Database Model Research Articles

Hybrid Anomaly Detection in Time Series by Combining Kalman Filters and Machine Learning Models.

Due to connectivity and automation trends, the medical device industry is experiencing increased demand for safety and security mechanisms. Anomaly detection has proven to be a valuable approach for ensuring safety and security in other industries, such as automotive or IT. Medical devices must operate across a wide range of values due to variations in patient anthropometric data, making anomaly detection based on a simple threshold for signal deviations impractical. For example, surgical robots directly contacting the patient's tissue require precise sensor data. However, since the deformation of the patient's body during interaction or movement is highly dependent on body mass, it is impossible to define a single threshold for implausible sensor data that applies to all patients. This also involves statistical methods, such as Z-score, that consider standard deviation. Even pure machine learning algorithms cannot be expected to provide the required accuracy simply due to the lack of available training data. This paper proposes using hybrid filters by combining dynamic system models based on expert knowledge and data-based models for anomaly detection in an operating room scenario. This approach can improve detection performance and explainability while reducing the computing resources needed on embedded devices, enabling a distributed approach to anomaly detection.

PERFORMANCE COMPARISON OF DERIVATIVE-FREE OPTIMIZATION METHODS

Structural optimization for parameters and non-parameters-based modelling are adopted in almost all area of science and engineering due to their inherent advantages in contrary to manual tuning. Typically, in the easiest case regression analysis is used to develop a data-based model for the future applications and forecasting. However, the aforementioned modelling approach may be suitable for well-defined data (i.e., elastic model) but might be difficult for complex data type where linear type model may show very poor performances. As a result, the adaptation of nonlinear type or complex models (e.g. a combination of linear and nonlinear parts) are unavoidable. For instance, modeling of a hysteresis type behavior would not be possible via any linear type models while a Bouc-Wen type nonlinear model would be a better choice. Hence, to avoid early mentioned issues and to achieve better performances, the derivative-free or search-algorithm optimization might be suitable alternatives. Herein, due to the underlying advantages, the derivative-free search algorithms have been adopted to optimize model performances and interpret both linear and nonlinear type data. To be precise, two different derivative-free algorithms namely, (i) Nelder-Mead Simplex Method, and (ii) Genetic Algorithm have been studied. The outcome of this study shows that in both former mentioned cases search-based algorithms have better performance in terms of capturing the behavior of the true data via optimization. The real-life applications of optimization algorithms are numerous as these tools are used in many branches of science and engineering.

A Graph Theory Approach for Spatial Data-Based Surface Water Flow Modeling

This research proposes an innovative approach that combines graph theory with spatial data to model surface water flow with the Single Flow Direction (SFD) concept, also known as the D8 algorithm. The objective is to show the water flow from the ground surface to a lower place. The research methodology involves collecting spatial data from the Digital Elevation Model (DEMNAS) in raster data type format. Test results show that the effectiveness of the graph approach in modeling water flow can produce clear flow output. This happens because each pixel traversed by water is connected by a line that forms a well-defined water flow path. This study significantly stimulates the development of more sophisticated modeling tools and practical applications in the future. This can help in more efficient management of water resources or produce more accurate flow modeling, contributing to improved understanding and better management of the environment.

DESIGNING A CUSTOMER LOYALTY PROGRAM WEB SERVICE

Currently, bonus programs from various companies are very popular, using which companies increase the loyalty of the company's customers, thereby ultimately increasing profits. The article describes the process of developing a web service of the company's loyalty program. The subject area has been studied, different types of loyalty programs have been considered, and its own loyalty program has been developed, which formed the basis for the model of the web service being developed. Next, an overview and analysis of development tools is performed, the most suitable technology stack for implementation is proposed using the example of the .Net Framework platform for implementing the server side and using open libraries to implement the user interface. Based on the analysis of the subject area and the development tools intended for use, a database model of the system was developed, and a database management system represented by MySQL was selected. The architecture of the system was designed using the MVC architectural pattern, Onion design patterns and Repositories, and a scheme for the implementation of the methods of operation of the web service with client accounting systems was proposed. A cashback service project has been developed, which is a web application that interacts with client programs. A module for integration into accounting systems has been developed, using the example of such software products as Topaz-gas station, Frontol and 1C.

Hybrid decision-making in atmospheric plasma spraying enables human–machine teaming

With the development of human-cyber-physical-production systems in intelligent manufacturing, cyber-supported production based on artificial intelligence is becoming an increasingly powerful means of controlling machines and collaborating with human users. Semi-autonomous systems with a medium degree of automation enable human-centered, flexible, and sustainable production, for instance, in hybrid decision-making. Especially in applications that do not meet the requirements for full automation and when humans are to be involved in their role as qualified decision-makers, teaming-capable systems are desirable and offer considerable advantages. This paper outlines the transdisciplinary concept of human–machine teaming and the role of human cognition in engineering tasks with multi-criteria decision-making. An illustrative real-life example from thermal spray technology is used to show how explainable artificial intelligence models offer targeted, hybrid cyber decision support. This new approach based on fuzzy pattern classifiers combines expert knowledge- and data-based modeling and enables a transparent interpretation of the results by the human user, as shown here using the example of test data from atmospheric plasma spraying. The method outlined can potentially be used to provide hybrid decision support for a variety of manufacturing processes and form the basis for advanced automation or teaming of humans and cyber-physical-production systems.

Anomaly Detection Industrial Items

Anomaly detection is one of the machines learning powered approach which identifies the various elements and objects from a big set of the data. The machine learning powered technique checks multiple objects with the help of differentiating properties and detects the objects which are dissimilar to another dataset. Anomaly detection has number of successful applications from security, medical, industrial and virtually all of the tasks which human may fail while identifying the differentiating properties. Machinery elements anomaly detection has been presented which identifies the differentiating objects of the four hundred images. Number of experiments were performed to detect the anomaly among the given 400 images. The training model has used bidirectional LSTM which gets a feature vector of twelve features, selected, and extracted with already defined algorithm. The dataset has been labelled manually and features were stored. While testing of the images, the study achieved 98% of the accuracy while detecting anomaly of the industrial elements. The system can detect anomalies in the presence of the varying colour and other properties. The accuracy can be increased by adding more images to the database and learning model more deeply. The study can be directed to various implementations including multiple learning implementations, applying on various other elements.

Towards Advancing Translators’ Guidance for Organisations Tackling Innovation Challenges in Manufacturing within an Industry 5.0 Context

Following the vision of the European Commission, organisations and workers establishing Industry 5.0 approaches aspire to more future-proof, resilient, sustainable, and human-centred European industries. In this contribution, we explore how technological innovations that contribute to a “win–win” interaction with involved stakeholders may be advanced in a human-centred and transparent proceeding supported by impartial expert translators who provide information or knowledge-based guidance for decision-makers, initiators and implementers in manufacturing innovation driven by sustainability. We elaborate a stepwise procedure for agreeing on milestones and conjointly treading the path towards solving innovation challenges during a translation process. We exemplify the technological aspects of such a process using an innovation case aiming at identifying parameters for enhancements in a vacuum-bagging process applied to the manufacturing of composite parts from prepregs based on condensation-curing matrix resins made from renewable resources. In detail, we present a straightforward design of an experimental approach varying the dwelling temperature and the temperature ramps during the curing of stacked prepregs. In this way, we demonstrate that for cured composites comprising a poly(furfuryl alcohol)-based matrix, the porosity and connected mechanical properties achieved with autoclave-free curing processes sensitively depend on these process parameters. Applying the resulting data-based model is shown to support decision-making for sustainable composite manufacture.

Data-based modelling of arrays of wave energy systems: Experimental tests, models, and validation

One of the key steps towards economic feasibility of wave energy conversion technology concerns scaling up to farms of multiple devices, in the attempt to reduce installation costs by sharing infrastructure, and a consequent drop in levelised cost of energy. Moreover, whenever wave energy systems are deployed in proximity (in so-called arrays), the exploitation of the hydrodynamic interactions between single devices is fully enabled, potentially increasing the final energy outcome. To achieve this in real (operational) time, the employed energy-maximising control strategies require control-oriented array models, able to efficiently describe the dynamics of these interconnected systems in a representative fashion. This can be, nonetheless, a difficult task when considering first principles alone, under small motion assumptions, for modelling purposes. Recognising the uncertainty associated to array numerical models obtained from the linearisation of simplified system equations around their equilibria, this paper presents models of several array configurations identified following a frequency domain approach on the basis of experimental data. Tailored tests on laboratory-scale devices have been designed and conducted in the Aalborg University (Denmark) wave tank facility, with the purpose of performing representative system identification of the wave energy systems arrays. The obtained models are validated on different representative sea states configurations, in controlled and uncontrolled motion operational conditions. The validation results are fully discussed and analysed in terms of standard error measures and time lag, while the obtained models are made freely accessible via a linked repository (named OCEAN), in the attempt to openly provide validated models for different array configurations.

Data-Based In-Cylinder Pressure Model with Cyclic Variations for Combustion Control: An RCCI Engine Application

Cylinder-pressure-based control is a key enabler for advanced pre-mixed combustion concepts. In addition to guaranteeing robust and safe operation, it allows for cylinder pressure and heat release shaping. This requires fast control-oriented combustion models. Over the years, mean-value models have been proposed that can predict combustion metrics (e.g., gross indicated mean effective pressure (IMEPg), or the crank angle where 50% of the total heat is released (CA50)) or models that predict the full in-cylinder pressure. However, these models are not able to capture cycle-to-cycle variations. The inclusion of the cycle-to-cycle variations is important in the control design for combustion concepts, like reactivity-controlled compression ignition, that can suffer from large cycle-to-cycle variations. In this study, the in-cylinder pressure and cycle-to-cycle variations are modelled using a data-based approach. The in-cylinder conditions and fuel settings are the inputs to the model. The model combines principal component decomposition and Gaussian process regression. A detailed study is performed on the effects of the different hyperparameters and kernel choices. The approach is applicable to any combustion concept, but is most valuable for advance combustion concepts with large cycle-to-cycle variation. The potential of the proposed approach is successfully demonstrated for a reactivity-controlled compression ignition engine running on diesel and E85. The average prediction error of the mean in-cylinder pressure over a complete combustion cycle is 0.051 bar and of the corresponding mean cycle-to-cycle variation is 0.24 bar2. This principal-component-decomposition-based approach is an important step towards in-cylinder pressure shaping. The use of Gaussian process regression provides important information on cycle-to-cycle variation and provides next-cycle control information on safety and performance criteria.

Revolutionising Egyptian pavement design: a comprehensive E* database and advanced modeling approach for contextually informed performance predictions

ABSTRACT This research assesses the dynamic modulus (E*) as a pivotal rheological attribute for characterizing viscoelastic behaviour in various indigenous asphalt mixtures across diverse scenarios. An exhaustive investigation involving 32 laboratory-designed asphalt mixes and two field mixes explores the influence of traffic loading patterns, air voids, binder sources, and climatic conditions on E*. Utilizing Superpave gyratory compaction, 68 E*-specimens are compacted and subjected to E*-testing, leading to master curve development. Evaluation of NCHRP 1-37A and NCHRP 1-40D Witczak models demonstrates their fair to excellent accuracy in predicting E* for Egyptian mixes. The NCHRP 1-40D model stands out with an R² of 0.94, showcasing superior accuracy and minimal bias. This study integrates AASHTOW are Pavement ME Design (PMED) to analyse pavement sections, revealing the impact of design factors, characterized by measured E*, on predicted pavement performance, encompassing asphalt concrete rutting, alligator cracking, longitudinal cracking, and terminal international roughness index. The investigation highlights the marginal impact of substituting predicted E* for measured E* on AASHTOWare pavement performance indicators for Egyptian mixes under default binder characteristic values. This research contributes valuable insights into the interplay of E* and pavement performance, facilitating wellinformed decisions in pavement design and analysis across diverse conditions in the Egyptian context.

Use of Web-GIS Database Model to Accommodate Public Participation in the Improvement of Sustainable Road and Bridge Management: A Conceptual Idea

Sustainability is usually associated with energy source, ecological, soil and environmental aspects. Periodical road and bridge preservation is required to ensure the increasing of accessibility and mobility level, to increase regional economics’ growth, to minimize unplanned or unpredicted environmental consequences and to improve institutional arrangement capability as well. Road and bridge preservation is commonly managed based on its level of damage and constrained in budgeting. Poor public policy makes it worst. Therefore, the issue of strengthening the capacity of regional road and bridge management institutions is deemed important and urgent not only to reduce roads and bridges preservation cost but also to increase regional economics’ growth. Therefore, a proper road preservation type and priority should be determined based on technical, socio-economics, environment and public policy consideration. However, annual road and bridge functional condition data collection took high cost because it has been carried out by engineering consultant. The aim of this study is resolve it by providing a public participation portion in a road and bridge data base model. They are simply asked to send a real time condition of road s or bridge nearby to the local authority. The results strongly indicate that cost saved obtained from it could be focused to road and bridge preservation cost.

Жобаны басқарудың жүйелік моделі

The rapid development of project management and its wide application in practice required the creation of an appropriate professional infrastructure. The analysis of project management methods is carried out. The directions of research in this area are considered. The analysis of control models from the point of view of systems theory is carried out. Analysis of operational and project management to establish common elements. The purpose of using the result when building a database model of a project management system

Early detection of nasopharyngeal carcinoma through machine-learning-driven prediction model in a population-based healthcare record database.

Early diagnosis and treatment of nasopharyngeal carcinoma (NPC) are vital for a better prognosis. Still, because of obscure anatomical sites and insidious symptoms, nearly 80% of patients with NPC are diagnosed at a late stage. This study aimed to validate a machine learning (ML) model utilizing symptom-related diagnoses and procedures in medical records to predict nasopharyngeal carcinoma (NPC) occurrence and reduce the prediagnostic period. Data from a population-based health insurance database (2001-2008) were analyzed, comparing adults with and without newly diagnosed NPC. Medical records from 90 to 360 days before diagnosis were examined. Five ML algorithms (Light Gradient Boosting Machine [LGB], eXtreme Gradient Boosting [XGB], Multivariate Adaptive Regression Splines [MARS], Random Forest [RF], and Logistics Regression [LG]) were evaluated for optimal early NPC detection. We further use a real-world data of 1 million individuals randomly selected for testing the final model. Model performance was assessed using AUROC. Shapley values identified significant contributing variables. LGB showed maximum predictive power using 14 features and 90 days before diagnosis. The LGB models achieved AUROC, specificity, and sensitivity were 0.83, 0.81, and 0.64 for the test dataset, respectively. The LGB-driven NPC predictive tool effectively differentiated patients into high-risk and low-risk groups (hazard ratio: 5.85; 95% CI: 4.75-7.21). The model-layering effect is valid. ML approaches using electronic medical records accurately predicted NPC occurrence. The risk prediction model serves as a low-cost digital screening tool, offering rapid medical decision support to shorten prediagnostic periods. Timely referral is crucial for high-risk patients identified by the model.

Micro-electro-mechanical acoustofluidic mixing system: A response surface-metaheuristic machine learning fusion framework

Micro-mixing is an essential step in various biological and chemical analyses and synthesis processes. In the present study, an acoustofluidic micromixer is designed, modeled, and its functionality has been investigated. Intelligent acoustofluidic is proposed as an evaluation-optimization method, based on Machine Learning algorithms. The first step involves designing a parametric geometry with multiple sharp edges. Following that, mathematical modeling of the system is carried out using Response Surface Methodology (RSM) and computational fluid dynamics (CFD). Geometrical parameters including the sharp edge tip angle and the channel thickness, along with the peak-to-peak actuation voltage were investigated. It was revealed that, due to the inherent nonlinearity of acoustic streaming, each design parameter could significantly impact the flow patterns and mixing performance of the acoustofluidic mixer. The mathematical surrogate model obtained from data-based modeling procedure is then employed for the artificial intelligence multi-objective optimization algorithms, i.e. Differential Evolution (DE) and Non-dominated Sorting Genetic Algorithm two (NSGA-II). The two objective functions are the mixing index and the mechanical energy consumption index (ME). The derived optimum solutions had low Mehcanical Energy (ME) values while yielding homogeneity in a wide range of applied voltages. This ensures the controllability of the proposed system, which is an invaluable feature in synthesis processes to determine the critical physical properties of the nanoparticles. Through the algorithm evaluation process, which was conducted to distinguish the more accurate and robust optimization method, it was revealed that the DE algorithm outperformed NSGA-II in generating optimum solutions with superior functionalities. In the final section, three optimum models, operating under three different Vp-p values, 10, 20, 30, were selected, and their acoustofluidic characteristics were analyzed. It was concluded that utilizing baffles with three sharp edges at low Vp-p values results in a 96% Mixing Index and 48% decrease in the power consumption of the system. However, at high Vp-p values, baffles with one sharp edge demonstrated superior mixing functionality, with a MI of 98%. Moreover, it is revealed that utilizing baffles with three-sharp edges results in an 80% increase of Mixing Index of the acoustofluidic micromixer.

Meta-analysis of machine learning models for the diagnosis of central precocious puberty based on clinical, hormonal (laboratory) and imaging data.

Central precocious puberty (CPP) is a common endocrine disorder in children, and its diagnosis primarily relies on the gonadotropin-releasing hormone (GnRH) stimulation test, which is expensive and time-consuming. With the widespread application of artificial intelligence in medicine, some studies have utilized clinical, hormonal (laboratory) and imaging data-based machine learning (ML) models to identify CPP. However, the results of these studies varied widely and were challenging to directly compare, mainly due to diverse ML methods. Therefore, the diagnostic value of clinical, hormonal (laboratory) and imaging data-based ML models for CPP remains elusive. The aim of this study was to investigate the diagnostic value of ML models based on clinical, hormonal (laboratory) and imaging data for CPP through a meta-analysis of existing studies. We conducted a comprehensive search for relevant English articles on clinical, hormonal (laboratory) and imaging data-based ML models for diagnosing CPP, covering the period from the database creation date to December 2023. Pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), summary receiver operating characteristic (SROC) curve, and area under the curve (AUC) were calculated to assess the diagnostic value of clinical, hormonal (laboratory) and imaging data-based ML models for diagnosing CPP. The I2 test was employed to evaluate heterogeneity, and the source of heterogeneity was investigated through meta-regression analysis. Publication bias was assessed using the Deeks funnel plot asymmetry test. Six studies met the eligibility criteria. The pooled sensitivity and specificity were 0.82 (95% confidence interval (CI) 0.62-0.93) and 0.85 (95% CI 0.80-0.90), respectively. The LR+ was 6.00, and the LR- was 0.21, indicating that clinical, hormonal (laboratory) and imaging data-based ML models exhibited an excellent ability to confirm or exclude CPP. Additionally, the SROC curve showed that the AUC of the clinical, hormonal (laboratory) and imaging data-based ML models in the diagnosis of CPP was 0.90 (95% CI 0.87-0.92), demonstrating good diagnostic value for CPP. Based on the outcomes of our meta-analysis, clinical and imaging data-based ML models are excellent diagnostic tools with high sensitivity, specificity, and AUC in the diagnosis of CPP. Despite the geographical limitations of the study findings, future research endeavors will strive to address these issues to enhance their applicability and reliability, providing more precise guidance for the differentiation and treatment of CPP.

Making geoscientific lab data FAIR: a conceptual model for a geophysical laboratory database

Abstract. The term of geoscientific laboratory measurements involves a variety of methods in geosciences. Accordingly, the resulting data comprise many different data types, formats, and sizes, respectively. Handling such a diversity of data, e.g. by storing the data in a generally applicable database, is difficult. Some discipline-specific approaches exist, but a geoscientific laboratory database that is generally applicable to different geoscientific disciplines has been lacking up to now. However, making research data available to scientists beyond a particular community has become increasingly important. Global working groups such as the Committee on Data of the International Science Council (CODATA) put effort in the development of tools to improve research data handling. International standards (e.g. ISO 19156) and ontologies (e.g. UCUM) provide a general framework for certain aspects that are elemental for the development of database models. However, these abstract models need to be adapted to meet the requirements of the geoscientific community. Within a pilot project of the NFDI4Earth initiative, we developed a conceptual model for a geoscientific laboratory database. To be able to handle the complex settings of geoscientific laboratory studies, flexibility and extensibility are key attributes of the presented approach. The model is intended to follow the FAIR (findability, accessibility, interoperability, and reusability) data principles to facilitate interdisciplinary applicability. In this study, we consider different procedures from existing database models and include these methods in the conceptual model.

An Investigation on the use of air quality models in ship emission forecasts

Today's world trade operates on the basis of maritime transportation. Combating the environmental impacts of maritime transportation has become a global concern. MARPOL Annex VI contains rules for the prevention of air pollution from ships. Air pollution represents a more abstract pollution than other annexes of MARPOL. In this context, the first and most critical step is to measure air pollution. Nowadays it is possible to calculate ship emissions with individual ship activity data instead of the traditional, fuel-based approach. One of the most ideal data sources for this calculation is AIS data. The critical question to be answered in the literature is how to obtain the highest resolution output using AIS data. Here air quality modelling gains strategic importance. In this study, air quality models used in ship emission calculations were examined with the PRISMA method and the most commonly used Eulerian and Lagrangian models were discussed. For this purpose, studies using AIS data in ship emission calculations were separated through the Scopus database and air quality modelling studies were filtered. Thus, the profile of air quality models used in ship-borne air pollution studies in the literature was obtained and a methodological reference source was created for future studies.

Performance Analysis and Improvement for CRUD Operations in Relational Databases from Java Programs Using JPA, Hibernate, Spring Data JPA

The role of databases is to allow for the persistence of data, no matter if they are of the SQL or NoSQL type. In SQL databases, data are structured in a set of tables in the relational database model, grouped in rows and columns. CRUD operations (create, read, update, and delete) are used to manage the information contained in relational databases. Several dialects of the SQL language exist, as well as frameworks for mapping Java classes (models) to a relational database. The question is what we should choose for our Java application, and why? A comparison of the most frequently used relational database management systems, mixed with the most frequently used frameworks should give us some guidance about when to use what. The evaluation is conducted based on the time taken for each CRUD operation to run, from thousands to hundreds of thousands of entries, using the possible combinations in the relational database system and the framework. Aiming to assess and improve the performance, the experiments included the possibility of warming-up the Java Virtual Machine before the execution of queries. Also, the research investigated the time spent using different methods of code to determine the critical regions (bottlenecks). Thus, the conclusions provide a comprehensive overview of the performances of Java applications accessing databases depending on the suite decisions considering the database type, the framework in use, and the type of operation, with clear comparisons between the alternatives, the key findings of the advantages and drawbacks of each of them, and supporting architects and developers in their technological decisions and improving the speed of their programs.

RRL: Recommendation Reverse Learning

As societies become increasingly aware of data privacy, regulations require that private information about users must be removed from both database and ML models, which is more colloquially called `the right to be forgotten`. Such privacy problems of recommendation systems, which hold large amounts of private data, are drawing increasing attention. Recent research suggests dividing the preference data into multiple shards and training submodels with these shards and forgetting users' personal preference data by retraining the submodels of marked shards. Despite the computational efficiency development compared with retraining from scratch, the overall recommendation performance deteriorates after dividing the shards because the collaborative information contained in the training data is broken. In this paper, we aim to propose a forgetting framework for recommendation models that neither separate the training data nor jeopardizes the recommendation performance, named Recommendation Reverse Learning (RRL). Given the trained recommendation model and marked preference data, we devise Reverse BPR Objective (RBPR Objective) to fine-tune the recommendation model to force it to forget the marked data. Nevertheless, as the recommendation model encode the complex collaborative information among users, we propose to utilize Fisher Information Matrix (FIM) to estimate the influence of reverse learning on other users' collaborative information and guide the updates of representations. We conduct experiments on two representative recommendation models and three public benchmark datasets to verify the efficiency of RRL. To verify the forgetting completeness, we use RRL to make the recommendation model poisoned by shilling attacks forget malicious users.

Abstract 2839: The mouse models of human cancer database (MMHCdb)

Abstract The laboratory mouse plays a pivotal role as an experimental system for investigations into the genetic and genomic basis of cancer susceptibility, initiation, and progression and for preclinical research on novel treatment strategies for cancer. Advances in mouse models include humanized mice, transplant compliant mouse strains, and panels of genetically diverse mice (e.g., Diversity Outbred and Collaborative Cross). The distributed and heterogenous nature of information about these model systems presents a significant challenge to finding and aggregating information about mouse models and to identifying the most relevant models for a particular research question. The Mouse Models of Human Cancer database (http://tumor.informatics.jax.org) is an expertly curated FAIR-compliant knowledgebase that applies community annotation standards and nomenclature standards for genes, alleles, and strains to support accurate and comprehensive search results for mouse models of human cancer. The data in MMHCdb are curated from peer-reviewed scientific publications and direct data submissions from individual investigators and large-scale programs. A primary focus of the resource is to reflect the impact that genetic background has on the cancer-related characteristics of mouse models. The database includes information on spontaneous and genetically engineered cancer models as well as Patient Derived Xenografts and diversity panels. The database supports faceted searching for models by multiple criteria including, tumor type, genetic background of the mouse model, somatic mutations, and publication. Model descriptions include summaries of observed frequencies of tumors observed in the model and histopathology images. Information in MMHCdb is linked to cancer model data from other bioinformatics resources including the Patient Derived Cancer Models database (PDCM), PathBase, the Gene Expression Omnibus (GEO), and ArrayExpress. MMHCdb is supported by NCI R01 CA089713 Citation Format: Dale A. Begley, Debra M. Krupke, Emily Jocoy, Steven B. Neuhauser, Carol J. Bult. The mouse models of human cancer database (MMHCdb) [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 2839.