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MuViS: Multimodal Virtual Sensing Benchmark
Authors:
Jens U. Brandt,
Noah C. Puetz,
Jobel Jose George,
Niharika Vinay Kumar,
Elena Raponi,
Marc Hilbert,
Thomas Bäck,
Thomas Bartz-Beielstein
Abstract:
Virtual sensing aims to infer hard-to-measure quantities from accessible measurements and is central to perception and control in physical systems. Despite rapid progress from first-principle and hybrid models to modern data-driven methods research remains siloed, leaving no established default approach that transfers across processes, modalities, and sensing configurations. We introduce MuViS, a…
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Virtual sensing aims to infer hard-to-measure quantities from accessible measurements and is central to perception and control in physical systems. Despite rapid progress from first-principle and hybrid models to modern data-driven methods research remains siloed, leaving no established default approach that transfers across processes, modalities, and sensing configurations. We introduce MuViS, a domain-agnostic benchmarking suite for multimodal virtual sensing that consolidates diverse datasets into a unified interface for standardized preprocessing and evaluation. Using this framework, we benchmark established approaches spanning gradient-boosted decision trees and deep neural network (NN) architectures, and show that none of these provides a universal advantage, underscoring the need for generalizable virtual sensing architectures. MuViS is released as an open-source, extensible platform for reproducible comparison and future integration of new datasets and model classes.
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Submitted 12 May, 2026; v1 submitted 13 March, 2026;
originally announced March 2026.
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A Novel XAI-Enhanced Quantum Adversarial Networks for Velocity Dispersion Modeling in MaNGA Galaxies
Authors:
Sathwik Narkedimilli,
N V Saran Kumar,
Aswath Babu H,
Manjunath K Vanahalli,
Manish M,
Aik Beng Ng,
Vinija Jain,
Aman Chadha
Abstract:
Current quantum machine learning approaches often face challenges balancing predictive accuracy, robustness, and interpretability. To address this, we propose a novel quantum adversarial framework that integrates a hybrid quantum neural network (QNN) with classical deep learning layers, guided by an evaluator model with LIME-based interpretability, and extended through quantum GAN and self-supervi…
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Current quantum machine learning approaches often face challenges balancing predictive accuracy, robustness, and interpretability. To address this, we propose a novel quantum adversarial framework that integrates a hybrid quantum neural network (QNN) with classical deep learning layers, guided by an evaluator model with LIME-based interpretability, and extended through quantum GAN and self-supervised variants. In the proposed model, an adversarial evaluator concurrently guides the QNN by computing feedback loss, thereby optimizing both prediction accuracy and model explainability. Empirical evaluations show that the Vanilla model achieves RMSE = 0.27, MSE = 0.071, MAE = 0.21, and R^2 = 0.59, delivering the most consistent performance across regression metrics compared to adversarial counterparts. These results demonstrate the potential of combining quantum-inspired methods with classical architectures to develop lightweight, high-performance, and interpretable predictive models, advancing the applicability of QML beyond current limitations.
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Submitted 30 July, 2026; v1 submitted 28 October, 2025;
originally announced October 2025.
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Comparative Analysis of Black Hole Mass Estimation in Type-2 AGNs: Classical vs. Quantum Machine Learning and Deep Learning Approaches
Authors:
Sathwik Narkedimilli,
Venkata Sriram Amballa,
N V Saran Kumar,
R Arun Kumar,
R Praneeth Reddy,
Satvik Raghav,
Manish M,
Aswath Babu H
Abstract:
In the case of Type-2 AGNs, estimating the mass of the black hole is challenging. Understanding how galaxies form and evolve requires considerable insight into the mass of black holes. This work compared different classical and quantum machine learning (QML) algorithms for black hole mass estimation, wherein the classical algorithms are Linear Regression, XGBoost Regression, Random Forest Regresso…
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In the case of Type-2 AGNs, estimating the mass of the black hole is challenging. Understanding how galaxies form and evolve requires considerable insight into the mass of black holes. This work compared different classical and quantum machine learning (QML) algorithms for black hole mass estimation, wherein the classical algorithms are Linear Regression, XGBoost Regression, Random Forest Regressor, Support Vector Regressor (SVR), Lasso Regression, Ridge Regression, Elastic Net Regression, Bayesian Regression, Decision Tree Regressor, Gradient Booster Regressor, Classical Neural Networks, Gated Recurrent Unit (GRU), LSTM, Deep Residual Networks (ResNets) and Transformer-Based Regression. On the other hand, quantum algorithms including Hybrid Quantum Neural Networks (QNN), Quantum Long Short-Term Memory (Q-LSTM), Sampler-QNN, Estimator-QNN, Variational Quantum Regressor (VQR), Quantum Linear Regression(Q-LR), QML with JAX optimization were also tested. The results revealed that classical algorithms gave better R^2, MAE, MSE, and RMSE results than the quantum models. Among the classical models, LSTM has the best result with an accuracy of 99.77%. Estimator-QNN has the highest accuracy for quantum algorithms with an MSE of 0.0124 and an accuracy of 99.75%. This study ascertains both the strengths and weaknesses of the classical and the quantum approaches. As far as our knowledge goes, this work could pave the way for the future application of quantum algorithms in astrophysical data analysis.
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Submitted 24 February, 2025; v1 submitted 21 February, 2025;
originally announced February 2025.
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FL-DECO-BC: A Privacy-Preserving, Provably Secure, and Provenance-Preserving Federated Learning Framework with Decentralized Oracles on Blockchain for VANETs
Authors:
Sathwik Narkedimilli,
Rayachoti Arun Kumar,
N. V. Saran Kumar,
Ramapathruni Praneeth Reddy,
Pavan Kumar C
Abstract:
Vehicular Ad-Hoc Networks (VANETs) hold immense potential for improving traffic safety and efficiency. However, traditional centralized approaches for machine learning in VANETs raise concerns about data privacy and security. Federated Learning (FL) offers a solution that enables collaborative model training without sharing raw data. This paper proposes FL-DECO-BC as a novel privacy-preserving, pr…
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Vehicular Ad-Hoc Networks (VANETs) hold immense potential for improving traffic safety and efficiency. However, traditional centralized approaches for machine learning in VANETs raise concerns about data privacy and security. Federated Learning (FL) offers a solution that enables collaborative model training without sharing raw data. This paper proposes FL-DECO-BC as a novel privacy-preserving, provably secure, and provenance-preserving federated learning framework specifically designed for VANETs. FL-DECO-BC leverages decentralized oracles on blockchain to securely access external data sources while ensuring data privacy through advanced techniques. The framework guarantees provable security through cryptographic primitives and formal verification methods. Furthermore, FL-DECO-BC incorporates a provenance-preserving design to track data origin and history, fostering trust and accountability. This combination of features empowers VANETs with secure and privacy-conscious machine-learning capabilities, paving the way for advanced traffic management and safety applications.
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Submitted 30 July, 2024;
originally announced July 2024.
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Class Specific Feature Selection for Interval Valued Data Through Interval K-Means Clustering
Authors:
D. S. Guru,
N. Vinay Kumar
Abstract:
In this paper, a novel feature selection approach for supervised interval valued features is proposed. The proposed approach takes care of selecting the class specific features through interval K-Means clustering. The kernel of K-Means clustering algorithm is modified to adapt interval valued data. During training, a set of samples corresponding to a class is fed into the interval K-Means clusteri…
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In this paper, a novel feature selection approach for supervised interval valued features is proposed. The proposed approach takes care of selecting the class specific features through interval K-Means clustering. The kernel of K-Means clustering algorithm is modified to adapt interval valued data. During training, a set of samples corresponding to a class is fed into the interval K-Means clustering algorithm, which clusters features into K distinct clusters. Hence, there are K number of features corresponding to each class. Subsequently, corresponding to each class, the cluster representatives are chosen. This procedure is repeated for all the samples of remaining classes. During testing the feature indices correspond to each class are used for validating the given dataset through classification using suitable symbolic classifiers. For experimentation, four standard supervised interval datasets are used. The results show the superiority of the proposed model when compared with the other existing state-of-the-art feature selection methods.
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Submitted 31 May, 2017;
originally announced May 2017.
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Symbolic Representation and Classification of Logos
Authors:
D. S. Guru,
N. Vinay Kumar
Abstract:
In this paper, a model for classification of logos based on symbolic representation of features is presented. The proposed model makes use of global features of logo images such as color, texture, and shape features for classification. The logo images are broadly classified into three different classes, viz., logo image containing only text, an image with only symbol, and an image with both text a…
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In this paper, a model for classification of logos based on symbolic representation of features is presented. The proposed model makes use of global features of logo images such as color, texture, and shape features for classification. The logo images are broadly classified into three different classes, viz., logo image containing only text, an image with only symbol, and an image with both text and a symbol. In each class, the similar looking logo images are clustered using K-means clustering algorithm. The intra-cluster variations present in each cluster corresponding to each class are then preserved using symbolic interval data. Thus referenced logo images are represented in the form of interval data. A sample logo image is then classified using suitable symbolic classifier. For experimentation purpose, relatively large amount of color logo images is created consisting of 5044 logo images. The classification results are validated with the help of accuracy, precision, recall, F-measure, and time. To check the efficacy of the proposed model, the comparative analyses are given against the other models. The results show that the proposed model outperforms the other models with respect to time and F-measure.
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Submitted 27 December, 2016;
originally announced December 2016.
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Delaunay Triangulation on Skeleton of Flowers for Classification
Authors:
Y H Sharath Kumar,
N Vinay Kumar,
D S Guru
Abstract:
In this work, we propose a Triangle based approach to classify flower images. Initially, flowers are segmented using whorl based region merging segmentation. Skeleton of a flower is obtained from the segmented flower using a skeleton pruning method. The Delaunay triangulation is obtained from the endpoints and junction points detected on the skeleton. The length and angle features are extracted fr…
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In this work, we propose a Triangle based approach to classify flower images. Initially, flowers are segmented using whorl based region merging segmentation. Skeleton of a flower is obtained from the segmented flower using a skeleton pruning method. The Delaunay triangulation is obtained from the endpoints and junction points detected on the skeleton. The length and angle features are extracted from the obtained Delaunay triangles and then are aggregated to represent in the form of interval-valued type data. A suitable classifier has been explored for the purpose of classification. To corroborate the efficacy of the proposed method, an experiment is conducted on our own data set of 30 classes of flowers, containing 3000 samples.
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Submitted 6 September, 2016;
originally announced September 2016.
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Features Fusion for Classification of Logos
Authors:
N. Vinay Kumar,
Pratheek,
V. Vijaya Kantha,
K. N. Govindaraju,
D. S. Guru
Abstract:
In this paper, a logo classification system based on the appearance of logo images is proposed. The proposed classification system makes use of global characteristics of logo images for classification. Color, texture, and shape of a logo wholly describe the global characteristics of logo images. The various combinations of these characteristics are used for classification. The combination contains…
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In this paper, a logo classification system based on the appearance of logo images is proposed. The proposed classification system makes use of global characteristics of logo images for classification. Color, texture, and shape of a logo wholly describe the global characteristics of logo images. The various combinations of these characteristics are used for classification. The combination contains only with single feature or with fusion of two features or fusion of all three features considered at a time respectively. Further, the system categorizes the logo image into: a logo image with fully text or with fully symbols or containing both symbols and texts.. The K-Nearest Neighbour (K-NN) classifier is used for classification. Due to the lack of color logo image dataset in the literature, the same is created consisting 5044 color logo images. Finally, the performance of the classification system is evaluated through accuracy, precision, recall and F-measure computed from the confusion matrix. The experimental results show that the most promising results are obtained for fusion of features.
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Submitted 6 September, 2016;
originally announced September 2016.
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Ahb Compatible DDR Sdram Controller Ip Core for Arm Based Soc
Authors:
Dr. R. Shashikumar,
C. N. Vijay Kumar,
M. Nagendrakumar,
C. S. Hemanthkumar
Abstract:
DDR SDRAM is similar in function to the regular SDRAM but doubles the bandwidth of the memory by transferring data on both edges of the clock cycles. DDR SDRAM most commonly used in various embedded application like networking, image or video processing, Laptops ete. Now a days many applications needs more and more cheap and fast memory. Especially in the field of signal processing, requires sig…
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DDR SDRAM is similar in function to the regular SDRAM but doubles the bandwidth of the memory by transferring data on both edges of the clock cycles. DDR SDRAM most commonly used in various embedded application like networking, image or video processing, Laptops ete. Now a days many applications needs more and more cheap and fast memory. Especially in the field of signal processing, requires significant amount of memory. The most used type of dynamic memory for that purpose is DDR SDRAM. For FPGA design the IC manufacturers are providing commercial memory controller IP cores working only on their products. Main disadvantage is the lack of memory access optimization for random memory access patterns. The data path part of those controllers can be used free of charge. This work propose an architecture of a DDR SDRAM controller, which takes advantage of those available and well tested data paths and can be used for any FPGA device or ASIC design.(5). In most of the SOC design, DDR SDRAM is commonly used. ARM processor is widely used in SOCs; so that we focused to implement AHB compatible DDR SDRAM controller suitable for ARM based SOC design.
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Submitted 9 February, 2010;
originally announced February 2010.
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Identifying the Importance of Software Reuse in COCOMO81, COCOMOII
Authors:
CH. V. M. K. Hari,
Prof. Prasad Reddy P. V. G. D,
J. N. V. R Swarup Kumar,
G. SriRamGanesh
Abstract:
Software project management is an interpolation of project planning, project monitoring and project termination. The substratal goals of planning are to scout for the future, to diagnose the attributes that are essentially done for the consummation of the project successfully, animate the scheduling and allocate resources for the attributes. Software cost estimation is a vital role in preeminent…
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Software project management is an interpolation of project planning, project monitoring and project termination. The substratal goals of planning are to scout for the future, to diagnose the attributes that are essentially done for the consummation of the project successfully, animate the scheduling and allocate resources for the attributes. Software cost estimation is a vital role in preeminent software project decisions such as resource allocation and bidding. This paper articulates the conventional overview of software cost estimation modus operandi available. The cost, effort estimates of software projects done by the various companies are congregated, the results are segregated with the present cost models and the MRE (Mean Relative Error) is enumerated. We have administered the historical data to COCOMO 81, COCOMOII model and identified that the stellar predicament is that no cost model gives the exact estimate of a software project.
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Submitted 11 December, 2009;
originally announced December 2009.
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On the Refractive Index of Ageing Dispersions of Laponite
Authors:
N. V. N. Ravi Kumar,
K. Muralidhar,
Yogesh M. Joshi
Abstract:
Aqueous dispersion of Laponite at low ionic concentration is of interest since it undergoes structural evolution with respect to time, which is usually termed as ageing. In this work we study the refractive index behavior as a function of ageing time, concentration and temperature. We observed that the extended Lorenz-Lorentz equation fitted the refractive index dependence on concentration and t…
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Aqueous dispersion of Laponite at low ionic concentration is of interest since it undergoes structural evolution with respect to time, which is usually termed as ageing. In this work we study the refractive index behavior as a function of ageing time, concentration and temperature. We observed that the extended Lorenz-Lorentz equation fitted the refractive index dependence on concentration and temperature very well. The refractive index did not show any dependence on ageing time. However, the dependence of refractive index on concentration showed a marked change as the system underwent transition from an isotropic to a biphasic state. The slope of the refractive index-density data is remarkably close to that of water at all Laponite concentrations. In the context of transport phenomena, optical measurements such as interferometry can exploit the water-like behavior of Laponite dispersions.
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Submitted 10 January, 2008;
originally announced January 2008.