halftone color images or color images with selected colors, and some dealing with natural color images, which generate low quality of recovered secret. The proposed scheme deals with a color image in the RGB domain and generates gray shares for color images using color to gray and back through compression. These shares are encrypted into an innocent-looking gray cover image using a Discrete Cosine Transform (DCT) to make meaningful shares. Reconstruct a high-quality color image through the gray shares extracted from an innocent-looking gray cover image. Thus, using lower bandwidth for transmission and less storage

Bibliometric analysis of emerging technologies in the field of computer science helping in ovarian cancer research

This study is carried out to provide an analysis of the literature available at the intersection of ovarian cancer and computing. A comprehensive search was conducted using Scopus database for English-language peer-reviewed articles. The study administers chronological, domain clustering and text analysis of the articles under consideration to provide high-level concept map composed of specific words and the connections between them

ANALYSIS OF RECENT TRENDS IN MALWARE ATTACKS ON ANDROID PHONE: A SURVEY USING SCOPUS DATABASE

In past few years, smartphone use has shifted from professional access to personal need. Smartphone has now become an essential requirement to perform day to day activities. This has made smartphones unsecured and vulnerable to cyber threats and malware attacks. This study is also focused on finding intersection between malware attacks and Android OS considering Android as the most widely used mobile OS. A comprehensive search is conducted on Scopus Database for peer-reviewed articles. The study is carried out on bibliometric data of the considered articles to generate a highly useful concept map

Bibliometric Analysis of Machine Learning and Text Mining Algorithms for Diagnosis of Leukemia

Bibliometric analysis of leukemia area was carried out using information about publications from Scopus. The most productive journals, countries and authors were determined. The most frequently cited article and its citation history was described. A bibliometric map based on a citation network among countries was constructed

Blockchain-based Healthcare Portal – A Bibliometric Analysis

User privacy has been a topmost priority and one such domain where this is neglected is the area of healthcare. Our solution focuses on the idea of using blockchain to provide a platform for healthcare experts and patients, giving patients full control over the data that will be shared. This paper focuses on identifying current research that has been conducted in this area in the form of a bibliometric analysis. A bibliometric study on a research area involves a detailed analysis of citations and papers across a domain of study. The purpose of this study is a statistical analysis of publications which is so complex that it is close to impossible to understand trends merely based on knowledge and experience. There are specific tools required to recognize these trends based on the bibliometric data. This paper will give an outlook on the areas of blockchain that were explored by various papers, the criteria and pattern followed by the combination of papers, and a cumulative statistical analysis of the papers that were fetched for the purpose of our study from the Scopus database. The most popular visualization software tool VOSviewer was taken into use

Analysis of Recent Trends in Continuous Sign Language Recognition using NLP

Oralism is an ideology and practice that advocates communication that is based solely on speech. This practice is encouraged from a pretty early age in our country. As a consequence, the hard of hearing are constantly forced to negotiate with schools, colleges, organisations, workspaces, and families that don’t acknowledge the need and preference for sign language over oral languages. This results in inconsideration of an entire community for admissions, jobs and general social position. We aim to close that communication gap a little and take a step towards fighting the stigma associated with Sign Language. The aim is to provide a system for efficient communication with the deaf

An explainable AI-assisted web application in cancer drug value prediction

In recent years, there has been an increase in the interest in adopting Explainable Artificial Intelligence (XAI) for healthcare. The proposed system includes • An XAI model for cancer drug value prediction. The model provides data that is easy to understand and explain, which is critical for medical decision-making. It also produces accurate projections. • A model outperformed existing models due to extensive training and evaluation on a large cancer medication chemical compounds dataset. • Insights into the causation and correlation between the dependent and independent actors in the chemical composition of the cancer cell.While the model is evaluated on Lung Cancer data, the architecture offered in the proposed solution is cancer agnostic. It may be scaled out to other cancer cell data if the properties are similar. The work presents a viable route for customizing treatments and improving patient outcomes in oncology by combining XAI with a large dataset. This research attempts to create a framework where a user can upload a test case and receive forecasts with explanations, all in a portable PDF report

Data mining approaches to pneumothorax detection: Integrating mask-RCNN and medical transfer learning techniques

With the medical condition of pneumothorax, also known as collapsed lung, air builds up in the pleural cavity and causes the lung to collapse. It is a critical disorder that needs to be identified and treated right as it can cause breathing difficulties, low blood oxygen levels, and, in extreme circumstances, death. Chest X-rays are frequently used to diagnose pneumothorax. Using the Mask R-CNN model and medical transfer learning, the proposed work offers • A novel method for pneumothorax segmentation from chest X-rays. • A method that takes advantage of the Mask R-CNN architecture's for object recognition and segmentation. • A modified model to address the issue of segmenting pneumothoraxes and then polish it using a sizable dataset of chest X-rays.The proposed method is tested against other pneumothorax segmentation techniques using a dataset of ‘chest X-rays’ with ‘pneumothorax annotations. The test findings demonstrate that proposed method outperforms other cutting-edge techniques in terms of segmentation accuracy and speed. The proposed method could lead to better patient outcomes by increasing the precision and effectiveness of pneumothorax diagnosis and therapy. Proposed method also benefits other medical imaging activities by using the medical transfer learning approaches which increases the precision of computer-aided diagnosis and treatment planning

International Nosocomial Infection Control Consortium report, data summary of 50 countries for 2010-2015: Device-associated module

•We report INICC device-associated module data of 50 countries from 2010-2015.•We collected prospective data from 861,284 patients in 703 ICUs for 3,506,562 days.•DA-HAI rates and bacterial resistance were higher in the INICC ICUs than in CDC-NHSN's.•Device utilization ratio in the INICC ICUs was similar to CDC-NHSN's. Background: We report the results of International Nosocomial Infection Control Consortium (INICC) surveillance study from January 2010-December 2015 in 703 intensive care units (ICUs) in Latin America, Europe, Eastern Mediterranean, Southeast Asia, and Western Pacific. Methods: During the 6-year study period, using Centers for Disease Control and Prevention National Healthcare Safety Network (CDC-NHSN) definitions for device-associated health care-associated infection (DA-HAI), we collected prospective data from 861,284 patients hospitalized in INICC hospital ICUs for an aggregate of 3,506,562 days. Results: Although device use in INICC ICUs was similar to that reported from CDC-NHSN ICUs, DA-HAI rates were higher in the INICC ICUs: in the INICC medical-surgical ICUs, the pooled rate of central line-associated bloodstream infection, 4.1 per 1,000 central line-days, was nearly 5-fold higher than the 0.8 per 1,000 central line-days reported from comparable US ICUs, the overall rate of ventilator-associated pneumonia was also higher, 13.1 versus 0.9 per 1,000 ventilator-days, as was the rate of catheter-associated urinary tract infection, 5.07 versus 1.7 per 1,000 catheter-days. From blood cultures samples, frequencies of resistance of Pseudomonas isolates to amikacin (29.87% vs 10%) and to imipenem (44.3% vs 26.1%), and of Klebsiella pneumoniae isolates to ceftazidime (73.2% vs 28.8%) and to imipenem (43.27% vs 12.8%) were also higher in the INICC ICUs compared with CDC-NHSN ICUs. Conclusions: Although DA-HAIs in INICC ICU patients continue to be higher than the rates reported in CDC-NSHN ICUs representing the developed world, we have observed a significant trend toward the reduction of DA-HAI rates in INICC ICUs as shown in each international report. It is INICC's main goal to continue facilitating education, training, and basic and cost-effective tools and resources, such as standardized forms and an online platform, to tackle this problem effectively and systematically