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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Saraf, Priyanka Tharaniesh, P.R. Singh, Soumyendra Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Mathematics Chennai India

ISBN: (纸本)9798350372892

Skin illnesses encompass a wide range of problems that affect the skin and can have significant effects on an individual's general health and well-being. The skin is a complex organ that affects not only physical protection but also mental well-being and general quality of life. The frequency of skin disorders, which manifest as redness, itching, discoloration, blisters, and rashes, highlights the need for prompt identification and treatment in a variety of demographic contexts. Dermatologists are essential to the diagnosis and treatment procedures since they are specialists in the medical treatment of the skin. Although earlier identification of certain disorders can greatly improve outcomes, the complexity of dermatology makes it difficult to effectively detect certain conditions at an early stage. The complicated nature of the diagnostic process results from the frequent need for intensive testing, which takes a long time. Furthermore, the subjectivity presents in dermatological diagnoses - which is shaped by the unique experiences and knowledge of practitioners - introduces heterogeneity that may compromise the validity of the findings. In order to improve the accuracy and effectiveness of illness identification in dermatology, this research introduces a unique deep learning technique. The technology's astounding 98 percent accuracy rate points to a potential advance in early intervention, opening the door to more potent therapies and, eventually, better patient outcomes. Beyond its direct consequences for dermatology, this study shows how artificial intelligence is revolutionizing the medical industry and is a major step toward utilizing cutting-edge technologies to support healthcare operations. A future where skin diseases are quickly and precisely recognized for the best possible patient care is promised by the promise of improved diagnostics and customized therapies as we negotiate the junction of technology and healthcare. © 2024 IEEE.

关键词： Dermatology

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ECG Signal Classification and Anomaly Detection using LSTM and HMM Models 11

ECG Signal Classification and Anomaly Detection using LSTM a...

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11th IEEE International Conference on Advances in computing and Communications, ICACC 2024

作者： Karthik, Garikipati Muppala, Tejaswi Vinitha Chowdary, A. Jayan, Sarada Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Mathematics Bengaluru India

ISBN: (纸本)9798350379136

Accurate detection and classification of electrocardiogram (ECG) signals is required for cardiac condition diagnosis and timely medical intervention. However, capturing the complex temporal dependencies in ECG data is difficult and hence may provide less effective performance comparatively. The project proposes an innovative approach to detecting and classifying the anomalies in ECG using LSTM and HMM. Preprocessing and normalization of the ECG data are done. After that, the sequence is prepared for input in LSTM, which builds a strong LSTM model with reinforced regularization techniques designed for feature learning from the ECG data. The features extracted by LSTM were concatenated with the original features and applied to train the HMM for anomaly detection based on estimating the likelihood of the log probabilities. Thresholding the log probabilities will locate anomalies. The next step is the implementation of HMM to classify ECG signals. The proposed method shows the framework comprehensively for effective detection of anomaly and classification, combining the strong points of both LSTM and HMM. The results obtained are employed to illustrate and prove the efficiency of the proposed combined approach and emphasise the importance of the combination of deep learning and statistical models for further analysis of ECG signals. © 2024 IEEE.

关键词： Markov processes

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A two-dimensional double layer-averaged model of hyperconcentrated turbidity currents with non-Newtonian rheology

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International Journal of Sediment Research 2023年第6期38卷 794-810页

作者： Yining Sun Ji Li Zhixian Cao Alistair George Liam Borthwick State Key Laboratory of Water Resources Engineering and Management Wuhan University Zienkiewicz Centre for Computational Engineering Faculty of Science and EngineeringSwansea University Institute for Infrastructure and Environment University of Edinburgh School of Engineering Computing and MathematicsUniversity of Plymouth

Hyperconcentrated turbidity currents typically display non-Newtonian characteristics that influence sediment transport and morphological evolution in alluvial ***,hydro-sedimentmorphological processes involving hyperconcentrated turbidity currents are poorly understood,with little known about the effect of the non-Newtonian *** current paper extends a recent twodimensional double layer-averaged model to incorporate non-Newtonian constitutive *** extended model is benchmarked against experimental and numerical data for cases including subaerial mud flow,subaqueous debris flow,and reservoir turbidity *** computational results agree well with observations for the subaerial mud flow and independent numerical simulations of subaqueous debris *** between the non-Newtonian and Newtonian model results become more pronounced in terms of propagation distance and sediment transport rate as sediment concentration *** model is then applied to turbidity currents in the Guxian Reservoir planned for middle Yellow River,China,which connects to a tributary featuring hyperconcentrated sediment-laden *** non-Newtonian model predicts slower propagation of turbidity currents and more significant bed aggradation at the confluence between the tributary Wuding River and the Yellow River in the reservoir than its Newtonian *** difference in model performance could be of considerable importance when optimizing reservoir operation schemes.

关键词： Double layer-averaged model Non-Newtonian rheology Mud flow Reservoir turbidity current Yellow River

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Convergent and Orthogonality Preserving Schemes for Approximating the Kohn-Sham Orbitals

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Numerical mathematics(Theory,Methods and Applications) 2023年第1期16卷 1-25页

作者： Xiaoying Dai Liwei Zhang Aihui Zhou LSEC Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China

To obtain convergent numerical approximations without using any orthogonalization operations is of great importance in electronic structure *** this paper,we propose and analyze a class of iteration schemes for the discretized Kohn-Sham Density Functional Theory model,with which the iterative approximations are guaranteed to converge to the Kohn-Sham orbitals without any orthogonalization as long as the initial orbitals are orthogonal and the time step sizes are given *** addition,we present a feasible and efficient approach to get suitable time step sizes and report some numerical experiments to validate our theory.

关键词： Gradient flow based model density functional theory orthogonality preserving scheme convergence temporal discretization

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AIR-SCAN: Aircraft Identification and Recognition using Deep Learning Scanning 11

AIR-SCAN: Aircraft Identification and Recognition using Deep...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Kumar, Aman Singh, Soumyendra Mohan G, Bharathi Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mathematics India

ISBN: (纸本)9798350350357

The research work explores advanced techniques in object detection frameworks, specifically focusing on Faster R-CNN and YOLOv8, to enhance the accuracy of military aircraft detection. Leveraging the multi-stage Faster R-CNN architecture, we investigate the impact of adjusting anchor ratios, systematically evaluating their influence on the model's ability to detect objects with diverse shapes. Departing from the conventional anchor-based approach, YOLOv8 introduces anchor-free detection, eliminating predefined anchors and increasing robustness to object scale variations. Our experimentation includes anchor ratio configurations and explores the innovative components of YOLOv8, such as the CSPDarknet53 backbone and the C2f module. It aims to contribute insights into optimizing military aircraft detection accuracy, catering to the dynamic and critical nature of aerial threats. By providing a comparative analysis of Faster R-CNN and YOLOv8, we shed light on their strengths and weaknesses, offering valuable considerations for deployment in military contexts. The paper's findings contribute to the continual advancement of computer vision applications in national security, emphasizing the importance of automated detection for efficient and timely threat identification. © 2024 IEEE.

关键词： National security

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Astronomical Image Clustering Using Spectral, K-Means and Mean Shift

Astronomical Image Clustering Using Spectral, K-Means and Me...

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2025 International Conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Mohite, Vaishnavi Shivaji Patra, Krishna Gayatri Sankar, C.H. Hari Jayan, Sarada Amrita School of Computing Department of Computer Science Bengaluru India Amrita School of Engineering Department of Mathematics Bengaluru India

ISBN: (纸本)9798331538538

By analyzing vast amounts of observational data through astronomical image analysis, you can gain valuable information about the structure of the universe. This paper presents a new method for clustering astronomical images that uses both spectral and K-means methods and also incorporates linear algebra and optimization principles. We propose a method that combines feature extraction techniques such as the Fourier transform with clustering algorithms to classify celestial objects based on their characteristics. The research examines the effectiveness of spectral clustering and K-means clustering in organizing astronomical images into meaningful groups, taking into account factors such as computer efficiency, clustering accuracy and interpretability. Experimenting with different datasets, including telescope images, we demonstrate the effectiveness of our approach in automating the classification of astronomical objects. Our results reveal insights into the underlying structure of astronomical knowledge and offer promising avenues for future research in computational astronomy. In general, this research advances both the field of astronomy and the application of mathematical methods to image analysis and clustering. © 2025 IEEE.

关键词： K-means clustering

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A Novel State Connection Strategy for Quantum computing to Represent and Compress Digital Images 48

A Novel State Connection Strategy for Quantum Computing to R...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Haque, Md Ershadul Paul, Manoranjan Ulhaq, Anwar Debnath, Tanmoy Charles Sturt University School of Computing Mathematics and Engineering BathurstNSW2795 Australia

ISBN: (纸本)9781728163277

Quantum image processing draws a lot of attention due to faster data computation and storage compared to classical data processing systems. Converting classical image data into the quantum domain and state label preparation complexity is still a challenging issue. The existing techniques normally connect the pixel values and the state position directly. Recently, the EFRQI (efficient flexible representation of the quantum image) approach uses an auxiliary qubit that connects the pixel-representing qubits to the state position qubits via Toffoli gates to reduce state connection. Due to the twice use of Toffoli gates for each pixel connection still it requires a significant number of bits to connect each pixel value. In this paper, we propose a new SCMFRQI (state connection modification FRQI) approach for further reducing the required bits by modifying the state connection using a reset gate rather than repeating the use of the same Toffoli gate connection as a reset gate. Moreover, unlike other existing methods, we compress images using block-level for further reduction of required qubits. The experimental results confirm that the proposed method outperforms the existing methods in terms of both image representation and compression points of view. © 2023 IEEE.

关键词： compression Quantum image representation SCMFRQI State preparation

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Video-Based Point Cloud Compression Using Density-Based Variable Size Hexahedrons

Video-Based Point Cloud Compression Using Density-Based Vari...

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2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Tohidi, Faranak Paul, Manoranjan School of Computing Mathematics and Engineering Charles Sturt University BathurstNSW2795 Australia

ISBN: (纸本)9798350313154

The most advanced standard for compressing dynamic point clouds, a recently created media format, is video-based point cloud compression (V-PCC). V-PCC relies on creating patches and converting 3D input into 2D frames to apply existing video coding. However, creating patches according to the normals and packing irregular projected patches produce many unoccupied pixels in 2D frames, affecting the temporal prediction and inefficiency of 2D video coding. In addition, unoccupied pixels increase the size of 2D frames, accordingly, the required bitrate. This paper introduces variable-sized hexahedrons segmentation as an alternative to patch creation to reduce the number of unoccupied pixels in the 2D frames. Furthermore, different areas of a point cloud are treated differently according to the density of those areas resulting in capturing points more accurately. This paper investigates combinations of different hexahedrons' sizes, and the experimental results demonstrate that the proposed method with the appropriate hexahedrons' sizes outperforms V-PCC. © 2023 IEEE.

关键词： Pixels

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Legal Contract Analysis and Risk Assessment Using Pre-Trained Legal-T5 and Law-GPT 3

Legal Contract Analysis and Risk Assessment Using Pre-Traine...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Poornima, A. Nagaraja, K.V. Venugopalan, Manju 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)9798331508456

The need for effective solutions in reviewing, risk identification, and compliance management of complex legal contracts has remained a critical challenge. This research explores how modern machine learning models like Legal T5, and Law GPT can automate contract analysis and improve review accuracy. Legal T5 summarizes the complex legal documents while Law GPT captures the critical risks. K-Means clustering was used to improve the contract segmentation by grouping similar clauses for a structured risk assessment. Tested on the CUAD dataset, the system's text summarizing findings showed coherence and a strong semantic similarity of 0.78, ensuring that the final summaries retained significant legal importance. The model achieved an average precision of 0.85, recall of 0.77, and F1-score of 0.80 across various risk categories. The findings prove that Legal T5, along with clustering, improves contract analysis with the ability to provide a context and structure-aware summary with better identification of risks. © 2025 IEEE.

关键词： Risk management

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Hydrodynamic Performance of An Integrated System of Breakwater and A Multi-Chamber OWC Wave Energy Converter

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China Ocean engineering 2024年第4期38卷 543-556页

作者： NING De-zhi ZHANG Xiang-yu WANG Rong-quan ZHAO Ming State Key Laboratory of Coastal and Offshore Engineering Dalian University of TechnologyDalian 116024China Dalian Key Laboratory of Offshore Renewable Energy Dalian University of TechnologyDalian 116024China School of Computing Engineering and MathematicsUniversity of Western SydneyPenrith NSW 2751Australia

A multi-chamber oscillating water column wave energy converter(OWC-WEC)integrated to a breakwater is *** hydrodynamic characteristics of the device are analyzed using an analytical model based on the linear potential flow theory.A pneumatic model is employed to investigate the relationship between the air mass flux in the chamber and the turbine *** effects of chamber width,wall draft and wall thickness on the hydrodynamic performance of a dual-chamber OWC-WEC are *** results demonstrate that the device,with a smaller front wall draft and a wider rear chamber exhibits a broader effective frequency *** device with a chamber-width-ratio of 1:3 performs better in terms of power ***,results from the analysis of a triplechamber OWC-WEC demonstrate that reducing the front chamber width and increasing the rearward chamber width can improve the total performance of the *** the number of chambers from 1 to 2 or 3 can widen the effective frequency bandwidth.

关键词： oscillating water column power extraction efficiency potential flow theory wave energy converter multi-chamber

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