Intelligence Analytics Research Articles

A lifecycle management system for capital construction facilities based on a digital cloud platform implementing information modeling methods and technologies

The first is the introduction of information modeling technology (IMT) capital construction facilities (CCF) at all stages of the life cycle (LC), as well as information modeling of urban areas for the tasks of urban building and planning. The second is the digitalization of procedures and services in the field of construction, the formation of unified accessible data sets and information platforms that implement a unified information space for the construction industry. To implement the presented tasks, it is proposed to create a distributed life cycle control system for capital construction facilities (DCS LC CCF) and form a digital cloud platform (digital infrastructure) for the implementation of investment and construction projects based on the use of information modeling technologies, artificial intelligence and predictive analytics.

Network Routing Issues in Global Geographic Information System

Research background: A geographic information system (GIS) is a computer-based information system that gathers, manages, and disseminates different geographic (related to earth) information to provide intelligent analytics necessary for prompt decisions and actions. A GIS can aggregate data from several different streams and turn it into a smart dashboard for different users to enhance productivity. The use of intelligent technologies, remote sensing, and ad-hoc wireless networks has significantly improved GIS. In GIS, utilization of sensed data from different sources depends upon the use of efficient sensing equipment, smart mobile nodes, and network gateways and relays that can efficiently route traffic towards the sink or base station. GIS achieve attention in emergency response scenarios for their capability to collect, analyze, and process spatial-referred. Purpose of the article: Article is focused on studying and identifying suitable routing issues of wireless mobile nodes, which are the main transponders for smooth and errorless data transmission. Methods: We performed a simulation-based comparative study of three different routing protocols used in ad-hoc wireless networks, namely is to identify suitable routing protocols that can aid the GIS to improve its overall performance. We calculated and compared our results with different routing protocols. We were able, with the Random mobility model, to estimate the performance of various network parameters. Findings & Value added: We proposed a study in this work that includes geo-information services overall of routing protocols to support a team in stressful situations. The study shows that the AODV routing protocol performed better than the other two routing protocols (OLSR & TORA) under the given topology.

How to ensure the safety of industrial and production enterprises using video technology

It is possible to take control of all processes at the production facility and ensure its safety only with the help of a complex of modern technologies. In the article, the author uses the case of innovative solutions of the Chinese company Hikvision to consider the complexes of technical means that ensure the prevention of theft and non-production losses, perimeter protection, as well as fire safety and compliance with labor discipline. According to the author, the intelligent video analytics system is exactly the tool that allows the security service of an industrial or production facility to become a modern service for preventing incidents and losses.

ETHICAL ASPECTS OF THE USE OF INNOVATIVE INFORMATION TECHNOLOGIES IN CLINICAL TRIALS

A clinical trial, according to the WHO, “is any research study that prospectively assigns human participants or groups of humans to one or more health-related interventions to evaluate the effects on health outcomes. Interventions include but are not restricted to drugs, cells and other biological products, surgical procedures, radiological procedures, devices, behavioural treatments, process-of-care changes, preventive care, etc”.
 The application of innovative information technologies like artificial intelligence and big data analytics in clinical trial processes is a new challenge. Such systems are useful tools, and promise to enhance the healthcare management, and to optimize clinical outcomes and economic effectiveness. However, their use raises ethical and social issues.
 In this direction, the European Commission in June 2018 set up the High Level Expert Group on AI, which offers guidance on a comprehensive framework for trustworthy AI. Trustworthy AI consists of three components, which should be met during the entire life cycle of the system: (1) it should be lawful, (2) it should be ethical, and (3) it should be robust.
 In this article we used the focus group methodology to obtain information from experts about the ethical aspects raised when innovative information technologies like artificial intelligence and big data analytics are used in clinical trials. Feedback from the experts was also gathered regarding the usage of the proposed guidelines for trustworthy AI, as an evaluation tool for the particular case of clinical trials.

A computational framework for social-media-based business analytics and knowledge creation: empirical studies of CyTraSS

ABSTRACT Social media (SM) platforms greatly facilitate business information sharing, customer relationship building, and client emotion expression. However, managing knowledge acquired from SM messages is challenged by limited human cognitive capability. This paper describes a computational framework for developing intelligent SM-based business analytics and visualization. The research developed a proof-of-concept system named CyTraSS to support intelligent analyses and visualization of 2,318,691 messages posted by 740,070 users who discuss trafficking topics on Twitter. The results demonstrate theoretical insights and practical usability of the framework, enhance understanding of knowledge creation with SM technology, and provide novel findings for business managers and policy makers.

Toward Scalable Video Analytics Using Compressed-Domain Features at the Edge

Intelligent video analytics systems have come to play an essential role in many fields, including public safety, transportation safety, and many other industrial areas, such as automated tools for data extraction, and analyzing huge datasets, such as multiple live video streams transmitted from a large number of cameras. A key characteristic of such systems is that it is critical to perform real-time analytics so as to provide timely actionable alerts on various tasks, activities, and conditions. Due to the computation-intensive and bandwidth-intensive nature of these operations, however, video analytics servers may not fulfill the requirements when serving a large number of cameras simultaneously. To handle these challenges, we present an edge computing-based system that minimizes the transfer of video data from the surveillance camera feeds on a cloud video analytics server. Based on a novel approach of utilizing the information from the encoded bitstream, the edge can achieve low processing complexity of object tracking in surveillance videos and filter non-motion frames from the list of data that will be forwarded to the cloud server. To demonstrate the effectiveness of our approach, we implemented a video surveillance prototype consisting of edge devices with low computational capacity and a GPU-enabled server. The evaluation results show that our method can efficiently catch the characteristics of the frame and is compatible with the edge-to-cloud platform in terms of accuracy and delay sensitivity. The average processing time of this method is approximately 39 ms/frame with high definition resolution video, which outperforms most of the state-of-the-art methods. In addition to the scenario implementation of the proposed system, the method helps the cloud server reduce 49% of the load of the GPU, 49% that of the CPU, and 55% of the network traffic while maintaining the accuracy of video analytics event detection.

Rule‐based Verification of System Security using Feature‐Based Product Line Engineering

ABSTRACTSystems security engineering is a discipline to engineer a system of interest ensuring system functionality under disruptive conditions associated with misuse and malicious behavior. Today's cyber‐physical systems must survive in a constantly changing threat environment. Cybersecurity controls and cyber resiliency capabilities require continuous delivery to meet this challenge. This article describes creating and using a rule base as an integral part of a systems and software product line engineering (PLE) factory enabling continuous resilient and secure cyber‐physical systems delivery ensuring the system can function under disruptive conditions. A hypothetical intelligence gathering network comprising unmanned vehicles, a ground control segment, and an intelligence analytics segment is the system of interest. Systems engineering activities include continuously analyzing the cyber‐attack surface for each system variant in the product portfolio; creating or amending cyber resiliency capabilities and cybersecurity controls for each system variant addressing each attack vector; and a living rule base maintained within a PLE factory ensuring each system variant automatically generated by the factory contains the requisite system capabilities. Applying this approach results in rule‐based verification of continuously delivered cyber resiliency capabilities and cybersecurity controls for each fielded system variant. Moving at the speed of mission need is essential, and automated rule‐based verification of system deliveries meets that need.

Enhancing Extensive and Remote LoRa Deployments through MEC-Powered Drone Gateways.

The distribution of Internet of Things (IoT) devices in remote areas and the need for network resilience in such deployments is increasingly important in smart spaces covering scenarios, such as agriculture, forest, coast preservation, and connectivity survival against disasters. Although Low-Power Wide Area Network (LPWAN) technologies, like LoRa, support high connectivity ranges, communication paths can suffer from obstruction due to orography or buildings, and large areas are still difficult to cover with wired gateways, due to the lack of network or power infrastructure. The proposal presented herein proposes to mount LPWAN gateways in drones in order to generate airborne network segments providing enhanced connectivity to sensor nodes wherever needed. Our LoRa-drone gateways can be used either to collect data and then report them to the back-office directly, or store-carry-and-forward data until a proper communication link with the infrastructure network is available. The proposed architecture relies on Multi-Access Edge Computing (MEC) capabilities to host a virtualization platform on-board the drone, aiming at providing an intermediate processing layer that runs Virtualized Networking Functions (VNF). This way, both preprocessing or intelligent analytics can be locally performed, saving communications and memory resources. The contribution includes a system architecture that has been successfully validated through experimentation with a real test-bed and comprehensively evaluated through computer simulation. The results show significant communication improvements employing LoRa-drone gateways when compared to traditional fixed LoRa deployments in terms of link availability and covered areas, especially in vast monitored extensions, or at points with difficult access, such as rugged zones.

A Systematic Literature Review on Hospitality Analytics

With the growth of data generated by all systems involved in a hotel, terms like big data and business analytics (BA) gain strength within the hotel industry. Business analytics can be used in hospitality management to increase business knowledge and to improve the decision-making process. This study's main questions are: RQ1 – Which are the main research attributes studied in the past two decades related to analytics in the hospitality sector? RQ2 – What are the main differences between business intelligence and business analytics? RQ3 – What are the main trends in business analytics? RQ4 – Which are the main business intelligence perceptions and beliefs? To answer these research questions, this article provides a literature review to systematize the research made in business analytics information systems in the hospitality industry. The results can help identify different research attributes and the most relevant theories developed in the past two decades related to business analytics tools.

Towardprecision health: applying artificial intelligence analytics to digital health biometric datasets.

The rapid development of digital health devices has enabled patients to engage in their care to an unprecedented degree and holds the possibility of significantly improving the diagnosis, treatmentand monitoring of many medical conditions. Combined with the emergence of artificial intelligence algorithms, biometric datasets produced from these digital health devices present new opportunities to create precision-based, personalized approaches for healthcare delivery. For effective implementation of such innovations to patient care, clinicians will require an understanding of the types of datasets produced from digital health technologies; the types of analytic methods including feature selection, convolution neural networking, and deep learning that can be used to analyze digital data; and how the interpretation of these findings are best translated to patient care. In this perspective, we aim to provide the groundwork for clinicians to be able to apply artificial intelligence to this transformation of healthcare.

Data set of balance of Russia's regional economy in 2005-2024 based on the methodology of calculation of "underdevelopment whirlpools".

The article shows the possibilities of Data Set “Interactive Statistics and Intelligent Analytics of the Balanced State of the Regional Economy of Russia in Terms of Big Data and Blockchain – 2020”. For creation of the data set, we formed time rows of the values of the selected indicators, which characterize the balance of Russia's regional economy. The indicators are systematized and classified into two categories. The first category includes the indicators of the level of socio-economic development: GDP per capita and “underdevelopment whirlpools”, balanced financial result of companies’ activities, population's employment level, and balance of the regional budget, calculated by finding the difference between revenues and expenditures. The second category includes the indicators of potential of socio-economic development: investments into fixed capital per capita, share of innovations-active organizations, share of innovative products, and digitization level. The data are collected in a table, based on which – with the help of programming and web-design – a data set is created – an interactive platform for working with data. The data set is available in Russian and English at the official web-site of the Institute of Scientific Communications [12]. The data set unifies and transfers into the digital form the data on the level and potential of Russia's regions’ potential of socio-economic development. The data set allows for flexible setting of data for any research. The data set allows – based on the statistics of a region's socio-economic development – determining its position in the Russian national ranking, category by the “TRMS” matrix, calculating the integral index for each region, and comparing its values. Using the proprietary methodology of calculation of “underdevelopment whirlpools” of Prof. Popkova, the data set reflects the balance of Russia's regional economy in 2005–2024. The data set allows for automatic visualization of data by creating a blockchain polygon of region's socio-economic development, which includes the chronogram of the region's development and the process of region's pulling into an “underdevelopment whirlpool”. The data set's data are presented in the form of an interactive map of Russia's regional economy. Map's color shows categories that are assigned to regions and the borders of regions, as well as information on each region's position in the 2020 rating. The data set allows for large-scale studies of Russia's regional economy with application of technologies of Big Data processing, machine learning, and intellectual analytics.

Connecting Data to Insight: A Pan-Canadian Study on AI in Healthcare.

Across Canada, healthcare leaders are exploring the potential of artificial intelligence and advanced analytics to transform the healthcare system. This report shares a summary of the current state of healthcare analytics across major hospitals and public healthcare agencies in Canada. We present information on the current level of investment, data governance maturity, analytics talent and tools and models being leveraged across the nation. The findings point to an opportunity for enhanced collaboration in advanced analytics and the adoption of nascent artificial intelligence technologies in healthcare. The recommendations will help drive adoption in Canada, ultimately improving the patient experience and promoting better health outcomes for Canadians.

Data intelligence and risk analytics

Data intelligence and risk analytics

Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analytics.

Video coding, which targets to compress and reconstruct the whole frame, and feature compression, which only preserves and transmits the most critical information, stand at two ends of the scale. That is, one is with compactness and efficiency to serve for machine vision, and the other is with full fidelity, bowing to human perception. The recent endeavors in imminent trends of video compression, e.g. deep learning based coding tools and end-to-end image/video coding, and MPEG-7 compact feature descriptor standards, i.e. Compact Descriptors for Visual Search and Compact Descriptors for Video Analysis, promote the sustainable and fast development in their own directions, respectively. In this paper, thanks to booming AI technology, e.g. prediction and generation models, we carry out exploration in the new area, Video Coding for Machines (VCM), arising from the emerging MPEG standardization efforts1. Towards collaborative compression and intelligent analytics, VCM attempts to bridge the gap between feature coding for machine vision and video coding for human vision. Aligning with the rising Analyze then Compress instance Digital Retina, the definition, formulation, and paradigm of VCM are given first. Meanwhile, we systematically review state-of-the-art techniques in video compression and feature compression from the unique perspective of MPEG standardization, which provides the academic and industrial evidence to realize the collaborative compression of video and feature streams in a broad range of AI applications. Finally, we come up with potential VCM solutions, and the preliminary results have demonstrated the performance and efficiency gains. Further direction is discussed as well.

Human Trafficking Network Investigations: The Role of Open Source Intelligence and Large-Scale Data Analytics in Investigating Organized Crime

Human Trafficking Network Investigations: The Role of Open Source Intelligence and Large-Scale Data Analytics in Investigating Organized Crime

CHALLENGES FOR LEGAL EDUCATION IN THE ERA OF I.R.4.0

The formulation of the National Policy on Industrial Revolution 4.0 (IR4.0) and Internet of things signals the Malaysian government readiness to come up with the necessary policy framework for the digitalised era. It has been said that universities’ curriculum structure and instructional design must also be revisited to ready the graduate to face the onslaught of technological revolution. Through content analysis of relevant literature, this paper analyses the myriad ways in which legal education has been impacted by IR4.0 both in terms of the ‘body of knowledge’ as well as the ‘skill sets’ required for law students to survive in the era of automatous systems. The paper is structured to first explain the concept of IR4.0 and how some national countries leverage the digital technology to improve their economy or facilitate social transformation. The paper proceeds with a discussion of how autonomous system, artificial intelligence and data analytics can enhance the instructional design of teaching and researching law. In terms of the body of knowledge, most of the legal principles drawn for the brick and mortar environment are no longer relevant in the IR4.0 era. The paper reveals that the traditional method that focuses on the training of law graduates to think like a lawyer by understanding the reasoning in the judgement of cases or the preparation of conveyancing and court documents are no longer relevant in the IR4.0 era as these activities could easily be undertaken by bots. Instead, law students should be imparted with a multiplicity of human skills that could not be performed by autonomous systems such as those involving conscience, high level thinking, and emotion such as mediation, negotiation, counselling, court prosecution, advocacy, witness examination, plea mitigation as well as social skills, resource management skills, technical skills and most importantly system skills.

A Service-Oriented Foundation for Big Data

This paper provides a service-oriented foundation for big data. The foundation has two parts. Part 1 reveals 10 big characteristics of big data. Part 2 presents a service-oriented framework for big data. The framework has fundamental, technological, and socio-economic levels. The fundamental level has four big fundamental characteristics of big data: big volume, big velocity, big variety, and big veracity. The technological level consists of three big technological characteristics of big data: Big intelligence, big analytics, big infrastructure. The socioeconomic level has three big socioeconomic characteristics of big data: big service, big value, and big market. The article looks at each level of the proposed framework from a service-oriented perspective. The multi-level framework will help organizations and researchers understand how the 10 big characteristics relate to big opportunities, big challenges, and big impacts arising from big data. The proposed approach in this paper might facilitate the research and development of big data, big data analytics, business intelligence, and business analytics.

Digital confidence, experience and motivation in physiotherapists: A UK-wide survey

Background: Healthcare digital transformation should focus on the use of innovative technologies to enhance quality, safety, efficiency of care services, and patient experience. Subsequently, the roles and skills of healthcare staff will change, requiring evaluation and elevation of digital literacy across the physiotherapy profession. Aim: To evaluate the confidence, motivation and competence of digital technologies in a cohort of UK physiotherapists (juxtaposed with a wider group of allied health professionals). Methods: A quantitative, cross-sectional online questionnaire of physiotherapists and other allied health professionals (AHPs) in the UK. Results: 282 responses from AHPs were received with 279 complete responses for further analysis (including 126 physiotherapists). Physiotherapists report moderate-high levels of confidence in the use of digital devices (7.6 ±1.77), and high levels of motivation in learning about digital technology (8.7 ±1.6). Physiotherapists self-rate their knowledge regarding the benefits of digital transformation as high (72%). Physiotherapists show a strong preference for daily communication via telephone (82%) and email (97%). Conclusion: Physiotherapists demonstrate moderate-high levels of confidence and motivation to use digital technologies (in line with those seen in other AHPs). Higher degrees of competence are reported regarding understanding the benefits of digital technology, and lower competence is reported regarding topic areas such as artificial intelligence and data analytics.

Data-Driven Intelligence on Innovation and Competition: Patent Overlay Network Visualization and Analytics

ABSTRACT Technology positions of firms may determine their competitive advantages and innovation capabilities. While a tangible understanding of technology positions can inform competitive intelligence, they are heterogeneous, intangible and difficult to analyze. We introduce a data-driven network visualization and analysis methodology to assess and compare the technology positions of firms for competitive intelligence analytics based on patent data. This article demonstrates the methodology via comparative analyses of multiple firms for strategic insights on innovation and competition.

Guest Editorial: Special Section on Latest Advances on Industrial Intelligent Video Systems and Analytics

Guest Editorial: Special Section on Latest Advances on Industrial Intelligent Video Systems and Analytics