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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Nuthalapati Sudha Sai Bhargav Kasetty K. Sai Madhuri Jajala Nikitha Issac Neha Margret K. Rajakumar School of Engineering Mahindra University Hyderabad Telangana India School of Computer Science and Engineering V.I.T – University Vellore Tamilnadu India Dept. of Computer Science and Data Science BVRIT Narsapur Hyderabad Telangana India Dept. of CSE – AIML & IoT VNR VIET Hyderabad Telangana India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

The diagnosis of a range of eye disorders needs to categorize the retinal vessels. Computerized implementation of this process is becoming increasingly essential for automated screening systems for retinal diseases. To achieve a more accurate extraction of the retinal vessels, a new pre-processing step is proposed. These proposed pre-processes are also compared to other algorithms to assess their impact. The proposed pre-processing process consists of two phases. The first phase is the implementation and validation of the pre-processing modules, and the second phase is the implementation of these pre-processes onto the retinal vessels that were to be extracted. To achieve a significantly improved segmented vessel image, the proposed pre-process phase employs a common image-processing technique. In recent years, there has been a great deal of focus on retinal vessel identification studies, and the importance of assessing and confirming the findings of retinal vessel segmentation.

关键词： Image segmentation Analytical models Accuracy computational modeling Retinal vessels Diseases

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Driver Behavior Identification with Facial Recognition and Deep Learning

Driver Behavior Identification with Facial Recognition and D...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Ajay Sudhir Bale T N Siva Kumar Keerthana Lal Arulananth T S Naveen Ghorpade Sinchana. K. P Dept. of ECE New Horizzon College of Engineering Bengaluru India Dept. of Cyber Security & Data Science Bapatla Engineering College Autonomous Bapatla Andhra Pradesh India Dept. of ECE Amrita School of Engineering Bengaluru India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad Telangana India Dept. of CSE Vijaya Vittala Institute of Technology Bengaluru India Dept. of CSE Don Bosco Institute of Technology Bengaluru India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Driving entails a series of actions demanding intense focus. Some- times, these actions get overshadowed by other behaviors such as enjoy engaging in ingesting activities, conversing, using a cellphone, tuning the radio, or even drowsiness. These behaviors stand as the primary culprits behind today's traffic accidents. Thus, it's imperative to create applications that can forewarn drivers. This study proposes a streamlined convolutional neural network structure for identifying driver behaviors, thereby enhancing the accuracy of the alert system and minimizing traffic collisions. This network combines modules for extracting features and making classifications. By merging these components, the feature extraction within the neural network is designed to be more potent and efficient. The model aims to decrease computational costs and amplify data flow within the network by employing dept.-wise separable convolutions. Additionally, the Convolution Block Attention Module aids the network in prioritizing vital features, resulting in improved performance for various computer vision tasks. The classifier unit includes global average pooling and a softmax layer to compute class probabilities. The overall architecture optimizes network parameters while upholding classification precision. Three standard datasets are used for both training and assessing the whole network.

关键词： Training Accuracy computational modeling Feature extraction Real-time systems Telecommunication computing Convolutional neural networks Driver behavior Tuning Videos

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Mechanisms of Mesoscale Hydride Morphology and Reorientation in a Polycrystal Investigated Using Phase-Field Modeling 20

Mechanisms of Mesoscale Hydride Morphology and Reorientation...

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20th International Symposium on Zirconium in the Nuclear Industry

作者： Simon, Pierre-Clément A. Chen, Long-Qing Daymond, Mark R. Motta, Arthur T. Tonks, Michael R. Ken and Mary Alice Lindquist Department of Nuclear Engineering The Pennsylvania State University 233 Hallowell Building University ParkPA16802 United States Computational Mechanics and Materials Department Idaho National Laboratory Idaho FallsID83415 United States Department of Materials Science and Engineering The Pennsylvania State University University ParkPA16802 United States Dept. of Mechanical and Materials Engineering Queen's University KingstonONK7L 2N8 Canada Dept. of Materials Science and Engineering University of Florida GainesvilleFL32611 United States

ISBN: (纸本)9780803177376

This study focuses on the precipitation of nanoscale hydrides in polycrystalline zirconium as a first step to predicting the hydride morphology observed experimentally and investigating the mechanisms responsible for hydride reorientation at the mesoscale. A quantitative phase-field model, which includes the elastic anisotropy of the nanoscale zirconium hydride system, is developed to investigate the mechanism of hydride reorientation in which the presence of an applied hoop stress promotes hydride precipitation in grains with basal poles aligned with the circumferential direction. Although still elongated along the basal plane of the hexagonal matrix, nanoscale hydrides growing in grains oriented perpendicular to the applied stress appear radial at the mesoscale. Thus, a preferential hydride precipitation in grains with basal poles aligned parallel to the applied stress could account for mesoscale hydride reorientation. This mechanism is consistent with experimental observations performed in other studies. Copyright © 2023 by ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959.

关键词： Zirconium compounds

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Optimized Predictive Storage Resource Allocation Through Neural Forecasting for Cloud Environments

Optimized Predictive Storage Resource Allocation Through Neu...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Sridevi Ponmalar, P. Adithya Pothan Raj, V. Jebaseelan, S. D. Sundarsingh Phade, Gayatri Srithar, V. Harikrishnan, R. School of computing Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Campus Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Dept. of Cse Chennai India Sathyabama Institute of Science and Technology Dept of Eee Chennai119 India Sandip Institute of Technology & Research Centre Nashik India PeriyarManiammai Institute of Science & Technology Department of Computer Applications Thanjavur India Symbiosis Institute of Technology Symbiosis International Deemed University Pune Campus Pune412115 India

ISBN: (纸本)9798331543617

The demand for efficient and scalable management of storage resources is growing enormously with the rapid growth in data-intensive applications within the cloud environment. Optimized PSRANF is the state-of-the-art approach towards dynamic storage resource allocation, based on the forecast of future demands in storage. PSRANF utilizes state-of-the-art neural forecasting with Long Short-Term Memory (LSTM) networks and Transformer designs to analyze the pattern of historical storage usage for highly accurate future need predictions. By proactively adjusting storage capacity to these predictions, PSRANF strikes an optimal balance between performance and cost by reserving just-in-time resources. It ensures that there is no risk of over-provisioning, which would be too costly. In the same breath, the risk of under-provisioning, which leads to performance bottlenecks, is avoided. What is more, the system will apply prioritization, placing the data of higher priority or frequently accessed data into faster storage tiers of a higher class of performance and less important classes of data into cost-effective storage. The adaptive feedback loop continuously refines the forecasting model, which, over time, improves accuracy and allows the system to handle changes in workload patterns. PSRANF combines neural forecasting with resource allocation in such a way that is cost-effective;thus, it is very well placed for cloud environments where the demand for storage is dynamic in nature and requires intelligent data-driven approaches. The AWS implementation of PSRANF has turned out to be quite a helpful tool in optimizing the storage resource allocation process. While it is able to predict 98.5% of the storage needs correctly by achieving a cost saving of up to 18.7%, managing data from 97.8% to 99.2%, and resources from 95.4%, it's robust for improvements in cloud storage management and operational efficiency. © 2024 IEEE.

关键词： Storage efficiency

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Heart Abnormality Detection Through Neural Network

Heart Abnormality Detection Through Neural Network

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Sarada B Smitha Chowdary Ch Jampani Satish Babu K. Kavitha Dept. of Data Science Malla Reddy Engineering College for Women(JNTUH) Secunderabad India Dept. of CSE Koneru Lakshmiah Education Foundation Guntur India Dept. of Computer Science P.B.Siddhartha College of Arts and Science Vijayawada India

This research study aims to develop an algorithm using phonocardiograms and Deep learning to differentiate between normal and abnormal heart sounds. The increase in cardiovascular diseases (CVD's), primarily caused by unhealthy habits, necessitates efficient diagnosis. The study utilized 1055 data samples, with 833 being normal and 182 abnormal, each lasting 5 seconds. Logistic regression and a three-layered neural network were initially applied, yielding accuracies of 42% and 63%, respectively. By extracting features using spectral roll-off and reapplying the models, accuracies improved to 79% and 86%. Up sampling the data further increased the accuracies to 86% for logistic regression and 89% for the three-layered neural network. The findings demonstrate the potential of audio-based algorithms in diagnosing cardiovascular diseases accurately.

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Ocular Cataract Identification Using Deep Convolutional Neural Networks

Ocular Cataract Identification Using Deep Convolutional Neur...

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International Conference on Advances in Big data, Computing and data Communication Systems (icABCD)

作者： Feliciana M. E. Manuel Saide Manuel Saide Felermino D. M. A. Ali Sanae Lotfi Dept. of Computer Engineering Faculty of Engineering Lurio University Pemba Mozambique Center for Data Science New York University New York USA

Ocular cataract is among diseases that result in blindness if not treated in time. It affects people worldwide, primarily in underdeveloped countries. This health problem affects the quality of patients' lives. However, early diagnosis avoids blindness and allows the patient to have appropriate treatment. Developing countries, especially those with low income, have a precarious health system, even in the ophthalmology sector, where equipment is lacking. This research aims to develop a deep learning-based model to detect ocular cataracts based on retinal images. We collect 1000 retinal images from Kaggle, which are then equally divided into two classes: with and without cataracts. We then use several neural architectures to correctly classify these images, including ResNet18, ResNet34, InceptionResNetV2, and InceptionV4. We demonstrate that ResNet18 outperforms the other architectures, reaching 95.5% accuracy score. Our results suggest that deep convolutional neural networks can achieve a significant performance in ocular cataracts classification using retinal images.

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The Impact of the Energy Storage System Operation Strategy on Power Grid Reliability

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Transactions of the Korean Institute of Electrical Engineers 2021年第12期70卷 1820-1826页

作者： Kim, Beopsoo Lee, Dayoung Rusetskii, Nikita Shusterzon, Konstantin Sidorov, Denis Kim, Insu Dept. of Electrical Engineering Inha University Korea Republic of Dept. of Electrical and Computer Engineering Inha University Korea Republic of School of Information Technology and Data Science Irkutsk National Research Technical University Russia Energy Systems Institute Siberian Branch Russian Academy of Sciences Russia

Based on the empirical data published by the Korea Electric Power Exchange and the Korea Meteorological Administration, we present a methodology that predicts the system marginal prices. For this purpose, this study uses the long-short term memory (LSTM) technique, which is commonly used in time-series prediction problems. As a result, we present a strategy for operating energy storage systems (ESSs) from the perspective of a prosumer rather than a system operator or conventional utilities. This study also examines the effects of the proposed prosumer actions on the system. The proposed operating strategy is established based on dynamic programming. In particular, this study takes the proposed operation strategy of ESSs the point of view of system reliability. © The Korean Institute of Electrical Engineers This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://***/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

关键词： Reliability

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Summarization of Investment Reports Using Pre-trained Model 14

Summarization of Investment Reports Using Pre-trained Model

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14th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2023

作者： Sakaji, Hiroki Kobayashi, Ryotaro Izumi, Kiyoshi Mitsugi, Hiroyuki Kuramoto, Wataru The University of Tokyo School of Engineering Tokyo Japan Daiwa Institute of Research Ltd. Frontier Technologies Research Center. Data-driven Science Dept. Tokyo Japan Daiwa Asset Management Co.Ltd. Fund Management Division Tokyo Japan

ISBN: (纸本)9798350324228

In this paper, we attempt to summarize monthly reports as investment reports. Fund managers have a wide range of tasks, one of which is the preparation of investment reports. In addition to preparing monthly reports on fund management, fund managers prepare management reports that summarize these monthly reports every six months or once a year. The preparation of fund reports is a labor-intensive and time-consuming task. Therefore, in this paper, we tackle investment summarization from monthly reports using transformer-based models. There are two main types of summarization methods: extractive summarization and abstractive summarization, and this study constructs both methods and examines which is more useful in summarizing investment reports. © 2023 IEEE.

关键词： Investments

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Neural Network Coding in data Compression Systems in Communication Channels 6

Neural Network Coding in Data Compression Systems in Communi...

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6th International Scientific Conference on Information, Control, and Communication Technologies, ICCT 2022

作者： Berezkin, Aleksandr Kukunin, Dmitry Kirichek, Ruslan State University of Telecommunications Bonch-Bruevich Saint-Petersburg Dept. of Software Engineering and Computer Science St. Petersburg Russia Bonch-Bruevich Saint-Petersburg State University of Telecommunications Dept. of Communication Networks and Data Transmission St. Petersburg Russia

ISBN: (数字)9781665493673

ISBN: (纸本)9781665493673

The article describes the use of a neural network implementation of BCH codes encoding and decoding devices for error correction in on-the-fly data compression systems. The user payload vector is encoded in the BCH code with additional redundancy, which provides linear data separability and better convergence when training the autoencoder. On the receiving side, the decoding device operates in the error correction mode, which makes it possible not to impose strict requirements on the accuracy of data recovery when training the autoencoder. Examples of such devices are described in the article. © 2022 IEEE.

关键词： data compression

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Blockchain-Based Music Streaming Services: A Novel Approach to Transparent Artist Control

Blockchain-Based Music Streaming Services: A Novel Approach ...

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Rashmi Ashtagi Tanmay Deshmukh Swati Jadhav Ranjeet Bidwe Afsha Akkalkot Amar Buchade Nilesh Uke Sudipta Banerjee Dept. of Computer Engineering Vishwakarma Institute of Technology Pune India Dept. of CSE Symbiosis Institute of Technology Pune India Dept. Name of Computer Engineering COEP Technological University Pune India Dept. of Artificial Intelligence Data Science Vishwakarma Institute Technology Pune India Indira College of Engineering and Management Pune India Symbiosis Institute of Technology Pune India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Artists have historically used music as a form of personal expression. However, due to the centralized nature of music streaming services, artists receive only a small percentage of the revenue generated and have no control over their content produced. To address these issues, a revolutionary technique is needed that provides artists with greater control and transparency over their output. This paper focuses on a decentralized strategy that utilizes blockchain technology to facilitate direct interaction between artists and users, enabling artists to upload their music, sell songs, and receive royalties. The implementation of blockchain technology could potentially revolutionize the music industry by ensuring that rightful owners receive payment for their music while reducing the waste of paying intermediaries. Without intermediaries involved in the distribution process, the music industry could pay out more royalties to rights holders automatically and instantaneously. The proposed solution also addresses piracy concerns through the use of encryption and watermarking techniques. Blockchains provide security, confidentiality, tamper-proofing, traceability, control, and oversight over information. By developing a secure, self-installing, cross-platform reader positioned on the header of the digital content file, The research empowers data owners to manage the flow of information even after it has been made available. The suggested approach enables content owners to control their digital content and monitor its most popular components after distribution. The blockchain-based system also benefits from token distribution among the content’s owner, distributor, and consumer. The simulation results demonstrate that our approach is capable of overcoming the flaws of the existing system.

关键词： Industries Access control Simulation Smart contracts Process control Watermarking Blockchains Encryption Monitoring

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