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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Rajendrakumar, S. Chowta, Yashasvi Tadikonda, G Harshavardhan Reddy, Sathi Lakshmi Priyatha Karthik, Mukkanti Venkata Sai Shanmugam, Selvanayaki Kolandapalayam Thangavel, Senthil Kumar Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Chemical Engineering and Materials Science Coimbatore641112 India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore641112 India Ashland University Department of Mathematics and Computer Science OH United States

ISBN: (纸本)9798350376425

This research study proposes a new online platform, Native Nest, to address the challenges faced by the commerce industry in Ooty District. This initiative provides a sustainable platform for registered tribal artisans to connect with buyers, promoting their cultural heritage and products. The platform features a user-friendly interface, adjustable layout, and reliable infrastructure, ensuring a seamless experience for both clients and administrators. QR codes strategically placed throughout Ooty facilitate easy access to the platform, expanding its reach to tourists and locals. Native Nest offers detailed product information and enables online purchasing, eliminating the need for middlemen and providing a convenient shopping experience. By empowering tribal communities and promoting their cultural heritage, Native Nest contributes to the socio-economic development of the region. This initiative enables cultural exchange and expands the market share of tribal arts and expressions, creating a more comprehensive and sustainable marketplace. © 2024 IEEE.

关键词： Marketplaces

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Generative Adversarial Network for Image Reconstruction from Human Brain Activity

Generative Adversarial Network for Image Reconstruction from...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Tanner, Tim Cutsuridis, Vassilis School of Computer Science University of Lincoln Lincoln United Kingdom School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom

Decoding of brain activity with machine learning has enabled the reconstruction of thoughts, memories and dreams. In this study, we designed a methodology for reconstructing visual stimuli (digits) from human brain activity recorded during passive visual viewing. Using the MindBigData EEG dataset, we preprocessed the signals and cleaned them from noise, muscular artifacts and eye blinks. Using the Common Average Reference (CAR) method and past studies’ results we reduced the available electrodes from 14 to 4 keeping only those containing discriminative features associated with the visual stimulus. A convolutional neural network (CNN) was then trained to encode the signals and classify the images. A 92% classification performance was achieved post-CAR. Three variations of an auxiliary conditional generative adversarial network (AC-GAN) were evaluated for decoding the latent feature vector with its class embedding and generating black-and-white images of digits. Our objective was to create an image similar to the presented stimulus through the previously trained GANs. An average 65% reconstruction score was achieved by the AC-GAN without a modulation layer, a 60% by the AC-GAN with modulation layer and multiplication, and a 63% by the AC-GAN with modulation and concatenation. Rapid advances in generative modeling promise further improvements in reconstruction performance. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

关键词： Generative adversarial networks

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256 Metaverse Records Dataset 24

256 Metaverse Records Dataset

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32nd ACM International Conference on Multimedia, MM 2024

作者： Steinert, Patrick Wagenpfeil, Stefan Frommholz, Ingo Hemmje, Matthias L. Faculty of Mathematics and Computer Science University of Hagen Hagen Germany Faculty of Business Computing and Software Engineering Pfh University of Applied Sciences Göttingen Germany School of Mathematics and Computer Science University of Wolverhampton Wolverhampton United Kingdom

ISBN: (纸本)9798400706868

The metaverse is a developing field that crosses several areas of multimedia research. In this paper, we introduce the 256-MetaverseRecords dataset, a novel and extensive collection of annotated screen recordings in the form of videos from various virtual worlds of the metaverse. We describe the process of creating the dataset, the quality criteria for the annotations, and the exploration of the dataset. We also show four experiments to evaluate the performance of different feature extraction methods for Metaverse Recordings (MVRs): MVR segmentation, audio event detection, and object and interaction detection based on this dataset. Our results demonstrate that existing methods have limitations and leave challenges in dealing with the diversity and complexity of metaverse data, and that more research is needed to develop metaverse-specific techniques. Our dataset can serve as a valuable resource for the research community and foster the development of new applications and solutions for the metaverse. © 2024 Owner/Author.

关键词： dataset

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On the Convergence of a Crank-Nicolson Fitted Finite Volume Method for Pricing European Options under Regime-Switching Kou’s Jump-Diffusion Models

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Advances in Applied mathematics and Mechanics 2023年第5期15卷 1290-1314页

作者： Xiaoting Gan Junfeng Yin Rui Li School of Mathematics and Computer Science Chuxiong Normal UniversityChuxiongYunnan 675000China School of Mathematical Sciences Tongji UniversityShanghai 200092China College of Mathematics Physics and Information Engineering Jiaxing UniversityJiaxingZhejiang 314001China

In this paper,we construct and analyze a Crank-Nicolson fitted finite volume scheme for pricing European options under regime-switching Kou’s jumpdiffusion model which is governed by a system of partial integro-differential equations(PIDEs).We show that this scheme is consistent,stable and monotone as the mesh sizes in space and time approach zero,hence it ensures the convergence to the solution of continuous ***,numerical experiments are performed to demonstrate the efficiency,accuracy and robustness of the proposed method.

关键词： European option pricing regime-switching Kou’s jump-diffusion model partial integro-differential equation fitted finite volume method Crank-Nicolson scheme

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Comparative Analysis of DFT, DCT, and DWT Transformations with ADMM and l1-Optimization for Compressed Sensing in Meningioma Tumor MRI Image Reconstruction 1

Comparative Analysis of DFT, DCT, and DWT Transformations wi...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Narendran, Advik Hothri, Anantha Saga, Hemanth Jayan, Sarada Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bangalore India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Mathematics Bangalore India

ISBN: (纸本)9798350376517

In the context of modern medical imaging in MRI, it is becoming increasingly challenging to minimize the amount of time taken for scanning and data burden that is expected to be processed while still ensuring that high quality images are obtained. This study sought to compare the performances of different transforms in compressed sensing including DFT, DCT, and DWT based on conjugate ADMM and l1-optimization for reconstruction of meningioma tumor MRI images. The objective of this initiative is to entail the determination of the optimal transform and optimization methodologies whose outcomes will provide the best image reconstruction quality with the lowest computational complexities. Through the use of compressed sensing, the number of measurements used was much lower than normally expected, lowering the amount of data used. It was found DWT may be preferred for non-time-sensitive applications that demand the highest quality images, DCT's balanced attributes render it generally the best choice for the broadest range of medical imaging applications, expected to lead to a significant enhancement in the MRI diagnosis, patient comfort and the overall healthcare delivery system costs. The report outlines the method utilized for this study and the results observed using these techniques in real-world medical environments. © 2024 IEEE.

关键词： Magnetic resonance imaging

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A Systematic Review of Federated Learning Mechanism for The Internet of Medical Things 1

A Systematic Review of Federated Learning Mechanism for The ...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Aravind, P. Jerlin, M Asha Devarajan, Ganesh Gopal Nagarajan, Senthil Murugan School of Computer Science and Engineering Vellore Institute of Technology Chennai India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Ghaziabad201204 India Medicine University of Luxembourg Faculty of Science Technology Department of Mathematics Luxembourg

ISBN: (纸本)9798350353648

The rapid evolution of healthcare technology, no-tably the integration of the Internet of Medical Things (loMT), has revolutionized patient care, diagnosis, and treatment method-ologies. However, this progress introduces substantial challenges, primarily concerning the security, privacy, and interoperability of sensitive medical data. Federated Learning, a decentralized ma-chine learning approach, emerges as a possible solution to address these difficulties since it allows for collaborative model training without requiring raw patient data exchange. This systematic review explores and evaluates various FL mechanisms tailored for 10MT applications. The review examines existing literature, encompassing the amalgamation of loT and healthcare, the challenges faced in traditional health care systems, and the role of FL in mitigating these challenges. It highlights applications, privacy-preserving techniques, security measures, advantages, limitations, and potential enhancements in utilizing FL within 10MT environments. The review also presents a structured methodology for evaluating FL paradigms, encompassing performance metrics, privacy/security analysis, advantages/limitations assessment, and comparative analysis. Moreover, the paper pro-poses an innovative framework for federated learning in 10MT, detailing its architecture, modules, algorithms, functionalities, ethical considerations, and expected impact. Through a comprehensive analysis and synthesis of existing literature and proposed strategies, this review aims to provide insights for health care stakeholders in selecting and implementing optimal FL strategies for collaborative medical advancements while upholding stringent data privacy and security standards in 10MT. © 2024 IEEE.

关键词： Federated learning

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A Clustering Approach on Unveiling Global Development Disparities 5

A Clustering Approach on Unveiling Global Development Dispar...

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Ankireddypalli, Okesh Arella, Mouhitha Gujjula, Saranya Jayan, Sarada Amrita School of Computing Department of Computer Science & Engineering Amrita Vishwa Vidyapeetham Bengaluru India Amrita School of Engineering Department of Mathematics Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331528539

This paper seeks to go further and use clustering, hypothesis testing as well as attribute independence analysis to delve deeper into the world development indicators such as birth rate, infant mortality, business taxes rates etc., in a bid to pinpoint key relationships in the underlying socio-economic environment of the world today. Regional classifications, based on socio- economic development indicators, show that there are groupings of countries with similar development characteristics and this makes it easier to review, and analyze regional disparities. Hypothesis testing allows us to determine if there is indeed a correlation, such as between the business tax rates and Gross domestic product [GDP] growth or spending on health care and life expectancy. Also, the interdependence and the extent of the impact of these factors may be determined by identifying how precisely each component correlates with the others. And comparative analysis of these indicators were performed using the clustering techniques such as K means, hierarchical clustering, Density-Based Spatial Clustering of Applications with Noise [DBSCAN] and Spectral. The silhouette score for the clusters using K means and Hierarchical clustering shows up better analysis of the development status of the countries based on the world development features. © 2024 IEEE.

关键词： Inflation

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Quantum-Driven Big Data Processing

Quantum-Driven Big Data Processing

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Handa, Devansh Nikhil, Kindi Krishna Duvarakanath, S. Murali, K. Amrita School Of Computing Amrita Vishwa Vidyapeetham Department Of Computer Science And Engineering Bengaluru India Amrita School Of Engineering Amrita Vishwa Vidyapeetham Department Of Mathematics Bengaluru India

ISBN: (纸本)9798350371567

The proposed study realizes a novel quantum machine learning (QML) architecture that allows heuristic function evaluation and can actually perform quantum circuits during massive data processing. The Quantum-Circuit fusion or the Quantum-Data fusion approach to information technology is the latest strategy revolutionizing the major applications with vast computing needs in present time. Our method, which is task - agnostic, is aimed to increase the machine operations speed and scale as the concepts of quantum computing are applied. In this research article, the theoretical background and perspectives are described, so generally, the idea of the combination of quantum circuits is clarified. The research delivery shows as well of this combination as its importance and possibilities, offering a new and cool method in the competitive era of data-driven computational sciences, especially in the field of quantum machine learning. Within classical machine quantum-circuit fusion the potential capability to handle a large data set and obtain the new viewpoints consequently quantum machine learning progress represents a promising route. © 2024 IEEE.

关键词： Timing circuits

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Real Time Object Detection using Markov Random Fields: Video Anomaly Detection 5

Real Time Object Detection using Markov Random Fields: Video...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Chennupati, Anjali Prahas, Bhamidipati Ghali, Bharadwaj Aaditya Murali, K. Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Mathematics Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350361155

This research focuses on the field of video anomaly detection, using the combined strength of the Markov Random Fields (MRFs) and autoencoder systems. The image-segmentation model proposed is based on the use of MRF, which is being used its spatial-temporal abilities to, for one, improve its ability to differentiate abnormalities in video content. The use of MRFs and autoencoders for the integration of the MRFs and autoencoders forms a comprehensive representation of the video data, improving the detection of abnormal activities. A wide range of testing is performed on videos randomly selected from the AVENUE dataset in order to emphasize the effectiveness of the proposed method, which outperforms other approaches. In particular, the approach significantly reduces the computation time of the anomaly detection by 60% when compared to the conventional frame extraction method. This efficiency improvement is an indication of the feasibility of this model in detecting anomalies in practical surveillance applications and has the required attributes that can be used to detect an anomaly in the surveillance system within a short time. The results reveal the potential benefits of the proposed framework for the improvement of the anomaly detection accuracy and operational effectiveness, adding to its practical viability being deployed in surveillance settings. © 2024 IEEE.

关键词： Anomaly detection

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StyleMask: Disentangling the Style Space of StyleGAN2 for Neural Face Reenactment 17

StyleMask: Disentangling the Style Space of StyleGAN2 for Ne...

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17th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2023

作者： Bounareli, Stella Tzelepis, Christos Argyriou, Vasileios Patras, Ioannis Tzimiropoulos, Georgios School of Computer Science and Mathematics Kingston University London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom

ISBN: (纸本)9798350345445

In this paper we address the problem of neural face reenactment, where, given a pair of a source and a target facial image, we need to transfer the target's pose (defined as the head pose and its facial expressions) to the source image, by preserving at the same time the source's identity characteristics (e.g., facial shape, hair style, etc), even in the challenging case where the source and the target faces belong to different identities. In doing so, we address some of the limitations of the state-of-the-art works, namely, a) that they depend on paired training data (i.e., source and target faces have the same identity), b) that they rely on labeled data during inference, and c) that they do not preserve identity in large head pose changes. More specifically, we propose a framework that, using unpaired randomly generated facial images, learns to disentangle the identity characteristics of the face from its pose by incorporating the recently introduced style space S [1] of StyleGAN2 [2], a latent representation space that exhibits remarkable disentanglement properties. By capitalizing on this, we learn to successfully mix a pair of source and target style codes using supervision from a 3D model. The resulting latent code, that is subsequently used for reenactment, consists of latent units corresponding to the facial pose of the target only and of units corresponding to the identity of the source only, leading to notable improvement in the reenactment performance compared to recent state-of-the-art methods. In comparison to state of the art, we quantitatively and qualitatively show that the proposed method produces higher quality results even on extreme pose variations. Finally, we report results on real images by first embedding them on the latent space of the pretrained generator. We make the code and the pretrained models publicly available at: https://***/StelaBou/StyleMask. © 2023 IEEE.

关键词： 3D modeling

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