Artificial Intelligence Machine Learning Research Articles

Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention

The abstract is "The rapid evolution of cyber threats necessitates innovative defenses, particularly in the domains of risk assessment and fraud detection. This paper explores the integration of Artificial Intelligence (AI) and Adversarial Machine Learning (ML) techniques as a formidable strategy against increasingly sophisticated cyber-attacks. We present a comprehensive framework that leverages AI to dynamically assess cybersecurity risks and detect fraudulent activities with unprecedented accuracy and speed. Firstly, we delve into the foundational principles of adversarial machine learning, outlining how these techniques can be employed to simulate potential cyber threats, thereby enabling the development of more resilient AI-driven cybersecurity systems. We highlight the dual role of adversarial ML in both enhancing security defenses and potentially serving as a vector for sophisticated attacks, underscoring the importance of developing robust, adversarial-resistant models. Subsequently, we introduce a novel adaptive risk assessment methodology that incorporates real-time data analysis, machine learning algorithms, and predictive modeling to accurately identify and prioritize threats. This method adapts to the evolving digital landscape, ensuring that cybersecurity measures are always one step ahead of potential attackers. In the context of fraud detection, we explore -how AI algorithms can sift through vast datasets to detect anomalies and patterns indicative of fraudulent behavior. Through case studies and empirical analysis, we demonstrate the effectiveness of AI in identifying fraud across various sectors, from financial transactions to online identity verification processes. Our research contributes to the cybersecurity field by providing a detailed examination of how AI and adversarial ML can be harnessed to fortify digital defenses, improve risk assessment techniques, and enhance fraud detection capabilities. The insights garnered from this study not only advance theoretical understanding but also offer practical guidance for organizations seeking to implement AI-driven security solutions. As cyber threats continue to evolve, the integration of AI and adversarial ML in cybersecurity strategies will be paramount in safeguarding digital assets and maintaining the integrity of online systems."

Machine-learning models evaluate embryo morphokinetics to detect subclinical signs of heat stress in embryos produced by multiple ovulation embryo transfer

Increased demand for animal-based protein, driven by the in­creasing world population, has emphasized the use of bovine embryo transfer (ET). However, this demand is predominantly in locations where environmental conditions, like heat stress, are inhibiting reproductive performance. Heat stressed do­nors have decreased viable blastocyst production, greater vari­ability in embryonic gene expression and lower ET pregnancy outcomes in current literature. These embryonic changes are nonidentifiable to the human eye, highlighting the need for solutions to identify compromised embryos and improve ET efficiencies. Machine-learning artificial intelligence has been used to evaluate health by assessing embryo morphokinetics, or time-specific morphological changes during embryo devel­opment, from videos of embryos in standard media. Thus, the objective was to use machine learning to detect real-time mor­phokinetic activity of embryos from donor cows undergoing multiple ovulation embryo transfer (MOET) based on seasonal exposure to heat stress.

Enhancing Mental Health Predictions: A Gradient Boosted Model for Sri Lankan Camp Refugees

This study explores the mental health challenges encountered by Sri Lankan camp refugees, a population often marginalized in mental health research, and analyzes a range of factors including socio-demographic characteristics, living conditions in camps, and psychological variables. In quantitative mental health research, linear regression serves as a conventional approach for assessing the influence of diverse factors on mental health outcomes. However, this method fails to accommodate non-linear relationships between mental health variables and predictors and relies on stringent model assumptions that often do not align with real-world conditions. This study introduces a model-agnostic, advanced machine learning/artificial intelligence (ML/AI) technique, glmboost, as a viable alternative to linear regression. The glmboost algorithm is capable of fitting non-linear prediction models while also conducting variable selection. Moreover, the coefficients obtained from the glmboost model retain the same interpretability as those derived from linear regression. While the glmboost model identifies several key factors including post-migration living difficulties, post-traumatic stress disorder, difficulty in sleeping, poor family functioning, and lower informal support from families as markers of declining mental well-being among the Sri Lankan refugees, the linear regression overlooks vital predictors such as family functioning and family support, highlighting the importance of utilizing advanced ML/AI techniques like glmboost to comprehensively capture complex relationships between predictor variables and mental health outcomes among refugee populations. Thus, by introducing a novel, data-driven approach to mental health risk assessment, this study paves the way for more precise and efficient analyses and interventions in refugee settings, with the potential for improved resource allocation and personalized support, thus revolutionizing mental health service delivery in challenging environments. Additionally, it contributes to the academic discussion on refugee mental health while emphasizing the pivotal role of advanced data analytics in addressing complex health issues within humanitarian contexts.

DETEK-AI: A Web-based Deepfake Detection System

AI-driven deepfake bots pose a serious risk to society, security, and trust, which necessitates the development of reliable techniques to detect and mitigate their threat, as their proliferation leads to disinformation, deceit, and harm. Deepfake detection systems can improve cybersecurity, safeguard digital identities, mitigate misinformation, protect reputations of individuals and organizations, assure ethical use of AI technology, empower people and organizations, support regulatory efforts, and contribute to a safer digital ecosystem. Detek AI is a frontrunner in innovative technology aimed at combating deepfake content, safeguarding individuals and organizations from misinformation, and upholding the integrity of online platforms. Keyword— Deepfake Technology, Deepfake Detection, Convolutional Neural Networks, Artificial Intelligence bots, Machine Learning Artificial Intelligence, Face Morphing Detection, Deep Learning, Benford’s Law.

Cost-sensitive ordinal classification methods to predict SARS-CoV-2 pneumonia severity.

To study the suitability of costsensitive ordinal artificial intelligence-machine learning (AIML) strategies in the prognosis of SARS-CoV-2 pneumonia severity. Observational, retrospective, longitudinal, cohort study in 4 hospitals in Spain. Information regarding demographic and clinical status was supplemented by socioeconomic data and air pollution exposures. We proposed AI-ML algorithms for ordinal classification via ordinal decomposition and for cost-sensitive learning via resampling techniques. For performance-based model selection, we defined a custom score including per-class sensitivities and asymmetric misprognosis costs. 260 distinct AI-ML models were evaluated via 10 repetitions of 5×5 nested cross-validation with hyperparameter tuning. Model selection was followed by the calibration of predicted probabilities. Final overall performance was compared against five well-established clinical severity scores and against a 'standard' (non-cost sensitive, non-ordinal) AI-ML baseline. In our best model, we also evaluated its explainability with respect to each of the input variables. The study enrolled n = 1548 patients: 712 experienced low, 238 medium, and 598 high clinical severity. d = 131 variables were collected, becoming d ' = 148 features after categorical encoding. Model selection resulted in our best-performing AI-ML pipeline having: a) no imputation of missing data, b) no feature selection (i.e. using the full set of d ' features), c) 'Ordered Partitions' ordinal decomposition, d) cost-based reimbalance, and e) a Histogram-based Gradient Boosting classifier. This best model (calibrated) obtained a median accuracy of 68.1% [67.3%, 68.8%] (95% confidence interval), a balanced accuracy of 57.0% [55.6%, 57.9%], and an overall area under the curve (AUC) 0.802 [0.795, 0.808]. In our dataset, it outperformed all five clinical severity scores and the 'standard' AI-ML baseline. We conducted an exhaustive exploration of AI-ML methods designed for both ordinal and cost-sensitive classification, motivated by a real-world application domain (clinical severity prognosis) in which these topics arise naturally. Our model with the best classification performance exploited successfully the ordering information of ground truth classes, coping with imbalance and asymmetric costs. However, these ordinal and cost-sensitive aspects are seldom explored in the literature.

Waste-to-energy poly-generation scheme for hydrogen/freshwater/power/oxygen/heating capacity production; optimized by regression machine learning algorithms

Utilization of machine learning techniques in the analysis and enhancement of poly-generation energy systems improves their efficiency and sustainability. Also, waste-to-energy systems propose a hopeful answer for both waste management and sustainable energy and water production. The production of hydrogen and freshwater through these systems not only provides valuable resources but also contributes to environmental sustainability. This research utilizes artificial intelligence's machine learning algorithms to examine and enhance a waste-to-energy system within a poly-generation energy system that generates hydrogen, freshwater, power, oxygen, and hot air. The integrated system consists of a waste combustion chamber, a proton exchange membrane electrolyzer, a supercritical carbon dioxide Brayton cycle, and a desalination system. Based on the findings, the machine learning algorithms exhibiting R-squared values exceeding 99% are considered strong fits. Additionally, all algorithms with high predicted R-squared values have the capability to accurately forecast new data using the provided training data. The emissions of 669.7 g/kWh and an efficiency of 69.62% are achieved when the pressure ratio is 10.73 and the temperature is 863.6 °C under optimal conditions. The accuracy and validity of the machine learning techniques are further confirmed by the strong agreement with thermodynamic modeling.

UTILIZAREA IMAGINILOR SATELITARE ÎN AGRICULTURĂ. STUDIU DE CAZ

All sizes of farms can benefit from satellite imagery, not only big producers. When paired with artificial intelligence (AI) and deep machine learning techniques, satellite photography becomes an effective tool for monitoring agricultural conditions and anticipating issues in the field. As a result, using satellite photos to guide crop farming choices can help determine when to apply nutrients and irrigation. This paper focuses on monitoring through satellite sensors with an emphasis on the facilities offered by the European Copernicus Program through Sentinel-2 satellites the crops from a farm from Calarasi County, Borcea commune.

ENHANCING OIL AND GAS EXPLORATION EFFICIENCY THROUGH AI-DRIVEN SEISMIC IMAGING AND DATA ANALYSIS

This paper delves into the advancements in AI-driven seismic imaging and data analysis techniques aimed at augmenting the efficiency of oil and gas exploration. We explore various AI algorithms and machine learning models that have been deployed to interpret seismic data, predict subsurface structures, and identify potential hydrocarbon reservoirs with unprecedented precision. Furthermore, we discuss the integration of big data analytics and high-performance computing in handling vast volumes of seismic data, thereby facilitating rapid decision-making in exploration projects. Through case studies and empirical evidence, we highlight the tangible benefits and potential challenges associated with the adoption of AI-driven seismic imaging and data analysis in the oil and gas industry. Ultimately, this paper underscores the transformative impact of AI technologies in optimizing exploration workflows and maximizing resource discovery while mitigating risks and reducing operational costs. In the pursuit of optimizing oil and gas exploration, the integration of artificial intelligence (AI) methodologies has emerged as a transformative force. This paper examines the evolving landscape of AI-driven seismic imaging and data analysis techniques, aimed at enhancing efficiency within the exploration domain. By harnessing AI algorithms and machine learning models, seismic data interpretation is propelled to unprecedented levels of accuracy, enabling the prediction of subsurface structures and the identification of potential hydrocarbon reservoirs with enhanced precision. Moreover, the synergistic fusion of big data analytics and high-performance computing facilitates the processing of vast seismic datasets, expediting decision-making processes in exploration endeavors. Through a synthesis of case studies and empirical evidence, this paper elucidates the tangible benefits and potential challenges associated with AI adoption in the oil and gas sector. By amplifying exploration workflows, mitigating risks, and curbing operational costs, AI-driven seismic imaging and data analysis stand poised to revolutionize the landscape of oil and gas exploration, catalyzing sustainable resource discovery in an evolving energy paradigm. Keywords: AI-Driven Seismic Imaging, Oil And Gas Exploration, Data Analysis, Efficiency Optimization, Geophysical Insights, Seismic Interpretation.

CAN ARTIFICIAL INTELLIGENCE ACCURATELY DETECT URINARY STONES? A SYSTEMATIC REVIEW.

To perform a systematic review on artificial intelligence(AI) performances to detect urinary stones. A PROSPERO-registered(CRD473152) systematic search of Scopus, Web of Science, Embase and PubMed databases was performed to identify original research articles pertaining to AI stone detection or measurement, using search terms("automatic" OR "machine learning" OR "convolutional neural network" OR "artificial intelligence" OR "detection" AND "stone volume"). Risk-of-bias(RoB) assessment was performed according to the Cochrane RoB tool, the Joanna Briggs Institute Checklist for nonrandomized studies and the Checklist for Artificial Intelligence in Medical Imaging(CLAIM). Twelve studies were selected for final review, including 3 multicenter and 9 single-center retrospective studies. 11 studies completed at least 50% of the CLAIM checkpoints and only one presented a high ROB. All included studies aimed to detect on kidney(5/12, 42%), ureter(2/12, 16%) or urinary(5/12, 42%) stones on Non-Contrast Computed Tomography(NCCT), but 42% intended to automate measurement. Stone distinction from vascular calcification interested 2 studies. All studies used AI machine learning network training and internal validation, but a single one provided an external validation. Trained networks achieved stone detection with sensitivity, specificity and accuracy rates ranging from 58,.7 to 100%, 68,.5 to 100% and 63 to 99,.95%, respectively. Detection Dice score ranged from 83% to 97%. A high correlation between manual and automated Stone Volume(r=0,.95) was noted. Differentiate distal ureteral stones and phleboliths seemed feasible. Artificial Intelligence processes can achieve automated urinary stone detection from NCCT. Further studies should provide urinary stones detection coupled with phlebolith distinction and an external validation, and include anatomical abnormalities and urological foreign bodies (ureteral stent and nephrostomy tubes) cases.

Development and validation of AI/ML derived splice-switching oligonucleotides.

Splice-switching oligonucleotides (SSOs) are antisense compounds that act directly on pre-mRNA to modulate alternative splicing (AS). This study demonstrates the value that artificial intelligence/machine learning (AI/ML) provides for the identification of functional, verifiable, and therapeutic SSOs. We trained XGboost tree models using splicing factor (SF) pre-mRNA binding profiles and spliceosome assembly information to identify modulatory SSO binding sites on pre-mRNA. Using Shapley and out-of-bag analyses we also predicted the identity of specific SFs whose binding to pre-mRNA is blocked by SSOs. This step adds considerable transparency to AI/ML-driven drug discovery and informs biological insights useful in further validation steps. We applied this approach to previously established functional SSOs to retrospectively identify the SFs likely to regulate those events. We then took a prospective validation approach using a novel target in triple negative breast cancer (TNBC), NEDD4L exon 13 (NEDD4Le13). Targeting NEDD4Le13 with an AI/ML-designed SSO decreased the proliferative and migratory behavior of TNBC cells via downregulation of the TGFβ pathway. Overall, this study illustrates the ability of AI/ML to extract actionable insights from RNA-seq data.

Identifying necrotizing soft tissue infection using infectious fluid analysis and clinical parameters based on machine learning algorithms

BackgroundDetermining the presence of necrotizing soft tissue infection (NSTI) poses a significant hurdle. As of late, there has been a notable increase in the application of artificial intelligence (AI) machine learning techniques in identifying diseases, a shift that can be attributed to their exceptional efficiency, unbiased nature, and high precision. MethodsInformation was gathered from a cohort of 13 patients suffering from NSTI, alongside 12 patients with cellulitis. The construction of NSTI diagnostic machine learning models utilized four different algorithms, specifically random forest, k-nearest neighbors (KNN), support vector machine (SVM), and logistic regression. These models were constructed based on 28 distinctive attributes identified through statistical examination. Following this, the diagnostic efficiency of each algorithms was evaluated. A novel random forest model, streamlined for clinical use, was later developed by focusing on 6 attributes that had the most pronounced influence on the accuracy of our initial random forest model. ResultsThe following data was noted regarding the sensitivity and specificity of the four NSTI diagnostic models:logistic regression displayed 78.2 % and 83.7 %, KNN presented 79.1 % and 87.1 %, SVM showed 83.5 % and 86.3 %, and random forest exhibited 89.6 % and 92.9 %, respectively. In comparison, lactate levels in fluid demonstrated 100 % sensitivity and 76.9 % specificity at an optimal cut-off point of 69.6 mg/dL. Among all four machine learning models, random forest outperformed the others and also showed better results than fluid lactate. A newly constructed random forest model, created using 6 of the 13 identified features, displayed promising results in diagnosing NSTI, having a sensitivity and specificity of 90.2 % and 92.2 %, respectively. ConclusionsDeveloping a diagnostic model for NSTI employing the random forest algorithm has resulted in a diagnostic technique that is more efficient, cost-effective, and expedient. This approach could provide healthcare practitioners with the tools to identify and manage NSTI with greater efficacy.

Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania.

Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management. This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)-powered cough audio classifier for detecting these respiratory conditions in rural Tanzania. This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be nonintrusive and user-friendly, will facilitate the collection of diverse cough sound samples from patients attending outpatient clinics in 20 health care facilities in the Shinyanga region. The collected cough sound data will undergo rigorous analysis, using advanced AI signal processing and machine learning techniques. By comparing acoustic features and patterns associated with TB, asthma, and COPD, a robust algorithm capable of automated disease discrimination will be generated facilitating the development of a smartphone-based cough sound classifier. The classifier will be evaluated against the calculated reference standards including clinical assessments, sputum smear, GeneXpert, chest x-ray, culture and sensitivity, spirometry and peak expiratory flow, and sensitivity and predictive values. This research represents a vital step toward enhancing the diagnostic capabilities available in outpatient clinics, with the potential to revolutionize the field of respiratory disease diagnosis. Findings from the 4 phases of the study will be presented as descriptions supported by relevant images, tables, and figures. The anticipated outcome of this research is the creation of a reliable, noninvasive diagnostic cough classifier that empowers health care professionals and patients themselves to identify and differentiate these respiratory diseases based on cough sound patterns. Cough sound classifiers use advanced technology for early detection and management of respiratory conditions, offering a less invasive and more efficient alternative to traditional diagnostics. This technology promises to ease public health burdens, improve patient outcomes, and enhance health care access in under-resourced areas, potentially transforming respiratory disease management globally. PRR1-10.2196/54388.

AI-Powered Revolution: Transforming Industrial Production through Automation

The integration of artificial intelligence (AI) tools has propelled automation in industrial production to new heights, ushering in a wave of transformative advancements. This research delves into the profound impact of AI tools on industrial production, elucidating their pivotal role in driving efficiency, cost reduction, and productivity enhancements. By leveraging AI algorithms and machine learning techniques, manufacturers can optimize operations with unprecedented precision and agility. However, alongside the promises of increased efficiency come significant challenges and opportunities. This paper navigates through the complexities of implementing AI-driven automation in manufacturing processes, addressing issues such as data security, workforce adaptation, and ethical considerations. Through rigorous secondary data analysis and research methodology, this study endeavours to shed light on the current state of automation in industrial production while offering valuable insights into its future prospects and implications for the manufacturing industry.

The Impact of Business Continuity on Supply Chain Practices and Resilience Due to COVID-19

Background: Business continuity entails the potential negative consequences of uncertainty on a firm’s ability to achieve strategic objectives. The COVID-19 pandemic significantly impacted business continuity due to lockdowns, travel restrictions, and social distancing measures. Consequently, firms adopted specific supply chain (SC) practices to effectively navigate this global crisis. Methods: This research adopted a stochastic approach based on Bayesian Networks to evaluate the implications of business continuity on firms’ decisions to embrace SC practices, focusing on omnichannel strategies, SC coordination, and technologies such as artificial intelligence systems, big data and machine learning, and mobile applications. Results: Our findings revealed that firms facing disruption in a single performance area can apply specific strategies to maintain resilience. However, multiple areas of underperformance necessitate a varied approach. Conclusions: According to our empirical analysis, omnichannel strategies are critical when disruptions simultaneously impact quality, inventory, sales, and ROI, particularly during major disruptions such as the COVID-19 pandemic. AI and big data become vital when multiple risks coalesce, enhancing areas such as customer service and supply chain visibility. Moreover, supply chain coordination and mobile app adoption are effective against individual performance risks, proving crucial in mitigating disruption impacts across various business aspects. These findings help policy-makers and business owners to have a better understanding of how business continuity based on performance resistance to disruptions pushes companies to adopt different practices including new technologies and supply chain coordination. Accordingly, they can use the outputs of this study to devise strategies for improving resilience considering their supply chain vulnerabilities.

Leveraging Modeling and Simulation to Enhance the Efficiency of Bioequivalence Approaches for Generic Drugs: Highlights from the 2023 Generic Drug Science and Research Initiatives Public Workshop.

The 2023 Generic Drug Science and Research Initiative Public Workshop organized by the U.S. Food and Drug Administration (FDA) discussed the research needs to improve and enhance bioequivalence (BE) approaches for generic drug development. FDA takes such research needs and panel discussions into account to develop its Generic Drug User Fee Amendments III (GDUFA III) Science and Research Initiatives specific to generics. During the five workshop sessions, presentations and panel discussions focused on identifying and addressing scientific gaps and research needs related to nitrosamine impurity issues, BE assessment for oral products, innovative BE approaches for long-acting injectable products, alternative BE approaches for orally inhaled products, and advanced BE methods for topical products. Specifically, this report highlights the discussions on how to improve BE assessment for developing generic drug products based on research priorities for leveraging quantitative methods and modeling, as well as artificial intelligence/machine learning (AI/ML).

Abstract LB122: JNJ-87890387, a novel ENPP3 bispecific T-cell redirector (ENPP3xCD3) with tumor selectivity through targeting apical surface antigens

Abstract Clinical success of T-cell redirection therapy for solid tumors has been limited potentially due to a lack of target specificity against cancer, leading to on-target off-tumor toxicity at sub-efficacious doses. ENPP3 (ectonucleotide pyrophosphatase/phosphodiesterase family member 3) is a transmembrane protein that was identified with the help of an artificial intelligence machine learning algorithm. ENPP3 is characterized by apically restricted expression in most normal tissues and elevated, depolarized expression in several solid tumors, thus potentially providing tumor selectivity. ENPP3 expression exhibits a high prevalence in several solid tumors including renal cell carcinoma (RCC) - clear cell (93%) and papillary (78%), lung adenocarcinoma (50%), endometrioid uterine (53%) and ovarian (47%) cancers, colorectal carcinoma (51%), and hepatocellular carcinoma (HCC, 47%). JNJ-87890387 is a fully human bispecific antibody with high affinity and specific binding to ENPP3 on tumor cells and lower affinity binding to cluster of differentiation (CD)3 on T-cells. In vitro, JNJ-87890387 demonstrated potent (pM) T-cell activation and cytotoxicity against tumor cell lines with different levels of endogenous ENPP3 membrane expression, and absence of killing against cells lacking ENPP3 expression, confirming JNJ-87890387 specificity. In vivo, JNJ-87890387 demonstrated ENPP3-expression-dependent potent anti-tumor activity in multiple cell line-derived and patient tumor-derived RCC and HCC xenograft models with complete tumor regressions, and evidence of dose-dependent CD4+ and CD8+ T cell infiltration into the tumors. Preclinical safety assessment in the cynomolgus monkey with a tool molecule using the therapeutic ENPP3 binding arm but with a cynomolgus CD3 cross-reactive arm only identified toxicities consistent with the modality. In summary, these data support JNJ-87890387’s clinical development with an ongoing first-in-human Phase I study to evaluate the safety and preliminary anti-tumor activity in advanced-stage solid tumors with a high prevalence of ENPP3 expression. Citation Format: Smruthi Vijayaraghavan, Pankaj Seth, Annmarie Winkis, Kristen Chevalier, Alexa Marthaler, Katrin Sproesser, Vince Torti, Nirav Shah, Eilyn Lacy, Anna-Lena Frisk, Heather Deutsch, Gerald Chu, Michael Sharp, Jonathan Pabalan, Cholpon Tilegenova, Katherine Thorton, Josh Lauring, Bethany Mattson, Ken Tian, Jacqueline Kinyamu-Akunda, Laurie Lenox, Wan Cheung Cheung, Sheri Moores, Sylvie Laquerre. JNJ-87890387, a novel ENPP3 bispecific T-cell redirector (ENPP3xCD3) with tumor selectivity through targeting apical surface antigens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB122.

A Machine Learning Algorithm Improves the Diagnostic Accuracy of the Histologic Component of Antibody Mediated Rejection (AMR-H) in Cardiac Transplant Endomyocardial Biopsies.

BackgroundPathologic antibody mediated rejection (pAMR) remains a major driver of graft failure in cardiac transplant patients. The endomyocardial biopsy remains the primary diagnostic tool but presents with challenges, particularly in distinguishing the histologic component (pAMR-H) defined by 1) intravascular macrophage accumulation in capillaries and 2) activated endothelial cells that expand the cytoplasm to narrow or occlude the vascular lumen. Frequently, pAMR-H is difficult to distinguish from acute cellular rejection (ACR) and healing injury. With the advent of digital slide scanning and advances in machine deep learning, artificial intelligence technology is widely under investigation in the areas of oncologic pathology, but in its infancy in transplant pathology. For the first time, we determined if a machine learning algorithm could distinguish pAMR-H from normal myocardium, healing injury and ACR. Materials and MethodsA total of 4,212 annotations (1,053 regions of normal, 1,053 pAMR-H, 1,053 healing injury and 1,053 ACR) were completed from 300 hematoxylin and eosin slides scanned using a Leica Aperio GT450 digital whole slide scanner at 40X magnification. All regions of pAMR-H were annotated from patients confirmed with a previous diagnosis of pAMR2 (>50% positive C4d immunofluorescence and/or >10% CD68 positive intravascular macrophages). Annotations were imported into a Python 3.7 development environment using the OpenSlide™ package and a convolutional neural network approach utilizing transfer learning was performed. ResultsThe machine learning algorithm showed 98% overall validation accuracy and pAMR-H was correctly distinguished from specific categories with the following accuracies: normal myocardium (99.2%), healing injury (99.5%) and ACR (99.5%). ConclusionOur novel deep learning algorithm can reach acceptable, and possibly surpass, performance of current diagnostic standards of identifying pAMR-H. Such a tool may serve as an adjunct diagnostic aid for improving the pathologist's accuracy and reproducibility, especially in difficult cases with high inter-observer variability. This is one of the first studies that provides evidence that an artificial intelligence machine learning algorithm can be trained and validated to diagnose pAMR-H in cardiac transplant patients. Ongoing studies include multi-institutional verification testing to ensure generalizability.

인공지능 기반의 자동화된 통합보안관제시스템 모델 연구

In today's growing threat environment, rapid and effective detection and response to security events is essential. To solve these problems, many companies and organizations respond to security threats by introducing security control systems. However, existing security control systems are experiencing difficulties due to the complexity and diverse characteristics of security events. In this study, we propose an automated integrated security control system model based on artificial intelligence. It is based on deep learning, an artificial intelligence technology, and provides effective detection and processing functions for various security events. To this end, the model applies various artificial intelligence algorithms and machine learning methods to overcome the limitations of existing security control systems. The proposed model reduces the operator's workload, ensures efficient operation, and supports rapid response to security threats.

Abstract 4915: Identification and validation of novel prognostic genetic markers in HER2-negative advanced gastric cancer (AGC) by artificial intelligence (AI) deep learning and machine learning algorithm

Abstract Introduction: Despite advances in anticancer treatments, there has been urgent need to identify novel prognostic markers for HER2-negative AGC. We propose AI based deep learning and machine learning pipeline to explore genetic markers that predict patient response to chemotherapy in HER2-negative AGC. Methods: We retrospectively assessed 179 stage IV HER2-negative AGC patients who have been treated with first-line chemotherapy in Yonsei Cancer Center, Korea between 2015 and 2021. In-house targeted sequencing panel data (CancerSCANTM and CancerMaster) were used to identify candidate prognostic markers. DeepSurv was mainly used to analyze the progression-free survival (PFS), which is a deep learning algorithm that can investigate prognostic roles. The SHapley Additive exPlanations (SHAP) method was applied to open the deep learning models’ black-box and calculate the importance ranking of gene features. Machine learning models-random survival forest, elastic-net, and lasso-were also investigated to identify potentially meaningful gene variants. To rank the gene features, we calculated variable importance (VIMP) for random survival forest, and penalized regression coefficients for elastic-net and lasso. All deep learning and machine learning models were trained using 5-folds cross-validation in 1,000 iterations of re-sampling bootstrapping with hyperparameter tuning by grid search. To classify responder or non-responder related genetic markers to chemotherapy, we used the signs of averaged penalize coefficients obtained from elastic-net and lasso. Based on the median linear predictor with candidate gene features, patients were classified into high-risk and low-risk group. Results: A total of 7,824 common variants were analyzed and 20 prognostic genetic markers were identified based on the top average values of SHAP, VIMP, and penalized regression coefficients. The top 10 non-responder genetic markers are included as follows: PARP4(p.S873N), CHUK(p.V268I), BARD1(359_365del), CREBBP(p.Y1125F), BARD1(p.P24S), PHLPP2(p.R1312Q), FAT3(p.Q3375R), CASP5(p.T106A), PHLPP2(p.I544V), and PTCH2(p.T988M). Otherwise, the top 10 responder genetic markers are defined as follows: IL7R(p.I66T), NOTCH4(L16delinsLL), FGFR3, BCL2A1(p.G82D), ZNF217(p.T548I), BRCA1(p.K1183R), FANCA(p.A412V), ADGRA2, CDKN2B, and FANCM(p.S175F). The high-risk group had worse PFS compared with low-risk group (median PFS 4.1 vs. 9.1 months; P-value <0.0001). Conclusion: This study showed the potential of AI deep learning and machine learning pipeline that employs an innovate prognostic genetic markers for HER2-negative AGC. Current ongoing analyses of larger cohort with more comprehensive clinical data will be presented. Citation Format: Sejung Park, Seok-Jae Heo, Choong-Kun Lee, Yaeji Lee, Woo Sun Kwon, Jingmin Che, Minseok Kim, Hyun Cheol Chung, Sun Young Rha. Identification and validation of novel prognostic genetic markers in HER2-negative advanced gastric cancer (AGC) by artificial intelligence (AI) deep learning and machine learning algorithm [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4915.

PersistVerify: Federated model ownership verification with spatial attention and boundary sampling

Federated learning, known for its emphasis on privacy and resource efficiency, has emerged as a transformative paradigm in the fields of artificial intelligence and industrial machine learning. However, with the advancement and widespread adoption of this technology, the concern of model theft in federated learning models becomes increasingly prominent. While current endeavors concentrate on protecting individual deep learning models, limited attention has been dedicated to those involved in federated learning, especially within decentralized environments. To fill this void, we introduce PersistVerify, an innovative approach for verifying model copyright within the federated learning framework. PersistVerify integrates boundary sample selection and spatial attention mechanisms, enhancing the robustness and confidentiality of neural network backdoor watermarks to ensure secure verification of model ownership. PersistVerify constructs a confidential and robust watermark dataset through three sequential steps: latent representation extraction, boundary sample selection, and spatial attention-based trigger embedding. Our research addresses the pervasive challenge of reliable model ownership verification in federated learning, demonstrating the efficacy of PersistVerify. This study provides valuable insights and methodologies to maintain the confidentiality and reliability of neural network backdoor watermarks in the domains of artificial intelligence and federated learning.