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IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Ummi Athiyah Supriadi Rustad Moch Arief Soeleman Muljono Muhamad Akrom Faculty of Computer Science Dian Nuswantoro University Semarang Indonesia Department of Data Science Telkom University Purwokerto Indonesia

ISBN: (数字)9798350368086

ISBN: (纸本)9798350368093

Colorectal cancer is one of the leading causes of cancer death worldwide, so accurate early detection is needed. Endoscopic imaging technology plays a vital role in diagnosis, but the presence of outliers in endoscopic images can significantly affect the accuracy of the classification model. This study addresses this challenge by applying Histogram of Oriented Gradients (HOG) for feature extraction and K-means Clustering for outlier detection and removal. A dataset of colon endoscopic images was obtained from the public database Gastrovision, which was divided into two classes: normal and abnormal conditions. This study applied a two-step modeling process, first with the original dataset and then with the cleaned dataset after outlier removal. The results showcased a significant improvement in model performance after outlier handling, with accuracy increasing from $61 \%$ to $85 \%$. The Area Under Curve (AUC) of the ROC curve also increased from 0.84 to 0.92, indicating a better model ability to distinguish between normal and abnormal conditions. These findings emphasize the importance of data quality and effective outlier management in training a machine-learning model for colon cancer endoscopic image conditions.

关键词： Training Accuracy Satellites Endoscopes Neural networks Imaging Machine learning Colorectal cancer data models Image classification

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CYCLICAL WEIGHT CONSOLIDATION: TOWARDS SOLVING CATASTROPHIC FORGETTING IN SERIAL FEDERATED LEARNING A PREPRINT

arXiv

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arXiv 2024年

作者： Song, Haoyue Wang, Jiacheng Wang, Liansheng National Institute for Data Science in Health and Medicine Xiamen University China Department of Computer Science Xiamen University China

Federated Learning (FL) has gained attention for addressing data scarcity and privacy concerns. While parallel FL algorithms like FedAvg exhibit remarkable performance, they face challenges in scenarios with diverse network speeds and concerns about centralized control, especially in multi-institutional collaborations like the medical domain. Serial FL presents an alternative solution, circumventing these challenges by transferring model updates serially between devices in a cyclical manner. Nevertheless, it is deemed inferior to parallel FL in that (1) its performance shows undesirable fluctuations, and (2) it converges to a lower plateau, particularly when dealing with non-IID data. The observed phenomenon is attributed to catastrophic forgetting due to knowledge loss from previous sites. In this paper, to overcome fluctuation and low efficiency in the iterative learning and forgetting process, we introduce cyclical weight consolidation (CWC), a straightforward yet potent approach specifically tailored for serial FL. CWC employs a consolidation matrix to regulate local optimization. This matrix tracks the significance of each parameter on the overall federation throughout the entire training trajectory, preventing abrupt changes in significant weights. During revisitation, to maintain adaptability, old memory undergoes decay to incorporate new information. Our comprehensive evaluations demonstrate that in various non-IID settings, CWC mitigates the fluctuation behavior of the original serial FL approach and enhances the converged performance consistently and significantly. The improved performance is either comparable to or better than the parallel vanilla. © 2024, CC BY.

关键词： Iterative methods

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A Blockchain-Based Architecture for Interoperable Healthcare data Exchange

A Blockchain-Based Architecture for Interoperable Healthcare...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Niranjan Sahi Anda Liang Sean McHale Wyn Van Devanter Peng Zhang Department of Computer Science Vanderbilt University Nashville TN USA Department of Computer Science and Data Science Institute Vanderbilt University Nashville TN USA

Patients and healthcare providers often face challenges when dealing with creating, reading, updating, sharing, and deleting operations on individual healthcare data due to incompatible electronic medical record systems and the inability to provide necessary data. These challenges can drastically prolong both a patient's diagnosis time and the time until the operation. There are many informational discrepancies when attempting to provide adequate healthcare background - especially with new providers when a full healthcare history is required. To aid in increasing interoperability, we propose a decentralized data-sharing approach. While current centralized solutions own patients’ healthcare data, we seek to put data back in the hands of the patients using blockchain as a decentralized database. In essence, our data-sharing architecture and set of rules leverage a permissioned blockchain network, Hyperledger Fabric, to give each patient on the network the ability to own and control the flow of their healthcare data.

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HealthBridge: A Decentralized and Interoperable Healthcare App for Rare Disease Communities

HealthBridge: A Decentralized and Interoperable Healthcare A...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ziyuan Guo Yiwei Wang Anda Liang Wyn Van Devanter Peng Zhang Department of Computer Science Vanderbilt University Nashville TN USA Department of Computer Science and Data Science Institute Vanderbilt University Nashville TN USA

Traditional centralized database systems share many weaknesses, including the overhead of sharing medical documentation, data fragmentation, and susceptibility to centralized data attacks. Those problems would especially influence rare disease patient communities given their higher need for medical data interoperability and security. In this paper, we present an alternative approach to managing healthcare data that is tailored to the rare disease community. Specifically, we design and implement HealthBridge, a decentralized and interoperable mobile application with distributed ledger technology. The underlying permissioned network utilizes Hyperledger Fabric and has a modular design, empowering efficient data transactions directly between trusted entities with predefined smart contracts that regulate access permissions. For the application design, we focus on the needs of the rare disease community, prioritizing ease of use without compromising data security. In particular, our application provides a delegate feature, aimed at supporting rare disease patients with additional needs. Through this feature, if needed, patients can grant pre-defined access permissions to delegates through smart contracts. While the overall implementation of HealthBridge is still in the developmental stages, it presents a proof of concept and a promising system to support the underfunded rare disease community in terms of healthcare interoperability through a permissioned blockchain network.

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Feature Selection Guided by CVOA Metaheuristic for Deep Neural Networks: Application to Multivariate Time Series Forecasting 18th

Feature Selection Guided by CVOA Metaheuristic for Deep Ne...

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18th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2023

作者： Jiménez-Navarro, Manuel Jesús Restrepo-Estrada, Camilo Melgar-García, Laura Gutierrez-Aviles, David Department of Computer Languages and Systems University of Seville Seville Spain Faculty of Economic Sciences University of Antioquia Medellín Colombia Data Science and Big Data Lab Pablo de Olavide University Seville Spain

ISBN: (纸本)9783031425288

In the workflow of machine/deep learning, a common question that data scientists ask before training a model is whether to feed the model with the entire set of input dataset features or just a subset of them. In this scenario, the precise selection of features from the input data has a significant impact on the efficiency of model training and the final performance, especially in datasets with many variables. This decision is typically made before model training and in a static way (i.e., the feature selection is applied, and then the model is trained). This paper presents a new methodology based on metaheuristics to dynamically select features. This approach has been applied to train a deep neural network to solve two problems: univariate and multivariate time series forecasting. The performance of the proposed method has been compared to the random forest model (a well-known machine learning model with a highly efficient internal feature selection method). The proposed method outperformed the random forest model in terms of mean squared error, demonstrating the validity of the approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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Detection of Number Plate in Vehicles using Deep Learning based Image Labeler Model

Detection of Number Plate in Vehicles using Deep Learning ba...

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2023 IEEE International Conference on Recent Trends in Electronics and Communication, ICRTEC 2023

作者： Gupta, Shashi Kant Saxena, Surabhi Khang, Alex Hazela, Bramah Dixit, Chandra Kumar Haralayya, Bhadrappa Eudoxia Research University Department of Cse United States Pes University Department of Computer Applications Bengaluru560085 India University of Science and Technology Ai and Data Scientist Department of Information Technology Viet Nam University of Science and Technology Ai and Data Scientist Department of Information Technology United States Amity School of Engineering and Technology Lucknow Amity University Computer Science and Engineering Uttar Pradesh India Dr. Shakuntala Misra National Rehabilitation University Department of Physics UP Lucknow226017 India Lingaraj Appa Engineering College Bidar Department of Mba Karanataka Bidar585403 India

ISBN: (纸本)9798350396195

Deep learning is playing a vital role in most of the applications being used in today's technological era. There seems to be no application where the use of deep learning concepts has not been used yet. One such application is the detection of number plates of vehicles on roads. The incidence of automobile crashes has greatly expanded along with the population growth. Their needs to be system that could automatically detect the vehicle's number plate whenever necessary. The situation arises when any of the rider does not obey to the traffic rules, or skips the traffic signals or indulges in any other sort of activities such as accidents. In these situations, the rider tries to run away. In order to detect the person, these kind of deep learning models helps us to detect the vehicle. Image labelers on the roadsides. Need of sensors are required to store the data and they need to be deployed on the roadside. These labelers are used to track the vehicles that are not obeying the traffic rules. With the use of sensors, it could be stated that the use of IoT also plays in the deployment of the current work when it comes to real-time. The model initially is trained with numerous number plates. The number plates have various patterns of texts and numbers and it is essential for the deep learning model to get trained. With successful training, the model is able to detect the new number plates. The model proposed in the current paper is compared with the other existing systems and proves to be efficient and cost-efficient than them. © 2023 IEEE.

关键词： Vehicles

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A Novel Pipeline for Virus Integration Sites Detection in Tumor Genomes Using Deep Learning

A Novel Pipeline for Virus Integration Sites Detection in Tu...

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IEEE International Conference on Big data

作者： Lorrayya Williams Vibhuti Gupta Department of Computer Science & Data Science School of Applied Computational Sciences Meharry Medical College Nashville TN USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Cancer is one of the leading causes of death worldwide. Pathogenic viruses are estimated to be responsible for 15% of all human cancers globally and pose significant threats to public health. Viruses integrate their genetic material into the host genome, increasing the risk of cancer promoting changes in it. To understand the molecular mechanisms of virus-mediated cancers, it is crucial to identify viral insertion sites in cancer genomes. However, this effort is hindered by the rapidly increasing volume of tumor sequencing data, along with the challenges of accurate data analysis caused by high viral mutation rates and the difficulty of aligning short reads to the reference genome. Thus it is crucial to develop an efficient method for virus integration site detection in tumor genomes. This paper proposes a novel pipeline to identify viral integration sites leveraging deep Convolutional Neural Networks (CNN). Our contributions are twofold: (i) We propose and integrate three novel matrix generation methods into the pipeline, developed after aligning the host and viral genomes with their respective reference genomes.; (ii) We employ one-hot encoded images with reduced computational complexity to represent viral integration sites and harness the capabilities of Deep CNN networks for detection. The paper illustrates our proposed approach and presents experiments conducted using both synthetic and real sequencing data. Our preliminary experimental results are promising, showcasing the effectiveness of the proposed methods in detecting viral integration sites.

关键词： Sequential analysis Accuracy computer viruses Pipelines Genomics Convolutional neural networks Bioinformatics Viruses (medical) Cancer Tumors

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Development and Characterization of Zinc Dry Electrodes for Wearable Electrophysiology 46

Development and Characterization of Zinc Dry Electrodes for ...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Rizq, Cassia D'Amico, Alessandro Truel, Aidan Faybishenko, Joelle Lee, Min Suk Kim, Jeong-Hoon Cauwenberghs, Gert De Sa, Virginia R. University of California San Diego Department of Bioengineering La Jolla United States University of California San Diego Department of Cognitive Science La Jolla United States University of California San Diego Halicioǧlu Data Science Institute La Jolla United States University of California San Diego Department of Electrical and Computer Engineering La Jolla United States

ISBN: (纸本)9798350371499

Zinc dry electrodes were fabricated and investigated for wearable electrophysiology recording. Results from electrochemical impedance spectroscopy and electromyography functionality testing show that zinc electrodes are suitable for use in electrophysiology. Two electrode configurations were tested: a standard disc and a custom tripolar concentric ring configuration. However, no functional benefit was observed with the tripolar concentric ring electrodes as compared to the disc electrodes. © 2024 IEEE.

关键词： Electromyography

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Compact Multitasking Multichromosome Genetic Algorithm for Heuristic Selection in Ontology Matching

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6752-6766页

作者： Xue, Xingsi Lin, Jerry Chun-Wei Su, Tong Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350118 China Silesian University of Technology Department of Distributed Systems and IT Devices Akademicka Gliwice44-100 Poland Fujian University of Technology School of Computer Science and Mathematics Fujian Fuzhou350118 China

Ontology matching (OM) is critical for knowledge integration and system interoperability on the semantic web, tasked with identifying semantically related entities across different ontologies. Despite its importance, the complexity of terminology semantics and the large number of potential matches present significant challenges. Existing methods often struggle to balance between accurately capturing the multifaceted nature of semantic relationships and computational efficiency. This work introduces a novel approach, a compact multitasking multichromosome genetic algorithm for Heuristic selection (HS) in OM, designed to navigate the nuanced hierarchical structure of ontologies and diverse entity mapping preferences. Our method combines compact genetic algorithms with multichromosome optimization for entity sequencing and assigning HS, alongside an adaptive knowledge transfer mechanism to finely balance exploration and exploitation efforts. Evaluated on the ontology alignment evaluation initiative's benchmark, our algorithm demonstrates superior ability to produce high-quality ontology alignments efficiently, surpassing comparative methods in both effectiveness and efficiency. These findings underscore the potential of advanced genetic algorithms in enhancing OM processes, offering significant contributions to the broader AI field by improving the interoperability and knowledge integration capabilities of semantic web technologies. © 2024 IEEE.

关键词： Genetic algorithms

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A solution for resource allocation through complex systems in fog computing for the internet of things

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Computing 2024年第7期106卷 2107-2131页

作者： Faraji, Fatimah Javadpour, Amir Sangaiah, Arun Kumar Zavieh, Hadi Department of Computer Engineering Yazd University Safaeih Yazd100190 Iran Harbin Institute of Technology Shenzhen10587 China ADiT-Lab Electrotechnics and Telecommunications Department ISMAI and INESC TEC Instituto Politécnico de Viana do Castelo ISMAI Porto10587 Portugal International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Economic and Statistics Guangzhou Higher Education Mega Center Guangzhou University Guangzhou510006 China Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon

To manage the IoT resources to process the massive amount of collected data in complex systems, Allocation of tasks to nodes to increase user satisfaction has been noticed by researchers. In allocating tasks, the necessity of a powerful auxiliary platform is felt. Cloud and fog networks are two suitable options for performing and completing tasks on the Internet of Things. In the cloud network, due to offloading tasks to remote computing centers, problems such as increasing complete time, increasing traffic in the network, and high energy consumption for the Internet of Things network occur. The fog network solves the problem of completing tasks due to reducing the geographical distance between Internet of Things devices and processor nodes. The devices in the fog network cooperate with the Internet of Things network’s diverse and variable nodes with time computing, storage, and processing resources. In this heterogeneous environment, the non-cooperation and dishonesty of malicious fog nodes through misrepresentation of shared resources and non-completion of tasks can disrupt the performance of the Internet of Things network. As a result, two fundamental challenges in the heterogeneous fog network include providing resources to allocate tasks and recognizing valid fog nodes. This paper focuses on creating a resource provider network to allocate tasks to authentic fog nodes with appropriate resources. When an Internet of Things node faces a shortage of resources and link prediction, it can form a resource supply network with neighboring nodes to solve the two stated challenges. For this purpose, in the first stage, efforts are made to select the most appropriate options among the neighboring nodes based on specific criteria. In the second step, reliable nodes are identified based on a trust mechanism, including resource trust and entity trust. In the last step, tasks are divided among reliable devices with sufficient resources. The evaluation of the proposed method i

关键词： Quality of service

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