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

作者： Nandini, Bandaru Jaya Sahithi, Mamidi Leha Harshika, Siddareddy Gari Jayan, Sarada Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India Amrita School of Engineering Department of Mathematics Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331528539

In the complex realm of medical imaging, noise poses a significant challenge as every pixel can convey crucial information that might save lives. Various factors, including environmental noise, sensor noise, and the intrinsic constraints of imaging equipment, can cause denoising in images. This study introduces a novel image denoising approach specifically applied to the Alzheimer's dataset, which is crucial for understanding and diagnosing Alzheimer's disease. The dataset comprises five distinct classes, each representing different stages or types of the disease, making it particularly significant in the medical field. Theproposed method leverages a combination of Discrete Fourier Transform (DFT), Inverse Discrete Fourier Transform (IDFT), Butterworth low-pass filtering, and Non-local Means denoising to enhance image quality. By employing Fourier Transform, the study performs frequency domain analysis on the image data, enabling the extraction of vital information while reducing noise. Furthermore, K-means clustering is used to categorize noisy and denoised images based on visual similarities. To validate the effectiveness of the denoising technique, statistical evaluations are conducted through hypothesis testing, comparing noise levelsand quality metrics between noisy and denoised images. Thisstudy not only offers a comprehensive approach to addressing the challenges of image denoising in medical imaging but alsohas significant societal benefits. By improving the clarity of images used in Alzheimer's research, the study contributes to more accurate diagnoses and better understanding of the disease, potentially leading to improved patient outcomes and advancing public health efforts in combating Alzheimer's disease. © 2024 IEEE.

关键词： Butterworth filters

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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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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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A Reinforcement Approach for Fleet Optimization in Multi-Agent Autonomous Cars 3

A Reinforcement Approach for Fleet Optimization in Multi-Age...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Pandey, Mayank Kunda, Naga Sai Shreya Pranave, K.C. Murali, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Mathematics Bengaluru India

ISBN: (纸本)9798350369083

The problems of fleet optimisation and safety are significant in the field of multi-agent autonomous automobile systems. This study focuses on the exploration of the application of reinforcement learning (RL) in tackling the challenges associated with fleet management and safety within multi-agent autonomous vehicle systems. Using Q-learning, self-driving taxis are trained to efficiently handle passenger requests and complex route planning in a simulated setting. The studys' contributions include the creation of a Q-learning algorithm optimized for taxi routing, a simplified yet robust training environment. By conducting detailed simulation tests, the research demonstrates how RL can substantially improve the efficiency of vehicle fleets and enhance passenger satisfaction, thereby emphasizing its critical role in advancing autonomous vehicle technology. The results provide useful information about how RL-based methods can transform the way we design and operate transportation systems, making them safer and more effective for cities of the future. © 2024 IEEE.

关键词： Multi agent systems

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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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Two-step Runge–Kutta methods for Volterra integro-differential equations

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International Journal of Computer mathematics 2024年第1期101卷 37-55页

作者： Wen, Jiao Huang, Chengming Guan, Hongbo College of Mathematics and Information Science Zhengzhou University of Light Industry Zhengzhou China School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan China

In this paper, we investigate two-step Runge–Kutta methods to solve Volterra integro-differential equations. Two-step Runge–Kutta methods increase the order of convergence in comparing the classical Runge–Kutta method without extra computational cost. First, the local order conditions and convergence theorem are derived. Then, stability properties of two-step Runge–Kutta methods corresponding to the basic and convolution test equations are analysed. Furthermore, one-stage method with order four and two-stage method with order six are constructed and we plot the stability regions. Numerical examples are presented to confirm the theoretical analyses. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Stability

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Comparative Analysis of Bitcoin Price Prediction Models: LSTM, BiLSTM, ARIMA and Transformers 15

Comparative Analysis of Bitcoin Price Prediction Models: LST...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

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

ISBN: (纸本)9798350370249

The proposed work explores the significance of Bitcoin in today's financial landscape and its role as a decentralized store of value and hedge against economic uncertainty. The diverse forecasts for Bitcoin prices are proposed and the importance of accurate prediction models. Specifically, it emphasizes the effectiveness of Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) models in capturing the complex dynamics of cryptocurrency markets, offering insights for traders, investors, and researchers. The growing importance of Deep Neural Networks (DNNs) is highlighted by analyzing historical market data and forecasting future price movements. The proposed work concludes by underscoring the evolution of Bitcoin price prediction methodologies from traditional models like ARIMA to advanced techniques like Bi-LSTM, and Auto-regressive EncoderDecoder Transformer, enhancing financial security and decision-making in the cryptocurrency market. The Bi-LSTM worked by providing a 0.9832 R2 score. © 2024 IEEE.

关键词： Decentralized finance

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Quantum-Blockchain Healthcare System for Invasive and No-Invasive-IoMT Data 20th

Quantum-Blockchain Healthcare System for Invasive and No-I...

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20th International Conference on Mobile Web and Intelligent Information Systems, MobiWIS 2024

作者： Grønli, Tor Morten Lakhan, Abdullah Younas, Muhammad School of Economics Innovations and Technology Kristiania University College Oslo Norway School of Engineering Computing and Mathematics Oxford Brookes University Oxford United Kingdom

ISBN: (纸本)9783031680045

These days, the Internet of Medical Things (IoMT) is a new paradigm where different bio-sensors, such as invasive and non-invasive, generate data to monitor the healthcare of the human body. However, the IoMT application consisted of sensor data (e.g., pacemaker heartbeat detection, blood pressure monitoring, and ingestible gas). These sensor data must be connected to the remote server for decision-making and analysis. Therefore, security and processing time are key issues for IoMT applications in distributed fog cloud networks. This paper presents the Quantum-Blockchain Healthcare System for Invasive and Non-Invasive IoMT Data. The paper has dual objectives: to minimize the processing time and security risk of IoMT data in distributed fog cloud networks and to design a novel quantum blockchain for IoMT application. In this paper, we present quantum key distribution blockchain scheduling (QKDBS), which consists of local scheduling of sensor data and remote scheduling with the quantum blockchain key distribution in the system. Simulations show that QKDBS works better than any other blockchain or quantum method for running IoMT applications on network nodes that are spread out. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Pacemakers

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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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