Multi-layer Deep Neural Network Research Articles

Survival Analysis Based Qos Recommendation for Bus Transportation using Deep Learning

Public transportation play an important role in metropolitan cities.Bus services are an important part of public transportation systems, which are frequently chosen because they are convenient, economical, environmentally beneficial, and less taxing on the infrastructure. The temporal patterns of bus transportation incidents are examined in this study using survival analysis, with a focus on the amount of time until certain events like delays, malfunctions, or safe arrivals at destinations. High-dimensional data is being generated at an increasing rate due to technological advancements. As a result, it is quite difficult to analyze such a dataset adequately. Algorithms for machine learning (ML) have become popular tools for modeling complex and nonlinear interactions in a variety of real-world applications, including as the analysis of high-dimensional survival data. Multilayer Deep Neural Network (DNN) models have achieved impressive results recently. Thus, using Keras and TensorFlow, a Cox-based DNN prediction survival model (DNNSurv model) was created. Its findings, however, were limited to survival datasets with large sample sizes or high dimensionality. Using high dimensional survival data, the proposed work will evaluate the DNNSurv model's prediction ability and compare it to a well-known machine learning survival prediction model (random survival forest). The Proposed Work also offers the best settings for a number of hyperparameters, including tuning parameter selection, for this reason. The suggested approach showed through data analysis that, when compared to the ML model, the DNNSurv model performed better overall in terms of the two primary survival prediction evaluation measures (i.e., the concordance index and the time-dependent AUC).

Cryptocurrency returns prediction using candlestick patterns analysis and multi-layer deep LSTM neural networks

Cryptocurrency returns prediction using candlestick patterns analysis and multi-layer deep LSTM neural networks

An inter-modal attention-based deep learning framework using unified modality for multimodal fake news, hate speech and offensive language detection

Fake news, hate speech and offensive language are related evil triplets currently affecting modern societies. Text modality for the computational detection of these phenomena has been widely used. In recent times, multimodal studies in this direction are attracting a lot of interests because of the potentials offered by other modalities in contributing to the detection of these menaces. However, a major problem in multimodal content understanding is how to effectively model the complementarity of the different modalities due to their diverse characteristics and features. From a multimodal point of view, the three tasks have been studied mainly using image and text modalities. Improving the effectiveness of the diverse multimodal approaches is still an open research topic. In addition to the traditional text and image modalities, we consider image–texts which are rarely used in previous studies but which contain useful information for enhancing the effectiveness of a prediction model. In order to ease multimodal content understanding and enhance prediction, we leverage recent advances in computer vision and deep learning for these tasks. First, we unify the modalities by creating a text representation of the images and image–texts, in addition to the main text. Secondly, we propose a multi-layer deep neural network with inter-modal attention mechanism to model the complementarity among these modalities. We conduct extensive experiments involving three standard datasets covering the three tasks. Experimental results show that detection of fake news, hate speech and offensive language can benefit from this approach. Furthermore, we conduct robust ablation experiments to show the effectiveness of our approach. Our model predominantly outperforms prior works across the datasets.

Advancements in remote sensing: Harnessing the power of artificial intelligence for scene image classification

<abstract> <p>The Remote Sensing Scene Image Classification (RSSIC) procedure is involved in the categorization of the Remote Sensing Images (RSI) into sets of semantic classes depending upon the content and this procedure plays a vital role in extensive range of applications, like environment monitoring, urban planning, vegetation mapping, natural hazards' detection and geospatial object detection. The RSSIC procedure exploits Artificial Intelligence (AI) technology, mostly Machine Learning (ML) techniques, for automatic analysis and categorization of the content, present in these images. The purpose is to recognize and differentiate the land cover classes or features in the scene, namely crops, forests, buildings, water bodies, roads, and other natural and man-made structures. RSSIC, using Deep Learning (DL) techniques, has attracted a considerable attention and accomplished important breakthroughs, thanks to the great feature learning abilities of the Deep Neural Networks (DNNs). In this aspect, the current study presents the White Shark Optimizer with DL-driven RSSIC (WSODL-RSSIC) technique. The presented WSODL-RSSIC technique mainly focuses on detection and classification of the remote sensing images under various class labels. In the WSODL-RSSIC technique, the deep Convolutional Neural Network (CNN)-based ShuffleNet model is used to produce the feature vectors. Moreover, the Deep Multilayer Neural network (DMN) classifiers are utilized for recognition and classification of the remote sensing images. Furthermore, the WSO technique is used to optimally adjust the hyperparameters of the DMN classifier. The presented WSODL-RSSIC method was simulated for validation using the remote-sensing image databases. The experimental outcomes infer that the WSODL-RSSIC model achieved improved results in comparison with the current approaches under different evaluation metrics.</p> </abstract>

Perceptron Theory Can Predict the Accuracy of Neural Networks.

Multilayer neural networks set the current state of the art for many technical classification problems. But, these networks are still, essentially, black boxes in terms of analyzing them and predicting their performance. Here, we develop a statistical theory for the one-layer perceptron and show that it can predict performances of a surprisingly large variety of neural networks with different architectures. A general theory of classification with perceptrons is developed by generalizing an existing theory for analyzing reservoir computing models and connectionist models for symbolic reasoning known as vector symbolic architectures. Our statistical theory offers three formulas leveraging the signal statistics with increasing detail. The formulas are analytically intractable, but can be evaluated numerically. The description level that captures maximum details requires stochastic sampling methods. Depending on the network model, the simpler formulas already yield high prediction accuracy. The quality of the theory predictions is assessed in three experimental settings, a memorization task for echo state networks (ESNs) from reservoir computing literature, a collection of classification datasets for shallow randomly connected networks, and the ImageNet dataset for deep convolutional neural networks. We find that the second description level of the perceptron theory can predict the performance of types of ESNs, which could not be described previously. Furthermore, the theory can predict deep multilayer neural networks by being applied to their output layer. While other methods for prediction of neural networks performance commonly require to train an estimator model, the proposed theory requires only the first two moments of the distribution of the postsynaptic sums in the output neurons. Moreover, the perceptron theory compares favorably to other methods that do not rely on training an estimator model.

Deep Multi-Layer Neural Network with Variable-Depth Output

In this study, a deep multi-layer neural network (DMLNN) with variable-depth output (VDO), called VDO-DMLNN, is proposed for classification. Unlike the traditional DMLNN, for which a user must define the network architecture in advance, VDO-DMLNN is produced from the top–down, layer by layer, until the classification error rate of VDO-DMLNN no longer decreases. The user thus does not need to define the depth of VDO-DMLNN in advance. The combination of the genetic algorithm (GA) and the self-organizing feature map (SOFM), called GA–SOFM, is proposed to automatically generate the weights and proper number of nodes for each layer in VDO-DMLNN. In addition, the output nodes can be at different levels in VDO-DMLNN rather than all being at the last layer, as in the traditional DMLNN. Thus, the average of computing time required for the recognition of an input sample in VDO-DMLNN is less than that in traditional DMLNN when they have the same classification error rate. Finally, VDO-DMLNN is compared with some state-of-the-art neural networks in the experiments.

Deep Multilayer Neural Network with Weights Optimization-Based Genetic Algorithm for Predicting Hypothyroid Disease

Deep Multilayer Neural Network with Weights Optimization-Based Genetic Algorithm for Predicting Hypothyroid Disease

Stochastic Gradient Deep Multilayer Neural Network based Linear Congruential Generative Cryptosystem for Secured Data Communication in Cloud

Cloud computing is a kind of distributed computing that use a vast network of interconnected resources accessible over the internet. Security is a crucial concern in cloud computing due to the fact that users save their data on the cloud for convenient access from any location and at any time. Consequently, many users are worried about safeguarding their sensitive data in an unsafe location. Therefore, cloud computing architecture requires an innovative cryptographic method that ensures the secrecy, authenticity, integrity, and non-repudiation of data transfer in the cloud. A new technique called SEMcrypt, which stands for Stochastic Gradient DEep Multilayer Neural Network based Linear Congruential Generative Cryptography, has been developed to enhance secure data transmission. SEMcrypt ensures higher data confidentiality and reduces the time required for communication between the cloud user (i.e., patient) and the server. The SEMcrypt approach has two distinct processes: categorization and secure data transport. Initially, the data is gathered from the patients and is used as input for the stochastic gradient regularised deep multilayer neural network. The deep neural network consists of one input layer, two hidden layers, and one output layer. At first, the information is gathered from the patients and sent to the input layer. Next, the patient data that has been gathered is examined in hidden layer 1 using the generalised Tikhonov regularisation function. The patient data that has been analysed is sent to hidden layer 2. The hyperbolic tangent activation function is used at that layer to classify the patient data. Subsequently, the categorised data undergoes encryption via the use of Linear Congruential Generative Goldwasser-Micali encryption, ensuring safe transfer of the data. Subsequently, the encrypted data is sent to the cloud server. The patient data is encrypted on the server side using the Linear Congruential Generative Goldwasser-Micali decryption technique to prevent unauthorised access or assaults. Consequently, the authorised recipient receives the unaltered information, which is then kept in the database for further analysis. Secured data transmission is achieved by ensuring better levels of data confidentiality and reducing the time required for the process. The experimental assessment focuses on criteria such as the time it takes to generate keys, the level of data confidentiality and integrity, the computing time, and the accuracy of categorization. The empirical findings demonstrate that our suggested SEMcrypt approach delivers efficient performance outcomes by attaining superior levels of data confidentiality and integrity within a minimal timeframe.

Attention-based generative adversarial networks improve prognostic outcome prediction of cancer from multimodal data.

The prediction of prognostic outcome is critical for the development of efficient cancer therapeutics and potential personalized medicine. However, due to the heterogeneity and diversity of multimodal data of cancer, data integration and feature selection remain a challenge for prognostic outcome prediction. We proposed a deep learning method with generative adversarial network based on sequential channel-spatial attention modules (CSAM-GAN), a multimodal data integration and feature selection approach, for accomplishing prognostic stratification tasks in cancer. Sequential channel-spatial attention modules equipped with an encoder-decoder are applied for the input features of multimodal data to accurately refine selected features. A discriminator network was proposed to make the generator and discriminator learning in an adversarial way to accurately describe the complex heterogeneous information of multiple modal data. We conducted extensive experiments with various feature selection and classification methods and confirmed that the CSAM-GAN via the multilayer deep neural network (DNN) classifier outperformed these baseline methods on two different multimodal data sets with miRNA expression, mRNA expression and histopathological image data: lower-grade glioma and kidney renal clear cell carcinoma. The CSAM-GAN via the multilayer DNN classifier bridges the gap between heterogenous multimodal data and prognostic outcome prediction.

Reliable adaptive edge-cloud collaborative DNN inference acceleration scheme combining computing and communication resources in optical networks

With the continuous development of the Artificial Intelligence of Things, deep neural network (DNN) models require a larger amount of computing capacity. The emerging edge-cloud collaboration architecture in optical networks is proposed as an effective solution, which combines edge computing with cloud computing to provide faster response and reduce the cloud load for compute-intensive tasks. The multi-layered DNN model can be divided into subtasks that are offloaded to edge and cloud servers for computation in this architecture. In addition, as bearer networks for computing capacity, once a server or link in optical networks fails, a large amount of data can be lost, so the robust reliability of the edge-cloud collaborative optical networks is very important. To solve the above problems, we design a reliable adaptive edge-cloud collaborative DNN inference acceleration scheme (RACAI) combining computing and communication resources. We formulate the RACAI into a mixed integer linear programming model and develop a multi-agent deep reinforcement learning algorithm (MADRL-RACIA) to jointly optimize DNN task partitioning, offloading, and protection. The simulation results show that compared with the benchmark schemes, the proposed MADRL-RACIA can provide a guarantee of reliability for more tasks under latency constraints and reduce the blocking probability.

Hybrid deep learning model for efficient state of charge estimation of Li-ion batteries in electric vehicles

State of charge (SoC) estimation is critical for the safe and efficient operation of electric vehicles (EVs). This work proposes a hybrid multi-layer deep neural network (HMDNN)-based approach for SoC estimation in EVs. This HMDNN uses Mountain Gazelle Optimizer (MGO) as a training algorithm for the deep neural network. Our method leverages the intrinsic relationship between the SoC and the voltage/current measurements of the EV battery to accurately estimate the SoC in real time. We evaluate our approach on a large dataset of real-world EV charging data and demonstrate its effectiveness in comparison to traditional SoC estimation methods. Four diverse Li-ion battery datasets of electric vehicles are employed which are the dynamic stress test (DST), Beijing dynamic stress test (BJDST), federal urban driving schedule (FUDS), and highway driving schedule (US06) with different temperatures of 0oC,25oC,45oC. The comparison is made with Mayfly Optimization Algorithm based DNN, Particle Swarm Optimization based DNN and Back-Propagation based DNN. The evaluation indices used are normalized mean square error (NMSE), root mean square error (RMSE), mean absolute error (MAE), and relative error (RE). The proposed algorithm achieves 0.1% NMSE and 0.3% RMSE on average on all datasets, which validates the effective performance of the proposed model. The results show that the proposed neural network-based approach can achieve higher accuracy and faster convergence than existing methods. This can enable more efficient EV operation and improved battery life.

Near Real-Time Flood Mapping with Weakly Supervised Machine Learning

Advances in deep learning and computer vision are making significant contributions to flood mapping, particularly when integrated with remotely sensed data. Although existing supervised methods, especially deep convolutional neural networks, have proved to be effective, they require intensive manual labeling of flooded pixels to train a multi-layer deep neural network that learns abstract semantic features of the input data. This research introduces a novel weakly supervised approach for pixel-wise flood mapping by leveraging multi-temporal remote sensing imagery and image processing techniques (e.g., Normalized Difference Water Index and edge detection) to create weakly labeled data. Using these weakly labeled data, a bi-temporal U-Net model is then proposed and trained for flood detection without the need for time-consuming and labor-intensive human annotations. Using floods from Hurricanes Florence and Harvey as case studies, we evaluated the performance of the proposed bi-temporal U-Net model and baseline models, such as decision tree, random forest, gradient boost, and adaptive boosting classifiers. To assess the effectiveness of our approach, we conducted a comprehensive assessment that (1) covered multiple test sites with varying degrees of urbanization, and (2) utilized both bi-temporal (i.e., pre- and post-flood) and uni-temporal (i.e., only post-flood) input. The experimental results showed that the proposed framework of weakly labeled data generation and the bi-temporal U-Net could produce near real-time urban flood maps with consistently high precision, recall, f1 score, IoU score, and overall accuracy compared with baseline machine learning algorithms.

Traffic congestion forecasting using multilayered deep neural network

ABSTRACT This study proposes a multilayered deep neural network (MLDNN) and a congestion index (CI) based on traffic density factor to forecast traffic congestion directly. Data were collected in Delhi city from a selected location using video cameras during peak hours of weekdays from Monday to Sunday to test the proposed model. Collected data were categorized in a matrix format in the intervals of five-minutes. The input matrix was divided into a number of intervals to train, validate, and test the MLDNN and baseline models, including support vector regression, multi-layer perceptron neural network, gated recurrent unit (GRU) neural network, long short-term memory (LSTM) neural network, convolutional neural network (CNN), CNN-GRU neural network, and CNN-LSTM neural network. Results of the study show that the MLDNN and proposed CI can be applied to predict traffic congestion successfully in heterogeneous traffic.

A novel discrete deep learning-based intelligent methodology for energy consumption classification

Energy consumption classification is one of the most widely-used approaches in the energy area that is applied in various applications such as household, commercial, urban, rural, industrial, etc. Energy consumption due to its substantial positive influence on the quality of made decisions, production and distribution management, and cost reduction, has considerable literature. In the classification literature, numerous statistical, intelligence, and hybrid methods have been developed and proposed in order to yield more accurate results. The common point of all these models is that whole of techniques follow a prevalent and repetitive procedure of learning process based on continuous distance-based cost function. While the objective function of classification is discrete. While the mismatch of the continuous cost function in classification problems with the discrete objective function causes them to be illogical or inefficient. Therefore, in this paper, a novel discrete learning process is offered to eliminate the inconsistency between the cost function and the classification objective function. The main difference between the proposed learning methodology rather than conventional versions is its cost function. In the proposed learning methodology, a mismatching function is considered as a cost function, which is dissimilar to previously developed ones, which are continuous functions based on distance, is a discrete function based on direction. In this way, in the proposed learning process, unknown parameters are discretely adjusted and at once jumped to the target. This is in contrast to conventional continuous learning algorithms in which the unknown parameters are continuously adjusted and step-by-step near the target. In this paper, deep multilayer neural networks (DMNs) have been exemplarily applied to implement the proposed learning algorithm. Although more consistency of goal and cost functions will logically has not a negative effect on the classification rate of classifiers. However, in this paper, to show the superiority of the proposed discrete deep multilayer neural network (DDMN) over conventional continuous-based classifiers, four energy consumption benchmark data sets have been employed. These data sets include the Tamilnadu Electricity Board Hourly Readings, Tetouan City Energy Consumption, Household Electric Power consumption, and steel industry Energy Consumption. Empirical results indicate that the proposed DDMN model, as pre-expected, can yield better performance in all cases than the DMN model. The DDMN model can achieve a 93.27% classification rate on average and upgrade its classic version approximately by 4.77%.

A Novel Deep-Learning-Based CADx Architecture for Classification of Thyroid Nodules Using Ultrasound Images.

Nodules of thyroid cancer occur in the cells of the thyroid as benign or malign types. Thyroid sonographic images are mostly used for diagnosis of thyroid cancer. The aim of this study is to introduce a computer-aided diagnosis system that can classify the thyroid nodules with high accuracy using the data gathered from ultrasound images. Acquisition and labeling of sub-images were performed by a specialist physician. Then the number of these sub-images were increased using data augmentation methods. Deep features were obtained from the images using a pre-trained deep neural network. The dimensions of the features were reduced and features were improved. The improved features were combined with morphological and texture features. This feature group was rated by a value called similarity coefficient value which was obtained from a similarity coefficient generator module. The nodules were classified as benign or malignant using a multi-layer deep neural network with a pre-weighting layer designed with a novel approach. In this study, a novel multi-layer computer-aided diagnosis system was proposed for thyroid cancer detection. In the first layer of the system, a novel feature extraction method based on the class similarity of images was developed. In the second layer, a novel pre-weighting layer was proposed by modifying the genetic algorithm. The proposed system showed superior performance in different metrics compared to the literature.

Generative Adversarial and Dual Layered Deep Classification Techniques for Improving Block Constructions in Public Cloud

This paper provides the Generative Adversarial and Dual Layered Deep Classification techniques to improve the drawbacks in the methods of Absolute Moment BTC (AMBTC) technique in reconstruction error rate of standard BTC model. The image blocks generation and compression are the main phases of BTC model. This can be applied for both colour images and grey scale images. However, the conventional BTC procedures lacks for edge reconstructions and noise reductions in the output images. The first technique GABTC is developed with multi-layered Deep Neural Network (DNN) structures with GA neural models. The integration of both GA models and BTC principles improve the quality of block constructions and reconstructions significantly. The second proposed work is adopted the Dual layered Deep Classification Technique. Handling the image database with minimal storage complexity, minimal computational complexity and optimal quality is a significant task. To obtain these solutions, many image processing techniques are evolved. In the domain, image compression and decompression are more needed at any cost for effectively handling the complex image databases. E-Learning resources are widely used around the internet based knowledge sharing environments. In the E-Learning environment, multiple types of data resources are managed. Particularly, organizing the images is more crucial task where multiple qualities of images are appeared inside the E-Learning network databases. This problem expects solutions from effective image compression techniques. Block Truncation Coding (BTC) and Absolute Moment BTC (AMBTC) are the techniques provide useful and easy implementations of E-Learning based image compression platform. At the same time, they are limited to image dissimilarity rate. To maintain the quality of images in both compression and decompression phases, multilevel image analysis models and training phases are required. In this regard, this proposed system develops a Dual Layered Deep Classification and Truncation (DLDCT) technique. DLDCT comprises the baseline benefits of BTC, multi-layered Support Vector Machine (SVM) units and Deep Layered Convolutional Neural Network (DLCNN) for producing classified range of image pixels and compressing the images under controlled circumstances. This proposed DLDCT makes the image compression and decompression with determined observations. This reduces real time errors occur during image reconstruction phases. This proposed system has been implemented and compared with existing works with respect to significant performance parameters.

A HYBRID ENSEMBLE LEARNING MODEL FOR EVALUATING THE SURFACE ROUGHNESS OF AZ91 ALLOY DURING THE END MILLING OPERATION

In metal-cutting operations, the surface roughness of the end product plays a significant role. It not only affects the aesthetic appearance of the end product but also signifies the product’s performance in the long run. Products with a high surface finish have higher endurance limits with negligible local stresses. On the other hand, products with rough surfaces are subjected to high stresses when they are engaged in various mechanical operations with varying loads. Surface roughness depends on various machining factors such as feed rate, depth of cut, cutting speed, or spindle speed. Therefore, it is required to predict surface roughness for the given machining parameters to reduce the cost and increase the life of the end product. In this work, an attempt has been made to evaluate the surface roughness of AZ91 alloy during the end milling operation. In this regard, various state-of-the-art ensemble learning models have been adopted and compared with the proposed hybrid ensemble model. The proposed hybrid ensemble model is the integration of random forest, gradient boosting, and a deep multi-layered neural network. In order to evaluate the performance of the proposed model, comparative analyses have been made in terms of mean square error, mean average error, and [Formula: see text] score. The result shows that the proposed hybrid model gives minimum error for surface roughness.

Modelling minimum miscibility pressure of CO2-crude oil systems using deep learning, tree-based, and thermodynamic models: Application to CO2 sequestration and enhanced oil recovery

The energy demand is still increasing across the globe, while environmental concerns about global warming effect and greenhouse gases have augmented recently. CO2 injection into mature oil reservoirs is an interesting operation that could help us supply the uprising demand while saving the environment. Having accurate knowledge about CO2 minimum miscibility pressure (MMP) is of utmost importance in designing a successful operation. This study mainly focuses on proposing several tools based on powerful tree-based and deep learning algorithms for estimating the MMP of CO2-crude oil system based on an extensive databank. The models employed in this study include extreme gradient boosting (XGBoost), categorical boosting (CatBoost), light gradient boosting machine (LGBM), random forest (RF), deep multi-layer neural network (deep MLN), deep belief network (DBN), and convolutional neural network (CNN). The models were trained and verified using 310 data points. Along with intelligent models, seven popular empirical correlations and two computational approaches, which are based on thermodynamics, were utilized to be compared with the proposed models. The outcomes expressed that the CatBoost model could estimate CO2 MMP values, using mole percent of volatile (C1 and N2) and intermediate (CO2, H2S, and C2–C5) fractions of oil, the average critical temperature of injection gas (Tcave), reservoir temperature (Tres), and molecular weight of C5+ fraction of oil (MWc5+) as input variables, with a total AARD of 1.34 %. Moreover, the variable impact examination showed that reservoir temperature greatly affects the MMP predictions. Finally, the Leverage approach verified the reliability of the databank and wide applicability domain of the developed CatBoost model spotting 5 outlier points (out of 310 points) only. The findings of this communication shed light on the high accuracy and reliability of CatBoost model in estimating CO2 MMP in a wide range of operational conditions.

Inverse Kinematic Modelling of a 3-DOF Robotic Manipulator using Hybrid Deep Learning Models

As the degrees of freedom (DOF) for a manipulator rise, so does the complexity of inverse kinematic modeling. This research provides an inverse kinematic model mapped with the aid of a Multilayer Deep Neural Network (DNN) trained using a unique meta-heuristic approach, namely the Gannet Optimization Algorithm (GOA), to decrease the computational weight and time lag for desired output transformation. The suggested design can automatically pick up on the kinematic characteristics of the manipulator. The sole observational basis for repeated learning is the link between input and output. Using the Robot Operating System (ROS), related simulations on a 3-DOF manipulator are performed. The simulation-generated dataset is split 65:35 for the purpose of training and testing the suggested model. Cost, time for the training data, mean relative error, normal mean square error, and mean absolute error for the test data are the metrics utilized for model validation. The efficacy and superiority of the suggested method are demonstrated by a comparison of the GOA-DNN model with the particle swarm optimization (PSO)-DNN and Grey Wolf Optimization (GWO)-DNN meta-heuristic DNN models.

Classification of Major Depressive Disorder using Machine Learning on brain structure and functional connectivity

Timely identification of the risk of having Major Depressive Disorder (MDD) using the advanced machine learning (ML) approach has been popularized to generate neural indicators of MDD. However, past ML studies have usually employed a comprehensive whole-brain approach, resulting in high computational cost that is hardly affordable in practical and clinical contexts. In this study, we took an alternative approach by first conducting a meta-analysis to identify brain regions of interest (ROIs) where structural and/or functional alterations had been consistently reported in MDD patients. We then utilized a nonlinear multilayer deep neural network method to evaluate the classification accuracies of MDD patients (N = 41, 29 females) and healthy individuals (N = 20, 12 females) based on the structural volume and resting-state functional connectivity (rsFC) of the ROIs. We found that the ML model based on brain volumes, with the inferior frontal cortex (IFC), amygdala, and the pons receiving the highest weights, achieved higher classification accuracy and sensitivity relative to the model based on rsFC. For ethical reasons, people with MDD remained taking antidepressants at the time of the study. Our findings highlight the critical role of the pons-cortico-limbic network in MDD, which is a significant insight for early identification and diagnosis of the illness for timely intervention.