AI-Based IoT Analytics on the Cloud for Diabetic Data Management System

It is very evident by looking at the current technological advancements that the interrelation and association of artificial intelligence (AI) and IoT in the Cloud have transformed the way healthcare has been working. AI and Cloud-empowered IoT boosts operational efficiency enhanced risk management. This combination creates products and services by enhancing the existing products while increasing scalability. To reduce costs, data analytics on the Cloud is much preferred in the current formation of technologies. This chapter focuses on the integration of different AI techniques in Cloud datasets for IoT data analytics. Analyzing, predicting, and making decisions by comparing the current data with historical data. The theory of AI-based IoT analytics will be much investigated with a healthcare application. Different approaches to implementing data analytics on the Cloud for a diabetic management system will be explored (human body). Finally, future trends and possible areas of research are also discussed.

Retrospective Analysis of Six Years of Acute Flaccid Paralysis Surveillance and Polio Vaccine Coverage Reported by Italy, Serbia, Bosnia and Herzegovina, Montenegro, Bulgaria, Kosovo, Albania, North Macedonia, Malta, and Greece

Here we analyzed six years of acute flaccid paralysis (AFP) surveillance, from 2015 to 2020, of 10 countries linked to the WHO Regional Reference Laboratory, at the Istituto Superiore di Sanità, Italy. The analysis also comprises the polio vaccine coverage available (2015–2019) and enterovirus (EV) identification and typing data. Centralized Information System for Infectious Diseases and Laboratory Data Management System databases were used to obtain data on AFP indicators and laboratory performance and countries’ vaccine coverage from 2015 to 2019. EV isolation, identification, and typing were performed by each country according to WHO protocols. Overall, a general AFP underreporting was observed. Non-Polio Enterovirus (NPEV) typing showed a high heterogeneity: over the years, several genotypes of coxsackievirus and echovirus have been identified. The polio vaccine coverage, for the data available, differs among countries. This evaluation allows for the collection, for the first time, of data from the countries of the Balkan area regarding AFP surveillance and polio vaccine coverage. The need, for some countries, to enhance the surveillance systems and to promote the polio vaccine uptake, in order to maintain the polio-free status, is evident.

The Feasibility of Using UAV Structure from Motion Photogrammetry to Extract HBIM of the Great Ziggurat of UR

Culture heritage reflects nation’s legacy and therefore should be protected from damage in order to pass it to future generations. Recently, such protection can be applied by 3D digitization techniques such as conservation, restoration, documentation, etc. The 3D digitalization of heritage assets has encountered numerous focus in the last two decades due to the development in data capturing techniques and technological advancement in 3D remote sensing (RS) approaches such as photogrammetry and laser scanning. However, the abundance of 3D information resources and spatial data modelling and analysis methods have urged stakeholders to adopt intelligent 3D data management system so called Building Information Modelling (BIM) to facilitate data approaching and management. Historic Building Information Model (HBIM) is a special case of the BIM system, however it reflects the possibility to apply the BIM technology to the historical and heritage buildings. In this research, Structure from motion (SfM) photogrammetric routine based on Unmanned Aerial Vehicles (UAVs) images was applied to build HBIM of the Great Ziggurat of Ur in the south of Iraq. Based on the 3D geometric and texturing information extracted through photogrammetry and the historical information provided, virtual reconstruction has been carried out using (HBIM) technology. This was achieved by applying realistic materials and texturing information in order to document the building, which is directed to investigate the feasibility of implementing image-based 3D modelling within HBIM environments. Restoring the missing parts of the Ziggurat temple was also a focus of this research by implementing reverse engineering methodologies based on available information considered within the extracted HBIM. This can successfully represent a complete virtual reality model and a management information system of the Ziggurat building to be passed to future generations. The work also includes data assessment and validation with the as-built model generated from reference measurements within Computer Aided Design (CAD) environment.

The main drivers for the occurrence of six red-listed epiphytic bryophytes and lichens in the boreo-nemoral forest landscape, Latvia

Forest landscape plays a significant role in rare cryptogam distribution. However, data about the environmental demands of rare epiphytic bryophytes and lichens in boreo-nemoral forest landscapes are not complete. In this study, we focused on finding the main environmental predictors influencing the occurrence of three red-listed epiphytic bryophytes and three red-listed epiphytic lichens in the Latvian boreo-nemoral forest landscape. We obtained the records of species from the Natural Data Management System OZOLS database, which is a national information system on all rare taxa. We analyzed the occurrence of species in relation to forest stand age and area, forest type, heterogeneity and tree bark pH class. We found that selected red-listed bryophyte and lichen occurrence was mainly influenced by forest stand age and area. However, each of the red-listed epiphytic bryophyte and lichen has their own ecological demands in the boreo-nemoral landscape.

A database system for querying of river networks: facilitating monitoring and prediction applications

The increasing availability of real-time in situ measurements and remote sensing observations have the potential to contribute to the optimization of water resources management. Global challenges such as climate change, intensive agriculture and urbanization put a high pressure on our water resources. Due to recent innovations in measuring both water quantity and quality, river systems can now be monitored in real time at an unprecedented spatial and temporal scale. To interpret the sensor measurements and remote sensing observations additional data for example on: the location of the measurement, upstream and downstream catchment characteristics, … are required. In this paper, we present a data management system to support flow-path related functionality for decision making and prediction modelling. Adding meta data sets and facilitating (near) real-time processing of sensor data questions are key concepts for the systems. The potential of the database framework for hydrological applications is demonstrated using different applications for the river system of Flanders. In one, the database framework is used to simulate the daily discharge for each segment within a catchment using a simple data-driven approach. The presented system is useful for numerous applications including pollution tracking, alerting and inter-sensor validation in river systems, or related networks.

Blockchain-Based Privacy-Preserving System for Genomic Data Management Using Local Differential Privacy

The advances made in genome technology have resulted in significant amounts of genomic data being generated at an increasing speed. As genomic data contain various privacy-sensitive information, security schemes that protect confidentiality and control access are essential. Many security techniques have been proposed to safeguard healthcare data. However, these techniques are inadequate for genomic data management because of their large size. Additionally, privacy problems due to the sharing of gene data are yet to be addressed. In this study, we propose a secure genomic data management system using blockchain and local differential privacy (LDP). The proposed system employs two types of storage: private storage for internal staff and semi-private storage for external users. In private storage, because encrypted gene data are stored, only internal employees can access the data. Meanwhile, in semi-private storage, gene data are irreversibly modified by LDP. Through LDP, different noises are added to each section of the genomic data. Therefore, even though the third party uses or exposes the shared data, the owner’s privacy is guaranteed. Furthermore, the access control for each storage is ensured by the blockchain, and the gene owner can trace the usage and sharing status using a decentralized application in a mobile device.

Management of COVID-19 Pandemic Data in India: Challenges Faced and Lessons Learnt

COVID-19 is an ongoing pandemic, which has already claimed millions of lives worldwide. In the absence of prior information on the pandemic, the governments can use generated testing data to drive policy decisions. Thus, a one-stop repository is essential to ensure sharing of clean, de-duplicated, and updated records to all the stakeholders. In India, the government initiated the testing through a network of VRDLs headed by the Indian Council of Medical Research (ICMR). Initially, the generated data were captured and shared in Excel sheets. As the number of cases increased, there was a need for a data management system to ensure reliable and up-to-date data to drive policy decisions. Thus, the data management team at ICMR initiated the development of a national COVID-19 testing data management tool that is currently maintaining all the data in a central hub. The first version of the tool was released in March 2020 and was subsequently modified with the changing testing guidelines and strategies. On completing one and a half years of managing the data and collecting approximately 550 million records, the team analyzed the challenges faced and the strategies used to ensure a seamless flow of data to the system and its real-time analysis. In this study, the entire duration of the pandemic has been divided into four different phases based on the resourcefulness of the country. Since the pandemic is currently ongoing, this study can be useful for countries in a different phase of pandemic facing similar challenges.

UDMS: User Data Management System Using Firebase Cloud

This paper aims at the creation of User Data Management System using Cloud Firebase. Firebase is a relatively new Technology for handling large amount of unstructured data. It is very necessary to store and secure our important data media for future use in case of emergency as a backup. We use a firebase cloud to save and retrieve Pictures, Documents, Videos and PDF’s. The present paper discuss the design and development of “UDMS” that can help the user to store their important data. Keywords: Java, Android, Firebase, Firebase Storage.

REKAYASA SISTEM INFORMASI MANAJEMEN PANTI ASUHAN MUHAMMADIYAH SUMBAWA BERBASIS WEB

Muhammadiyah Orphanage is a social welfare institution that shapes the growth and development of children who do not have families or who do not live with their families. The data management system currently running at the Muhammadiyah Orphanage is still manual which is recorded in a ledger and microsoft office word. Data processing like this will certainly make it difficult for administrators to present reports because it takes a relatively long time so as to allow errors in data management. In addition, there is no complete information about the Muhammadiyah orphanage so that donors must come to the orphanage to get information. The purpose of this thesis research is the Web-Based Engineering Information System of Muhammadiyah Sumbawa Orphanage Management. This system successfully designed and built using programming language PHP, HTML, CSS, MySQL Database, Sublime-Text Text Editor, qualitative research methods and using the Waterfall software development method. The final result of this thesis is Muhammadiyah Orphanage Management Information System Sumbawa Web-Based that will make it easier for administrators to manage orphanage data and make it easier for donors to find information about Muhammadiyah orphanages.

Modernisation of urban governance: An approach of ‘Blockchain + Big Data’

Mathematics is a prerequisite for the development of blockchain technology. The deeply penetrated mathematical ideas support the establishment of the trust mechanism of the whole blockchain system, which makes the blockchain technology autonomous, decentralised, not so easy to tamper, open, anonymous and also possesses other characteristics. Due to these characteristics, the introduction of blockchain will greatly solve a series of problems faced by the quality and acquisition of big data in cities, and release more data vitality. Based on the perspective of chain blocks and big data fusion, this paper puts forward that data are the foundation of modern urban governance. Data management has become the key to modern urban governance. It puts forward that the building of a big data management system based on blockchain will strengthen the construction of the intelligent city and modernisation of urban governance capabilities.