Graphical Framework Research Articles

Mixed-variable graphical modeling framework towards risk prediction of hospital-acquired pressure injury in spinal cord injury individuals

Developing machine learning (ML) methods for healthcare predictive modeling requires absolute explainability and transparency to build trust and accountability. Graphical models (GM) are key tools for this but face challenges like small sample sizes, mixed variables, and latent confounders. This paper presents a novel learning framework addressing these challenges by integrating latent variables using fast causal inference (FCI), accommodating mixed variables with predictive permutation conditional independence tests (PPCIT), and employing a systematic graphical embedding approach leveraging expert knowledge. This method ensures a transparent model structure and an explainable feature selection and modeling approach, achieving competitive prediction performance. For real-world validation, data of hospital-acquired pressure injuries (HAPI) among individuals with spinal cord injury (SCI) were used, where the approach achieved a balanced accuracy of 0.941 and an AUC of 0.983, outperforming most benchmarks. The PPCIT method also demonstrated superior accuracy and scalability over other benchmarks in causal discovery validation on synthetic datasets that closely resemble our real dataset. This holistic framework effectively addresses the challenges of mixed variables and explainable predictive modeling for disease onset, which is crucial for enabling transparency and interpretability in ML-based healthcare.

The Fundamental Role of Linkage Uncertainty in Epidemiological Analysis of Big Data

BackgroundEpidemiologists increasingly work with linked “big data”. Uncertainty in record linkage may lead to biased inferences but is often overlooked. We evaluate the impact of linkage uncertainty on statistical inference in linked big data. MethodsWe developed a graphical framework for describing linkage uncertainty when linking multiple representations of the same entity, applied to de-identified data from South Africa’s national laboratory database. Through simulation, we systematically introduced linkage errors and measured their impact on overall accuracy (sensitivity, positive predictive value (PPV)). We evaluate how linkage errors affect bias and variance in point estimates for a hypothetical parameter of interest in clinical epidemiology: 24-month retention in care for HIV patients. We compare the roles of sampling error vs. linkage error as fundamental sources of uncertainty in datasets of varying sizes. ResultsWe simulated a population of 14,393 HIV patients, with a “true” 24-month retention of 38.7%. There were 338,056 true links. Introducing 4,200 false links reduced PPV by 5%. Removing 21,500 existing links decreased sensitivity by 5%. From 10 simulation runs, a 95% sensitivity led, on average, to a 7.4% overestimate in entries to care and a 2.2% (range: 2.1-2.4%) underestimate in 24-month retention. A 95% PPV resulted, on average, in a 7.5% underestimate in entries to care and a 1.8% (range: 1.5-2.0%) overestimate in 24-month retention. ConclusionWe observe that in a large sample, linkage uncertainty minimally impacts variance in point estimates but has a potentially large influence on the magnitude and direction, distinguishing it from typical sampling errors.

Qualitative evaluation of a molecular point-of-care testing study for influenza in UK primary care.

Influenza contributes to the surge in winter infections and the consequent winter pressures on the health service. Molecular point-of-care testing(POCT) for influenza might improve patient management by providing rapid and accurate clinical diagnosis to inform the timely initiation of antiviral therapy and reduce unnecessary admissions and antibiotics use. To explore factors that influence the adoption or non-adoption of POCT in English general practices and provide insights to enable its integration into routine practice workflows. A qualitative implementation evaluation was conducted in ten general practices within the English national sentinel network (Oxford-RCGP Research and Surveillance Centre), from April to July 2023. Using the nonadoption, abandonment, scale-up, spread, and sustainability framework, data collection and analysis were conducted across ten practices. We made ethnographic observations of the POCT workflow and surveyed the practice staff for their perspectives on POCT implementation. Data were analysed using a mix of descriptive statistics, graphical modelling techniques and framework approach. Ethnographic observations identified two modes of POCT integration into practice workflow: 1) clinician POCT workflow - typically involving batch testing due to time constraints, 2) research nurse/healthcare assistant POCT workflow - characterised by immediate testing of individual patients. Survey indicated that most primary care staff considered the POCT training offered was sufficient, and these practices were ready for change and had the capacity and resources to integrate POCT in workflows. General practices should demonstrate flexibility in the workflow and workforce they deploy to integrate POCT into routine clinical workflow.

ConfluentFUCCI for fully-automated analysis of cell-cycle progression in a highly dense collective of migrating cells.

Understanding mechanisms underlying various physiological and pathological processes often requires accurate and fully automated analysis of dense cell populations that collectively migrate. In such multicellular systems, there is a rising interest in the relations between biophysical and cell cycle progression aspects. A seminal tool that led to a leap in real-time study of cell cycle is the fluorescent ubiquitination-based cell cycle indicator (FUCCI). Here, we introduce ConfluentFUCCI, an open-source graphical user interface-based framework that is designed, unlike previous tools, for fully automated analysis of cell cycle progression, cellular dynamics, and cellular morphology, in highly dense migrating cell collectives. We integrated into ConfluentFUCCI's pipeline state-of-the-art tools such as Cellpose, TrackMate, and Napari, some of which incorporate deep learning, and we wrap the entire tool into an isolated computational environment termed container. This provides an easy installation and workflow that is independent of any specific operation system. ConfluentFUCCI offers accurate nuclear segmentation and tracking using FUCCI tags, enabling comprehensive investigation of cell cycle progression at both the tissue and single-cell levels. We compare ConfluentFUCCI to the most recent relevant tool, showcasing its accuracy and efficiency in handling large datasets. Furthermore, we demonstrate the ability of ConfluentFUCCI to monitor cell cycle transitions, dynamics, and morphology within densely packed epithelial cell populations, enabling insights into mechanotransductive regulation of cell cycle progression. The presented tool provides a robust approach for investigating cell cycle-related phenomena in complex biological systems, offering potential applications in cancer research and other fields.

A modeling framework for detecting and leveraging node-level information in Bayesian network inference.

Bayesian graphical models are powerful tools to infer complex relationships in high dimension, yet are often fraught with computational and statistical challenges. If exploited in a principled way, the increasing information collected alongside the data of primary interest constitutes an opportunity to mitigate these difficulties by guiding the detection of dependence structures. For instance, gene network inference may be informed by the use of publicly available summary statistics on the regulation of genes by genetic variants. Here we present a novel Gaussian graphical modeling framework to identify and leverage information on the centrality of nodes in conditional independence graphs. Specifically, we consider a fully joint hierarchical model to simultaneously infer (i) sparse precision matrices and (ii) the relevance of node-level information for uncovering the sought-after network structure. We encode such information as candidate auxiliary variables using a spike-and-slab submodel on the propensity of nodes to be hubs, which allows hypothesis-free selection and interpretation of a sparse subset of relevant variables. As efficient exploration of large posterior spaces is needed for real-world applications, we develop a variational expectation conditional maximization algorithm that scales inference to hundreds of samples, nodes and auxiliary variables. We illustrate and exploit the advantages of our approach in simulations and in a gene network study which identifies hub genes involved in biological pathways relevant to immune-mediated diseases.

DEVELOPMENT OF AN APPLICATION AS AN AUXILIARY TOOL FOR VERIFICATION OF AN ELECTROCARDIOGRAM CONCLUSION

Objective: Electrocardiography (ECG) is a key step in the diagnosis of cardiovascular pathology. Interpretation of the ECG is carried out by the doctor in the first minutes of severe patient admission and often determines further tactics of management. However, such an important diagnostic method is quite subjective, since it is highly dependent on the knowledge, and experience of the doctor and even on his attention to detail. The objective is to develop an application for automatic verification of various ECG criteria (exceptionally as an addition to the subjective interpretation of the doctor). Design and method: The desktop application was developed in Python (v3.10.5) by implementing its object-oriented programming concepts. Tkinter was chosen as a Graphical User Interface framework. The set of standard libraries has been supplemented by OpenCV (v4.6.0.66) for recognizing and digitizing ECG images, Pillow (v9.3.0) for manipulating the image and its metadata, NumPy (v1.23.5) for further analysis, and others. The initial version of the program includes STEMI recognition. Real ECGs printed on graph paper were used for debugging and testing. Results: When launching the application, the user is prompted to select a file containing an ECG image (a). After the required image has opened, the user should indicate the requested ECG-leads in the picture, following the simplest instructions (b). Then processing of the leads automatically starts. This phase is hidden from the user, and only a progress bar is displayed on the screen (c). Meanwhile, a number of manipulations are performed, such as converting the picture to a binary state, recognizing the ECG-line, and storing the coordinates of each pixel in the database. Objects of such classes as Isoline, and Cardiac Cycle with R wave, ST segment, etc. are created for each of the objects of the Lead class (d). Further geometric study of the pixel base and checking for the pathological ECG-signs on the backend form the final conclusion for the user (e). Conclusions: The developed application is relevant for use as an additional tool in the work and training of doctors. It can be expanded to cover a wide range of pathologies.

LoGo-GR: A Local to Global Graphical Reasoning Framework for Extracting Structured Information From Biomedical Literature.

In the biomedical literature, entities are often distributed within multiple sentences and exhibit complex interactions. As the volume of literature has increased dramatically, it has become impractical to manually extract and maintain biomedical knowledge, which would entail enormous costs. Fortunately, document-level relation extraction can capture associations between entities from complex text, helping researchers efficiently mine structured knowledge from the vast medical literature. However, how to effectively synthesize rich global information from context and accurately capture local dependencies between entities is still a great challenge. In this paper, we propose a Local to Global Graphical Reasoning framework (LoGo-GR) based on a novel Biased Graph Attention mechanism (B-GAT). It learns global context feature and information of local relation path dependencies from mention-level interaction graph and entity-level path graph respectively, and collaborates with global and local reasoning to capture complex interactions between entities from document-level text. In particular, B-GAT integrates structural dependencies into the standard graph attention mechanism (GAT) as attention biases to adaptively guide information aggregation in graphical reasoning. We evaluate our method on three publicly biomedical document-level datasets: Drug-Mutation Interaction (DV), Chemical-induced Disease (CDR), and Gene-Disease Association (GDA). LoGo-GR has advanced and stable performance compared to other state-of-the-art methods (it achieves state-of-the-art performance with 96.14%-97.39% F1 on DV dataset, advanced performance with 68.89% F1 and 84.22% F1 on CDR and GDA datasets, respectively). In addition, LoGo-GR also shows advanced performance on general-domain document-level relation extraction dataset, DocRED, which proves that it is an effective and robust document-level relation extraction framework.

Abstract 3526: GAIN-BRCA: A graphical explainable AI-net framework for breast cancer subtype classification using multiomics data

Abstract Higher standards for precise distinction and classification among the four breast cancer subtypes, i.e., Luminal A. Luminal B, HER-2-enriched, and Basal, are essential to delivering customized treatment in precision oncology. The effective integration of readily accessible multi-omics datasets from the same patient may improve the accuracy of subtype prediction algorithms and help decipher reliable molecular features for the prediction and prognosis of breast cancer. Previous models for predicting breast cancer subtypes relied mostly on concatenation and autoencoder methods but rarely utilized graph-based integration to leverage the biological associations among omics datatypes. To overcome these limitations, we developed a graph-based multi-omics integration method using features from mRNA, DNA methylation, and miRNA data and synthesized features from their interactions. GAIN-BRCA computes weightage scores from miRNA-mRNA and DNA methylation-mRNA interactions to derive a new transformed feature vector without compromising biological information. The neural network architecture was designed and trained on the transformed features to classify breast cancer subtypes. Neural network prediction accuracies were compared using concatenated, autoencoder, and GAIN-BRCA-based integrated datasets. GAIN-BRCA outperforms the existing similar models MOGONET (Acc: 0.72) and moBRCA-net (Acc: 0.86) with an accuracy of 0.91. These transformed features were then processed using SHAP, and the top 357 features were selected for functional characterization and pathway analysis. We identified specific pathways and genes related to each breast cancer subtype separately. The most enriched pathways found in each subtype are ribonucleotide reductase signaling and glucocorticoid signaling pathways in luminal A, PFKB4 signaling pathway in luminal B, adipogenesis and DNA methylation and transcriptional signaling in HER2+, and the role of OCT4 in mammalian embryonic stem cell pluripotency in basal. Similarly, the most discriminated gene features are BRAF, ESR1, KRAS, MAPK1, SMARCE1, PPP3CA, and TGFBR2 in luminal A, TGFB3 and NCOA3 in luminal B, HNF4A, SIN3A, and SOX9 in HER2+, and POU5F1 and FOXA2 in basal subtypes. The GAIN-BRCA framework, combined with SHAP, weighs independent omics as cross-disciplinary rather than multidisciplinary and presents a distinctive picture for each subtype. As a result, the framework presents the importance of features about the corresponding subtype, which might lead to a greater understanding of the molecular basis of breast cancer subtypes. Citation Format: Jai Chand Patel, Sushil Kumar Shakyawar, Sahil Sethi, Chittibabu Guda. GAIN-BRCA: A graphical explainable AI-net framework for breast cancer subtype classification using multiomics data [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 3526.

A3C: An Image-Association-Based Computing Device Authentication Framework for People with Upper Extremity Impairments

Current computing device authentication often presents accessibility barriers for people with upper extremity impairments (UEI). In this paper, we present a framework called Accessible image-Association-based Authentication for Computing-devices (A3C) , a novel recognition-based graphical authentication framework specifically designed for people with UEI to authenticate to their computing devices. A3C requires users to provide a set of primary images that the user knows that are recognizable to them and subsequently associate each primary image with a secondary image. To evaluate the efficacy of the A3C framework, we instantiated the framework by implementing a version of A3C called A3C-FA , which uses images of faces of people the user knows as the primary image and animal images as the secondary image. We then performed three studies to evaluate A3C-FA: a shoulder-surfing attack study ( N=319 ), a close-adversary attack study ( N=268 ), and a usability study with people with UEI ( N=14 ). We found that A3C was robust against both shoulder-surfing and close-adversary attacks. We also performed a detailed study to evaluate the accessibility of A3C-FA. Our participants reported that A3C-FA was more usable and more secure than the authentication approaches with which they were familiar. Based on these findings, we suggest four areas of future research to further improve the design of A3C framework.

Impact of a spherical interface on a concentrical spherical droplet

<p>In this paper, an analytical and numerical technique are examined in order to analyse the Stokes flow determination problem due to a viscous sphere droplet moving at a concentric instantaneous position inside a spherical interface separating finite and semi-infinite immiscible fluid phases. Here, when only one of the three phases of the fluid (micropolar fluid) has a microstructure, attention is focused on this case. The motion is considered when Reynolds- and capillary-numbers are low, and the droplet surface and the fluid-fluid interface have insignificant deformation. A general solution is obtained in a spherical coordinate system based on a concentric position to analyse the slow axisymmetric movement of the micropolar fluid, considering microrotation and velocity components. Boundary conditions are initially fulfilled at the fluid-fluid interface and subsequently at the droplet surface. The normalised hydrodynamic drag force applying to a moving viscous droplet appears to be a function of the droplet-to-interface radius ratio, which increases monotonically and becomes unbounded when the droplet surface touches the fluid-fluid interface. The numerical outcomes of the normalised drag force acting on the viscous droplet are derived for different values of the parameters, and are presented in a tabular and graphical framework. A comparison was made between our numerical outcomes for the drag force and the pertinent data for the special cases found in the literature.</p>

GRNN-Based Real-Time Fault Chain Prediction

This paper proposes a data-driven graphical framework for the real-time search of risky cascading fault chains (FCs). While identifying risky FCs is pivotal to alleviating cascading failures, the complex spatio-temporal dependencies among the components of the power system render challenges to modeling and analyzing FCs. Furthermore, the real-time search of risky FCs faces an inherent combinatorial complexity that grows exponentially with the size of the system. The proposed framework leverages the recent advances in graph recurrent neural networks to circumvent the computational complexities of the real-time search of FCs. The search process is formalized as a partially observable Markov decision process (POMDP), which is subsequently solved via a time-varying graph recurrent neural network (GRNN) that judiciously accounts for the inherent temporal and spatial structures of the data generated by the system. The key features of this structure include (i) leveraging the spatial structure of the data induced by the system topology, (ii) leveraging the temporal structure of data induced by system dynamics, and (iii) efficiently summarizing the system's history in the latent space of the GRNN. The proposed framework's efficiency is compared to the relevant literature on the IEEE 39-bus New England system and the IEEE 118-bus system.

MatSciRE: Leveraging pointer networks to automate entity and relation extraction for material science knowledge-base construction

Material science literature is a rich source of factual information about various categories of entities (like materials and compositions) and various relations between these entities, such as conductivity, voltage, etc. Automatically extracting this information to generate a material science knowledge base is a challenging task. In this paper, we propose MatSciRE (Material Science Relation Extractor), a Pointer Network-based encoder–decoder framework, to jointly extract entities and relations from material science articles as a triplet (entity1,relation,entity2). Specifically, we target the battery materials and identify five relations to work on — conductivity, coulombic efficiency, capacity, voltage, and energy. Our proposed approach achieved a much better F1-score (0.771) than a previous attempt using ChemDataExtractor (0.716). The overall graphical framework of MatSciRE is shown in Fig. 1. The material information is extracted from material science literature in the form of entity–relation triplets using MatSciRE.

PPFGED: Federated learning for graphic element detection with privacy preservation in multi-source substation drawings

In digital recognition of substation drawings, a significant challenge is how to accurately detect complex graphic elements in each drawing. Existing detection schemes often operate in isolation, with data dispersed across multiple sources, which impedes the development of high-accuracy models. A straightforward solution of consolidating drawings from different substations for training does not satisfy the stringent security requirements of substations. To address these challenges, we propose a novel graphic element detection framework based on federated learning for multi-source substation drawings. Our framework involves a server provider, multiple power companies, and distributed substations, and it employs a universal model generation scheme that protects data privacy. Local substations utilize the designed universal graphic element extraction scheme to categorize drawings and extract smaller elements, achieving enhanced accuracy. Subsequently, we design a privacy-enhanced diffusion model for substations to protect elements before uploading them. Power companies then train local model and apply a designed dimension-compressed local differential privacy scheme to the model parameters to protect the training process. To deter server provider from maliciously exploiting model, a game theory-based optimization solution is designed between server provider and power companies. Finally, experiments conducted using substation drawings provided by multiple cooperating units demonstrate that our framework can accurately perform drawing classification and graphic element extraction, and while obtaining the strongest privacy guarantees and low time cost, it achieves an intelligent system for graphic element detection with an accuracy of 88.14% across different regional substations.

Aggregation of nanoparticles in flow of Carreau fluid containing gyrotactic microorganisms on extendable cylinder with viscous dissipation aspects by performing numerical simulations

Owing to the excellent utilizations of nanotechnology in industrial processes, thermal engineering, cooling processes, solar systems, and so forth, current contribution is articulated. This investigation aims to explore the physical characterization of Carreau nanofluid over a linearly extendable cylinder along with the appliance of bioconvection phenomenon. Self-propagated movement of gyrotactic microorganisms sustained by buoyancy forces along with additional thermal aspects of viscous dissipation are engrossed in the study. The leading governing system is mutated into as set of (nonlinear) ordinary differential equations by means of adequate similar alterations. The attended resultant problem is contested with fourth-order Runge-Kutta integration and shooting schemes. A detailed graphical framework is constituted in a comparative manner for cylindrical and flat surfaces to examine the thermo-physical attributes of involved physical parameters on associated profiles. The impact of identical parameters on the skin friction factor and rate of transfers by quantitative and graphical display is also presented.

Bayesian Learning of Graph Substructures

Graphical models provide a powerful methodology for learning the conditional independence structure in multivariate data. Inference is often focused on estimating individual edges in the latent graph. Nonetheless, there is increasing interest in inferring more complex structures, such as communities, for multiple reasons, including more effective information retrieval and better interpretability. Stochastic blockmodels offer a powerful tool to detect such structure in a network. We thus propose to exploit advances in random graph theory and embed them within the graphical models framework. A consequence of this approach is the propagation of the uncertainty in graph estimation to large-scale structure learning. We consider Bayesian nonparametric stochastic blockmodels as priors on the graph. We extend such models to consider clique-based blocks and to multiple graph settings introducing a novel prior process based on a Dependent Dirichlet process. Moreover, we devise a tailored computation strategy of Bayes factors for block structure based on the Savage-Dickey ratio to test for presence of larger structure in a graph. We demonstrate our approach in simulations as well as on real data applications in finance and transcriptomics.

Pedagogical proposal of tactile graphical framework for delimiting permanent preservation areas for the visually impaired

One of the biggest challenges for visually impaired people is learning cartographic content. Among the cartographic contents, the conceptualization and delimitation of preservation areas is an applied example with extreme relevance for environmental education. This article presents the process of developing a tactile graphic scheme for environmental education for visually impaired people regarding the delimitation of permanent preservation areas around springs and rivers. The creation of the teaching resource used easily acquired materials and followed format recommendations indicated by academic literature on tactile maps. The didact resource has textual content in Braille and Roman numerals, as well as legend and graphic scale in high relief. Special care was taken so that the teaching resource can be used in an inclusive manner, involving, at the same time, blind, partially visually impaired and students with full vision, to promote an inclusive educational environment. This resource can be a useful tool for teaching cartography topics applied to environmental conservation contexts.

Graphical structures for design and verification of quantum error correction

We introduce a high-level graphical framework for designing and analysing quantum error correcting codes, centred on what we term the coherent parity check (CPC). The graphical formulation is based on the diagrammatic tools of the ZX-calculus of quantum observables. The resulting framework leads to a construction for stabilizer codes that allows us to design and verify a broad range of quantum codes based on classical ones, and that gives a means of discovering large classes of codes using both analytical and numerical methods. We focus in particular on the smaller codes that will be the first used by near-term devices. We show how CSS codes form a subset of CPC codes and, more generally, how to compute stabilizers for a CPC code. As an explicit example of this framework, we give a method for turning almost any pair of classical codes into a CPC code. Further, we give a simple technique for machine search which yields thousands of potential codes, and demonstrate its operation for distance 3 and 5 codes. Finally, we use the graphical tools to demonstrate how Clifford computation can be performed within CPC codes. As our framework gives a new tool for constructing small- to medium-sized codes with relatively high code rates, it provides a new source for codes that could be suitable for emerging devices, while its ZX-calculus foundations enable natural integration of error correction with graphical compiler toolchains. It also provides a powerful framework for reasoning about all stabilizer quantum error correction codes of any size.

Drivers of green economy in an emerging market: Generic and sector-specific insights

The study examines the drivers of the green economy generally and with a focus on sectors regarded as environmental-sensitive hotspots like waste, agriculture, forestry, energy, and transport sectors. The study was grounded on both the natural resource-based view and institutional theories. Creswell's (2014) thematic analysis and the graphic framework of Miles and Huberman (1984, 1994) were used to address the respective objectives of the study. A total of 10 interviewees were selected purposively based on their expertise and the institutions in question who provided responses that were analysed. The results show that the three generic drivers of the green economy include pollution prevention revealed by the impact of climate change, and environmental degradation cost, environmental stewardship portrayed by inefficient resource utilization and global demand for change, as well as, sustainable development manifesting from a futuristic drive, a focus on people-planet-profit, and rejection of environmentally unfriendly development models. The findings further reveal the differences and interrelatedness of the drivers of the green economy within the 5 hotspot sectors under investigation. The results of the study reveal that there are different and sector-specific strategies that policymakers and practitioners have to apply in their quest to drive a green economy at the central government, local government and grassroots levels. The study proposes the development of a comprehensive national green economy policy and also the promotion of peer-to-peer knowledge transfer networks structured as Communities of Practice. This study is one of the few that explores the concept of the green economy within the macroeconomic level and is novel in attaching theoretical lenses.

Counterfactual reasoning in space and time: Integrating graphical causal models in computational movement analysis

AbstractMovement analysis is distinguished by an emphasis on understanding via observation and association. However, an important component of movement from the human and computer modeling perspective is the processes that bring about movement behavior in the first place. This article contextualizes the graphical causal modeling framework (for association, intervention, and counterfactual causal analysis) in GIScience, and more specifically within movement analysis studies. This is done by modeling the movement behavior of football players, applied to spatiotemporal data generated by an agent‐based simulation. The movement dataset is thoroughly analyzed to infer the statistical associations among its variables, to estimate the effect of an intervention on some of those variables, and to answer a few counterfactual questions from the observations. We conclude that causal graphs (i.e., directed acyclic graphs), if implemented correctly, can assist analysts in infering causal relations from movement data. This research suggests the integration of causal graphs and agent‐based paradigms as one solution for computational movement analysis.

An Advanced Knowledge Based Graphical Authentication Framework with Guaranteed Confidentiality and Integrity

The information and security systems largely rely on passwords,which remain the fundamental part of any authentication process. The conventional authentication method based on alphanumerical username and password suffer from significant disadvantages. The graphical password-based authentication system has recently been introduced as an effective alternative. Although the graphical schemes effectively generate the passwords with better flexibility and enhanced security, the most common problem with this is the shoulder surfing attack. This paper proposes an effective 3D graphical password authentication system to overcome such drawbacks. The system is based on the selection of click points for generating passwords. The proposed work involved a training phase for evaluating the model in terms of the success rate. The overall evaluations of the model in terms of password entropy, password space, login success rates, and prediction probability in the shoulder surfing and guessing attacks proved that the model is more confidential and maintains a higher range of integrity than the other existing models.