Medical health systems have been concentrating on artificial intelligence techniques for speedy diagnosis. However, the recording of health data in a standard form still requires attention so that machine learning can be more accurate and reliable by considering multiple features. The aim of this study is to develop a general framework for recording diagnostic data in an international standard format to facilitate prediction of disease diagnosis based on symptoms using machine learning algorithms. Efforts were made to ensure error-free data entry by developing a user-friendly interface. Furthermore, multiple machine learning algorithms including Decision Tree, Random Forest, Naive Bayes and Neural Network algorithms were used to analyze patient data based on multiple features, including age, illness history and clinical observations. This data was formatted according to structured hierarchies designed by medical experts, whereas diagnosis was made as per the ICD-10 coding developed by the American Academy of Ophthalmology. Furthermore, the system is designed to evolve through self-learning by adding new classifications for both diagnosis and symptoms. The classification results from tree-based methods demonstrated that the proposed framework performs satisfactorily, given a sufficient amount of data. Owing to a structured data arrangement, the random forest and decision tree algorithms’ prediction rate is more than 90% as compared to more complex methods such as neural networks and the naïve Bayes algorithm.

From an individual’s perspective, technological advancement has merits and demerits. Video captured by surveillance cameras while a person goes about their daily life may improve their personal safety but the images collected may also represent an invasion of their privacy. Because of the ease of digital information sharing, there exists a need to protect that visual information from illegal utilization by untrusted parties. The European parliament has ratified the General Data Protection Regulation (GDPR), which has been effective since May 2018 with a view to ensuring the privacy of European Union (EU) citizens’ and visitors’ personal data. The regulation has introduced data safeguards through Pseudonymisation, Encryption, and Data protection-by-design. However, the regulation does not assist with technical and implementation procedures, such as video redaction, to establish those safeguards. This paper refers to the GDPR term “personal data” as “visual personal data” and aims to discuss regulatory safeguards of visual privacy, such as reversible protection, from the technological point-of-view. In the context of GDPR, the roles of machine learning (i.e. within computer vision), image processing, cryptography, and blockchain are explored as a way of deploying Data Protection-by-Design solutions for visual surveillance data. The paper surveys the existing market-based data protection solutions and provides suggestions for the development of GDPR-compliant Data Protection-by-Design video surveillance systems. The paper is also relevant for those entities interacting with EU citizens from outside the EU and for those regions not currently covered by such regulation that may soon implement similar provisions.

Instrumentation and methods for rapid screening and selection of plants with improved water use efficiency are essential to address current issues of global food and fuel security. A new imaging system that combines chlorophyll fluorescence and thermal imaging has been developed to generate images of assimilation rate (A), stomatal conductance (g_s), and intrinsic water use efficiency (WUE_i) from whole plants or leaves under controlled environmental conditions. This is the first demonstration of the production of images of WUE_i and the first to determine images of g_s from themography at the whole-plant scale. Data are presented illustrating the use of this system for rapidly and non-destructively screening plants for alterations in WUE_i by comparing Arabidopsis thaliana mutants (OST1-1) that have altered WUE_i driven by open stomata, with wild-type plants. This novel instrument not only provides the potential to monitor multiple plants simultaneously, but enables intra- and interspecies variation to be taken into account both spatially and temporally. The ability to measure A, g_s, and WUE_i progressively was developed to facilitate and encourage the development of new dynamic protocols. Images illustrating the instrument’s dynamic capabilities are demonstrated by analysing plant responses to changing photosynthetic photon flux density (PPFD). Applications of this system will augment the research community’s need for novel screening methods to identify rapidly novel lines, cultivars, or species with improved A and WUE_i in order to meet the current demands on modern agriculture and food production.

For a number of years, scientists have been trying to develop aids that can make visually impaired people more independent and aware of their surroundings. Computer-based automatic navigation tools are one example of this, motivated by the increasing miniaturization of electronics and the improvement in processing power and sensing capabilities. This paper presents a complete navigation system based on low cost and physically unobtrusive sensors such as a camera and an infrared sensor. The system is based around corners and depth values from Kinect’s infrared sensor. Obstacles are found in images from a camera using corner detection, while input from the depth sensor provides the corresponding distance. The combination is both efficient and robust. The system not only identifies hurdles but also suggests a safe path (if available) to the left or right side and tells the user to stop, move left, or move right. The system has been tested in real time by both blindfolded and blind people at different indoor and outdoor locations, demonstrating that it operates adequately.

Using dynamic surveillance cameras for security has significantly increased the privacy concerns for captured individuals. Malicious users may misuse these videos by performing Replay and/or Man-in-the-Middle attacks during storage or recording over the network. Considering these risks, this paper proposes an effective security application SecureCam based on selective detection (focused moving objects) and protection using encryption. For object detection, this paper implements a novel low computational unsupervised learning algorithm i.e. Motion-Fusion (MF) for more precise motion detection in the mobile camera videos. After that, selective encryption (SE) is applied by the lightweight Chacha20 cipher to the detected video parts. Proposed SecureCam is extensively evaluated based on performance analysis, security analysis and computational complexity. For object detection, the comparative evaluation shows that the MF algorithm outperforms traditional state-of-the-art dense optical flow (DOF) algorithm with an average (mean) difference increase: in the accuracy of 54%; and in the precision of 42% making it computationally effective for such videos. The visual results with 21% encryption space ratio (ESR) indicate that the videos are sufficiently protected against identification. Overall comparative evaluation with existing approaches also affirm the significance and utility of proposed SecureCam for internet of multimedia things (IoMT) environment.