Intelligent Framework Research Articles

“Planetary phase shift” as a new systems framework to navigate the evolutionary transformation of human civilisation

Purpose The paper aims to address a gap in foresight study and practice relating to the lack of unifying theoretical systems frameworks capable of examining empirical data from across a wide range of different ecological, social, political and economic systems. It attempts to develop a new “collective forward intelligence” that can not only make sense of these disparate trends and processes as symptoms of a wider planetary system but also, on this basis, construct accurate and plausible future scenarios to underpin national and international decision-making. Design/methodology/approach This study conducts a transdisciplinary integration of C. S. Holling’s adaptive cycle with phase-transition phenomena across biology, physics and chemistry, applied on societal and civilisational scales. A systems methodology is then applied to integrate historical and empirical data across the energy, food, transport, materials and information sectors of civilisation’s production system. Findings The paper develops planetary phase shift theory as a new collective forward intelligence framework for foresight study and practice, formalising the notion that humanity has arrived at an unprecedented historic and geological turning point. It finds that multiple global crises across both earth and human systems are symptoms of the last stages of the life-cycle of global industrialisation civilisation, which is the potential precursor either for collapse, or for a new civilisational life-cycle that may represent a new stage in the biological and cultural evolution of the human species. Research limitations/implications The research sets out a new empirically grounded theoretical framework for complex scenario analysis. This can develop more robust approaches to foresight study and practice, scenario development and forecasting. It suggests the need for a new research programme to understand the dynamics of the planetary phase shift and its diverse implications for societies, industry, technology and politics. The research is limited in that the current paper does not explore how it can be applied in this way. It identifies broad scenarios for a post-industrial civilisational life-cycle but does not identify the variety of complex subsets of these. Practical implications The paper provides powerful practical implications to develop new methodology based on planetary phase shift theory for strategic planning, risk assessment and management, as well as public policy and decision-making. Social implications The paper suggests the urgency and necessity of bold and radical societal transformation and implies key areas for civil society to focus on in innovating new values, worldviews and operating systems with a focus on the next life-cycle. Originality/value To the best of the author’s knowledge, this paper provides the first integrated transdisciplinary theoretical and empirical framework to understand how the interplay of earth system crises, societal change and technology disruptions is driving large-scale civilisational transformation with complex local ramifications.

An intelligent framework for attack detection in the internet of things using coati optimization

An intelligent framework for attack detection in the internet of things using coati optimization

Artificial Intelligences in Industrial Robots: A Framework Based on Gardner’s Multiple Intelligences

Industrial robots, both in manufacturing and non-manufacturing sectors, are evolving rapidly, driven by advancements in artificial intelligence (AI). This paper presents a comprehensive survey of industrial robots, framed through the lens of Howard Gardner’s theory of multiple intelligences. By categorising various AI capabilities in industrial robots—such as visual recognition, decision-making, and collaborative interaction—based on Gardner’s intelligence framework, we provide a novel taxonomy that bridges human cognitive abilities and artificial systems. The survey explores the historical development of industrial robots, the current state of AI implementation, and future trends in robotics. Additionally, we discuss the implications of these advancements for industries and their workforce, as well as the ethical considerations surrounding the growing autonomy of AI systems. This paper aims to serve as a reference point for researchers and professionals seeking to understand the intersection of cognitive science and industrial AI, highlighting the potential and challenges of integrating AI into robotic systems.

Establishing Medical Intelligence-Leveraging Fast Healthcare Interoperability Resources to Improve Clinical Management: Retrospective Cohort and Clinical Implementation Study.

FHIR (Fast Healthcare Interoperability Resources) has been proposed to enable health data interoperability. So far, its applicability has been demonstrated for selected research projects with limited data. This study aimed to design and implement a conceptual medical intelligence framework to leverage real-world care data for clinical decision-making. A Python package for the use of multimodal FHIR data (FHIRPACK [FHIR Python Analysis Conversion Kit]) was developed and pioneered in 5 real-world clinical use cases, that is, myocardial infarction, stroke, diabetes, sepsis, and prostate cancer. Patients were identified based on the ICD-10 (International Classification of Diseases, Tenth Revision) codes, and outcomes were derived from laboratory tests, prescriptions, procedures, and diagnostic reports. Results were provided as browser-based dashboards. For 2022, a total of 1,302,988 patient encounters were analyzed. (1) Myocardial infarction: in 72.7% (261/359) of cases, medication regimens fulfilled guideline recommendations. (2) Stroke: out of 1277 patients, 165 received thrombolysis and 108 thrombectomy. (3) Diabetes: in 443,866 serum glucose and 16,180 glycated hemoglobin A1c measurements from 35,494 unique patients, the prevalence of dysglycemic findings was 39% (13,887/35,494). Among those with dysglycemia, diagnosis was coded in 44.2% (6138/13,887) of the patients. (4) Sepsis: In 1803 patients, Staphylococcus epidermidis was the primarily isolated pathogen (773/2672, 28.9%) and piperacillin and tazobactam was the primarily prescribed antibiotic (593/1593, 37.2%). (5) PC: out of 54, three patients who received radical prostatectomy were identified as cases with prostate-specific antigen persistence or biochemical recurrence. Leveraging FHIR data through large-scale analytics can enhance health care quality and improve patient outcomes across 5 clinical specialties. We identified (1) patients with sepsis requiring less broad antibiotic therapy, (2) patients with myocardial infarction who could benefit from statin and antiplatelet therapy, (3) patients who had a stroke with longer than recommended times to intervention, (4) patients with hyperglycemia who could benefit from specialist referral, and (5) patients with PC with early increases in cancer markers.

An Intelligent Framework for Sleep Stage Scoring by 1D-Convolutional Neural Network with Bi-LSTM Layer Using EEG signal

An Intelligent Framework for Sleep Stage Scoring by 1D-Convolutional Neural Network with Bi-LSTM Layer Using EEG signal

An Intelligent Framework of Equipment Fault Diagnosis Based on Knowledge Graph

Equipment fault information typically exhibits the characteristics of fragmentation and diversified structure. The existing fault diagnosis methods are incapable of fully exploiting the prior knowledge and expert knowledge within the field, and the diagnosis results are overly one-sided. Given that it is challenging to obtain effective fault diagnostic knowledge for complex equipment and complex faults, this paper proposes the application of the domain knowledge graph (KG) for fault diagnosis. Different from the existing fault diagnosis methods involving the KG, our framework consists of two major parts. The first part is to construct an equipment fault KG from semi-structured and unstructured text. We further enrich the graph knowledge through knowledge completion, which can furnish high-quality knowledge sources for downstream applications. The second part is to employ the built fault KG either online or offline for fault diagnosis. We offer two approaches: the deep learning plus KG approach; the question-answering approach. The former not only guarantees the diagnostic accuracy but also provides more comprehensive diagnostic information. This constitutes the online utilization of the KG. The latter realizes the offline use of the KG, providing users with a natural and user-friendly manner to retrieve fault diagnosis information. We demonstrate and verify our proposed framework in the context of the bearing fault diagnosis.

Indetermsoft-Set-Based D* Extra Lite Framework for Resource Provisioning in Cloud Computing

Cloud computing is an immensely complex, huge-scale, and highly diverse computing platform that allows the deployment of highly resource-constrained scientific and personal applications. Resource provisioning in cloud computing is difficult because of the uncertainty associated with it in terms of dynamic elasticity, rapid performance change, large-scale virtualization, loosely coupled applications, the elastic escalation of user demands, etc. Hence, there is a need to develop an intelligent framework that allows effective resource provisioning under uncertainties. The Indetermsoft set is a promising mathematical model that is an extension of the traditional soft set that is designed to handle uncertain forms of data. The D* extra lite algorithm is a dynamic heuristic algorithm that makes use of the history of knowledge from past search experience to arrive at decisions. In this paper, the D* extra lite algorithm is enabled with the Indetermsoft set to perform proficient resource provisioning under uncertainty. The experimental results show that the performance of the proposed algorithm is found to be promising in performance metrics such as power consumption, resource utilization, total execution time, and learning rate. The expected value analysis also validated the experimental results obtained.

Deep learning-powered efficient characterization and quantification of microplastics

Characterizing and quantifying microplastics (MPs) are time-consuming and labor-intensive tasks traditionally. This paper presents an artificial intelligence (AI) framework aiming to automate these tasks by integrating computer vision and deep learning techniques. The approach leverages Fourier Transform Infrared (FTIR) spectra and visual images. Primary novelties of this research involve the development of: (1) an AI framework integrating efforts of data processing, analytics, visualization, and human-computer interaction; (2) a method for transforming FTIR data into contour images; (3) data augmentation strategies for resolving data scarcity and imbalance issues; (4) deep learning models for identifying MPs; (5) computer vision algorithms for quantifying MPs; and (6) an engineer-friendly graphic user interface (GUI) for enhancing data accessibility. The AI framework has been applied to polyethylene, polypropylene, polystyrene, polyamide, ethylene-vinyl acetate, and cellulose acetate. Results confirmed the efficacy of the framework, exhibiting high accuracy scores in classification (98 %), segmentation (99 %), and quantification (96 %) tasks. This research advances the capability of automatic assessment of MPs.

An Intelligent Framework for Performance Optimization of Telemedicine Center with Trust incorporating decision-making styles

An Intelligent Framework for Performance Optimization of Telemedicine Center with Trust incorporating decision-making styles

Development trends and knowledge framework of artificial intelligence (AI) applications in oncology by years: a bibliometric analysis from 1992 to 2022

PurposeOncology is the primary field in medicine with a high rate of artificial intelligence (AI) use. Thus, this study aimed to investigate the trends of AI in oncology, evaluating the bibliographic characteristics of articles. We evaluated the related research on the knowledge framework of Artificial Intelligence (AI) applications in Oncology through bibliometrics analysis and explored the research hotspots and current status from 1992 to 2022.MethodsThe research employed a scientometric methodology and leveraged scientific visualization tools such as Bibliometrix R Package Software, VOSviewer, and Litmaps for comprehensive data analysis. Scientific AI-related publications in oncology were retrieved from the Web of Science (WoS) and InCites from 1992 to 2022.ResultsA total of 7,815 articles authored by 35,098 authors and published in 1,492 journals were included in the final analysis. The most prolific authors were Esteva A (citaition = 5,821) and Gillies RJ (citaition = 4288). The most active institutions were the Chinese Academy of Science and Harward University. The leading journals were Frontiers ın Oncology and Scientific Reports. The most Frequent Author Keywords are "machine learning", "deep learning," "radiomics", "breast cancer", “melanoma” and "artificial intelligence," which are the research hotspots in this field. A total of 10,866 Authors' keywords were investigated. The average number of citations per document is 23. After 2015, the number of publications proliferated.ConclusionThe investigation of Artificial Intelligence (AI) applications in the field of Oncology is still in its early phases especially for genomics, proteomics, and clinicomics, with extensive studies focused on biology, diagnosis, treatment, and cancer risk assessment. This bibliometric analysis offered valuable perspectives into AI's role in Oncology research, shedding light on emerging research paths. Notably, a significant portion of these publications originated from developed nations. These findings could prove beneficial for both researchers and policymakers seeking to navigate this field.

An Open Annotated Dataset and Baseline Machine Learning Model for Segmentation of Vertebrae with Metastatic Bone Lesions from CT.

Automatic analysis of pathologic vertebrae from computed tomography (CT) scans could significantly improve the diagnostic management of patients with metastatic spine disease. We provide the first publicly available annotated imaging dataset of cancerous CT spines to help develop artificial intelligence frameworks for automatic vertebrae segmentation and classification. This collection contains a dataset of 55 CT scans collected on patients with various types of primary cancers at two different institutions. In addition to raw images, data include manual segmentations and contours, vertebral level labeling, vertebral lesion-type classifications, and patient demographic details. Our automated segmentation model uses nnU-Net, a freely available open-source framework for deep learning in healthcare imaging, and is made publicly available. This data will facilitate the development and validation of models for predicting the mechanical response to loading and the resulting risk of fractures and spinal deformity.

How can geostatistics help us understand deep learning? An exploratory study in SAR-based aircraft detection

Deep Neural Networks (DNNs) have garnered significant attention across various research domains due to their impressive performance, particularly Convolutional Neural Networks (CNNs), known for their exceptional accuracy in image processing tasks. However, the opaque nature of DNNs has raised concerns about their trustworthiness, as users often cannot understand how the model arrives at its predictions or decisions. This lack of transparency is particularly problematic in critical fields such as healthcare, finance, and law, where the stakes are high. Consequently, there has been a surge in the development of explanation methods for DNNs. Typically, the effectiveness of these methods is assessed subjectively via human observation on the heatmaps or attribution maps generated by eXplanation AI (XAI) methods. In this paper, a novel GeoStatistics Explainable Artificial Intelligence (GSEAI) framework is proposed, which integrates spatial pattern analysis from Geostatistics with XAI algorithms to assess and compare XAI understandability. Global and local Moran’s I indices, commonly used to assess the spatial autocorrelation of geographic data, assist in comprehending the spatial distribution patterns of attribution maps produced by the XAI method, through measuring the levels of aggregation or dispersion. Interpreting and analyzing attribution maps by Moran’s I scattergram and LISA clustering maps provide an accurate global objective quantitative assessment of the spatial distribution of feature attribution and achieves a more understandable local interpretation. In this paper, we conduct experiments on aircraft detection in SAR images based on the widely used YOLOv5 network, and evaluate four mainstream XAI methods quantitatively and qualitatively. By using GSEAI to perform explanation analysis of the given DNN, we could gain more insights about the behavior of the network, to enhance the trustworthiness of DNN applications. To the best of our knowledge, this is the first time XAI has been integrated with geostatistical algorithms in SAR domain knowledge, which expands the analytical approaches of XAI and also promotes the development of XAI within SAR image analytics.

Advanced artificial intelligence framework for T classification of TNM lung cancer in 18FDG-PET/CT imaging

The integration of artificial intelligence (AI) into lung cancer management offers immense potential to revolutionize diagnostic and treatment strategies. The aim is to develop a resilient AI framework capable of two critical tasks: firstly, achieving accurate and automated segmentation of lung tumors and secondly, facilitating the T classification of lung cancer according to the ninth edition of TNM staging 2024 based on PET/CT imaging. This study presents a robust AI framework for the automated segmentation of lung tumors and T classification of lung cancer using PET/CT imaging. The database includes axial DICOM CT and18FDG-PET/CT images. A modified ResNet-50 model was employed for segmentation, achieving high precision and specificity. Reconstructed 3D models of segmented slices enhance tumor boundary visualization, which is essential for treatment planning. The Pulmonary Toolkit facilitated lobe segmentation, providing critical diagnostic insights. Additionally, the segmented images were used as input for the T classification using a CNN ResNet-50 model. Our classification model demonstrated excellent performance, particularly for T1a, T2a, T2b, T3 and T4 tumors, with high precision, F1 scores, and specificity. The T stage is particularly relevant in lung cancer as it determines treatment approaches (surgery, chemotherapy and radiation therapy or supportive care) and prognosis assessment. In fact, for Tis-T2, each increase of one centimeter in tumor size results in a worse prognosis. For locally advanced tumors (T3-T4) and regardless of size, the prognosis is poorer. This AI framework marks a significant advancement in the automation of lung cancer diagnosis and staging, promising improved patient outcomes.

A Comprehensive Framework for Machine Learning-Based Threat Intelligence in Health Information Systems

This research work provides a comprehensive architecture of Machine Learning based threat intelligence particularly for Health Information System (HIS). The number of cybersecurity threats executed by healthcare companies is even higher since healthcare organizations continue to introduce digitized data into medical data. This work employs complex machine learning techniques from the MIMIC-III Critical Care Database to develop a practical threat identification and mitigation system. In this case, the strategy of analysis involves selection of data, data processing, modeling and real time dangers identification considering both supervised and unsupervised learning. The results reveal that the proposed framework covers high performance indicators such as: accuracy that equals 97.92%, and the level of precision and recall which also equal 90% ROC AUC has reached 0.94. These results demonstrate that the framework can identify and categorise cybersecurity risks in systems of health information on a regular basis. It not only increases threat perception but also makes the system internally valuable for healthcare IT professionals since it contains real-time monitoring and anomaly detection functionality. Therefore, this study stands in support of the ongoing efforts to enhance the security of the healthcare bodies on the use of policies on cybersecurity so as to ensure the protection of individual patient’s information against new forms of threats.

Taking aim at crime: evaluating evidence in a crime gun intelligence approach

ABSTRACT Identifying effective strategies to combat firearm-related crimes is a growing priority in the United States. The crime gun intelligence (CGI) framework, centered on evidence-based approaches, is a key approach to accomplish this. This article explores the relative contributions of various evidence types to case clearance. We examine gun-related crimes investigated by the Manchester Police Department (MPD) in New Hampshire from 2017 to 2022 to see how the use of NIBIN, e-Trace, firearm recovery, physical evidence, social media, and video evidence impacted the likelihood of arrest. Results suggest that recovering firearms and having video evidence are key elements for increasing the likelihood of an arrest. CGI-related evidence and other evidence types were typically predicted to have no significant effects. NIBIN and physical evidence were associated with negative effects on arrest in some instances. The practical implications of these findings, as well as their limitations and directions for future research, will also be discussed.

Pathology Report Interpretation and Disease Diagnosis Using Fuzzy Logic Embedded in an Artificial Intelligence Framework: A New Paradigm for Digital Technologies

Pathology Report Interpretation and Disease Diagnosis Using Fuzzy Logic Embedded in an Artificial Intelligence Framework: A New Paradigm for Digital Technologies

Enhancing Clustering Performance of Failed Test Cases during HIL Simulation: A Study on Deep Auto-Encoder Structures and Hyperparameter Tuning

Over the last decade, hardware-in-the-loop (HIL) simulation has been established as a safe, efficient, reliable, and flexible method for performing real-time simulation. Furthermore, in the automotive sector, the HIL system has been recommended in the ISO 26262 standard as a powerful platform for performing realistic simulation during system integration testing. As a result of performing HIL black-box tests, the results of executing test cases (TCs) are reported as pass/fail without determining the nature and root causes of the underlying failures. The conventional analysis process of the failed TCs relies on expert knowledge. The higher the number of failed TCs, the higher the cost of manual analysis in terms of time and effort. In light of the shortcomings of existing methodologies, this study presents a novel intelligent framework capable of analyzing failed TCs without the need for expert knowledge or code access. To this end, a convolutional auto-encoder-based deep-learning approach is proposed to extract representative features from the textual description of the failed TCs. Furthermore, k-means-based clustering is used to categorize the extracted features according to their respective failure classes. To illustrate the effectiveness and validate the performance of the proposed method, a virtual test drive with real-time HIL simulation is presented as a case study. The proposed model exhibits superior clustering performance compared to other standalone k-means algorithms, as demonstrated by the David Bouldin Index (DBI) and accuracy values of 0.5184 and 94.33%, respectively. Furthermore, the proposed model shows a significant advantage in terms of feature extraction and clustering performance compared to the current state-of-the-art fault-analysis method. The proposed approach not only supports the validation process and improves the safety and reliability of the systems but also reduces the costs of manual analysis in terms of time and effort.

Explainable AI for Transparent MRI Segmentation: Deep Learning and Visual Attribution in Clinical Decision Support

For medical diagnosis and therapy planning, the importance of accurate MRI segmentation cannot be overemphasized. Conversely, the inscrutability of deep learning models remains obstacles to their application in therapeutic contexts. In this article, an interpretability artificial intelligence framework is introduced. It combines an MRI segmentation model based on deep learning, visual attribution algorithms and natural language explanations. EXPERIMENT The dataset is consisting of plenty of different types of brain MRI scans, and used to test the architecture. The average of Dice score of our method is 88.7% and 92.3% for segmentation of tumor and categorization of tissues, respectively. Both are pretty epic scores. The insights extracted from both the visual attributions and textual explations improve our understanding of how the model arrives at its decisions, thereby increasing the transparency and interpretability of the model. believe this approach to explainable artificial intelligence can help to close the gap between state-of-the-art performance in MRI segmentation and clinical interpretability, by increasing the transparency of complex models and facilitating their implementation into a clinical workflow. Conclusion Our approach may have implications in the transparent and reliable development of AI-based decision support systems for medical imaging

Autonomous AI Systems for Continuous Learning and Real-Time Decision Making

Autonomous AI systems are upsetting navigation by constantly learning and pursuing ongoing choices without human oversight. These frameworks have applications across businesses like medical services, money, and assembling, where ongoing information is fundamental for functional achievement. This paper investigates the parts of independent simulated intelligence frameworks, including constant learning models, continuous information handling structures, and dynamic procedures. We likewise look at the difficulties such frameworks face, including information security, reasonableness, and taking care of edge cases. Through contextual analyses in various enterprises, we show the capability of independent computer-based intelligence frameworks to reshape the fate of savvy navigation. At last, we propose future bearings for research, zeroing in on moral structures and half and half learning models to further develop flexibility and straightforwardness in these frameworks.

Cancelable palmprint: intelligent framework toward secure and privacy-aware recognition system

Cancelable template protection techniques are indispensable to provide essential security and privacy privileges in biometric systems. This paper introduces an efficient cancelable palmprint recognition technique based on multi-level transformations. Gabor filtering with feature remapping is introduced for extracting highly discriminative features. Histogram remapping is applied to nonlinearly transform the downsampled Gabor features to be normally distributed. This feature-remapping step is proposed to enhance the discriminatory power and alleviate the effect of feature variability and image artifacts. Comb filtering is applied to the mapped features as a first protection layer. To provide security guarantees against linkability attacks, index-based locality-sensitive hashing (LSH) is introduced as a second protection layer to transform the comb-filtered mapped real-valued features into maximum-ranked indices. Recognition is performed in the secured domain to accelerate matching and to preserve the user’s privacy. Results show that the proposed scheme provides a large re-issuance ability, protects templates from being inverted, and demonstrates strong unlinkability across different databases. In addition, favorable verification/identification accuracy is obtained and the system satisfies the needs for real-time applications. A global measure D↔sys\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${D}_{\\leftrightarrow }^{sys}$$\\end{document} value of 0.01 is obtained, and thus, correlation attacks are mitigated. The recognition results for the legitimate scenario are 100% identification accuracy and 0% EER. For the worst case (same token scenario), the corresponding results are 99.752% and 0.31%, respectively.