Human Activity and Motion Pattern Recognition within Indoor Environment Using Convolutional Neural Networks Clustering and Naive Bayes Classification Algorithms

Human Activity Recognition (HAR) systems are designed to read sensor data and analyse it to classify any detected movement and respond accordingly. However, there is a need for more responsive and near real-time systems to distinguish between false and true alarms. To accurately determine alarm triggers, the motion pattern of legitimate users need to be stored over a certain period and used to train the system to recognise features associated with their movements. This training process is followed by a testing cycle that uses actual data of different patterns of activity that are either similar or different to the training data set. This paper evaluates the use of a combined Convolutional Neural Network (CNN) and Naive Bayes for accuracy and robustness to correctly identify true alarm triggers in the form of a buzzer sound for example. It shows that pattern recognition can be achieved using either of the two approaches, even when a partial motion pattern is derived as a subset out of a full-motion path

Comprehensive Survey of Multimedia Steganalysis: Techniques, Evaluations, and Trends in Future Research

During recent years, emerging multimedia processing techniques with information security services have received a lot of attention. Among those trends are steganography and steganalysis. Steganography techniques aim to hide the existence of secret messages in an innocent-looking medium, where the medium before and after embedding looks symmetric. Steganalysis techniques aim to breach steganography techniques and detect the presence of invisible messages. In the modern world, digital multimedia such as audio, images, and video became popular and widespread, which makes them perfect candidates for steganography. Monitoring this huge multimedia while the user communicates with the outside world is very important for detecting whether there is a hidden message in any suspicious communication. However, steganalysis has a significant role in many fields, such as to extract the stego-message, to detect suspicious hidden messages and to evaluate the robustness of existing steganography techniques. This survey provides the general principles of hiding secret messages using digital multimedia as well as reviewing the background of steganalysis. In this survey, the steganalysis is classified based on many points of view for better understanding. In addition, it provides a deep review and summarizes recent steganalysis approaches and techniques for audio, images, and video. Finally, the existing shortcomings and future recommendations in this field are discussed to present a useful resource for future research

Comprehensive Survey of Multimedia Steganalysis: Techniques, Evaluations, and Trends in Future Research

During recent years, emerging multimedia processing techniques with information security services have received a lot of attention. Among those trends are steganography and steganalysis. Steganography techniques aim to hide the existence of secret messages in an innocent-looking medium, where the medium before and after embedding looks symmetric. Steganalysis techniques aim to breach steganography techniques and detect the presence of invisible messages. In the modern world, digital multimedia such as audio, images, and video became popular and widespread, which makes them perfect candidates for steganography. Monitoring this huge multimedia while the user communicates with the outside world is very important for detecting whether there is a hidden message in any suspicious communication. However, steganalysis has a significant role in many fields, such as to extract the stego-message, to detect suspicious hidden messages and to evaluate the robustness of existing steganography techniques. This survey provides the general principles of hiding secret messages using digital multimedia as well as reviewing the background of steganalysis. In this survey, the steganalysis is classified based on many points of view for better understanding. In addition, it provides a deep review and summarizes recent steganalysis approaches and techniques for audio, images, and video. Finally, the existing shortcomings and future recommendations in this field are discussed to present a useful resource for future research

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population