Malaria in the rural and remote regions of tropical countries remain a major public health challenge. Early diagnosis and prompt effective treatment are the basis for the management of malaria and for reducing malaria mortality and morbidity worldwide and the key to malaria elimination. While Rapid Diagnostic Test (RDT) remains the current mainstay testing malaria infections, it is usually used in conjunction with clinical findings and lab tests of blood films through Microscopy- the gold standard of malaria diagnosis. Recent reports suggest that the accuracy of RDTs could be compromised due to parasite antigen gene deletion(s), and the lack of expertise and high turnover time makes microscopy impractical to be used in rural and remote areas which impede the diagnosis and treatment of the disease. Delay in receiving treatment for uncomplicated malaria is reported to increase the risk of developing severe malaria and mortality. Thus, the need to develop advanced, faster, and smarter tools for malaria diagnosis is paramount, specially to reinforce the gold standard method, i.e., malaria microscopy which is a full-proof tool given the limitations be addressed. Deep learning-based methods have proven to provide human expert level performance on object detection/classification on image data. Such methods can be utilized for automation of repetitive task in assessing large number of microscope images of blood samples. In this paper, we propose a novel approach to improve the performance of deep learning models through consistent labelling of ground truth bounding box for the task of pathogen detection on microscope images of thick blood smears. Recommendations are made on the reliability and repeatability testing of the trained models. A custom deep learning architecture (YOLO-mp) is developed based on the design criteria of optimizing accuracy and speed of detection with minimal resources. The custom three-layered YOLO-mp-3l and four-layered YOLO-mp-4l models achieved the best mAP scores of 93.99 (@IoU=0.5) and 94.07 (@IoU=0.5), respectively outperforming standard YOLOv4 (mAP 92.56 @IoU=0.5) for detection of malaria pathogen on a public dataset of thick blood smear microscope images captured using phone camera. YOLO-mp-3l (BFLOPs =21.8, model size =24.5Mb) and YOLO-mp-4l (BFLOPs=24.477, model size =25.4Mb) outperformed standard YOLOv4 (BFLOPs=127.232, model size = 244Mb) in terms of computation and memory requirements proving them suitable to run on low resource devices.

The data management process is characterised by a set of tasks where data quality management (DQM) is one of the core components. Data quality, however, is a multidimensional concept, where the nature of the data quality issues is very diverse. One of the most widely anticipated data quality challenges, which becomes particularly vital when data come from multiple data sources which is a typical situation in the current data-driven world, is duplicates or non-uniqueness. Even more, duplicates were recognised to be one of the key domain-specific data quality dimensions in the context of the Internet of Things (IoT) application domains, where smart grids and health dominate most. Duplicate data lead to inaccurate analyses, leading to wrong decisions, negatively affect data-driven and/or data processing activities such as the development of models, forecasts, simulations, have a negative impact on customer service, risk and crisis management, service personalisation in terms of both their accuracy and trustworthiness, decrease user adoption and satisfaction, etc. The process of determination and elimination of duplicates is known as deduplication, while the process of finding duplicates in one or more databases that refer to the same entities is known as Record Linkage. To find the duplicates, the data sets are compared with each other using similarity functions that are usually used to compare two input strings to find similarities between them, which requires quadratic time complexity. To defuse the quadratic complexity of the problem, especially in large data sources, record linkage methods, such as blocking and sorted neighbourhood, are used. In this paper, we propose a six-step record linkage deduplication framework. The operation of the framework is demonstrated on a simplified example of research data artifacts, such as publications, research projects and others of the real-world research institution representing Research Information Systems (RIS) domain. To make the proposed framework usable we integrated it into a tool that is already used in practice, by developing a prototype of an extension for the well-known DataCleaner. The framework detects and visualises duplicates thereby identifying and providing the user with identified redundancies in a user-friendly manner allowing their further elimination. By removing the redundancies, the quality of the data is improved therefore improving analyses and decision-making. This study makes a call for other researchers to take a step towards the “golden record” that can be achieved when all data quality issues are recognised and resolved, thus moving towards absolute data quality.

Data migration is required to run data-intensive applications. Legacy data storage systems are not capable of accommodating the changing nature of data. In many companies, data migration projects fail because their importance and complexity are not taken seriously enough. Data migration strategies include storage migration, database migration, application migration, and business process migration. Regardless of which migration strategy a company chooses, there should always be a stronger focus on data cleansing. On the one hand, complete, correct, and clean data not only reduce the cost, complexity, and risk of the changeover, it also means a good basis for quick and strategic company decisions and is therefore an essential basis for today’s dynamic business processes. Data quality is an important issue for companies looking for data migration these days and should not be overlooked. In order to determine the relationship between data quality and data migration, an empirical study with 25 large German and Swiss companies was carried out to find out the importance of data quality in companies for data migration. In this paper, we present our findings regarding how data quality plays an important role in a data migration plans and must not be ignored. Without acceptable data quality, data migration is impossible.

Chinese calligraphy, revered globally for its therapeutic and mindfulness benefits, encompasses styles such as regular (Kai Shu), running (Xing Shu), official (Li Shu), and cursive (Cao Shu) scripts. Beginners often start with the regular script, advancing to more intricate styles like cursive. Each style, marked by unique historical calligraphy contributions, requires learners to discern distinct nuances. The integration of AI in calligraphy analysis, collection, recognition, and classification is pivotal. This study introduces an innovative convolutional neural network (CNN) architecture, pioneering the application of CNN in the classification of Chinese calligraphy. Focusing on the four principal calligraphy styles from the Tang dynasty (690–907 A.D.), this research spotlights the era when the traditional regular script font (Kai Shu) was refined. A comprehensive dataset of 8282 samples from these calligraphers, representing the zenith of regular style, was compiled for CNN training and testing. The model distinguishes personal styles for classification, showing superior performance over existing networks. Achieving 89.5–96.2% accuracy in calligraphy classification, our approach underscores the significance of CNN in the categorization of both font and artistic styles. This research paves the way for advanced studies in Chinese calligraphy and its cultural implications.

The identification of malaria infection using microscope images of blood smears is considered as a ‘gold standard’. The diagnosis of malaria needs expert microscopists which are scarce in remote areas where malaria is endemic. Therefore, it is desirable to automate the repetitive task of pathogen detection in the blood samples received as microscope images. This study provides an easy to use and deploy method for implementing a malaria pathogen detection software- the Intelligent Suite. The Intelligent Suite features a graphical user interface (GUI) implemented using ‘cvui’ library to interact with the OpenVINO’s inference engine for model optimisation and deployment across several inference devices. The intelligent Suite uses a custom YOLO-mp-3l model trained on Darknet framework for detection of malaria pathogen in thick smear microscope images. Moreover, the Intelligent Suite provides user interface for inference device/mode selection, alter model parameters, and generate detection reports along with the model performance metrics. The Intelligent Suite was executed on a CPU computer with model inference running on a plug-and-play Neural Compute Stick (NCS2) and performance reported.