Intelligence Analytics Research Articles

Semantical video coding: Instill static-dynamic clues into structured bitstream for AI tasks

Traditional media coding schemes typically encode image or video into a semantic-unknown binary stream, which fails to directly support downstream intelligent tasks at the bitstream level. Semantically Structured Image Coding (SSIC) (Sun et al., 2020) makes the first attempt to enable partial-decoding image intelligent task analysis via a Semantically Structured Bitstream (SSB). However, the SSIC considers image coding and its generated SSB only contains the static object information. In this paper, we propose an advanced Semantically Structured Video Coding (SSVC). Video signals contain more rich dynamic motion information and redundancy. Thus, we present a reformulation of semantically structured bitstream (SSB) in SSVC which contains both static object characteristics and dynamic motion clues. Specifically, we introduce optical flow to encode continuous motion information and reduce cross-frame redundancy via a predictive coding architecture, then the optical flow and residual information are reorganized into SSB, which enables the proposed SSVC could better adaptively support video-based downstream intelligent applications. Extensive experiments on various vision tasks demonstrate that the proposed SSVC framework could directly support multiple intelligent tasks just depending on a partially decoded bitstream, saving bitrate consumption for intelligent analytics.

SDN Application Plane Where Intelligence Meets Networking

The software-defined networking (SDN) paradigm has revolutionized the way we think about network management and control. The SDN application plane is a critical component of this paradigm, where intelligent applications are leveraged to drive network behavior and optimize network performance. In this abstract, we explore the role of the SDN application plane in bridging the gap between traditional network management and the intelligent, dynamic network of the future. We discuss the challenges and opportunities of deploying SDN applications, including the need for standardized APIs and the importance of intelligent analytics and machine learning techniques. Finally, we highlight the transformative potential of the SDN application plane, from enabling new applications and services to improving network security and resilience. By bringing intelligence and networking together in new and innovative ways, the SDN application plane promises to revolutionize the way we design, manage, and secure our networks.

Hospital data analytics for business intelligence- an analytical tool for patient feedback analysis

Hospital data analytics for business intelligence- an analytical tool for patient feedback analysis

Application of the program for artificial intelligence analytics of paper text and segmentation by specified parameters in clinical practice

The development of novel technologies using elements of artificial intelligence (AI) in medicine is addressed to practical clinical implementation and provision of key issues, including improvement in the use of routine clinical data, aimed at practical relevance, standardization, confidentiality and patient safety.Aim. To evaluate the effectiveness of the RuPatient electronic heart record (EHR) system in real clinical practice for extracting and structuring medical data.Material and methods. Extraction and recognition of data using EHR from various following sources: outpatient records, statements, routine medical reports, epicrisis and other structured and unstructured medical information based on the developed technology of intelligent text analytics, optical character recognition, for specified words and phrases, and the use of machine learning elements. A particular criterion for evaluating the effectiveness of EHR is the time spent on filling out electronic medical records compared to real clinical practice.Results. The time of entering and processing information by the recognition system of medical documentation included in the RuPatient EHR was shorter than in standard practice (20,3±1,4 minutes, 25,1±1,5 minutes, respectively, p<0,001), the average time of recognition of documents was 30±4,3 seconds. During the ROC analysis, we determined that the threshold value that allows high accuracy to recognize images of discharge epicrisis using the RuPatient system was 83,5% with an area under the curve (AUC) value of 0,76.Conclusions. The developed RuPatient EHR has a medical documentation recognition module for creating structured data based on AI technology elements and can be used in creating an electronic medical history and accumulation of structured data for the implementation of tasks for the practical and scientific use of big data and AI projects in medicine. When using the RuPatient system, the burden on medical staff during document management can be reduced and access to primary medical information simplified.

Exploring the path of network security and student privacy protection in smart campus based on Markov model

Abstract With the advent of technological society, data and intelligence have become the directional trend of development, and the network security of smart campus has become the focus of public attention day by day. The personalization of college students and the development of intelligent analytics have brought about a whole new change in privacy protection. The intertwining of campus networks and privacy protection is a complex and very real issue, and research based on the privacy protection of college students has become more urgent. Using Markov model as a computational analysis tool, this paper deeply investigates the security and usage degree of smart campus network security technology, and it propose a complete Markov- network security technology system. The calculation shows that the intrusion detection system has the highest security of 51% and the widest usage of 63%. This is followed by firewall technology with 19% security and 26% usage. Based on the above techniques, the system security of the proposed Markov-network security technique is 57%, which is much better than the 43% of the traditional network model.

Participant Selection for Federated Learning With Heterogeneous Data in Intelligent Transport System

Intelligent Transportation Systems (ITS) utilises the growing trend of both communication technologies and intelligent analytics to make transportation systems more smart and efficient. Federated Learning, a privacy-preserving machine learning paradigm shows promise in being applied in this field. However, the data and device heterogeneity, and highly dynamic environment in ITS pose challenges to the performance of federated learning. One of the recent approaches to address the challenges are to choose proper participants from available clients during training. However, this research field is not fully investigated yet, and many works are still based on the classic random-based selection scheme. In this paper, we present Newt, an enhanced federated learning approach. On one hand, it includes a new client selection utility that explores the trade-off between accuracy performance in each round and system progress. On the other hand, it highlights a feedback control on the selector. Specifically, we implement a control on the selection frequency as a new dimension of client selection method design. We evaluate the proposed system with DNN training tasks on large scale FEMNIST-based datasets that are of different heterogeneity properties. The experiments show that our method outperforms the other baseline methods by as large as 20%.

Optimizing database efficiency: Empowering systems with data mining

Databases are critical for storing structured data, but deriving insights remains challenging. This paper investigates integrating classification, clustering, association rules, and anomaly detection within database architectures to enable intelligent analytics. A unified architecture is proposed along with an asynchronous incremental learning technique to efficiently handle dynamic data. Comprehensive experiments on diverse real-world datasets demonstrate 10–25% improvements in metrics like query latency, accuracy, and costs compared to conventional integration approaches. Emerging applications in multimedia, spatiotemporal, and IoT mining are discussed. The holistic convergence of multiple techniques is highlighted as the key innovation in progressing towards next-generation intelligent databases powered by analytics.

Examining how and when digital platform capabilities drive technological innovation: a strategic information perspective

PurposeThis study aims to examine how and when digital platform capabilities drive technological innovation from a strategic information perspective, regarding information flow and information quality as mediators and business intelligence analytics as a moderator.Design/methodology/approachThis study engaged corporate executives in surveys and obtained 182 firm data and then tested the hypotheses using linear regression models and the robustness using the structural equation model.FindingsDigital platform capabilities drive corporate technological innovation through access to strategic information, i.e. strategic information mediates between digital platform capabilities and technological innovation. Business intelligence analytics increases the influence of strategic information on technological innovation.Practical implicationsThis study underlines the importance for companies to construct digital platform capabilities to access strategic information to achieve technological innovation and the role of business intelligence analytics in processing strategic information.Originality/valueThis study finds a new perspective, strategic information, to explain the mechanisms by which digital platform capabilities drive firms’ technological innovation. In addition, the robustness of the resource-based view in understanding digital platform capabilities is stressed.

Theoretical and methodological bases for the use of criminal analysis by operative units of law enforcement agencies in Ukraine

This article examines the concept, content and nature of intelligence-led policing. The role of criminal analysis, supplemented by criminal intelligence, in countering crime is examined. The specifics of interaction between operative units of law enforcement agencies and criminal analysis units are identified.
 The model of police activity guided by intelligence analytics is currently the most suitable for overcoming the existing challenges and threats in the field of fighting crime. ILP is a model of policing, according to which intelligence serves as the basis for conducting operations/investigations, and not vice versa. Criminal analysis and criminal intelligence are key elements of the Intelligence-Led Policing (ILP).
 The organization of police activities guided by ILP means the planning and management of police work activities, thanks to which the products of criminal analysis serve as the basis for determining strategic and operational goals in the field of preventing and stopping crime. This enables the effective allocation of resources based on the areas of activity of individual offenders, organised crime groups and the criminal environment as a whole.
 Criminal analysis is aimed at supporting effective law enforcement activities. The results of criminal analysis form the basis for the organization of police activities guided by intelligence analytics. That is, the products of criminal analysis serve as the basis for building the organization of police activity according to a new philosophy - a transition from reactive methods of police activity - reaction to a criminal event, to proactive - when police activity is guided by conclusions about the analysis of patterns and trends that are identified in the criminal environment. This approach allows you to take timely and effective measures regarding a problem or situation with the intention of eliminating or mitigating its consequences.
 The interaction between employees of operative units and criminal analysts has certain features and takes place mostly within the framework of criminal intelligence analysis. An important aspect in the effective cooperation of operatives and criminal analysts is the introduction of specialization in certain areas of work. Criminal analysis is aimed at supporting effective law enforcement activities. The results of criminal analysis form the basis for the organization of police activities guided by intelligence analytics. That is, the products of criminal analysis serve as the basis for building the organization of police activity according to a new philosophy - a transition from reactive methods of police activity - reaction to a criminal event, to proactive - when police activity is guided by conclusions about the analysis of patterns and trends that are identified in the criminal environment. This approach allows you to take timely and effective measures regarding a problem or situation with the intention of eliminating or mitigating its consequences.
 The interaction between employees of operative units and criminal analysts has certain features and takes place mostly within the framework of criminal intelligence analysis. An important aspect in the effective cooperation of operatives and criminal analysts is the introduction of specialization in certain areas of work.

$\tau\text{JOWL}$: A Systematic Approach to Build and Evolve a Temporal OWL 2 Ontology Based on Temporal JSON Big Data

Nowadays, ontologies, which are defined under the OWL 2 Web Ontology Language (OWL 2), are being used in several fields like artificial intelligence, knowledge engineering, and Semantic Web environments to access data, answer queries, or infer new knowledge. In particular, ontologies can be used to model the semantics of big data as an enabling factor for the deployment of intelligent analytics. Big data are being widely stored and exchanged in JavaScript Object Notation (JSON) format, in particular by Web applications. However, JSON data collections lack explicit semantics as they are in general schema-less, which does not allow to efficiently leverage the benefits of big data. Furthermore, several applications require bookkeeping of the entire history of big data changes, for which no support is provided by mainstream Big Data management systems, including Not only SQL (NoSQL) database systems. In this paper, we propose an approach, named <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\tau \text{JOWL}$</tex> (temporal OWL 2 from temporal JSON), which allows users (i) to automatically build a temporal OWL 2 ontology of data, following the Closed World Assumption (CWA), from temporal JSON-based big data, and (ii) to manage its incremental maintenance accommodating the evolution of these data, in a temporal and multi-schema environment.

Machine-learned digital phase switch for sustainable chemical production

Batch or semi-batch chemical reaction units often requires multiple operational phases to convert reactants to valuable products. In various chemical production facilities, the switching decision of such operational phases has to be confirmed and registered by the operating personnel. Imprecise switching of phases can waste a significant amount of time and energy for the reaction unit, which gives negative plant sustainability and costs. Additionally, automation for phase switching is rarely used due to the challenges of batch-to-batch variance, sensor instability, and various process uncertainties. Here, we demonstrate that by using a machine learning approach which includes optimized noise removal methods and a neural network (that was neural architecture searched), the real-time reaction completion could be precisely tracked (R2 > 0.98). Furthermore, we show that the latent space of the evolved neural network could be transferred from predicting reaction completion to classifying the reaction operational phase via optimal transfer learning. From the optimally transfer learned network, a novel phase switch index is proposed to act as a digital phase switch and is shown to be capable of reducing total reactor operation time, with the verification of an operator. These intelligent analytics was studied on a reactive distillation unit for a reaction of monomers and acids to polyester in the Netherlands. The combined analytics gave a potential of 5.4% reaction batch time saving, 10.6% reaction energy savings, and 10.5% carbon emissions reduction. For the operator, this method also saves up to 6 h during the end discharge of the reaction.

Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021)

Big data technology and predictive analytics exhibit advanced potential for business intelligence (BI), especially for decision-making. This study aimed to explore current research studies, historic developing trends, and the future direction. A bibliographic study based on CiteSpace is implemented in this paper, 681 non-duplicate publications are retrieved from databases of Web of Science Core Collection (WoSCC) and Scopus from 2000 to 2021. The countries, institutions, cited authors, cited journals, and cited references with the most academic contributions were identified. Social networks and collaborations between countries, institutions, and scholars are explored. The cross degree of disciplinaries is measured. The hotspot distribution and burst keyword historic trend are explored, where research methods, BI-based applications, and challenges are separately discussed. Reasons for hotspots bursting in 2021 are explored. Finally, the research direction is predicted, and the advice is delivered to future researchers. Findings show that big data and AI-based methods for BI are one of the most popular research topics in the next few years, especially when it applies to topics of COVID-19, healthcare, hospitality, and 5G. Thus, this study contributes reference value for future research, especially for direct selection and method application.

Memory optimization at Edge for Distributed Convolution Neural Network

AbstractInternet of Things (IoT) edge intelligence has emerged by optimizing the deep learning (DL) models deployed on resource‐constraint devices for quick decision‐making. In addition, edge intelligence reduces network overload and latency by bringing intelligent analytics closer to the source. On the other hand, DL models need a lot of computing resources. As a result, they have high computational workloads and memory footprint, making it impractical to deploy and execute on IoT edge devices with limited capabilities. In addition, existing layer‐based partitioning methods generate many intermediate results, resulting in a huge memory footprint. In this article, we propose a framework to provide a comprehensive solution that enables the deployment of convolutional neural networks (CNNs) onto distributed IoT devices for faster inference and reduced memory footprint. This framework considers a pretrained YOLOv2 model, and a weight pruning technique is applied to the pre‐trained model to reduce the number of non‐contributing parameters. We use the fused layer partitioning method to vertically partition the fused layers of the CNN and then distribute the partition among the edge devices to process the input. In our experiment, we have considered multiple Raspberry Pi as edge devices. Raspberry Pi with a neural computing stick is a gateway device to combine the results from various edge devices and get the final output. Our proposed model achieved inference latency of 5 to 7 seconds for to fused layer partitioning for five devices with a 9% improvement in memory footprint.

Exploratory Research on Sweetness Perception: Decision Trees to Study Electroencephalographic Data and Its Relationship with the Explicit Response to Sweet Odor, Taste, and Flavor.

Using implicit responses to determine consumers’ response to different stimuli is becoming a popular approach, but research is still needed to understand the outputs of the different technologies used to collect data. During the present research, electroencephalography (EEG) responses and self-reported liking and emotions were collected on different stimuli (odor, taste, flavor samples) to better understand sweetness perception. Artificial intelligence analytics were used to classify the implicit responses, identifying decision trees to discriminate the stimuli by activated sensory system (odor/taste/flavor) and by nature of the stimuli (‘sweet’ vs. ‘non-sweet’ odors; ‘sweet-taste’, ‘sweet-flavor’, and ‘non-sweet flavor’; and ‘sweet stimuli’ vs. ‘non-sweet stimuli’). Significant differences were found among self-reported-liking of the stimuli and the emotions elicited by the stimuli, but no clear relationship was identified between explicit and implicit data. The present research sums interesting data for the EEG-linked research as well as for EEG data analysis, although much is still unknown about how to properly exploit implicit measurement technologies and their data.

An Imperative Role of Digitalization in Monitoring Cattle Health for Sustainability

In the current context, monitoring cattle health is critical for producing abundant milk to satisfy population growth demand and also for attaining sustainability. Traditional methods associated with cattle health must be strengthened in order to overcome the concern of detecting diseases based on the health condition. This problem has moved attention toward digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), cloud computing, edge/fog computing, big data, blockchain, drones, robotics, and augmented reality (AR)/virtual reality (VR), as these technologies have proved for real-time monitoring, intelligent analytics, secure data distribution, and real-time visual experience. The purpose of this study is to examine and discuss many cattle health disorders, as well as to address the fundamental notion of digital technologies, as well as the significance of these technologies for cattle health. Furthermore, the article addressed the different devices that integrated IoT and AI for cattle health monitoring, in which the previous architecture of cattle health monitoring is presented. Based on the review, the article discusses the challenges and suggests recommendations that can be implemented for the future work

Key Challenges and Emerging Technologies in Industrial IoT Architectures: A Review.

The Industrial Internet of Things (IIoT) is bringing evolution with remote monitoring, intelligent analytics, and control of industrial processes. However, as the industrial world is currently in its initial stage of adopting full-stack development solutions with IIoT, there is a need to address the arising challenges. In this regard, researchers have proposed IIoT architectures based on different architectural layers and emerging technologies for the end-to-end integration of IIoT systems. In this paper, we review and compare three widely accepted IIoT reference architectures and present a state-of-the-art review of conceptual and experimental IIoT architectures from the literature. We identified scalability, interoperability, security, privacy, reliability, and low latency as the main IIoT architectural requirements and detailed how the current architectures address these challenges by using emerging technologies such as edge/fog computing, blockchain, SDN, 5G, Machine Learning, and Wireless Sensor Networks (WSN). Finally, we discuss the relation between the current challenges and emergent technologies and present some opportunities and directions for future research work.

Validating Accuracy of an Internet-Based Application against USDA Computerized Nutrition Data System for Research on Essential Nutrients among Social-Ethnic Diets for the E-Health Era.

Internet-based applications (apps) are rapidly developing in the e-Health era to assess the dietary intake of essential macro-and micro-nutrients for precision nutrition. We, therefore, validated the accuracy of an internet-based app against the Nutrition Data System for Research (NDSR), assessing these essential nutrients among various social-ethnic diet types. The agreement between the two measures using intraclass correlation coefficients was good (0.85) for total calories, but moderate for caloric ranges outside of <1000 (0.75) and >2000 (0.57); and good (>0.75) for most macro- (average: 0.85) and micro-nutrients (average: 0.83) except cobalamin (0.73) and calcium (0.51). The app underestimated nutrients that are associated with protein and fat (protein: −5.82%, fat: −12.78%, vitamin B12: −13.59%, methionine: −8.76%, zinc: −12.49%), while overestimated nutrients that are associated with carbohydrate (fiber: 6.7%, B9: 9.06%). Using artificial intelligence analytics, we confirmed the factors that could contribute to the differences between the two measures for various essential nutrients, and they included caloric ranges; the differences between the two measures for carbohydrates, protein, and fat; and diet types. For total calories, as an example, the source factors that contributed to the differences between the two measures included caloric range (<1000 versus others), fat, and protein; for cobalamin: protein, American, and Japanese diets; and for folate: caloric range (<1000 versus others), carbohydrate, and Italian diet. In the e-Health era, the internet-based app has the capacity to enhance precision nutrition. By identifying and integrating the effects of potential contributing factors in the algorithm of output readings, the accuracy of new app measures could be improved.

Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions.

There has been rapid growth in the use of artificial intelligence (AI) analytics in medicine in recent years, including in rheumatic and musculoskeletal diseases (RMDs). Such methods represent a challenge to clinicians, patients, and researchers, given the "black box" nature of most algorithms, the unfamiliarity of the terms, and the lack of awareness of potential issues around these analyses. Therefore, this review aims to introduce this subject area in a way that is relevant and meaningful to clinicians and researchers. We hope to provide some insights into relevant strengths and limitations, reporting guidelines, as well as recent examples of such analyses in key areas, with a focus on lessons learned and future directions in diagnosis, phenotyping, prognosis, and precision medicine in RMDs.

Spatio-temporal monitoring of health facility-level malaria trends in Zambia and adaptive scaling for operational intervention

BackgroundThe spatial and temporal variability inherent in malaria transmission within countries implies that targeted interventions for malaria control in high-burden settings and subnational elimination are a practical necessity. Identifying the spatio-temporal incidence, risk, and trends at different administrative geographies within malaria-endemic countries and monitoring them in near real-time as change occurs is crucial for developing and introducing cost-effective, subnational control and elimination intervention strategies.MethodsThis study developed intelligent data analytics incorporating Bayesian trend and spatio-temporal Integrated Laplace Approximation models to analyse high-burden over 32 million reported malaria cases from 1743 health facilities in Zambia between 2009 and 2015.ResultsThe results show that at least 5.4 million people live in catchment areas with increasing trends of malaria, covering over 47% of all health facilities, while 5.7 million people live in areas with a declining trend (95% CI), covering 27% of health facilities. A two-scale spatio-temporal trend comparison identified significant differences between health facilities and higher-level districts, and the pattern observed in the southeastern region of Zambia provides the first evidence of the impact of recently implemented localised interventions.ConclusionsThe results support our recommendation for an adaptive scaling approach when implementing national malaria monitoring, control and elimination strategies and a particular need for stratified subnational approaches targeting high-burden regions with increasing disease trends. Strong clusters along borders with highly endemic countries in the north and south of Zambia underscore the need for coordinated cross-border malaria initiatives and strategies.

AI-Driven Anomaly Detection: A New Frontier in Web Application Security

In an era where cyber threats are not just evolving but escalating at an alarming rate, traditional cybersecurity measures are proving inadequate to safeguard sensitive digital assets. This research confronts the pressing need for a radical transformation in cybersecurity practices through the aggressive integration of Artificial Intelligence (AI) models. By harnessing the power of AI, we aim to redefine the landscape of cybersecurity, enabling organizations to preemptively identify, analyze, and neutralize threats before they manifest into catastrophic breaches. This study meticulously examines the integration of AI across key cybersecurity domains: threat modeling, real-time threat analysis, automated backup management, disaster recovery strategies, and rigorous source code review. As cybercriminals employ increasingly sophisticated tactics to exploit vulnerabilities, it becomes imperative for organizations to adopt a proactive stance powered by AI-driven automation and intelligent analytics. We delve into the fundamental reasons behind the urgent necessity for AI in cybersecurity, spotlighting the escalating complexity of cyberattacks and the inadequacy of conventional defenses. Current practices are critically analyzed to expose gaps and highlight the dire need for robust solutions that can adapt and evolve. The research provides a blueprint for leveraging cutting-edge AI technologies to transform threat identification and mitigation processes, fostering a security culture that is anticipatory rather than reactive.