Browsing by Author "Raghavendra, B.S."

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1-D CNN for Mineral Classification using Hyperspectral Data
(Institute of Electrical and Electronics Engineers Inc., 2023) Yadav, P.P.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.
Hyperspectral Image (HSI) is a potent remote sensing (RS) technique, capturing images over numerous narrow, contiguous spectral bands. Unlike traditional RS methods, HSI offers detailed spectral insights for each pixel, enhancing comprehension of the Earth's surface and its contents. Initially intended for mining and geology, its application has expanded across various domains. Yet, mineral identification poses challenges due to spectral signature variations and limited ground truth. Despite various advanced algorithms, including machine learning, no dedicated Deep Learning (DL) expert system exists for mineral classification in HSI. DL models require abundant training data and ground-truth, which are scarce in hyperspectral mineral data. Introducing the 1-D CNN model as a proposed method, we focus on enhancing mineral classification by increasing the available training data. The utilization of augmented training samples through the 1-D CNN model tackles the challenge of limited ground truth data, enabling accurate classification of mineral classes. Â© 2023 IEEE.
A Meaningful Reformulation of Relative Spectral Discrimination Power to Analyze Hyperspectral Data
(Institute of Electrical and Electronics Engineers Inc., 2023) Yadav, P.P.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.
Spectral matching algorithms (SMAs) discriminate and distinguish spectral signatures of earth surface features by comparing with their ground-truth spectra. Though different SMAs developed based on different theoretical strategies, choosing an effective SMA is still a challenging task. To study the performance of SMAs in distinguishing spectral signatures, few performance measure are developed and relative spectral discrimination power (RSDPW) is one such a measure. RSDPW discriminates how one spectral signature is distinct from another relative to a reference spectral signature. Classical way of measuring RSDPW do not takes into account of spectral matching between the two spectral signatures to be discriminated. Therefore, in this paper, a reformulation for RSDPW is presented to get a good idea about the spectral diversity of spectral signatures to measure RSDPW in a more meaningful manner and also to make it perspicacious. The experimental results show that the proposed reformulated RSDPW not only a meaningful way to measure it but also robust/standard enough to compare various SMAs by measuring it. Additionally, the range of RSDPW values for different levels of discrimination is demonstrated for the present study. Â© 2023 IEEE.
Approximate Finite Rate of Innovation Based Seismic Reflectivity Estimation
(Birkhauser, 2024) Sudhakar Reddy, P.S.; Raghavendra, B.S.; Narasimhadhan, A.V.
Reflectivity inversion is an important deconvolution problem in reflection seismology that helps to describe the subsurface structure. Generally, deconvolution techniques iteratively work on the seismic data for estimating reflectivity. Therefore, these techniques are computationally expensive and may be slow to converge. In this paper, a novel method for estimating reflectivity signals in seismic data using an approximate finite rate of innovation (FRI) framework, is proposed. The seismic data is modeled as a convolution between the Ricker wavelet and the FRI signal, a Dirac impulse train. Relaxing the accurate exponential reproduction limitation given by generalised Strang-Fix (GSF) conditions, we develop a suitable sampling kernel utilizing Ricker wavelet which allows us to estimate the reflectivity signal. The experimental results demonstrate that the proposed approximate FRI framework provides a better reflectivity estimation than the deconvolution technique for medium-to-high signal-to-noise ratio (SNR) regimes with nearly 18% of seismic data. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
ATGP based Change Detection in Hyperspectral Images
(IEEE Computer Society, 2022) Yadav, P.P.; Bobate, N.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.
Hyperspectral images (HSIs) due to advancements in spatial-spectral resolutions and availability of multi-temporal information is in demand for many remote sensing (RS) applications including change detection (CD). The high dimensionality of HSIs and limited availability of HSI-CD data sets with ground-truth change maps make CD not so easy but a difficult task. Though there are many classical and deep learning (DL) based algorithms to detect changes, the performance of classical algorithms is not up to the satisfactory level and the final performance of DL models depend on efficiency of pre-detection techniques which provide prior knowledge on changed and unchanged areas that are required to get appropriate training samples to learn to detect changes. Classical and DL approaches consider change information at pixel level i.e. pixel to pixel change either by comparing the corresponding pixels alone or with their local neighborhood pixels. Therefore, identification of features for every pixel that relate the most significant information of the whole HSI-CD data in a simple and an efficient way to detect changes effectively is the need of the hour. In addition, there is not much comprehensive study on developing CD algorithms that not only simple to use but also as efficient as that of DL models is available. Therefore, in this paper, an endmember based feature extraction is proposed to detect changes in HSI. An automatic target generation process (ATGP) algorithm is adapted to extract endmembers present in the HSI-CD data set. Then, various spectral matching algorithms are used to measure endmember relations for all the pixels so that dimensionality of the data is reduced as well as the effective features to detect changes can be extracted. The experimental results on three benchmark HSI-CD data sets show that proposed ATGP based change vector analysis (CVA) algorithm yields remarkable results on comparing both with the classical as well as DL based CD approaches. Â© 2022 IEEE.
CloudX-net: A robust encoder-decoder architecture for cloud detection from satellite remote sensing images
(Elsevier B.V., 2020) Kanu, S.; Khoja, R.; Lal, S.; Raghavendra, B.S.; Cs, A.
Cloud Detection is an important pre-processing step for any application involving remote sensing data. This paper presents a deep learning based CloudX-Net architecture, that can detect cloud cover with improved accuracy in comparison to the benchmark from satellite remote sensing images. The proposed CloudX-Net model reduces the number of parameters needed for accurate predictions and thus make deep learning based cloud detection method very efficient. Atrous Spatial Pyramid Pooling (ASPP) and Separable convolution are used to optimize the network. For experimentation, we have used Landsat 8 images and 38-Cloud dataset and trained the architectures using Soft Jaccard loss function. Comparing several quantifying metrics result from various recent deep learning architectures proves the efficiency and effectiveness of the proposed CloudX-Net model for cloud detection from satellite images. The source code and data are available at https://github.com/shyamfec/CloudXNet. © 2020 Elsevier B.V.
Deep Learning Models for Classification of Lung Cancer Types from Histopathology Images
(Institute of Electrical and Electronics Engineers Inc., 2025) Kumar, S.V.; Swapna, H.; Raghavendra, B.S.
the most deadly and often diagnosed cancer is lung cancer, among other types of cancers all over the world. being diagnosed early can save the patient's life and improve their five-year survival rate. In this context, accurately recognizing the types of lung cancer from histopathology images is essential because it helps doctors decide which cancer types need further therapy. In this paper, to identify types of lung cancer from histopathology images, a deep learning framework based on binary and multi-classification approaches has been proposed. The framework utilizes the concept of transfer learning, and the Weighted average ensemble approach is used for the multi-classification model. The performance of the proposed model is examined using the LC25000 dataset, which is made freely available, and compared with the current approaches for classifying cancer types. It is observed that for the binary classification problem, InceptionV3, EfficicientNetB1, and multi-classification using the Weighted average ensemble approach have provided better results. The figures of merit achieved are recall, average accuracy, precision, and F1-score of 98.88%, 98.77%,98.77%, and 98.77%, respectively Â© 2025 IEEE.
Effectiveness of Phase Correlation Spectral Similarity Measure in Distinguishing Target Signatures for Hyperspectral Data Analysis
(Institute of Electrical and Electronics Engineers Inc., 2020) Yadav, P.P.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.
Hyperspectral imaging is one of the most information-rich sources of remote sensing data that exists. It can capture the entire, continuous spectrum of color and light. Feature extraction techniques that are selected for identifying diagnostic features influence classification accuracy. Spectral matching algorithms like similarity measures are developed to compare spectral features of materials with their reference spec-tral signatures in identifying different earth surfaces. Similarity measures are used as simple feature extraction techniques in target identification using hyperspectral data. Though there are several similarity measures, selecting a robust similarity measure requires further investigation. Influence of similarity measures are not studied much in distinguishing spectrally distinct signatures. In this article, we propose to study the performance of similarity measures in (i) discriminating endmember signatures (ii) mixed pixel identification (iii) clustering signatures of different classes and (iv) endmember extraction. Experimental results show an effective and a robust performance of proposed phase correlation similarity measure among all other similarity measures compared for all the problems under investigation. Â© 2020 IEEE.
Efficient deep learning architecture with dimension-wise pyramid pooling for nuclei segmentation of histopathology images
(Elsevier Ltd, 2021) Aatresh, A.A.; Yatgiri, R.P.; Chanchal, A.K.; Kumar, A.; Ravi, A.; Das, D.; Raghavendra, B.S.; Lal, S.; Kini, J.
Image segmentation remains to be one of the most vital tasks in the area of computer vision and more so in the case of medical image processing. Image segmentation quality is the main metric that is often considered with memory and computation efficiency overlooked, limiting the use of power hungry models for practical use. In this paper, we propose a novel framework (Kidney-SegNet) that combines the effectiveness of an attention based encoder-decoder architecture with atrous spatial pyramid pooling with highly efficient dimension-wise convolutions. The segmentation results of the proposed Kidney-SegNet architecture have been shown to outperform existing state-of-the-art deep learning methods by evaluating them on two publicly available kidney and TNBC breast H&E stained histopathology image datasets. Further, our simulation experiments also reveal that the computational complexity and memory requirement of our proposed architecture is very efficient compared to existing deep learning state-of-the-art methods for the task of nuclei segmentation of H&E stained histopathology images. The source code of our implementation will be available at https://github.com/Aaatresh/Kidney-SegNet. © 2021 Elsevier Ltd
Gradient Based Spectral Similarity Measure for Hyperspectral Image Analysis
(Institute of Electrical and Electronics Engineers Inc., 2021) Yadav, P.P.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.
Spectral matching algorithms or similarity measures dis-criminate spectral signatures of unknown materials by comparing them with the reference spectral signatures. Spectral matching algorithms play an important role in identifying different earth surface features using hyperspectral image analysis. Identification of diagnostic features and discriminating spectral signatures, especially of mineral signatures, which exhibit subtle differences among themselves is still a challenging task. Thus, developing or coming up with a new spectral matching algorithms is expected. Therefore, in this paper, we present a gradient based spectral similarity measure (GSSM) that captures the diagnostic (absorption) features to measure the degree of closeness between spectral signatures. Effectiveness of the proposed GSSM in distinguishing spectrally distinct signatures is studied with that of other spectral matching algorithms in (i) discriminating endmember signatures (ii) mixed pixel identification (iii) clustering spectral signatures of different classes and (iv) endmember extraction. Experimental results on a spectral library of 5 minerals formed using a benchmark mineral dataset called Cuprite data clearly show that the proposed GSSM is capable of (i) discriminating different spectral signatures in a better manner and also (ii) bringing into notice of the user the locations of diagnostic features by highlighting them to recognize easily. Â© 2021 IEEE.
Gradient Correlation Incorporated Similarity Measures in Matching Spectral Signatures
(Institute of Electrical and Electronics Engineers Inc., 2022) Yadav, P.P.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.
Hyperspectral images provide ample information which is needed to be analyzed carefully by the spectral processing algorithms for identifying objects, finding minerals etc. Spectral matching algorithms (SMAs) which make use of similarity measures discriminate and identify earth surface features by comparing spectral signatures with the ground-truth. SMAs that discriminate overall patterns capturing the diagnostic features of spectral signatures is of great use. In view of this, in this paper, we explore spectral gradient as a diagnostic feature to discriminate spectral signatures. Applicability of the proposed spectral gradient which is incorporated with SMAs in distinguishing spectrally distinct signatures is experimented in the following cases: (i) discriminating endmember signatures (ii) mixed pixel identification (iii) clustering spectral signatures of different classes and (iv) endmember extraction. Overall, the experimental results on a benchmark mineral Cuprite dataset library of five minerals have shown significantly improved performance in discriminating various spectral signatures. Â© 2022 IEEE.
Hidden Markov model-contourlet Hidden Markov tree based texture segmentation
(2004) Raghavendra, B.S.; Bhat, P.S.
Contourlets have emerged as a new mathematical tool for image processing and provide compact and decorrelated image representations. Hidden Markov modeling (HMM) of contourlet coefficients is a powerful approach for statistical processing of natural images. In this paper, we extended the hidden Markov modeling framework to contourlets and combined hidden Markov trees (HMT) with hidden Markov model to form HMM-Contourlet HMT model. The model is used for block based multiresolution texture segmentation. The performance of the HMM-Contourlet HMT texture segmentation method is compared with that of HMM-Real HMT and HMM-Complex HMT methods. The HMM-Contourlet HMT method provides superior texture segmentation results and excellent visual performance at small block sizes. � 2004 IEEE.
Hidden Markov model-contourlet Hidden Markov tree based texture segmentation
(2004) Raghavendra, B.S.; Subbanna Bhat, P.
Contourlets have emerged as a new mathematical tool for image processing and provide compact and decorrelated image representations. Hidden Markov modeling (HMM) of contourlet coefficients is a powerful approach for statistical processing of natural images. In this paper, we extended the hidden Markov modeling framework to contourlets and combined hidden Markov trees (HMT) with hidden Markov model to form HMM-Contourlet HMT model. The model is used for block based multiresolution texture segmentation. The performance of the HMM-Contourlet HMT texture segmentation method is compared with that of HMM-Real HMT and HMM-Complex HMT methods. The HMM-Contourlet HMT method provides superior texture segmentation results and excellent visual performance at small block sizes. Â© 2004 IEEE.
In this paper, block based texture segmentation is proposed based on contourlets and the hidden Markov model (HMM). Hidden Markov model is combined with hidden Markov tree (HMT) to form HMM-HMT model that models global dependency between the blocks in addition to the local statistics within a block. The HMM-HMT model is modified to use the contourlet transform, a new extension to the wavelet transform that forms a true basis for image representations. The maximum likelihood multiresolution segmentation algorithm is used to handle several block sizes at once. Since the algorithm works on the contourlet transformed image data, it can directly segment images without the need for transforming into the space domain. The experimental results demonstrate the competitive performance of the algorithm on contourlets with that of the other methods and excellent visual performance at small block sizes. The performance is comparable with that of wavelets and is superior at small block sizes. © Springer-Verlag 2004.
(Springer Verlag, Contourlet based multiresolution texture segmentation using contextual hidden markov models) Raghavendra, B.S.; Subbanna Bhat, P.
2004
INFLUENCE OF THE DARKEST PIXEL ON ENDMEMBERS INITIALIZATION
(Institute of Electrical and Electronics Engineers Inc., 2021) Yadav, P.P.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.
Endmember extraction is one of the necessary steps in hyperspectral data investigation. The eventual objective of hyperspectral data processing and analysis is to improve the accuracy of target identification, and the precise identification of endmembers is a challenging task. The several of the algorithms proposed for endmember extraction require knowledge on the targets for appropriate initialization. The endmember extraction algorithms (EEAs) rely on endmember initialization algorithms (EIAs) and in turn many EIAs also require the knowledge on targets of interest to begin the search process effectively. A comprehensive study on targets of interest to begin the search process is due expected. Therefore, in this paper, the concept of extreme or boundary pixels is explored to identify the targets of interest and the darkest pixel (a pixel with minimum length) is identified and proposed as a new target of interest to begin the endmember search process. The utility/influence of the proposed target of interest, i.e. the darkest pixel, is studied with that's of the brightest pixel (a pixel with maximum length) which has been in use as a popular target of interest for EIAs so far. An automatic target generation process (ATGP) and a similarity measures based endmember initialization algorithm (SMEIA) are adapted to test the proposed initialization strategy. The experimental results have revealed the usefulness of the darkest pixel as an additional target of interest in addition to the brightest pixel in searching for endmembers by EIAs. The strategy of choosing the darkest pixel enhanced the initial target knowledge and also improved the performance of the EIAs considerably. Â© 2021 IEEE.
LCSNet: Lightweight Caries Segmentation Network for the segmentation of dental caries using smartphone photographs
(Elsevier B.V., 2025) Radha, R.C.; Raghavendra, B.S.; Hota, R.K.; Vijayalakshmi, K.R.; Patil, S.; Narasimhadhan, A.V.
Dental caries is one of the major dental issues that is common among many individuals. It leads to tooth loss and affects the tooth root, creating a need to automatically detect dental caries to reduce treatment costs and prevent its consequences. The Lightweight Caries Segmentation Network (LCSNet) proposed in this study detects the location of dental caries by applying pixel-wise segmentation to dental photographs taken with various Android phones. LCSNet utilizes a Dual Multiscale Residual (DMR) block in both the encoder and decoder, adapts transfer learning through a pre-trained InceptionV3 model at the bottleneck layer, and incorporates a Squeeze and Excitation block in the skip connection, effectively extracting spatial information even from images where 95 % of the background and only 5 % represent the area of interest. A new dataset was developed by gathering oral photographs of dental caries from two hospitals, with advanced augmentation techniques applied. The LCSNet architecture demonstrated an accuracy of 97.36 %, precision of 73.1 %, recall of 70.2 %, an F1-Score of 71.14 %, and an Intersection-over-Union (IoU) of 56.8 %. Expert dentists confirmed that the LCSNet model proposed in this in vivo study accurately segments the position and texture of dental caries. Both qualitative and quantitative performance analyses, along with comparative analyses of efficiency and computational requirements, were conducted with other deep learning models. The proposed model outperforms existing deep learning models and shows significant potential for integration into a smartphone application-based oral disease detection system, potentially replacing some conventional clinically adapted methods. © 2025 The Authors.
Machine learning techniques for periodontitis and dental caries detection: A narrative review
(Elsevier Ireland Ltd, 2023) Radha, R.C.; Raghavendra, B.S.; Subhash, B.V.; Rajan, J.; Narasimhadhan, A.V.
Objectives: In recent years, periodontitis, and dental caries have become common in humans and need to be diagnosed in the early stage to prevent severe complications and tooth loss. These dental issues are diagnosed by visual inspection, measuring pocket probing depth, and radiographs findings from experienced dentists. Though a glut of machine learning (ML) algorithms has been proposed for the automated detection of periodontitis, and dental caries, determining which ML techniques are suitable for clinical practice remains under debate. This review aims to identify the research challenges by analyzing the limitations of current methods and how to address these to obtain robust systems suitable for clinical use or point-of-care testing. Methods: An extensive search of the literature published from 2015 to 2022 written in English, related to the subject of study was sought by searching the electronic databases: PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and ScienceDirect. Results: The initial electronic search yielded 1743 titles, and 55 studies were eventually included based on the selection criteria adopted in this review. Studies selected were on ML applications for the automatic detection of periodontitis and dental caries and related dental issues: Apical lessons, Periodontal bone loss, and Vertical root fracture. Conclusion: While most of the ML-based studies use radiograph images for the detection of periodontitis and dental caries, few pieces of the literature revealed that good diagnostic accuracy could be achieved by training the ML model even with mobile photos representing the images of dental issues. Nowadays smartphones are used in every sector for different applications. Training the ML model with as many images of dental issues captured by the smartphone can achieve good accuracy, reduce the cost of clinical diagnosis, and provide user interaction. © 2023 Elsevier B.V.
Magnetic resonance image reconstruction by nullspace based finite rate of innovation framework
(Association for Computing Machinery, 2021) Sudhakar Reddy, P.S.; Raghavendra, B.S.; Narasimhadhan, A.V.
The finite rate of innovation (FRI) framework has proved that it is possible to reconstruct the analog signals which have a finite number of parameters. FRI framework is used to reconstruct the images from undersampled magnetic resonance (MR) data. The reconstruction of the MR image from the MR data is a estimation problem, which can be solved by utilizing Prony's method. However, Prony's method involves solving the polynomial roots of the annihilating filter and this fact leads to an unstable reconstruction in the high noise scenario. In this paper, we introduce a novel reconstruction approach is also based on the annihilating filter. However, it involves the use of solutions of an underdetermined linear system. The simulation results of the proposed reconstruction approach show that the peak signal to noise ratio (PSNR) and the structural similarity index measure (SSIM) are higher magnitude than that of conventional FRI methods in the high noise scenario.1 Â© 2021 ACM.
Measurement and Evaluation of Human Vital Sign using 77GHz AWR1642 FMCW Radar Sensor
(Institute of Electrical and Electronics Engineers Inc., 2021) Srihari, P.; Vandana, G.S.; Raghavendra, B.S.
This paper proposes vital sign measurements of humans like heart rate (HR) and respiration rate (RR). The Texas Instruments AWR1642, frequency modulated continuous wave (FMCW) mmwave radar sensor, operating as 77-81 GHz frequency with 4GHz as the sweep bandwidth of the linear frequency modulated (LFM) waveform, is deployed to measure the HR and RR vital parameters. Here, twenty subjects (10 male; 10 female) experimental data has been collected using TI AWR1642 radar sensor, personal computer (PC), and DCA1000 data acquisition module. The HR and RR algorithm is applied to the process data to measure the HR and RR of these subjects. The male and female subjects' average HR is 78.587 and 77.827, respectively. Further, the average RR of the male and female subjects is 19.959 and 19.23, respectively. Furthermore, a pulse oximeter(POM) obtains HR ground truth information to validate the proposed method. The overall absolute mean error percentage(MEP) compared to pulse oximeter data is 4.7935% for 20 volunteers who have participated in the experiment. Â© 2021 IEEE.
Multitarget Detection and Tracking by Mitigating Spot Jammer Attack in 77-GHz mm-Wave Radars: An Experimental Evaluation
(Institute of Electrical and Electronics Engineers Inc., 2023) Kumuda, D.K.; Vandana, G.S.; Pardhasaradhi, B.; Raghavendra, B.S.; Srihari, P.; Cenkarmaddi, L.R.
Small form factor radar sensors at millimeter wavelengths find numerous applications in the industrial and automotive sectors. These radar sensors provide improved range resolution, good angular resolution, and enhanced Doppler resolution for short range and ultrashort ranges. However, it is challenging to detect and track the targets accurately when a radar is interfered by another radar. This article proposes an experimental evaluation of a 77-GHz IWR1642 radar sensor in the presence of a second 77-GHz AWR1642 radar sensor acting as a spot jammer. A real-time experiment is carried out by considering five different targets of various cross sections, such as a car, a larger size motorcycle, a smaller size motorcycle, a cyclist, and a pedestrian. The collected real-time data are processed by four different constant false alarm rate detectors, cell averaging (CA)-CFAR, ordered statistics (OS)-CFAR, greatest of CA (GOCA)-CFAR, and smallest of CA (SOCA)-CFAR. Following that, data from these detectors are fed into two different clustering algorithms (density-based spatial clustering of applications with noise (DBSCAN) and K-means), followed by the extended Kalman filter (EKF)-based tracker with global nearest neighbor (GNN) data association, which provide tracks of various targets with and without the presence of a jammer. Furthermore, four different metrics [tracks reported (TR), track segments (TSs), false tracks (FTs), and track loss (TL)] are used to evaluate the performance of various tracks generated for two clustering algorithms with four detection schemes. The experimental results show that the DBSCAN clustering algorithm outperforms the K-means clustering algorithm for many cases. © 2001-2012 IEEE.
RMDNet-Deep Learning Paradigms for Effective Malware Detection and Classification
(Institute of Electrical and Electronics Engineers Inc., 2024) S, S.; Lal, S.; Pratap Singh, M.; Raghavendra, B.S.
Malware analysis and detection are still essential for maintaining the security of networks and computer systems, even as the threat landscape shifts. Traditional approaches are insufficient to keep pace with the rapidly evolving nature of malware. Artificial Intelligence (AI) assumes a significant role in propelling its design to unprecedented levels. Various Machine Learning (ML) based malware detection systems have been developed to combat the ever-changing characteristics of malware. Consequently, there is a growing interest in exploring advanced techniques that leverage the power of Deep Learning (DL) to effectively analyze and detect malicious software. DL models demonstrate enhanced capabilities for analyzing extensive sequences of system calls. This paper proposes a Robust Malware Detection Network (RMDNet) for effective malware detection and classification. The proposed RMDNet model branches the input and performs depth-wise convolution and concatenation operations. The experimental results of the proposed RMDNet and existing DL models are evaluated on 48240 malware and binary visualization image dataset with RGB format. Also on the multi-class malimg and dumpware-10 datasets with grayscale format. The experimental results on each of these datasets demonstrate that the proposed RMDNet model can effectively and accurately categorize malware, outperforming the most recent benchmark DL algorithms. © 2013 IEEE.