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IEEE International Conference on Big data

作者： Seunghoon Han Sungsoo Kim Sungsu Lim Dept. of Computer Science & Engineering Chungnam National University Daejeon South Korea Smart Data Research Section ETRI Daejeon South Korea

Traffic forecasting is a crucial application of the Intelligent Transportation System (ITS), with research focusing on various methods, from classical statistical approaches to graph-based methods integrated with RNN-based approaches to capture spatial and temporal correlations simultaneously. During the traffic data collection phase, the absence of vehicles on each road or sensor malfunctions can result in the collection of traffic time series data as zeros. However, storing such zero values makes accurate traffic prediction more challenging. To address this challenge, we present a novel model for improving traffic forecasting using graph convolutional recurrent neural networks. The proposed method is evaluated on two real-world public benchmark datasets and compared with six baseline models, showcasing its superior performance.

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UATTA-ENS: Uncertainty Aware Test Time Augmented Ensemble for PIRC Diabetic Retinopathy Detection

arXiv

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arXiv 2022年

作者： Seth, Pratinav Khan, Adil Gupta, Ananya Mishra, Saurabh Kumar Bhandari, Akshat Dept. of Data Science and Computer Applications India Dept. of Mechanical Engineering India Dept. of Electronics and Comm. Engineering India Dept. of Computer Science and Engineering India Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Deep Ensemble Convolutional Neural Networks has become a methodology of choice for analyzing medical images with a diagnostic performance comparable to a physician, including the diagnosis of Diabetic Retinopathy. However, commonly used techniques are deterministic and are therefore unable to provide any estimate of predictive uncertainty. Quantifying model uncertainty is crucial for reducing the risk of misdiagnosis. A reliable architecture should be well-calibrated to avoid overconfident predictions. To address this, we propose a UATTA-ENS: Uncertainty-Aware Test-Time Augmented Ensemble Technique for 5 Class PIRC Diabetic Retinopathy Classification to produce reliable and well-calibrated predictions. Implementation is available at https://***/ptnv-s/UATTA-ENS. © 2022, CC BY.

关键词： Diagnosis

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Sustainable Energy Efficient Routing Protocol using Chimp algorithm and SOM for Underwater Wireless Sensor Networks

Sustainable Energy Efficient Routing Protocol using Chimp al...

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Recent Advances in science and engineering Technology (ICRASET), International Conference on

作者： T Philip Paul Arunodhayam Akhil Raj Vadde Thimmampali Prasanna Kumar Salaja Silas Shalem Preetham Gandu Chamarti Harshith Kumar Dept. of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Data Analytics Engineering College of Engineering and Computing George Mason University Fairfax VA United States

Underwater Wireless Sensor Networks (UWSNs) are increasingly being used for various applications, such as environmental monitoring, underwater exploration, and oceanographic research. protocols that consume less energy for underwater wireless sensor networks (UWSNs) are still a difficult undertaking because of the harsh conditions present underwater and the restricted battery capacity of the sensor nodes. This research paper proposes an optimized multi-Hop routing protocol using Chimp Optimization and Self-Organizing Map (SOM). The proposed technique is compared with three popular routing protocols - AODV, DSDV, and DSR in terms of network lifetime and packet delivery ratio. Chimp Optimization algorithm as well as SOM are used to improve network performance. The findings indicate that the put-forward technique exceeds the performance of existing routing protocols, as it increases network lifetime, PDR and PLR while decreasing packet loss ratio. The network lifetime of the put-forward technique is 1039.42 seconds, which is higher than AODV, DSDV, and DSR. Similarly, the PDR of the proposed technique is 97.38%, which is comparatively higher than the three routing protocols. Moreover, the PLR of the proposed technique is 2.62%, which is much lower than AODV, DSDV, and DSR. The proposed technique based on Chimp Optimization and SOM can effectively optimize the multi-Hop routing protocol in underwater wireless sensor networks to achieve sustainable energy efficiency, thus providing important insights for researchers and practitioners to enhance the performance of underwater wireless sensor networks.

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Performance Evaluation of data Imputation Methods for Graph Deep Learning-Based Traffic Prediction

Performance Evaluation of Data Imputation Methods for Graph ...

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IEEE International Conference on Big data

作者： Jeongseon Kim Sungsoo Kim Sungsu Lim Dept. of Computer Science & Engineering Chungnam National University Daejeon South Korea Smart Data Research Section ETRI Daejeon South Korea

We present five approaches for interpolating missing data in real-world traffic datasets using basic, statistical, and generative model methods. The effectiveness of these approaches is evaluated using graph deep learning models, and the results show that imputing missing data improves the performance of traffic prediction models, especially when dealing with a higher proportion of missing data. The experiments are conducted on two real-world datasets, METR-LA and PEMS-BAY, to evaluate the proposed methods comprehensively. The results demonstrate that imputing missing data significantly enhances the performance of traffic prediction models, particularly when dealing with a higher proportion of missing data.

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An Architecture for Dynamic Load Balancing in Cloud Environment

An Architecture for Dynamic Load Balancing in Cloud Environm...

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International Conference on Edge Computing and Applications (ICECAA)

作者： Gudivada Lokesh K. K. Baseer Dept. of Computer Science and Engineering Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati Department of Data Science School of Computing Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati

Clouds are highly customizable infrastructures that offer a platform as a service and let customers subscribe on a pay-as-you-go basis to their requirements. The straightforward service-oriented cloud computing model is gaining popularity around the world. The number of people using the Cloud is constantly growing. Clouds use modern data centers to manage a massive number of users. The reliability of the Cloud depends on load balancing. Balancing virtual machine loads lowers energy consumption and task rejections by optimizing resource utilization. One can increase performance while using fewer resources using load balancing, resource management, quality of service, etc. The difficulty of overloading and underloading virtual machines in cloud computing can be lessened by load balancing in the *** research study thoroughly examined the load-balancing algorithms found in the literature. First, the traditional approaches are analyzed before moving on to more recent work on load balancing with heterogeneous techniques. Along with the tools available for the current investigation, various metrics are used to evaluate the load-balancing algorithms. The proposed article will primarily serve to assist in the development of new algorithms in the future.

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Systems Modeling of Trust in AI-Enabled Medical Diagnosis

Systems Modeling of Trust in AI-Enabled Medical Diagnosis

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Annual IEEE Systems Conference

作者： Negin Moghadasi Misagh Piran Rupa S. Valdez Negar Moghaddasi Davis C. Loose Thomas L. Polmateer James H. Lambert Dept. of Systems and Information Engineering University of Virginia Johnson & Johnson Medtech Charlottesville USA Dept. of Radiology Nuclear Medicine and Molecular Imaging Heart and Diabetes Center North-Rhine Westphalia Ruhr University of Bochum Bad Oeyhausen Germany Dept. of Systems and Information Engineering School of Data Science University of Virginia Charlottesville USA Dept. of Dentistry Western University of Health Sciences Pomona USA Dept. of Engineering Systems and Environment University of Virginia Charlottesville USA Dept. of Engineering Systems and Environment CCALS University of Virginia Charlottesville USA Dept. of Systems and Information Engineering University of Virginia Charlottesville USA

ISBN: (数字)9798350358803

ISBN: (纸本)9798350358810

The progression of artificial intelligence, particularly in healthcare domain, has brought significant progress to treatment, drug discovery, disease diagnosis and more. The main contribution of this paper is the development of a scenario-based preferences risk register framework for quantifying AI related risks in disease diagnosis. Two case studies, one involving physician and the other one with involving patients as the main stakeholders, are compared to evaluate the effectiveness of the framework and to evaluate how the initiatives ranking orders will evolve based on the most and least disruptive scenarios for each cases. The framework is applied to realistic case studies on cardiac sarcoidosis, identifying success criteria, initiatives, emergent conditions, and the most and least disruptive scenarios. The success criteria align with the National Institute of Standards and Technology AI Risk Management Framework (NIST AI RMF) seven trustworthy AI characteristics. Finally, the framework identifies the most and least disruptive scenarios and the most important initiatives for both cases. Results show that Non-Interpretable AI and Lack of Human-AI Communications, Privacy Attacks, and Human Errors in Design, Develop, Measurement and Implementation are the most disruptive scenarios in both cases, however, scenario Cyber Security Threats also counted as other most disruptive scenarios to the initiatives ranked by patients. This adaptable framework has the potential not only to transfer findings to global healthcare systems but also to extend its effectiveness to various other domains, such as transportation, finance, environment, and design.

关键词： Privacy Measurement uncertainty Transportation NIST Systems modeling Registers Medical diagnosis

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ENHANCING PRINTED CIRCUIT BOARD DEFECT DETECTION THROUGH ENSEMBLE LEARNING

arXiv

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arXiv 2024年

作者： Law, Ka Nam Canaan Yu, Mingshuo Zhang, Lianglei Zhang, Yiyi Xu, Peng Liu, Jun Gao, Jerry Department of Applied Data Science San José State University San José United States Dept. of ISE Virginia Tech Blacksburg United States Dept. of Computer Engineering San José State University San José United States

The quality control of printed circuit boards (PCBs) is paramount in advancing electronic device technology. While numerous machine learning methodologies have been utilized to augment defect detection efficiency and accuracy, previous studies have predominantly focused on optimizing individual models for specific defect types, often overlooking the potential synergies between different approaches. This paper introduces a comprehensive inspection framework leveraging an ensemble learning strategy to address this gap. Initially, we utilize four distinct PCB defect detection models utilizing state-of-the-art methods: EfficientDet, MobileNet SSDv2, Faster RCNN, and YOLOv5. Each method is capable of identifying PCB defects independently. Subsequently, we integrate these models into an ensemble learning framework to enhance detection performance. A comparative analysis reveals that our ensemble learning framework significantly outperforms individual methods, achieving a 95% accuracy in detecting diverse PCB defects. These findings underscore the efficacy of our proposed ensemble learning framework in enhancing PCB quality control processes. © 2024, CC BY.

关键词： Adversarial machine learning

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Ethereum Blockchain-Based Design and Implementation of Electronic Records for Home Care

Ethereum Blockchain-Based Design and Implementation of Elect...

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Networking and Communications (ICNWC), International Conference on

作者： M. Jahir Pasha K. K. Baseer N. Vasavi K. Sreenivasulu Syed Mohammed Nadeem A. Chandra Obula Reddy Dept. of CSE(DS) Rajeev Gandhi Memorial College of Engineering and Technology Nandyal Andhra Pradesh India Gitam School of Technology Gitam (Deemed to be University) Bengaluru Karnataka India Dept. of CSE Ashoka Womens Engineering College Kurnool India Dept. of CSE G. Pullaiah College of Engineering and Technology Kurnool India Dept. of CSE-Data Science G. Pulla Reddy Engineering College(Autonomous) Kurnool India Dept. of CSE (AI&ML) Sri Venkateswara College of Engineering Kadapa India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

Blockchain's distributed and decentralized ledger, which conducts transactions over multiple computers and is digitally encrypted, will play a significant role in the future. It is meant to be transparent, impervious to tampering, and censorship-resistant. Building safe blocks joined by a chain containing transactions and a hash value is the fundamental idea underlying blockchain. Cryptographic hashes are used to join these blocks together to create an unchangeable, continuous chain. Many elements of the Ethereum blockchain can be useful when utilizing smart contracts. Thus, we make use of these smart contracts along with the many blockchain components that would be highly beneficial on the Ethereum network, and we thoroughly examine the transaction flow. Beyond only cryptocurrencies, Ethereum has been essential in the advancement of blockchain technology, opening the door for a wide range of decentralized projects and applications. In homecare, the low-secured records are utilized for family member and doctor consultations as well as the storage of diagnosis information. The majority of hospitals utilize email to share critical medical details due to its lower security and reliance on data. Most people are most familiar with blockchain technology as the foundation of cryptocurrencies like Bitcoin, where it serves as a safe and decentralized ledger for recording transactions. Its possibilities, however, go beyond cryptocurrency to a variety of other areas, such as banking and healthcare, where it might enable new kinds of decentralized apps and offer transparent and safe record-keeping. A blockchain system is an application code for a decentralized blockchain platform that runs and validates securely via a block-to-block network. Keeping a lot of data on the blockchain costs a lot of money, and exchanging health records is one issue that needs to be addressed. The recommended method for resolving this issue employs the Interplanetary File System (IPFS) to save costs

关键词： Computers Costs Codes Hospitals Smart contracts Decentralized applications Blockchains

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Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis

arXiv

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arXiv 2024年

作者： Li, Hongkang Wang, Meng Zhang, Shuai Liu, Sijia Chen, Pin-Yu Dept. Electrical Computer and System Engineering Rensselaer Polytechnic Institute TroyNY United States Dept. Data Science New Jersey Institute of Technology NewarkNJ United States Dept. Computer Science and Engineering Michigan State University East LansingMI United States IBM Thomas J. Watson Research Center Yorktown HeightsNY United States

Efficient training and inference algorithms, such as low-rank adaption and model pruning, have shown impressive performance for learning Transformer-based large foundation models. However, due to the technical challenges of the non-convex optimization caused by the complicated architecture of Transformers, the theoretical study of why these methods can be applied to learn Transformers is mostly elusive. To the best of our knowledge, this paper shows the first theoretical analysis of the property of low-rank and sparsity of one-layer Transformers by characterizing the trained model after convergence using stochastic gradient descent. By focusing on a data model based on label-relevant and label-irrelevant patterns, we quantify that the gradient updates of trainable parameters are low-rank, which depends on the number of label-relevant patterns. We also analyze how model pruning affects the generalization while improving computation efficiency and conclude that proper magnitude-based pruning has a slight effect on the testing performance. We implement numerical experiments to support our findings. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Can We Estimate a Useful Range of Prices for Popular Models of Used Cars Using Past data?

Can We Estimate a Useful Range of Prices for Popular Models ...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Md Shoaib Bhuiyan Dept. Health Data Science Faculty of Medical Engineering Suzuka University of Medical Science Suzuka Mie Japan

ISBN: (纸本)9781665492331

There are some models of cars that remain popular with the consumers, even if pre-used. However, they sell at a wide variety of prices. We built a dataset of past prices for two popular Toyota SUVs, Harrier and RAV4 using the final selling price obtained from a large Japanese auction house, USS. We then tried to predict a useful range of prices for each of these two models of pre-used Toyota SUVs, based on the parameters of that particular car like how many kilometers has it already travelled, whether the powertrain is hybrid or runs on gasoline only, which year was it originally built in, 2019 or in 2020 or later, etc. We also tried to find out whether options like having Sunroof/moonroof had any effect on the final bid of auction prices. Feature Selection and Feature Transformation were done using the Classification Learner App of the MATLAB Statistics and Machine Learning Toolbox version R2020b. Our analysis shows that we can establish a useful range of prices.

关键词： Machine learning Mechanical power transmission Predictive models Feature extraction data models Automobiles Petroleum

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