Multi-scale Architecture Research Articles

Autonomous self‐healing 3D micro‐suction adhesives for multi‐layered amphibious soft skin electronics

AbstractAutonomously self‐healing, reversible, and soft adhesive microarchitectures and structured electric elements could be important features in stable and versatile bioelectronic devices adhere to complex surfaces of the human body (rough, dry, wet, and vulnerable). In this study, we propose an autonomous self‐healing multi‐layered adhesive patch inspired by the octopus, which possess self‐healing and robust adhesion properties in dry/underwater conditions. To implement autonomously self‐healing octopus‐inspired architectures, a dynamic polymer reflow model based on structural and material design suggests criteria for three‐dimensional patterning self‐healing elastomers. In addition, self‐healing multi‐layered microstructures with different moduli endows efficient self‐healing ability, human‐friendly reversible bio‐adhesion, and stable mechanical deformability. Through programmed molecular behavior of microlevel hybrid multiscale architectures, the bioinspired adhesive patch exhibited robust adhesion against rough skin surface under both dry and underwater conditions while enabling autonomous adhesion restoring performance after damaged (over 95% healing efficiency under both conditions for 24 h at 30°C). Finally, we developed a self‐healing skin‐mountable adhesive electronics with repeated attachment and minimal skin irritation by laminating thin gold electrodes on octopus‐like structures. Based on the robust adhesion and intimate contact with skin, we successfully obtained reliable measurements during dynamic motion under dry, wet, and damaged conditions.image

A novel maternal thyroid disease prediction using multi-scale vision transformer architecture with improved linguistic hedges neural-fuzzy classifier.

Early pregnancy thyroid function assessment in mothers is covered. The benefits of using load-specific reference ranges are well-established. We pondered whether the categorization of maternal thyroid function would change if multiple blood samples obtained early in pregnancy were used. Even though binary classification is a common goal of current disease diagnosis techniques, the data sets are small, and the outcomes are not validated. Most current approaches concentrate on model optimization, focusing less on feature engineering. The suggested method can predict increased protein binding, non-thyroid syndrome (NTIS) (simultaneous non-thyroid disease), autoimmune thyroiditis (compensated hypothyroidism), and Hashimoto's thyroiditis (primary hypothyroidism). In this paper, we develop an automatic thyroid nodule classification system using a multi-scale vision transformer and image enhancement. Graph equalization is the chosen technique for image enhancement, and in our experiments, we used neural networks with four-layer network nodes. This work presents an enhanced linguistic coverage neuro-fuzzy classifier with chosen features for thyroid disease feature selection diagnosis. The training procedure is optimized, and a multi-scale vision transformer network is employed. Each hop connection in Dense Net now has trainable weight parameters, altering the architecture. Images of thyroid nodules from 508 patients make up the data set for this article. Sets of 80% training and 20% validation and 70% training and 30% validation are created from the data. Simultaneously, we take into account how the number of training iterations, network structure, activation function of network nodes, and other factors affect the classification outcomes. According to the experimental results, the best number of training iterations is 500, the logistic function is the best activation function, and the ideal network structure is 2500-40-2-1. K-fold validation and performance comparison with previous research validate the suggested methodology's enhanced effectiveness.

LID-Net: A lightweight image dehazing network for automatic driving vision systems

Visual system provides comprehensive road information for autonomous driving vehicles. Haze adversely affects the quality of driving images captured by onboard cameras, which poses a significant challenge to the safe operation of vehicles relying on pure vision-based autonomous driving solutions. To address the above issues, a lightweight image dehazing algorithm using a multi-scale architecture called LID-Net is proposed. LID-Net consists of Haze Extraction (HE) blocks and Haze Removal (HR) blocks. The HE block captures more haze features by employing a larger receptive field. The HR block introduces different attention weights to different regions to better remove haze of different concentrations. Furthermore, a Brightness Compensation unit is designed to address the issue of reduced brightness in images following dehazing. This unit compensates for the image's brightness without changing the color of the dehazed image. LID-Net is compared with other state-of-the-art methods on two real-world foggy weather datasets. The results indicate that LID-Net outperforms other methods in terms of dehazing effectiveness. LID-Net can efficiently process an image with a resolution of 1280 × 720 at 125 frames per second, which can fully meet the requirements of real-time processing of automatic driving systems.

Evolutionary Implications of Self-Assembling Cybernetic Materials with Collective Problem-Solving Intelligence at Multiple Scales.

In recent years, the scientific community has increasingly recognized the complex multi-scale competency architecture (MCA) of biology, comprising nested layers of active homeostatic agents, each forming the self-orchestrated substrate for the layer above, and, in turn, relying on the structural and functional plasticity of the layer(s) below. The question of how natural selection could give rise to this MCA has been the focus of intense research. Here, we instead investigate the effects of such decision-making competencies of MCA agential components on the process of evolution itself, using in silico neuroevolution experiments of simulated, minimal developmental biology. We specifically model the process of morphogenesis with neural cellular automata (NCAs) and utilize an evolutionary algorithm to optimize the corresponding model parameters with the objective of collectively self-assembling a two-dimensional spatial target pattern (reliable morphogenesis). Furthermore, we systematically vary the accuracy with which the uni-cellular agents of an NCA can regulate their cell states (simulating stochastic processes and noise during development). This allows us to continuously scale the agents' competency levels from a direct encoding scheme (no competency) to an MCA (with perfect reliability in cell decision executions). We demonstrate that an evolutionary process proceeds much more rapidly when evolving the functional parameters of an MCA compared to evolving the target pattern directly. Moreover, the evolved MCAs generalize well toward system parameter changes and even modified objective functions of the evolutionary process. Thus, the adaptive problem-solving competencies of the agential parts in our NCA-based in silico morphogenesis model strongly affect the evolutionary process, suggesting significant functional implications of the near-ubiquitous competency seen in living matter.

Towards strength-ductility synergy of titanium matrix composites through configuration design: Mesoscale deformation and damage behaviors

A novel multiscale configuration of two-scale network architectures and intragranular dispersoids was successfully constructed in the (TiBw+(TiZr)5Si3)/Ti55 composites via powder metallurgy. The well-designed composites exhibited a great improvement in tensile properties, with a substantial yield strength increment of ∼195 MPa while maintaining good plasticity. Compared with the homogeneous configuration, the multiscale configuration could significantly enhance the room-temperature plasticity by 7.5 times. To explore the contributions of configuration optimization to the strength-ductility synergy, statistical slip activities, strain evolutions and damage behaviors were investigated via quasi-in-situ and in-situ methods. It was found that the introduction of multiscale architectures could promote the activation of pyramidal slips, and lead to a more uniform activation of basal and prismatic slips. The reduction of elastic strain energy caused by the heterogeneous distribution of dislocations could also alleviate and suppress the propagation of microcracks. As for the configuration strengthening, the coupling effect of load-transfer strengthening and fine-grain strengthening was the dominant strengthening mechanism and was verified through theoretical calculations. The present work has correlated the mesoscale deformation behaviors with the superior mechanical properties, which could generically guide the design of high-performance titanium matrix composites.

MDC-RHT: Multi-Modal Medical Image Fusion via Multi-Dimensional Dynamic Convolution and Residual Hybrid Transformer.

The fusion of multi-modal medical images has great significance for comprehensive diagnosis and treatment. However, the large differences between the various modalities of medical images make multi-modal medical image fusion a great challenge. This paper proposes a novel multi-scale fusion network based on multi-dimensional dynamic convolution and residual hybrid transformer, which has better capability for feature extraction and context modeling and improves the fusion performance. Specifically, the proposed network exploits multi-dimensional dynamic convolution that introduces four attention mechanisms corresponding to four different dimensions of the convolutional kernel to extract more detailed information. Meanwhile, a residual hybrid transformer is designed, which activates more pixels to participate in the fusion process by channel attention, window attention, and overlapping cross attention, thereby strengthening the long-range dependence between different modes and enhancing the connection of global context information. A loss function, including perceptual loss and structural similarity loss, is designed, where the former enhances the visual reality and perceptual details of the fused image, and the latter enables the model to learn structural textures. The whole network adopts a multi-scale architecture and uses an unsupervised end-to-end method to realize multi-modal image fusion. Finally, our method is tested qualitatively and quantitatively on mainstream datasets. The fusion results indicate that our method achieves high scores in most quantitative indicators and satisfactory performance in visual qualitative analysis.

Multiscale-attention masked autoencoder for missing data imputation of wind turbines

High-quality data is essential for effective operation and maintenance of wind farms. However, data missing is a persistent issue in the supervisory control and data acquisition (SCADA) system, which seriously affects the data quality. To tackle the two limitations of current missing data imputation methods: the gap between training tasks and imputation tasks, and the inadequate extraction of correlations within SCADA data, this work proposes a data-driven framework named multiscale-attention masked autoencoder (MAMAE) for missing data imputation of wind turbines. The MAMAE employs masked autoencoding as a self-supervised training method, bridging the gap between the training and imputing task. Additionally, considering the importance of correlations in imputation for the SCADA data, a multiscale attention architecture built upon transformer is employed. Comprising four transformer stages, each applying attention mechanisms at distinct scales, the multiscale attention efficiently extracts feature, turbine, and temporal correlations. To ameliorate the problem of large computation cost caused by increased sequence length in different scales, localized attention is implemented in shifted windows, reducing the computational complexity from quadratic to a linear relationship with the sequence length. Furthermore, a turbine correlation-based feature combination method is proposed to coordinate with the multiscale attention and introduce turbine correlations into the imputation process. Experiments were conducted on a SCADA dataset collected in a real-world wind farm. The results show that the proposed method achieves higher accuracy than existing methods in most cases (especially in the cases with band missing and feature missing) and the ablation experiments verify the effectiveness of each proposed modification in improving accuracy or efficiency.

MMFG: Multimodal-based Mutual Feature Gating 3D Object Detection

To address the problem that image and point cloud features are fused in a coarse fusion way and cannot achieve deep fusion, this paper proposes a multimodal 3D object detection architecture based on a mutual feature gating mechanism. First, since the feature aggregation approach based on the set abstraction layer cannot obtain fine-grained features, a point-based self-attention mechanism module is designed. This module is added to the extraction branch of point cloud features to achieve fine-grained feature aggregation while maintaining accurate location information. Second, a new gating mechanism is designed for the deep fusion of image and point cloud. Deep fusion is achieved by mutual feature weighting between the image and the point cloud. The newly fused features are then fed into a feature refinement network to extract classification confidence and 3D target bounding boxes. Finally, a multi-scale detection architecture is proposed to obtain a more complete object shape. The location-based encoding feature algorithm is also designed to focus the interest points in the region of interest adaptively. The whole architecture shows outstanding performance on the KITTI3D and nuSenece datasets, especially at the difficult level. It shows that the framework solves the problem of low detection rates in LiDAR mode due to the low number of surface points obtained from distant objects.

Multiscale 3D printing via active nozzle size and shape control.

Three-dimensional (3D) printers extruding filaments through a fixed nozzle encounter a conflict between high resolution, requiring small diameters, and high speed, requiring large diameters. This limitation is especially pronounced in multiscale architectures featuring both bulk and intricate elements. Here, we introduce adaptive nozzle 3D printing (AN3DP), a technique enabling dynamic alteration of nozzle diameter and cross-sectional shape during printing. The AN3DP nozzle consists of eight independently controllable, tendon-driven pins arrayed around a flexible, pressure-resistant membrane. The design incorporates a tapered angle optimized for extruding shear-thinning inks and a pointed tip suitable for constrained-space printing, such as conformal and embedded printing. AN3DP's efficacy is demonstrated through the fabrication of components with continuous gradients, eliminating the need for discretization, and achieving enhanced density and contour precision compared to traditional 3D printing methods. This platform substantially expands the scope of extrusion-based 3D printers, thus facilitating diverse applications, including bioprinting cell-laden and hierarchical implants with bone-like microarchitecture.

Efficiency-Accuracy Trade-Off in Light Field Estimation with Cost Volume Construction and Aggregation.

The Rich spatial and angular information in light field images enables accurate depth estimation, which is a crucial aspect of environmental perception. However, the abundance of light field information also leads to high computational costs and memory pressure. Typically, selectively pruning some light field information can significantly improve computational efficiency but at the expense of reduced depth estimation accuracy in the pruned model, especially in low-texture regions and occluded areas where angular diversity is reduced. In this study, we propose a lightweight disparity estimation model that balances speed and accuracy and enhances depth estimation accuracy in textureless regions. We combined cost matching methods based on absolute difference and correlation to construct cost volumes, improving both accuracy and robustness. Additionally, we developed a multi-scale disparity cost fusion architecture, employing 3D convolutions and a UNet-like structure to handle matching costs at different depth scales. This method effectively integrates information across scales, utilizing the UNet structure for efficient fusion and completion of cost volumes, thus yielding more precise depth maps. Extensive testing shows that our method achieves computational efficiency on par with the most efficient existing methods, yet with double the accuracy. Moreover, our approach achieves comparable accuracy to the current highest-accuracy methods but with an order of magnitude improvement in computational performance.

Multi-scale quaternion CNN and BiGRU with cross self-attention feature fusion for fault diagnosis of bearing

In recent years, deep learning has led to significant advances in bearing fault diagnosis (FD). Most techniques aim to achieve greater accuracy. However, they are sensitive to noise and lack robustness, resulting in insufficient domain adaptation and anti-noise ability. The comparison of studies reveals that giving equal attention to all features does not differentiate their significance. In this work, we propose a novel FD model by integrating multi-scale quaternion convolutional neural network (MQCNN), bidirectional gated recurrent unit (BiGRU), and cross self-attention feature fusion (CSAFF). We have developed innovative designs in two modules, namely MQCNN and CSAFF. Firstly, MQCNN applies quaternion convolution to multi-scale architecture for the first time, aiming to extract the rich hidden features of the original signal from multiple scales. Then, the extracted multi-scale information is input into CSAFF for feature fusion, where CSAFF innovatively incorporates cross self-attention mechanism to enhance discriminative interaction representation within features. Finally, BiGRU captures temporal dependencies while a softmax layer is employed for fault classification, achieving accurate FD. To assess the efficacy of our approach, we experiment on three public datasets (CWRU, MFPT, and Ottawa) and compare it with other excellent methods. The results confirm its state-of-the-art, which the average accuracies can achieve up to 99.99%, 100%, and 99.21% on CWRU, MFPT, and Ottawa datasets. Moreover, we perform practical tests and ablation experiments to validate the efficacy and robustness of the proposed approach. Code is available at https://github.com/mubai011/MQCCAF.

WasteNet: A novel multi-scale attention-based U-Net architecture for waste detection in UAV images

Waste littering contributes to various ecological, aesthetic, economic, and social hazards. Given the dispersed nature of littered waste in public spaces, there is a need for a wider area of inspection to enable efficient detection of such waste. The study re-emphasizes the utility of UAVs for round-the-clock public space monitoring. The application of UAVs provides constant vigil over large public spaces. However, detecting the UAV-captured objects with precision is challenging as the far-off view further reduces the size of the target waste. Moreover, the waste objects have a multi-scale nature with different shapes, textures, and compositions. Given these varied challenges, this paper proposes a novel multi-scale attention-based deep network architecture (WasteNet) for efficient semantic segmentation of UAV-collected images to detect waste objects. Attention-based multi-scale fusion blocks (ABFBs) are introduced to improve semantic segmentation performance. These ABFBs can learn the contribution of different convolution scales during the training phase. The performance of the proposed WasteNet has been validated against seven other existing state-of-the-art waste localization methods. Moreover, an ablation study has been performed to derive the best architecture. The experimental results witnessed that WasteNet can detect litter from UAV-based images with a precision of 0.99, recall of 0.99, and MAP@50 of 0.95. For a better understanding of the results, the visuals reflecting an improved precision of waste boundary identification have also been included.

Multiscale Porous Architecture Consisting of Graphene Aerogels and Metastructures Enabling Robust Thermal and Mechanical Functionalities of Phase Change Materials

AbstractPassive thermal energy storage systems using phase change materials (PCMs) are promising for resolving temporal‐spatial overheating issues from small‐ to large‐scale platforms, yet their poor shape stability due to solid–liquid transition incurs PCM leakage and weak resistance against mechanical disturbance, limiting practical applications. While foam‐stable templates for PCMs have shown complement, they reveal massive leakage and collapse of liquefied PCMs under external loads or impacts. Herein, a multiscale porous architecture consisting of graphene aerogels (GAs) and meta structures enabling robust thermal‐mechanical functionalities of PCMs (3D‐MPGA) toward sustainable phase change thermal energy storage composites is reported. 3D‐printed mechanical metamaterials employing octet‐truss cells provide supportive strength and directionally‐assisted leakage reduction, while GAs serve as porous templates with surface‐interfacial contacts, thereby fixing paraffin wax as PCM inside their nano/micropores. The 3D‐MPGA shows intrinsic thermal characteristics of bulk PCMs, and improved thermal‐mechanical‐chemical stability, confirmed by long‐term heating‐cooling cycle tests over 10 h. Moreover, it exhibits highly reinforced strength (200–5000%) within a low density across ambient and melting temperatures, and maintains original shapes in the liquefied PCMs without severe leakage, against external loads. This work inspires rational strategies for advancing robust thermal‐mechanical functionalities for PCM‐based thermal energy storage systems.

GD-PAN: a multiscale fusion architecture applied to object detection in UAV aerial images

GD-PAN: a multiscale fusion architecture applied to object detection in UAV aerial images

Automatic Evaluation Method for Functional Movement Screening Based on Multi-Scale Lightweight 3D Convolution and an Encoder–Decoder

Functional Movement Screening (FMS) is a test used to evaluate fundamental movement patterns in the human body and identify functional limitations. However, the challenge of carrying out an automated assessment of FMS is that complex human movements are difficult to model accurately and efficiently. To address this challenge, this paper proposes an automatic evaluation method for FMS based on a multi-scale lightweight 3D convolution encoder–decoder (ML3D-ED) architecture. This method adopts a self-built multi-scale lightweight 3D convolution architecture to extract features from videos. The extracted features are then processed using an encoder–decoder architecture and probabilistic integration technique to effectively predict the final score distribution. This architecture, compared with the traditional Two-Stream Inflated 3D ConvNet (I3D) network, offers a better performance and accuracy in capturing advanced human movement features in temporal and spatial dimensions. Specifically, the ML3D-ED backbone network reduces the number of parameters by 59.5% and the computational cost by 77.7% when compared to I3D. Experiments have shown that ML3D-ED achieves an accuracy of 93.33% on public datasets, demonstrating an improvement of approximately 9% over the best existing method. This outcome demonstrates the effectiveness of and advancements made by the ML3D-ED architecture and probabilistic integration technique in extracting advanced human movement features and evaluating functional movements.

Multivariate mapping of seabed grain size parameters in the Bay of Fundy using convolutional neural networks

High-resolution seabed sediment information is critical for a range of marine spatial planning applications in multi-use shelf environments. To establish this information for the Bay of Fundy, Canada, legacy seabed sediment measurements were obtained from regional data compilations, and eight parameters describing the grain size were modelled across the extent of the bay using high resolution acoustic seafloor mapping and oceanographic datasets. This was achieved using a purpose-made convolutional neural network configured for geospatial modelling of multivariate grain size parameters. Shared information between the response parameters enabled model training with partially complete observations from the varied legacy data sources, and an explicit multiscale model architecture ensured that environmental predictors were implemented at appropriate scales for modelling each parameter. This avoids typical exhaustive exploration and selection of scale-specific predictor sets that often precede model building. Compositional grain size parameters were additionally accommodated using appropriate output activation functions, providing an efficient alternative to compositional data transformation and imputation. Results agreed well with our current understanding of the surficial geology of the bay, and cross-validation was used to quantitatively evaluate map predictions. Of the eight predicted parameters, the mean grain size and mud (clay and silt) fractions were predicted with high accuracy (> 50% variance explained); the accuracy of grain size skewness was comparatively low (24% variance explained). Exploration of variable importance suggested that compiled acoustic backscatter was the most important environmental variable for predicting the grain size, but that geographic information describing the latitude and longitude within the bay was also highly useful. We hypothesize an interaction between these variables that enables location-specific prediction. Data layers of predicted grain size parameter values are made available for further sedimentological and ecological exploration, and for marine spatial planning activities within the bay.

Multi-scale dispersion strengthening for high-temperature titanium alloys: Strength preservation and softening mechanisms

The long-lasting expectation “the hotter the engine, the better” calls for the development of high-temperature metallic alloys. Although the high specific strengths of titanium alloys are compelling for such applications, their deleterious softening beyond 600 °C imposes serious limitations. Much has been known for decades regarding the phase metallurgy for precipitation strengthening design in titanium alloys, however, the other facile strength promotion mechanism, dispersion strengthening, remains comparatively less-explored and unutilized. The present research concerns the multi-scale dispersion strengthening in titanium alloys, with mechanistic emphases on the critical plasticity micro-events that affect strength preservation. Due to the simultaneous introduction of intragranular dispersoids and intergranular reinforcers, the current titanium alloys present superior engineering tensile strength of 519 MPa at 700 °C. Throughout the examined 25–800 °C temperature range, noticeable softening induced by the thermal activation occurs above 600 °C, accompanied by evident strength loss. The temperature-dependence transition of dominated softening mechanisms from dynamic recovery to dynamic recrystallization has been clarified by theoretical calculations. Furthermore, the strengthening effect of multi-scale architectures is underpinned as the enhanced dislocation strengthening owing to the introduction of thermally-stable heterointerfaces, which could generically guide the design of similar heat-resistant titanium alloys.

CDAM-Net: Channel shuffle dual attention based multi-scale CNN for efficient glaucoma detection using fundus images

Glaucoma is a typical eye disorder that induces damage to the optic nerve due to increased intraocular pressure. It ultimately leads to partial or complete blindness without clinical reversal. Hence, it is of utmost importance to screen and detect glaucoma at an early stage. Most of the earlier glaucoma diagnosis methods rely on manual feature engineering, which is time-consuming and requires domain experts. Although recent methods, particularly, convolutional neural networks (CNNs) facilitate learning high-level feature representations from fundus images, they need extensive parameters and pose overfitting issues due to insufficient training samples. Further, conventional CNNs often ignore the minute changes in the lesion region. To overcome these issues, we propose a lightweight multi-scale CNN architecture called as CDAM-Net for effective glaucoma identification from retinal fundus images. Additionally, we introduce an attention module called channel shuffle dual attention (CSDA), comprising of a channel attention block, a spatial attention block and a channel shuffle unit, to focus on important regions in the fundus images, thereby extracting class-specific features. The CDAM-Net mainly consists of multi-scale feature representation (MFR) blocks that enable the extraction of multi-scale features from fundus images. Each MFR block is followed by a CSDA module, which further helps enrich the feature representation. The CDAM-Net is evaluated on a retinal fundus image (RFI) dataset containing 1426 fundus images (837 glaucoma and 589 normal), and the results indicate that CDAM-Net yields promising classification performance compared to existing techniques. Also, ablation studies are carried out to test the effectiveness of each component of the CDAM-Net.

Multi scale encoder-decoder network with Time Frequency Attention and S-TCN for single channel speech enhancement

The goal of speech enhancement is to restore clean speech in noisy environments. Acoustic scenarios with low signal-to-noise ratios (SNR) make it quite challenging to extract the target speech from its noise. In the current study, to enhance noisy speech, we propose a feature recalibration based multi-scale convolutional encoder-decoder architecture with squeeze temporal convolutional networks (S-TCN) bottleneck. Each multi-scale convolutional layer in encoder and decoder is followed by time-frequency attention module (TFA). The recalibration based multi-scale 2D convolution layers are used to extract local and contextual information. Additionally, the recalibration network is equipped with a gating mechanism to control the flow of information among the layers, enabling weighting of the scaled features for noise suppression and speech retention. The fully connected layer (FC) in the bottleneck part of encoder-decoder contains a few neurons, which capture the global information from the multi-scale 2D convolution layer and reduce parameters. A S-TCN, inspired by the popular temporal convolutional neural network (TCNN), is inserted between the encoder and the decoder to model long-term dependencies in speech. The TFA is a highly efficient network component, that operates through two simultaneous attentions, one focused on time frames, and the other on frequency channels. These attentions work together to explicitly exploit positional information to create a two-dimensional attention map to effectively capture the significant time-frequency distribution of speech. Utilizing the common voice dataset, our proposed model consistently enhances results compared to the current benchmarks, as demonstrated by two extensively utilized objective measures PESQ and STOI. The proposed model shows significant improvements, with average PESQ and STOI scores increasing by 45.7% and 23.8% respectively for seen background noises, and by 43.5% and 21.4% for unseen background noises, when compared to the quality of noisy speech. Tests validate that the proposed approach outperforms numerous cutting-edge algorithms.

Fake news detection in Dravidian languages using multiscale residual CNN_BiLSTM hybrid model

Fake news detection is the process of identifying news that contain purposeful misinformation disseminated through traditional news sources or social media platforms. Detecting fake news in Dravidian languages poses unique challenges due to the linguistic diversity and limited resources available for these languages. In this study, we propose a novel approach for fake news detection in Dravidian languages, leveraging contextualized word embeddings from MuRIL. MuRIL provides contextual embeddings that capture nuanced linguistic nuances, making it well-suited for the complexities of Dravidian languages. The proposed hybrid model combines multiscale residual CNN and BiLSTM layers to capture local and global dependencies in textual data effectively. The multiscale architecture allows the model to extract features at various levels of granularity while the BiLSTM layer captures long-range dependencies and contextual information. We performed extensive experiments on diverse datasets in Dravidian languages to evaluate the proposed approach. The results demonstrate the effectiveness of our hybrid model in discerning between genuine and fake news articles.