Reversible data hiding (RDH) is a recently emerged research domain in the field of information security domain with broad applications in medical images and meta-data handling in the cloud. The amount of data required to handle the healthcare sector has exponentially increased due to the increase in the population. Medical images and various reports such as discharge summaries and diagnosis reports are the most common data in the healthcare sector. The RDH schemes are widely explored to embed the medical reports in the medical image instead of sending them as separate files. The receiver can extract the clinical reports and recover the original medical image for further diagnosis. This manuscript proposes an approach that uses a new lossless compression-based RDH scheme that creates vacant room for data hiding. The proposed scheme uses run-length encoding and a modified Elias gamma encoding scheme on higher-order bit planes for lossless compression. The conventional Elias gamma encoding process is modified in the proposed method to embed some additional data bits during the encoding process itself. The revised approach ensures a high embedding rate and lossless recovery of medical images at the receiver side. The experimental study is conducted on both natural images and medical images. The average embedding rate from the proposed scheme for the medical images is 0.75 bits per pixel. The scheme achieved a 0 bit error rate during image recovery and data extraction. The experimental study shows that the newly introduced scheme performs better when compared with the existing RDH schemes.
Road accidents caused due to drowsiness of the driver are quotidian. As per the World Health Organization global report, India has the highest number of road accidents, and about half or greater number are because of drowsy driving, and this has become a major issue. Real-time drowsiness detection models detect when the driver is feeling drowsy by monitoring behavioural aspects or by using physiological sensors. Though the use of bio-sensors gives more accurate results, they are intrusive and distract the driver. We have developed and implemented a behavioural-based drowsiness detection algorithm that monitors the movement of the face and closeness of eyes to detect and alert a drowsy driver. We successfully implemented our algorithm in Matlab-2020 software, where we took a live video from a webcam and processed each frame to classify it as either drowsy or not. We also tested on a dataset featuring live driving subjects and achieved 90% accuracy with 84% precision. If drowsiness is detected, a system audio alert is generated to alert the driver. In case eyes or face are not detected in a frame, we by default classified it as drowsy and produced the alert message because a false negative is more dangerous than a false positive, and thus attained a high recall of 98%.
Sign language has been used for a long time by deaf and mute people to communicate their thoughts and feelings. Since there is no universal sign language, the needy people use country-specific sign languages. For example, American Sign Language (ASL) is popularly used by Americans and Indian Sign Language (ISL) is commonly practised in India. Communication between two people who know the specific sign language is quite easy. But, if a mute person wants to communicate with another person who is not familiar with sign language, it is a difficult task, and a sign language interpreter is required to translate the signs. This issue motivated the computer scientist to work on automated sign language recognition systems that are capable of recognizing the signs from specific sign languages and converting them into text information or audio so that the common people can understand it easily. This chapter will be a useful reference for the researchers who are planning to start their research study in the domain of sign language recognition.
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