Network Model Research Articles

On null models for temporal small-worldness in brain dynamics

Abstract Brain dynamics can be modeled as a temporal brain network starting from the activity of different brain regions in functional magnetic resonance imaging (fMRI) signals. When validating hypotheses about temporal networks, it is important to use an appropriate statistical null model that shares some features with the treated empirical data. The purpose of this work is to contribute to the theory of temporal null models for brain networks by introducing the random temporal hyperbolic (RTH) graph model, an extension of the random hyperbolic (RH) graph, known in the study of complex networks for its ability to reproduce crucial properties of real-world networks. We focus on temporal small-worldness which, in the static case, has been extensively studied in real-world complex networks and has been linked to the ability of brain networks to efficiently exchange information. We compare the RTH graph model with standard null models for temporal networks and show it is the null model that best reproduces the small-worldness of resting brain activity. This ability to reproduce fundamental features of real brain networks, while adding only a single parameter compared with classical models, suggests that the RTH graph model is a promising tool for validating hypotheses about temporal brain networks.

Performance evaluation of multiclass classification models for ToN-IoT network device datasets

Internet of things (IoT) technology has empowered tangible objects to establish internet connections, facilitating data exchange with computational capabilities. With significant potential across sectors like healthcare, environmental monitoring, and industrial control, IoT represents a promising technological advancement. This study explores datasets from ToN-IoT’s IoT devices, focusing on multi-class classification, including normal and attack classes, with an additional aim of identifying potential attack sub-classes. Datasets comprise various IoT devices, such as refrigerators, garage doors, global positioning systems (GPS) sensors, motion lights, modbus devices, thermostats, and weather sensors. Comparative analysis is conducted between two prominent multiclass classification models, extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM), utilizing accuracy and computational time metrics as evaluation criteria. Research findings highlight that the LightGBM model achieves superior accuracy at 78%, surpassing XGBoost 74.31%. However, XGBoost demonstrates an advantage with a shorter computational time of 1.23 seconds, compared to LightGBM 6.79 seconds. This study not only provides insights into multiclass classification model selection but also underscores the crucial consideration of the trade-off between accuracy and computational efficiency in decision-making. Research contributes to advancing our understanding of IoT security through effective classification methodologies. The findings offer valuable information for researchers and practitioners, emphasizing the nuanced decisions needed when selecting models based on specific priorities like accuracy and computational efficiency.

Novel method of image analysis that combines Radon and Fourier image transformations for the purpose of differentiating between malignant and benign colonic biopsies.

Colorectal cancer is the third most common type of cancer. It develops slowly as a polyp that can turn into a cancerous tumor. This study aimed to develop a decision-making algorithm of microscopic images using texture analysis that is orientation free, to be used for automated classification of normal and neoplastic (malignant or premalignant) colonic biopsies. Forty-nine colonic adenocarcinomas, 41 adenomas and a control group of adjacent normal colonic mucosa were included in the texture analysis. Radon transform followed by the Fast Fourier transform were applied to the images. Subsequently, the gray level co-occurrence matrix (GLCM) transform was applied allowing the extraction of four textural variables (homogeneity, contrast, correlation, and entropy). For classification and prediction of the diagnosis, a statistical multivariate regression model and a neural network (NNET) model were used and compared. The statistical model provided a sensitivity of 71.3% and a specificity of 50% (Area under the ROC curve: 0.67) for classifying the neoplastic and the normal images, respectively. The NNET model was superior to the statistical model and produced a sensitivity of 97.9% and specificity of 88% (Area under the ROC curve: 0.92). To our knowledge, this is the first study that used a combination of Radon, FFT, and GLCM transformations in order to overcome the tissue orientation problem in texture analysis of microscopic images of colonic biopsies. The NNET classifier trained by the extracted textural features proved to be superior to the statistical classifier, thus predicting colonic neoplasia with high accuracy. RESEARCH HIGHLIGHTS: We propose a novel decision-making algorithm of orientation invariant image texture analysis, fast and easily implemented for automated differentiation between benign and neoplastic epithelial tumors of the colon. This method can reduce the turnaround time allowing to prioritize the biopsies during their examination and diagnosis by the pathologist.

Pipeline Corrosion Prediction and Uncertainty Analysis with an Ensemble Bayesian Neural Network Approach

This study developed an ensemble Bayesian Neural Network (BNN) model for pipeline corrosion prediction incorporating uncertainty analysis. The performance of the proposed BNN model was compared with different empirical and data-driven models. Shapley Additive Explanation (SHAP) values were used to quantify the importance of input factors. Results indicate that the ensemble BNN model demonstrates superior prediction accuracy, achieving the highest R2 value of 0.949, with the lowest Mean Absolute Error (MAE) of 0.33mm and Root Mean Square Error (RMSE) of 0.449mm on corrosion depth. It outperforms other models in predicting maximum corrosion depth and uniquely captures uncertainty information, which deterministic models cannot provide. Key factors influencing corrosion include chloride content, pH, pipe-to-soil potential, and pipeline age. Specifically, conditions such as a pipe-to-soil potential exceeding -0.85V, a pipeline age over 20 years, and pH values below 7 are found to significantly accelerate corrosion. The model also illustrates the effects of different influencing factors on corrosion growth uncertainty. Higher chloride content, lower pH values, and higher pipe-to-soil potentials may expand the uncertainty bounds. The proposed model presents the dynamic changes in prediction uncertainty over time, which is usually ignored in previous models. It can improve the precision and reliability of long-term predictions concerning pipeline corrosion depth. This knowledge of corrosion uncertainty is crucial for making informed decisions about pipeline inspection and maintenance strategies.

A Deep Artificial Neural Network Model for Predicting the Mechanical Behavior of Triply Periodic Minimal Surfaces under Damage Loading

A Deep Artificial Neural Network Model for Predicting the Mechanical Behavior of Triply Periodic Minimal Surfaces under Damage Loading

Investigation of Visual Language Landscape of Tourist Attractions from Multimodal Perspective

With the development of economic globalization, the tourism industry has been welcomed by the public. The visual language landscape of tourist attractions can not only assist tourists to play and watch the project, but if it is properly planned, the language landscape can also become a major feature and highlight of the scenic spot. Therefore, how to set up and construct the visual language landscape of tourist attractions is a problem that needs to be considered in each region. In response to the above problems, on the basis of understanding the concept types of the visual language landscape of tourist attractions, this article conducts in-depth research and investigation on the visual language landscape of tourist attractions, combining the evaluation dataset in the multimodal perspective and the Convolutional Neural Network (CNN) –Recurrent Neural Network (RNN) model based on semantic regularization. This article conducted a comparative experiment on each model on the NUS-WIDE dataset and the MS-COCO dataset. The experimental results showed that it was crucial to give full play to the expressive power of the CNN. Compared to the NUS-WIDE dataset, the MS-COCO dataset brought less additional boost by leveraging social media tags. The CIDEr score of the CNN-RNN model based on semantic regularization was improved by 11.4%, which placed the foundation for the investigation and analysis of the linguistic landscape of tourist attractions.

Hybrid Aquila optimizer for efficient classification with probabilistic neural networks

The model of a probabilistic neural network (PNN) is commonly utilized for classification and pattern recognition issues in data mining. An approach frequently used to enhance its effectiveness is the adjustment of PNN classifier parameters through the outcomes of metaheuristic optimization strategies. Since PNN employs a limited set of instructions, metaheuristic algorithms provide an efficient way to modify its parameters. In this study, we have employed the Aquila optimizer algorithm (AO), a contemporary algorithm, to modify PNN parameters. We have proposed two methods: Aquila optimizer based probabilistic neural network (AO-PNN), which uses both local and global search capabilities of AO, and hybrid Aquila optimizer and simulated annealing based probabilistic neural network (AOS-PNN), which integrates the global search abilities of AO with the local search mechanism of simulated annealing (SA). Our experimental results indicate that both AO-PNN and AOS-PNN perform better than the PNN model in terms of accuracy across all datasets. This suggests that they have the potential to generate more precise results when utilized to improve PNN parameters. Moreover, our hybridization technique, AOS-PNN, is more effective than AO-PNN, as evidenced by classification experiments accuracy, data distribution, convergence speed, and significance. We have also compared our suggested approaches with three different methodologies, namely Coronavirus herd immunity optimizer based probabilistic neural network (CHIO-PNN), African buffalo algorithm based probabilistic neural network (ABO-PNN), and β-hill climbing. We have found that AO-PNN and AOS-PNN have achieved significantly higher classification accuracy rates of 90.68 and 93.95, respectively.

Comparative temporal dynamics of individuation and perceptual averaging using a biological neural network model

Analyzing visual scenes and computing ensemble statistics, known as perceptual averaging, is crucial for the stable sensory experience of a cognitive agent. Despite the apparent simplicity of applying filters to scenes, the challenge arises from our brain’s seamless transition between summarization and individuation across various reference frames (retinotopic, spatiotopic, and hemispheric). In this study, we explore the capability of a neural network to dynamically switch between individuation and summarization. Our chosen computational model, a fully connected on-center off-surround recurrent neural network previously employed for enumeration/individuation, demonstrates the potential to extract both summary statistics and achieve high individuation accuracy. Notably, our results show that the individuation accuracy can reach close to perfection within a presentation duration of 100 ms, but not so for summarization. We have also shown a spatially varying excitation version of the network that can explain quite a few interesting spatio-temporal patterns of perception. These findings not only highlight the feasibility of such a neural network but also provide insights into the temporal dynamics of ensemble perception.

Flexible gating between subspaces in a neural network model of internally guided task switching.

Behavioral flexibility relies on the brain's ability to switch rapidly between multiple tasks, even when the task rule is not explicitly cued but must be inferred through trial and error. The underlying neural circuit mechanism remains poorly understood. We investigated recurrent neural networks (RNNs) trained to perform an analog of the classic Wisconsin Card Sorting Test. The networks consist of two modules responsible for rule representation and sensorimotor mapping, respectively, where each module is comprised of a circuit with excitatory neurons and three major types of inhibitory neurons. We found that rule representation by self-sustained persistent activity across trials, error monitoring and gated sensorimotor mapping emerged from training. Systematic dissection of trained RNNs revealed a detailed circuit mechanism that is consistent across networks trained with different hyperparameters. The networks' dynamical trajectories for different rules resided in separate subspaces of population activity; the subspaces collapsed and performance was reduced to chance level when dendrite-targeting somatostatin-expressing interneurons were silenced, illustrating how a phenomenological description of representational subspaces is explained by a specific circuit mechanism.

A Mean Field to Capture Asynchronous Irregular Dynamics of Conductance-Based Networks of Adaptive Quadratic Integrate-and-Fire Neuron Models.

Mean-field models are a class of models used in computational neuroscience to study the behavior of large populations of neurons. These models are based on the idea of representing the activity of a large number of neurons as the average behavior of mean-field variables. This abstraction allows the study of large-scale neural dynamics in a computationally efficient and mathematically tractable manner. One of these methods, based on a semianalytical approach, has previously been applied to different types of singleneuron models, but never to models based on a quadratic form. In this work, we adapted this method to quadratic integrate-and-fire neuron models with adaptation and conductance-based synaptic interactions. We validated the mean-field model by comparing it to the spiking network model. This mean-field model should be useful to model large-scale activity based on quadratic neurons interacting with conductance-based synapses.

A Network Analysis of the Fear Avoidance Model of Genital Pain

ABSTRACT Using a novel data-driven network approach, this study aimed to examine the interconnection between the key elements of the Fear-Avoidance Model of female genital pain – sexual arousal, fear-avoidant cognitions, and motivational coping – and its associated factors to predict the intensity and frequency of genital pain across women over time. Network modeling allowed for a comprehensive evaluation of the Fear-Avoidance model while capturing the dynamic features of genital pain. We estimated a cross-sectional and a temporal, contemporaneous, and between-persons network model on convenience-based data of 543 female students (mean age = 23.7 years, SD = 3.6) collected at three time points. Results showed that lubrication, pain catastrophizing, pain avoidance, fear-avoidance beliefs, sexual satisfaction, anxiety, and frequency of coital and non-coital sex predicted pain, with lubrication being the most consistent predictor across estimations. The network of women with recurrent genital pain showed a similar pattern as the network of the total sample, except that pain avoidance and fear-avoidance beliefs rather than pain catastrophizing predicted pain directly, and frequency of coital and non-coital sexual activities played a more prominent role. These results suggest that the main problem of genital pain centers around women not being sufficiently aroused during intercourse and inadequate ways of pain coping, which are critical targets of cognitive-behavioral therapy treatment and should be developed further.

Deep Learning Models of Multi-Scale Lesion Perception Attention Networks for Diagnosis and Staging of Pneumoconiosis: A Comparative Study with Radiologists.

Accurate prediction of pneumoconiosis is essential for individualized early prevention and treatment. However, the different manifestations and high heterogeneity among radiologists make it difficult to diagnose and stage pneumoconiosis accurately. Here, based on DR images collected from two centers, a novel deep learning model, namely Multi-scale Lesion-aware Attention Networks (MLANet), is proposed for diagnosis of pneumoconiosis, staging of pneumoconiosis, and screening of stage I pneumoconiosis. A series of indicators including area under the receiver operating characteristic curve, accuracy, recall, precision, and F1 score were used to comprehensively evaluate the performance of the model. The results show that the MLANet model can effectively improve the consistency and efficiency of pneumoconiosis diagnosis. The accuracy of the MLANet model for pneumoconiosis diagnosis on the internal test set, external validation set, and prospective test set reached 97.87%, 98.03%, and 95.40%, respectively, which was close to the level of qualified radiologists. Moreover, the model can effectively screen stage I pneumoconiosis with an accuracy of 97.16%, a recall of 98.25, a precision of 93.42%, and an F1 score of 95.59%, respectively. The built model performs better than the other four classification models. It is expected to be applied in clinical work to realize the automated diagnosis of pneumoconiosis digital chest radiographs, which is of great significance for individualized early prevention and treatment.

Tell Me What You Do, I’ll Tell You Who You Are: Predicting Offender-Victim Relationships in Sexual Homicide

ABSTRACT This study fills a gap in the existing literature by differentiating between sexual homicides committed by strangers and those by acquaintances. Utilizing data from the Sexual Homicide International Database, which encompasses 772 cases from France and Canada, the research focuses on using victimological and crime scene characteristics to predict the victim-offender relationship in sexual homicides. Employing a comprehensive methodological approach, the study uses bivariate analysis, sequential binary logistic regression, and an artificial neural network (ANN) model. These methods help in examining the correlations and predictive values of various factors in determining the nature of the victim-offender relationship. The findings highlight significant differences in the modus operandi of stranger and acquaintance offenders. Stranger offenders are more likely to exhibit violent, premeditated actions involving weapons, while acquaintance offenders tend to use verbal aggression, exploiting their existing relationship with the victim. Theoretically, results provide empirical insights into the dynamics of sexual homicides, expanding the understanding of offender behavior and crime scene analysis. Practically, it offers valuable guidance for law enforcement in criminal investigations and resource allocation.

Sensing Volatile Pollutants with Spin-Coated Films Made of Pillar[5]arene Derivatives and Data Validation via Artificial Neural Networks.

Different types of solvents, aromatic and aliphatic, are used in many industrial sectors, and long-term exposure to these solvents can lead to many occupational diseases. Therefore, it is of great importance to detect volatile organic compounds (VOCs) using economic and ergonomic techniques. In this study, two macromolecules based on pillar[5]arene, named P[5]-1 and P[5]-2, were synthesized and applied to the detection of six different environmentally volatile pollutants in industry and laboratories. The thin films of the synthesized macrocycles were coated by using the spin coating technique on a suitable substrate under optimum conditions. All compounds and the prepared thin film surfaces were characterized by NMR, Fourier transform infrared (FT-IR), elemental analysis, atomic force microscopy (AFM), scanning electron microscopy (SEM), and contact angle measurements. All vapor sensing measurements were performed via the surface plasmon resonance (SPR) optical technique, and the responses of the P[5]-1 and P[5]-2 thin-film sensors were calculated with ΔI/Io × 100. The responses of the P[5]-1 and P[5]-2 thin-film sensors to dichloromethane vapor were determined to be 7.17 and 4.11, respectively, while the responses to chloroform vapor were calculated to be 5.24 and 2.8, respectively. As a result, these thin-film sensors showed a higher response to dichloromethane and chloroform vapors than to other harmful vapors. The SPR kinetic data for vapors validated that a nonlinear autoregressive neural network was performed with exogenous input for the best molecular modeling by using normalized reflected light intensity values. It can be clearly seen from the correlation coefficient values that the nonlinear autoregressive with exogenous input artificial neural network (NARX-ANN) model for dichloromethane converged more successfully to the experimental data compared to other gases. The correlation coefficient values of the dichloromethane modeling results were approximately 0.99 and 0.98 for P[5]-1 and P[5]-2 thin-film sensors, respectively.

Dynamic simulation-assisted Gaussian mixture alignment approach for fault diagnosis of rotation machinery under small samples

Abstract Obtaining a substantial number of actual samples for rotating machinery in an industrial setting can be challenging, particularly when faulty samples are acquired under hazardous working conditions. The issue of insufficient samples hinders the effective training of reliable fault diagnosis models, impeding the industrial implementation of advanced intelligent methods. This study proposes an innovative Dynamic Simulation-assisted Gaussian Mixture Alignment model (DSGMA) to address the challenge of applying fault diagnosis technologies, with its performance mined by advanced transfer algorithms. Specifically, we establish a fault dynamics model for rotating machinery and acquire a substantial amount of simulated data as the source domain to facilitate the training of the deep neural network model. Subsequently, we propose a Gaussian mixture-guided domain alignment approach that assigns a domain-independent Gaussian distribution to each category as prior knowledge, with the parameters calculated using limited actual samples. Diagnostic knowledge is transferred from the source domain to the target domain by minimizing the Kullback-Leibler divergence between the features of the simulated samples and the Gaussian mixture priors. Furthermore, the DSGMA model incorporates Gaussian clustering loss to augment the clustering capability of samples belonging to the same category from real devices and enhances the computational stability of the parameters in the Gaussian mixture model. The efficacy of the DSGMA method is validated using three publicly available datasets and compared against five widely adopted methods. The experimental findings illustrate that DSGMA exhibits superior diagnostic and robust capabilities, facilitating efficient fault diagnosis under scenarios of small samples.

AI-Enabled Portable E-Nose Regression Predicting Harmful Molecules in a Gas Mixture.

The biomimetic electronic nose (e-nose) technology is a novel technology used for the identification and monitoring of complex gas molecules, and it is gaining significance in this field. However, due to the complexity and multiplicity of gas mixtures, the accuracy of electronic noses in predicting gas concentrations using traditional regression algorithms is not ideal. This paper presents a solution to the difficulty by introducing a fusion network model that utilizes a transformer-based multikernel feature fusion (TMKFF) module combined with a 1DCNN_LSTM network to enhance the accuracy of regression prediction for gas mixture concentrations using a portable electronic nose. The experimental findings demonstrate that the regression prediction performance of the fusion network is significantly superior to that of single models such as convolutional neural network (CNN) and long short-term memory (LSTM). The present study demonstrates the efficacy of our fusion network model in accurately predicting the concentrations of multiple target gases, such as SO2, NO2, and CO, in a gas mixture. Specifically, our algorithm exhibits substantial benefits in enhancing the prediction performance of low-concentration SO2 gas, which is a noteworthy achievement. The determination coefficient (R2) values of 93, 98, and 99% correspondingly demonstrate that the model is very capable of explaining the variation in the concentration of the target gases. The root-mean-square errors (RMSE) are 0.0760, 0.0711, and 3.3825, respectively, while the mean absolute errors (MAE) are 0.0507, 0.0549, and 2.5874, respectively. These results indicate that the model has relatively small prediction errors. The method we have developed holds significant potential for practical applications in detecting atmospheric pollution detection and other molecular detection areas in complex environments.

D-SPIN constructs gene regulatory network models from multiplexed scRNA-seq data revealing organizing principles of cellular perturbation response.

Gene regulatory networks within cells modulate the expression of the genome in response to signals and changing environmental conditions. Reconstructions of gene regulatory networks can reveal the information processing and control principles used by cells to maintain homeostasis and execute cell-state transitions. Here, we introduce a computational framework, D-SPIN, that generates quantitative models of gene regulatory networks from single-cell mRNA-seq datasets collected across thousands of distinct perturbation conditions. D-SPIN models the cell as a collection of interacting gene-expression programs, and constructs a probabilistic model to infer regulatory interactions between gene-expression programs and external perturbations. Using large Perturb-seq and drug-response datasets, we demonstrate that D-SPIN models reveal the organization of cellular pathways, sub-functions of macromolecular complexes, and the logic of cellular regulation of transcription, translation, metabolism, and protein degradation in response to gene knockdown perturbations. D-SPIN can also be applied to dissect drug response mechanisms in heterogeneous cell populations, elucidating how combinations of immunomodulatory drugs can induce novel cell states through additive recruitment of gene expression programs. D-SPIN provides a computational framework for constructing interpretable models of gene-regulatory networks to reveal principles of cellular information processing and physiological control.

IMUPF-BIN: A new autonomous search method for radioactive sources

To solve the problem of mobile robots autonomously searching for unknown radioactive sources, this paper proposes an automatic source search strategy, abbreviated as IMUPF-BIN: the location of the radioactive source is estimated by improved unscented particle filtering (IMUPF) algorithm, and the search path is predicted and planned by bioinspired neural network (BIN) model. First, the search environment is expressed as an occupied grid map, and the BIN model is combined with the grid occupied map to obtain the environment information in the search process. Then, the unscented particle filter (UPF) which is used for source parameter estimation is improved by introducing the idea of linear optimization to improve the efficiency of particle usage and the diversity of particles. Further, the BIN model is introduced to reduce the influence of obstacles on the movement of the robot, thereby improving the search accuracy and efficiency of radioactive source. After the robot detects the radiation count value, the parameter information of the source is estimated through the IMUPF algorithm. By using the activity value of the BIN model and distributed model predictive control (DMPC) to plan the path, while continuously iterating the search process, the location of the radioactive source is finally determined. The simulation results show that in without obstacles environment, the localization accuracy of the IMUPF algorithm is improved by 85.6% and 52.8% compared to the standard particle filtering (PF) and UPF algorithm respectively, and the search time of the IMUPF-BIN algorithm is reduced by 75.9% compared to the gradient search (GS) algorithm. In obstacles environment, the localization accuracy of the IMUPF algorithm is improved by 80.8% and 35.9% compared to the PF and UPF algorithms respectively. In addition, the GS algorithm fails to search for the radioactive source due to the shield of obstacles, while the IMUPF-BIN algorithm can successfully search for radioactive source in a shorter search time.

Breaking the silence: empowering the mute-deaf community through automatic sign language decoding.

The objective of this study is to develop a system for automatic sign language recognition to improve the quality of life for the mute-deaf community in Egypt. The system aims to bridge the communication gap by identifying and converting right-hand gestures into audible sounds or displayed text. To achieve the objectives, a convolutional neural network (CNN) model is employed. The model is trained to recognize right-hand gestures captured by an affordable web camera. A dataset was created with the help of six volunteers for training, testing, and validation purposes. The proposed system achieved an impressive average accuracy of 99.65 % in recognizing right-hand gestures, with high precision value of 95.11 %. The system effectively addressed the issue of gesture similarity between certain alphabets by successfully distinguishing between their respective gestures. The proposed system offers a promising solution for automatic sign language recognition, benefiting the mute-deaf community in Egypt. By accurately identifying and converting right-hand gestures, the system facilitates communication and interaction with the wider world. This technology has the potential to greatly enhance the quality oflife for individuals who are unable to speak or hear, promoting inclusivity and accessibility.

A Novel Liver Tumor segmentation of Adverse Propagation Advanced Swin Transformer Network with Mask region-based convolutional neural networks

The diagnosis and treatment of liver diseases from computed tomography (CT) images is an indispensable task for segmentation of Liver & its tumours. Due to the uneven presence, fuzzy borders, diverse densities, shapes and sizes of lesions segmentation of liver & its tumour is a difficult task. At this point we mainly focused on deep learning algorithms for segmenting liver and its tumour from abdominal CT scan images thereafter minimising the time & energy used for a liver diseases diagnosis This study aims to classify and segment liver tumors using a novel deep learning-based model. A Mask region-based convolutional neural network (Mask R-CNN) model is proposed for multiorgan segmentation to aid esophageal radiation treatment. Due to the fact that organ boundaries may be fuzzy and organ shapes are various, original Mask R-CNN works well on natural image segmentation while leaves something to be desired on the multiorgan segmentation task. Addressing it, the advantages of this method are threefold: (1) a ROI (region of interest) generation method is presented in the RPN (region proposal network) which is able to utilize multiscale semantic features. (2) A prebackground classification subnetwork is integrated to the original mask generation branch to improve the precision of multiorgan segmentation. The segmented image is added to the Adverse Propagation Advanced Swin Transformer Network (APESTNet) to prevent overfitting. The proposed model is based on CT volume slices of patients with liver tumors and evaluated on the public 3D dataset IRCADB01.The proposed model dataset is split into training as 60% and testing as 40 % for classification. The proposed model's accuracy and recall are 99.23 % and 98.24 %, respectively.