The Comparison of Routing Algorithm for SDN Network using AI for Future Network

With growing interest in applying artificial intelligence (AI) to population breast cancer screening, the evidence base has expanded rapidly. This systematic review aims to systematically review and summarise the published evidence on the use of AI in breast cancer screening. We conducted a systematic review of primary studies assessing AI for screening mammography, extracting test-accuracy metrics (sensitivity, specificity, recall and cancer detection rates) and workflow outcomes. We searched the Cochrane Breast Cancer Group Specialised Register, Cochrane CENTRAL, PubMed, Embase (Elsevier), Scopus, ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform from January 2012 to June 2025; we also screened reference lists of included studies and relevant reviews. No language restrictions were applied. Primary studies evaluating AI for screening mammography (digital mammography or digital breast tomosynthesis) in asymptomatic women, assessing AI as a standalone reader or AI-assisted radiologist workflows versus radiologists alone. Eligible designs included randomised trials, prospective paired reader studies, real-world implementation/registry cohorts, retrospective cohorts and multireader-multicase reader studies conducted in population-based or opportunistic screening settings. Key outcomes included diagnostic accuracy metrics (eg, sensitivity, specificity, Area Under the Curve (AUC) and/or programme metrics (cancer detection rate (CDR), recall/abnormal interpretation rate, positive predictive value, arbitration/workload). We excluded protocols, pilot/feasibility studies, case reports, editorials and studies without relevant accuracy or screening outcomes. Two independent reviewers extracted data and assessed risk of bias. Study quality was appraised with Quality Assessment of Diagnostic Accuracy Studies-2 and an AI-specific critical appraisal tool, and findings were synthesised narratively with stratification by study design and AI integration role. 31 studies met the inclusion criteria, encompassing randomised controlled trials, prospective paired-reader studies, registry-based implementations and retrospective simulations, representing more than two million screening examinations across Europe, Asia, North America and Australia. When used as a second reader or within double-reading workflows, AI generally maintained or modestly increased sensitivity (up to +9 percentage points (PP)) while preserving or improving specificity. Triage and decision-referral configurations delivered the greatest operational benefit, reducing reading volumes by 40-90% while maintaining non-inferior cancer detection when thresholds were conservatively calibrated. Stand-alone AI achieved AUC values comparable to radiologists and similar cancer detection in real-world, non-enriched cohorts, although interval-cancer follow-up remains incomplete in several datasets. In prospective randomised evidence, including the Mammography Screening with Artificial Intelligence trial (MASAI) trial, AI-supported screening achieved higher CDRs (6.4 versus 5.0 per 1000; p=0.0021) with stable or reduced false-positive and recall rates. Across implementation and simulation settings, integration of AI reduced radiologist workload substantially, with triage and band-pass approaches reducing the number of reads by approximately 40-90%. Overall certainty is limited by heterogeneity across study designs, reliance on enriched datasets for some accuracy estimates and incomplete interval-cancer follow-up in several major studies. Contemporary AI systems show diagnostic performance that is broadly comparable to radiologists and can substantially reduce reading workload, particularly when used as a second reader or triage tool. Emerging prospective evidence supports their safe integration in these roles, although transparent reporting, standardised evaluation and long-term population studies are still required before considering AI as a stand-alone reader. AI may improve workflow efficiency and possibly cancer detection, but definitive evidence on safety, especially interval cancer outcomes, remains essential.

Abstract. In a science-based site selection procedure (StandAV), the Federal Republic of Germany is searching for a site with the best possible safety for a repository for high-level waste (HLW) over a period of 1 million years. This is a challenging task, given the size of Germany, its geological variability and the verification period required to ensure the best possible long-term safety. In addition, an enormous amount of heterogeneous geodata has to be processed. The application of artificial-intelligence-based methods in geosciences has great potential for dealing with large heterogeneous data sets. In the German Federal Office for the Safety of Nuclear Waste Management (BASE)-funded project “Application of artificial intelligence (AI) in the site selection process for a deep geological repository (FKZ 4721E03210)”, an interdisciplinary assessment tool is being developed to evaluate the applicability of AI methods in the geosciences. This contribution focuses on the potential and challenges of applying AI in geosciences with respect to key geological activities in the StandAV. Limitations that may arise from the use of AI with respect to key activities in the StandAV are highlighted. The necessary conditions for its future applicability are proposed. The results show that AI methods offer clear advantages over conventional methods for data management and handling large geological datasets and for modelling complex long-term and coupled geological processes. However, AI methods are generally only transferable to the geoscientific questions of the StandAV with methodological and subject-specific adaptations. Furthermore, the use of AI requires sufficient data, both in terms of quality and quantity. The study also shows that AI should only be used in a supportive way to address geological issues in key activities and should not have any decision-making power when used in StandAV. For example, AI can carry the risk of data and developer bias, which in turn can have serious consequences for the correct interpretation of results. High demands must be placed on the traceability of the AI methods used. AI methods that do not meet the transparency requirements of the StandAV carry a significant risk of jeopardising public confidence in the participation process. This could increase the general mistrust and scepticism towards AI in the public perception (Krob et al., 2023). It is strongly recommended that all methods should be evaluated and validated iteratively and that the results should be made available to the public when applied to the key activities of the StandAV.

<p> </p> <p>Addressing the global urgency for improved sustainable cities and communities, as per the United Nations Sustainable Development Goal (SDG) 11, requires innovative and disruptive approaches, which also include applying artificial intelligence (AI). While AI holds significant potential to address complex socio-economic and environmental challenges in cities, a comprehensive analysis of its applications and implications, particularly in urban contexts, is required to address the research gap in understanding how AI can be effectively deployed to meet the challenges. This paper reports on a study that evaluates how AI may facilitate achieving SDG 11. This assessment includes an expert-driven literature review, drawing insights from authoritative sources. In addition, a set of case studies illustrate practical applications of AI to improve urban sustainability. The combination of these approaches led to findings that underscore the pivotal role of AI in optimizing energy use, streamlining waste management, enhancing traffic flow, and contributing to environmental sustainability. However, according to the findings, AI implementation needs oversight to ensure it is ethical, inclusive, and privacyrespecting as an effective tool to aid decision-making. By fostering collaboration among planners, policymakers, and AI experts, the full potential of AI may be unlocked to shape sustainable urban environments and realize SDG 11.</p> <p> </p>

<p> </p> <p>Addressing the global urgency for improved sustainable cities and communities, as per the United Nations Sustainable Development Goal (SDG) 11, requires innovative and disruptive approaches, which also include applying artificial intelligence (AI). While AI holds significant potential to address complex socio-economic and environmental challenges in cities, a comprehensive analysis of its applications and implications, particularly in urban contexts, is required to address the research gap in understanding how AI can be effectively deployed to meet the challenges. This paper reports on a study that evaluates how AI may facilitate achieving SDG 11. This assessment includes an expert-driven literature review, drawing insights from authoritative sources. In addition, a set of case studies illustrate practical applications of AI to improve urban sustainability. The combination of these approaches led to findings that underscore the pivotal role of AI in optimizing energy use, streamlining waste management, enhancing traffic flow, and contributing to environmental sustainability. However, according to the findings, AI implementation needs oversight to ensure it is ethical, inclusive, and privacyrespecting as an effective tool to aid decision-making. By fostering collaboration among planners, policymakers, and AI experts, the full potential of AI may be unlocked to shape sustainable urban environments and realize SDG 11.</p> <p> </p>

This paper contains the design and development of an adaptive systems ( AdaptiveSystems Domain-Specific Language - DSL) framework to assist language developers and data scientists in their attempt to apply Artificial Intelligence (AI) algorithms in several application domains. Big-data processing and AI algorithms are at the heart of autonomics research groups among industry and academia. Major advances in the field have traditionally focused on algorithmic research and increasing the performance of the developed algorithms. However, it has been recently recognized by the AI community that the applicability of these algorithms and their consideration in context is of paramount importance for their adoption. Current approaches to address AI in context lie in two areas: adaptive systems research that mainly focuses on implementing adaptivity mechanisms (technical perspective) and AI in context research that focuses on business aspects (business perspective). There is currently no approach that combines all aspects required from business considerations to an appropriate level of abstraction. In this paper, we attempt to address the problem of designing adaptive systems and therefore providing AI in context by utilising DSL technology. We propose a new DSL ( AdaptiveSystems ) and a methodology to apply this to the creation of a DSL for specific application domains such as AdaptiveVLE (Adaptive Virtual Learning Environment) DSL. The language developer will be able to instantiate the AdaptiveSystems DSL to any application domain by using the guidelines in this paper with an integrated path from design to implementation. The domain expert will then be able to use the developed DSL (e.g. AdaptiveVLE DSL) to design and develop their application. Future work will include extension and experimentation of the applicability of this work to more application domains within British Telecom (BT) and other areas such as health care, finance, etc.

This chapter traces the ethical issues around applying artificial intelligence (AI) in education from the early days of artificial intelligence in education in the 1970s to the current state of this field, including the increasing sophistication of the system interfaces and the rise in data use and misuse. While in the early days most tools were largely learner-facing, now there are tools that are teacher-facing, supporting their management of the classroom, and administrator-facing, assisting in their management of cohorts of students. Learner-facing tools now take into account the affective and motivational aspects of learning as well as the cognitive. The rise of data collection and its associated analytic tools has enabled the development of dashboards for the dynamic management and reflective understanding of learners, teachers, and administrators. Ethical issues hardly figured in the early days of the field but now they loom large. This is because of the legitimate fears that learners’ and teachers’ autonomy will be compromised, that learner data will be collected and potentially misappropriated for other purposes, and that AI will introduce extra biases into educational decisions and increase existing inequity and also because of the scary reputation that AI has in general.

For the purpose of tracking several animal species, camera trapping is developing into a more reliable and popular technology. The idea of "citizen science"—incorporating members of the public into the research process—has been gaining momentum concurrently . As a result, millions of individuals have made contributions to research in numerous sectors. Despite early acknowledgment of camera traps' significance for public engagement, they were previously unsuited for citizen science. Academics are seeking assistance in categorizing film as a result of camera trap technological advancements that have made cameras more user-friendly, as well as the massive amounts of data that they now collect. Because of this, there are many camera trap efforts that now involve public participation, indicating that camera trap research is now a viable choice for citizen science. In order to categorize films, researchers are also applying artificial intelligence (AI). Although it has already been established that this rapidly developing field is useful, accuracy varies, Furthermore, AI does not provide the social and engagement advantages that citizen scientific endeavors provide. More attempts at fusing citizen science and AI are being suggested as a strategy to boost classification efficiency and accuracy while maintaining public interaction.

It is well understood that with a large number of data, an excellent interpretation of the subsurface condition can be produced, and also our understandings of the subsurface conditions can be improved significantly. However, having abundant subsurface geological and petrophysical data sometimes may not be possible, mainly due to budget issues. This situation can generate issues during hydrocarbon exploration and/or development activities.
 In this paper, the authors tried to apply artificial intelligence (AI) techniques to estimate outcomes values of particular wireline log data, using available petrophysic data. Two types of AI were selected and these are artificial neural network (ANN), and multiple linear regression (MLR). This research aims to advance our understanding of AI and its application in geology. There are three objectives of this study: (1) to estimate sonic log (DT) and density log (RhoB) using different types of AI (ANN and MLR); (2) to assess the best AI technique that can be used to estimate certain wireline log data; and (3) to compare the estimated wireline log values with the real, recorded values from the subsurface.
 Findings from this study show that ANN consistently provided a better accuracy percentage compared to MLR when estimating density log (RhoB). While using different set of data and technique, estimation of sonic log (DT) produced different accuracy level. Moreover, crossplot validation of the results show that the results from ANN analysis produced higher trendline reliability (R2) and correlation coefficient (R) than the results from MLR analysis. Comparison of the estimated RhoB and DT log data with the original recorded data shows minor mismatch. This is evident that AI technique can be a reliable solution to estimate particular outcomes of wireline log data, due to limited availability of the original recorded subsurface petrophysic data. It is expected that these findings would provide new insights into the application of AI in geology, and encourage the readers to explore and expand the many possibilities of the application of AI in geology.

They discovered that habitat degradation is a threat that impacts ecosystems and people across the globe. Therefore, as habitats decrease and species are threatened, improved strategies for managing such effects are inevitable. This paper focuses on how the loss of biological diversity may be complemented by applying artificial intelligence (AI). It stresses its role in monitoring and modeling the environment and the trends geared towards its saving. On top of the more specific use of AI for species counting and monitoring, change detection, and decision-making, the analysis provides an understanding of how AI technologies work in practice and how conservationists can benefit from it. This paper also outlines some of the critical threats related to AI opportunities in conservation and proposes strategies to address these threats. Therefore, it can be concluded that it is necessary and adequate to incorporate technology into their conservation measures to mitigate the global loss of biological diversity.

The expanding digitalization of routine diagnostic histological slides holds a potential to apply artificial intelligence (AI) to pathology, including bone marrow (BM) histology. In this perspective, we describe potential tasks in diagnostics that can be supported, investigations that can be guided, and questions that can be answered by the future application of AI on whole-slide images of BM biopsies. These range from characterization of cell lineages and quantification of cells and stromal structures to disease prediction. First glimpses show an exciting potential to detect subtle phenotypic changes with AI that are due to specific genotypes. The discussion is illustrated by examples of current AI research using BM biopsy slides. In addition, we briefly discuss current challenges for implementation of AI-supported diagnostics.

Responding to the vital role and robust development of smart warehouses, in this study, solutions for applying Artificial Intelligence (AI) to smart warehouse development, especially in Vietnam, were analyzed and proposed. The paper has investigated the factors affecting the application of AI for smart warehouse development in Vietnam through SWOT. Solution groups include (1) Investment decision support solutions that limit investment risks through solutions to test innovative approaches and algorithms, (2) Solutions for AI application in smart warehouse development, (3) Solutions to develop AI resources for smart warehouses. Therefore, by using SWOT analysis, this paper aims to generate value strategies AI-based solutions for the smart warehouse to improve the warehouse process efficiencies and strengthen the competitive advantage of the logistics industry in Vietnam.KeywordsArtificial intelligenceMachine learningDeep learningAI applicationSmart warehouseIndustry 4.0Digital transformation

In the scientific review study analyzes the state of scientific development of the problem of forming the readiness of future teachers to apply artificial intelligence (AI) in their professional activities. Based on the synthesis of contemporary domestic and international research, the main trends in the integration of AI into education are identified, and the theoretical and methodological foundations of teacher training in the digital society are outlined. The results of the nationwide study (2024) demonstrated a high level of educators’ interest in AI technologies, while simultaneously revealing a shortage of methodological, ethical, and pedagogical guidelines for their meaningful implementation. The study emphasizes that modern teacher education is shifting from a fragmented acquaintance with digital tools to the formation of holistic readiness to work with intelligent systems that integrate cognitive, operational, value-based, and reflective components. Special attention is given to research addressing the potential risks of automation, the decline of creativity, and the need to preserve humanistic values in education in the era of AI. It is substantiated that effective preparation of future teachers requires a systemic approach combining the development of digital culture, ethical responsibility, and critical thinking. The analysis shows the lack of a coherent methodological model for forming readiness to use AI, one that would be integrated with ethical, philosophical, and cognitive aspects of pedagogical activity. In the context of the Concept for the Development of Artificial Intelligence in Ukraine (2020), the relevance of forming teachers’ competencies necessary for the ethical, creative, and safe use of AI in the educational process is emphasized. The practical significance of the study lies in defining the scientific foundations for designing educational and professional development programs aimed at the responsible and effective integration of AI technologies into the pedagogical practice of higher education institutions.

Cognitive sensor systems (CSS) determine the future of inspection and monitoring systems for the nondestructive evaluation (NDE) of material states and their properties and key enabler of NDE 4.0 activities. CSS generate a complete NDE 4.0 data and information ecosystem, i. e. they are part of the materials data space and they are integrated in the concepts of Industry 4.0 (I4.0). Thus, they are elements of the Industrial Internet of Things (IIoT) and of the required interfaces. Applied Artificial Intelligence (AI) is a key element for the development of cognitive NDE 4.0 sensor systems. On the one side, AI can be embedded in the sensor’s microelectronics (e. g. neuromorphic hardware architectures) and on the other side, applied AI is essential for software modules in order to produce end-user-information by fusing multi-mode sensor data and measurements. Besides of applied AI, trusted AI also plays an important role in CSS, as it is able to provide reliable and trustworthy data evaluation decisions for the end user. For this recently rapidly growing demand of performant and reliable CSS, specific requirements have to be fulfilled for validation and qualification of their correct function. The concept for quality assurance of NDE 4.0 sensor and inspection systems has to cover all of the functional sub-systems, i. e. data acquisition, data processing, data evaluation and data transfer, etc. Approaches to these objectives are presented in this paper after giving an overview on the most important elements of CSS for NDE 4.0 applications. Reliable and safe microelectronics is a further issue in the qualification process for CSS.

SDVs get advanced as a result of applying artificial intelligence (AI), yet they become increasingly difficult to test. The conventional techniques such as the scripted examinations or random scenarios are not sufficient to test all of the likelihood of driving that may contain peculiar and hazardous elements. The present research paper claims to introduce a novel conceptualization using agentic AI and generative AI (with no contact to real-life phenomena) to experiment in SDV attributes more effectively. The agentic system of AI will be autonomous that is, they plan, learn and innovate overtime. The Generative AI assists in supporting millions of test cases, unusual road dynamics, driver behavior and cyber-attacks. It experimented with its framework using modified systems of driver-assistance (ADAS) and the experiment was very successful. In comparison, agentic AI contains three times more opportunities to go off the rails relative to scripted tests and exceeding a regulation failure relative to what random tests. It also enabled the vehicles to be competent to pass safety standards like FMVSS and the ISO 26262 standard as risks may be noticed promptly. The model minimized the use of the network without the report of mines to the testing speed and coverage which was enabled with a combination of edge resources and cloud-based resources. This was what was called continuous learning as the system continued to improve as more vehicles provided the data. The result has already been proven that AI-based testing is scalable, viable and applicable to use in complex conditions. According to the study, agentic AI could greatly contribute to smarter and safer SDV testing that would give engineers the opportunity to identify and eliminate issues extremely quickly. Such an approach will go toward the adoption of faster and have more confidence in self-driving cars.

In this research, the proposed solution is to apply Artificial Intelligence (AI) and the Internet of Things (IoT) in an attempt to modernize Smart Tourism Destinations (STDs). The synergy between the Internet of Things and the concepts of artificial intelligence is an unmatched gain to the sphere of tourism in numerous aspects, such as individual recommendations, access to information in real-time, etc. This was planned to conduct a deep investigation of the present state, issues and future of the STDs in the artificial intelligence and internet of things synergy. The paper starts to identify various AI methods besides data collection processes that can be provided by IoT and improve the quality of tourist experience and the process of destination management. Nonetheless, it is highly important to resolve these issues in regard to privacy and data security. Its core competence is to become the pioneer in provision of smart and people centric tourism destinations. The paper outlines the influence of AI-based technologies on Smart Tourism Destination, as well as returns to the necessity to study the ethical issues, quality of data, understandability, and collaboration between people and artificial intelligence.