Innovative Intelligence Research Articles

CNS: Application of Dynamic Resource Allocation Based on 5G Communication in Technology Innovation of Complex High-Tech Industries

In order to improve the technological innovation effect of compound high-tech industry, this paper studies its dynamic resource allocation technology with the support of 5G communication technology, and analyzes the application of 5G communication technology in technological innovation of compound high-tech industry. In the uplink and downlink of wireless energy-carrying communication system, this paper studies the joint optimization of subcarrier allocation and power allocation of uplink and downlink to maximize the uplink and downlink weighted sum rate of the system. Moreover, this paper adopts the joint optimization algorithm of power and sub-carrier based on Lagrangian dual method and ellipsoid method to obtain the maximum information transmission rate of uplink and downlink. In addition, this paper constructs an intelligent innovation management system. The research results show that the dynamic resource allocation method based on 5G communication proposed in this paper has a very good effect in the application of technological innovation in complex high-tech industries.

Earth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images

The continuous urbanisation in most Low-to-Middle-Income-Country (LMIC) cities is accompanied by rapid socio-economic changes in urban and peri-urban areas. Urban transformation processes, such as gentrification as well as the increase in poor urban neighbourhoods (e.g., slums) produce new urban patterns. The intersection of very rapid socio-economic and demographic dynamics are often insufficiently understood, and relevant data for understanding them are commonly unavailable, dated, or too coarse (resolution). Traditional survey-based methods (e.g., census) are carried out at low temporal granularity and do not allow for frequent updates of large urban areas. Researchers and policymakers typically work with very dated data, which do not reflect on-the-ground realities and data aggregation hide socio-economic disparities. Therefore, the potential of Earth Observations (EO) needs to be unlocked. EO data have the ability to provide information at detailed spatial and temporal scales so as to support monitoring transformations. In this paper, we showcase how recent innovations in EO and Artificial Intelligence (AI) can provide relevant, rapid information about socio-economic conditions, and in particular on poor urban neighbourhoods, when large scale and/or multi-temporal data are required, e.g., to support Sustainable Development Goals (SDG) monitoring. We provide solutions to key challenges, including the provision of multi-scale data, the reduction in data costs, and the mapping of socio-economic conditions. These innovations fill data gaps for the production of statistical information, addressing the problems of access to field-based data under COVID-19.

The Innovative Intelligence System—A Tool for Discovering Innovative Opportunities and for Ensuring the Sustainability and Business Prosperity

At present, in the field of building an innovative intelligence system in the company, considerable attention is paid especially to the technological side of this system and the description of its basic elements. The aim of the research is to provide a comprehensive view of building an innovative intelligence system in the company from managerial, information and organizational support. The research consists of questionnaire research and interviews conducted in Slovak companies with the intention to find out the current status of innovative intelligence systems in Slovakia by the content, organization, personnel and information aspects. The authors offer a summary of theoretical knowledge in the field of management—innovative intelligence systems. Summarizing these findings enriches the theoretical basis of management, especially in the field of innovation management. A detailed description of the individual processes, activities and actors of the innovative intelligence system model can serve for a better knowledge of the issue and its successful management. The proposed model of the innovative intelligence system includes the identification of individual actors, their tasks and the decomposition of its main process. The formulated recommendations are a valuable tool for company managers. Adherence to them will ensure the successful implementation and operation of an innovative intelligence system in the company.

Radiologists' Increasing Role in Population Health Management: AJR Expert Panel Narrative Review.

Population health management (PHM) is the holistic process of improving health outcomes of groups of individuals through the support of appropriate financial and care models. Radiologists' presence at the intersection of many aspects of health care, including screening, diagnostic imaging, and image-guided therapies, provides the opportunity for increased radiologist engagement in PHM. Furthermore, innovations in artificial intelligence and imaging informatics will serve as critical tools to improve value in health care through evidence-based and equitable approaches. Given radiologists' limited engagement in PHM to date, it is imperative to define the PHM priorities of the specialty so that radiologists' full value in improving population health is realized. The purpose of this expert review is to explore programs and future directions for radiologists in PHM.

Preparedness for Innovation in Times of Crisis: Lessons From the Initial COVID-19 Pandemic Response

In this paper, we explore preparedness strategies for the initial response to crisis-driven innovation, in the context of the Coronavirus Disease 2019 (COVID-19) pandemic. Using insights from semi-structured interviews with public health strategists, we apply analytic induction to investigate preparedness to innovate, and the main forms of crisis-driven innovation applied in the initial COVID-19 response. We thematically analyze measures of preparedness for the initial COVID-19 response and propose a framing aimed at boosting readiness for crisis-driven innovation. Our study finds themes on COVID-19 preparedness strategies involving combat-inspired public policies from assessed security, cyclic-oriented public projections of actionable safeguards, and continuity-driven public provisions for anticipated situations. The study also captures themes on crisis response as crisis-driven innovation in relation to digitalized, integrated, and tailored public services, initiatives, and systems. Our paper concludes with discussions on the implications of innovation intelligence for crisis preparedness, “ripple-inspired inside-out” view of crisis-driven innovation, “response as innovation” framings, and suggestions for further studies.

Novel Service Efficiency Evaluation and Management Model

Numerous scholars have invested in the research of service innovation management, hoping to find a more objective and scientific service efficiency evaluation and management model so as to stride forward towards the goal of smart innovation management. In the service operating system, the multi-workstation service operation is one of the common service operation models. Some studies have pointed out: apart from a good service attitude, the service operation time of each workstation is a key factor which measures the performance of the workstation’s service operation. Therefore, this paper proposed a standardized concept with a service operation efficiency evaluation index. This index is not only convenient and easy-to-use, but it also has a one-to-one mathematical relationship with the performance achievement rate. Next, the radar evaluation chart was employed to evaluate the service efficiency of each workstation. First, according to the upper confidence limit and the required value of the index, the minimum value (MV) of the index estimator was derived and marked on each radar line; at the same time, all MVs were connected to form a control block. When the point estimate of the index does not fall into the control block of the radar chart, it represents that the service operation efficiency of the workstation has not reached the required level, so it needs to be improved. Because this model can directly compare the point estimate of the index with the MV, it can judge whether the service operation efficiency reaches the required level. In this way, the advantage of simple and easy-to-use point estimate can be maintained, and the risk of misjudgment caused by sampling errors can be reduced as well, which is helpful for the service industry to move towards the goal of intelligent innovation management. This method is not only applied to the performance evaluation of the multi-workstation service operation process but also applicable to the performance evaluations of other service operations.

Artificial intelligence – the new suggestion for biomedicine, dentistry and healthcare

The development of technologies based on Artificial Intelligence (AI) and their application in medicine is growing rapidly. Innovations in digital technology, telemedicine, 5G technology and artificial intelligence (AI) create new opportunities for the development of the healthcare system. The aim of the present study is to explore the possibilities for the application of artificial intelligence in biomedicine, dentistry, healthcare and healthcare. In recent years there have been many major innovations, including the introduction of many new information and communication technologies. Digital innovations, including the further inclusion of telemedicine, the development of 5th generation wireless networks (5G) and artificial intelligence (AI) approaches, create an exceptional ecosystem for new health opportunities. The digital health sector creates a favorable environment for the provision of health services at a very high level.

Professionals\u2019 responses to the introduction of AI innovations in radiology and their implications for future adoption: a qualitative study

BackgroundArtificial Intelligence (AI) innovations in radiology offer a potential solution to the increasing demand for imaging tests and the ongoing workforce crisis. Crucial to their adoption is the involvement of different professional groups, namely radiologists and radiographers, who work interdependently but whose perceptions and responses towards AI may differ. We aim to explore the knowledge, awareness and attitudes towards AI amongst professional groups in radiology, and to analyse the implications for the future adoption of these technologies into practice.MethodsWe conducted 18 semi-structured interviews with 12 radiologists and 6 radiographers from four breast units in National Health Services (NHS) organisations and one focus group with 8 radiographers from a fifth NHS breast unit, between 2018 and 2020.ResultsWe found that radiographers and radiologists vary with respect to their awareness and knowledge around AI. Through their professional networks, conference attendance, and contacts with industry developers, radiologists receive more information and acquire more knowledge of the potential applications of AI. Radiographers instead rely more on localized personal networks for information. Our results also show that although both groups believe AI innovations offer a potential solution to workforce shortages, they differ significantly regarding the impact they believe it will have on their professional roles. Radiologists believe AI has the potential to take on more repetitive tasks and allow them to focus on more interesting and challenging work. They are less concerned that AI technology might constrain their professional role and autonomy. Radiographers showed greater concern about the potential impact that AI technology could have on their roles and skills development. They were less confident of their ability to respond positively to the potential risks and opportunities posed by AI technology.ConclusionsIn summary, our findings suggest that professional responses to AI are linked to existing work roles, but are also mediated by differences in knowledge and attitudes attributable to inter-professional differences in status and identity. These findings question broad-brush assertions about the future deskilling impact of AI which neglect the need for AI innovations in healthcare to be integrated into existing work processes subject to high levels of professional autonomy.

An Evaluation of the Yangtze River Economic Belt Manufacturing Industry Level of Intelligentization and Influencing Factors: Evidence from China

Over recent decades, the application of artificial intelligence methods in manufacturing has led to new spheres of research such as the Internet of Things, Cyber–Physical Systems, and Cloud Computing and Big Data, leading to the so-called Industry 4.0. However, to date, little research has been geared towards assessing the factors that influence intelligent manufacturing on a regional scale. Addressing this problem, this paper constructs an evaluation index system for the Yangtze River Economic Belt (YREB) intelligent manufacturing sector using eleven years (2008–2018) of provincial panel data. The entropy method is applied to three evaluation criteria, namely intelligent innovation, equipment, and profit, to construct an evaluation index system. An analysis of the results revealed that the level intelligentization of the manufacturing industry of the YREB increases yearly, and that intelligent innovations are notably occurring at a faster rate than profits. Disproportional enterprise returns on investment have occurred, which decreases enterprise motivation to be innovative in the first place. Additionally, it was also observed that FDI, financial development, government intervention, and the level of opening-up were the primary factors modulating regional intelligent manufacturing levels.

Dynamic customer churn prediction strategy for business intelligence using text analytics with evolutionary optimization algorithms

In the digital era, innovations in business intelligence are critical to staying competitive and popular across the growing business trends. Businesses have begun to investigate the next stage of data analytics and business intelligence solutions. On the other hand, Customer Churn Prediction (CCP) is a crucial process in business decision making, which properly identifies the churn users and takes necessary steps for customer retention. churn and non-churn customers have resembling features. Therefore, this research work designs a dynamic CCP strategy for business intelligence using text analytics with metaheuristic optimization (CCPBI-TAMO) algorithm. In addition, the chaotic pigeon inspired optimization based feature selection (CPIO-FS) technique is employed for the feature selection process and reduces computation complexity. Besides, long short-term memory (LSTM) with stacked auto encoder (SAE) model is applied to classify the feature reduced data. In the LSTM-SAE model, the ability of SAE in the detection of compact features is integrated into the classification capability of the LSTM model. Finally, the sunflower optimization (SFO) hyperparameter tuning process takes place to further improve the CCP performance. A detailed simulation analysis is performed on the benchmark customer churn prediction dataset and the experimental values highlighted the superior performance of the proposed model over the other compared methods with the maximum accuracy of 95.56%, 93.44%, and 92.74% on the applied dataset 1-3 respectively.

PHYRON: cognitive computing for the creation of an innovative Intelligence Experience Center

<p>This research presents the results of a project called “PHYRON: Cognitive Computing for the creation of an innovative Intelligence Experience Center”, funded by the Basque Government (Economic Development, Sustainability and Environment Department). The project started in April 2019 and it will end in December 2021. Its main objective was to arrange an industrial research about cognitive computing. The main aim was the application of these systems for the development of an Intelligent Experience Center (IExC) to facilitate: i) enrichment of processes, products and services, in general client experiences, ii) automatic generation of technical predictions related to the product and the client behaviour through the exploitation of acquired knowledge, and iii) rationalization and automation of the processes that are involved in the after sale services both at technical and management level. The technological outcome presented in this paper is built using cognitive engines to enable learning from the client experience, and predictive models to anticipate client necessities.</p>

Analytical innovation in intelligence systems: the US national security establishment and the craft of ‘net assessment’

ABSTRACT This article develops a theory of analytical innovation in intelligence systems using the craft of the ‘net assessment’ methodology in the US as a case study. Employing congruence method and process-tracing and drawing on multi-archival sources, the article demonstrates that at the roots of analytical innovation are three variables: the setting of a requirement, the conduct of methodological experiments and the synthesis of analytical knowledge. The study also reveals that the nature of analytical intelligence innovation is dyadic, consisting of an organizational and an ideational component.

SDoT-NFV: A Distributed SDN Based Security System with IoT for Smart City Environments

The Internet of Things (IoT) is a key developing innovation aimed at linking objects via the Internet. While, Software Defined Networking (SDN) is another modern network- ing domain intelligence innovation that increases network effi- ciency and enhances security, reliability, and protection through dynamic software programs. In this paper, we proposed a distributed secure SDoT-NFV architecture for smart cities with Network Function Virtualization (NFV) implementation. We integrated highly protected SDN that delivers better network ef- ficiency, protection, and privacy results. It also secures metadata within each layer as well as payload. In addition, this architecture attempted to implement a more efficient method for constructing a cluster via SDN. Moreover, SDN-IoT with the NFV ideas brings benefits in terms of energy conservation and load balancing to the relevant fields. In addition, several distributed controllers have suggested enhancing accessibility, integrity, anonymity, con- fidentiality, and so on. We also implemented an energy-efficient Cluster Head Selection (CHS) algorithm to make use of our proposed architecture. The network offers greater protection of each network layer as opposed to the traditional network in the proposed architecture. Lastly, we analyze the efficiency of the proposed architecture with different network parameters (throughput, RTT, and Time sequence) for smart cities.
 GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 7, Dec 2020 P 27-35

Information fusion for future COVID-19 prevention: continuous mechanism of big data intelligent innovation for the emergency management of a public epidemic outbreak

Information fusion is very effective and necessary to respond to a public epidemic outbreak such as COVID-19. Big data intelligent, as a product of information fusion, plays an important role in the prevention and control of COVID-19. The continuous mechanism of big data intelligent innovation (BDII) is fundamental to effectively prevent and control a public epidemic outbreak. In this study, the continuous mechanism of BDII was fused into a complex network, and a three-dimensional collaborative epidemic prevention model was constructed. Furthermore, adiabatic elimination principle was applied to explore the order parameter of the continuous mechanism. Finally, empirical analysis was conducted based on three-stage epidemic prevention strategies to reveal the effect of continuous epidemic prevention under different big data intelligent emergency management policy levels. The results of this study are as follows. Through the mutual influence and coupling of the subsystems, the continuous mechanism of BDII can be realized to manage a public epidemic outbreak emergency. The big data intelligent subsystem is integrated into the subsystems of public epidemic outbreak management and science and technology innovation. The big data intelligent emergency management policies play a positive role in the overall BDII for the continuous epidemic prevention of a public epidemic outbreak. The convention of BDII transformation is the continuous mechanism of BDII as the order parameter of a public epidemic outbreak. In the early stage of epidemic prevention, the convention is excessively pursued, while the neglect of BDII configuration is not conducive to the long-term collaborative governance of a public epidemic outbreak. The study provides practical guidelines for the formulation of fusion innovation policies, application of big data intelligent, and theoretical basis for the emergency management of a public epidemic outbreak in the medical field.

Modeling development mechanism of intelligent innovation potential of mining engineers

It is found that present-day mining engineers have a low and disbalanced intelligent innovation potential as compared with the Soviet-era specialists. It is of the current concern to reveal the intelligent innovation potential development mechanism of the engineers in order to stimulate innovative advancement of coal mining companies to keep the achieved level of their competitive abilities. Using the dramatic but successful innovation activity of Mining Engineer N. A. Chinakal as an example, the model of step-wise formation of cogni-creative and visenvolitive constituents of the intelligent innovation potential is constructed. The model visualizes the development mechanism and shows a clear skewness of the potential of present mining engineers toward the cogni-creative constituent. The unique level and balance of Chinakal’s innovation potential is demonstrated. The logics of engagement of an engineer’s brainpower resources and the contents of transition stages in mental work is displayed by the model of orbiting disclosure of a mining engineer’s brainpower resources during an innovation process. The model explains how intellect grows and expands while creating and implementing an innovation. Three zones, which are identified in the intelligent innovation potential of an engineer, illustrate the engineer’s possible positions and behavior in the course of industrial renovation. The stages of elaboration of an engineer’s innovation abilities and the contents of these stages along the way from an idea to an innovation are described. Incipience of special mental forces and accumulation of qualitative changes at the end of the first successful innovation cycle results in formation of the cumulative quality named the inn-experience. Multiplication of success in the next innovation cycles in different geological conditions ensures growth and enrichment of the inn-experience. Together with the accumulated package of put forward but unimplemened inventions, as well as with the experience gained in commissioning of innovations proposed by other inventors, the inn-experience composes the intelligent innovation potential of an engineer. The developed models and algorithm present the mechanism of the intelligent innovation potential which is the high-value intangible assets for the self-improvement of an engineer and for the competitive recovery of a mining company.

Cross‐item learning for volatile demand forecasting: An intervention with predictive analytics

AbstractDespite its importance to OM, demand forecasting has been perceived as a “problem‐solving” exercise; most empirical work in the field has focused on explanatory models but neglected prediction problems that are part of empirical science. The present study, involving one of the leading electronics distributors in the world, aims to improve prediction accuracy under high demand volatility for procurement managers to make better inventory decisions. In response to requests for an integrated forecasting methodology, we undertook an iterative process based on three guiding principles—data pooling, theory‐informed feature engineering, and ensemble‐based machine learning. The resulting framework managed to improve forecast accuracy significantly and is applicable to a broad range of situations. We present reflections and insights derived abductively through engagement with managers in this problem situation. This “problem‐driven” process corresponds to intervention as a research strategy that can foster theoretical and methodological innovations in OM. Our contribution goes beyond the development of the prediction framework as it elucidates ways OM researchers could leverage theoretical foundations to inform feature derivation and model construction. We posit that this work points to a way forward to the combination of OM principles with the emerging innovations in data science and artificial intelligence.

Musculoskeletal trauma and artificial intelligence: current trends and projections.

Musculoskeletal trauma accounts for a significant fraction of emergency department visits and patients seeking urgent care, with a high financial cost to society. Diagnostic imaging is indispensable in the workup and management of trauma patients. However, diagnostic imaging represents a complex multifaceted system, with many aspects of its workflow prone to inefficiencies or human error. Recent technological innovations in artificial intelligence and machine learning have shown promise to revolutionize our systems for providing medical care to patients. This review will provide a general overview of the current state of artificial intelligence and machine learning applications in different aspects of trauma imaging and provide a vision for how such applications could be leveraged to enhance our diagnostic imaging systems and optimize patient outcomes.

A Three-stage DEA Study on The Efficiency of Global Intelligent Innovation Based on Big Data

With the global intelligent technology coming, governments and organizations of various countries are speeding up the strategic layout of intelligent technology and competing to occupy the strategic commanding heights in global intelligent innovation. In this study, 101 countries from 18 regions were selected as samples. Based on the spiral collaborative innovation theory, from the perspectives of politics, industry, research, study and application, the number of Top 600 universities in the world majoring in computer science, R&D investment of hightech enterprises, and e-government investment level were selected as the input variables of intelligent innovation. Intelligence number of published papers linked with intelligent innovation, national high-tech exports, the government maturity level of artificial intelligence as the output variables. We use those input and output data to do the first stage DEA. Then, we choose the set up the number of national science and technology award, the average GDP growth rate nearly three years, intelligent technology industry in compensation levels, ICT development level as 4 environment variables, to figure out SFA analysis as the second stage DEA. Next we use three-stage DEA method to research global intelligent innovation efficiency with and without environment variables, and we find intelligent innovation efficiency presents obvious space characteristic: North America, Oceania and other regions to eliminate environment variables before and after the intelligent innovation efficiency is very high, West Africa, South Asia and other regions in the environment variable influences intelligent innovation efficiency improved significantly, and finally this study to provide path for intelligent innovation low efficiency region to improve their innovative efficiency of intelligent innovation development.

Model of Smart Parking System Based On Internet Of Things

Over increased automotive industry is being challenged to produce better quality products with intelligent innovations and lower cost. The Huge demand of the market is to get public parking area. The aim of this paper provides the highest quality equipment integrated by Smart Parking System Model. The model Provides simplify to controlling and monitoring of the parking area available which is connected to the Internet network, monitoring can be done remotely. Testing and data collection conducted in Jakarta by taking the data 24 hours during 6 months without stopping. The experiment result shows that the classification of availability of parking is approximately 78%. The results of the car sensor testing can be controlled in accordance with the parking area. Keywords: car, controlling, Internet of Things, monitoring, Smart Parking System

Can robotics be the future of ophthalmic surgery?

The future of robotic ophthalmologic surgery looks promising. Innovations in robotic technology and artificial intelligence may provide an ideal machine-human interface with planned and precise movements minimizing unwanted tissue damage and improving clinical outcomes.