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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Sumit Kushwaha K. Amuthachenthiru Geetha. K Jonnadula Narasimharao Dileep Kumar M Sai Sudha Gadde Department of Computer Applications University Institute of Computing Chandigarh University Punjab India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Faculty of Management Sciences Hensard University Bayelsa Nigeria Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Telangana India

Medical imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), X-rays, and ultrasound, are extensively employed in the healthcare industry for diagnostic purposes. Noise, on the other hand, can disturb these approaches and lead to inaccurate diagnosis. Due to this problem, the significance of noise removal technologies has grown in the field of medical imaging, specifically for the examination and understanding of medical images and anatomical structures. To tackle these challenges, a variety of denoising techniques have been developed, such as the Weiner filter (WF), Gaussian filter, and median filter. This work employs a Deep Learning (DL) method known as Convolutional Autoencoder (CAE) to remove noise from medical images. We gather chest X-ray images for analysis and focus on common noise types that affect medical images, such as Poisson and Gaussian noise. We evaluate the effectiveness of the suggested denoising method CAE by comparing it to the commonly employed WF methodology. The Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) are employed to assess and compare the performance of the WF and CAE in reducing Poisson and Gaussian noise. The study's findings indicate that the suggested denoising method based on CAE shows promising results in terms of improving quality.

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Customer Segmentation Using Credit Card data Analysis

Customer Segmentation Using Credit Card Data Analysis

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International Conference on Software Engineering Research, Management and Applications (SERA)

作者： Saikat Raj Santanu Roy Surajit Jana Soumyadip Roy Takaaki Goto Soumya Sen Computer Science and Engineering (Data Science) Future Institute of Engineering and Management Kolkata India A.K. Choudhury School of Information Technology University of Calcutta Kolkata India Faculty of Information Sciences and Arts Toyo University Japan

Customer segmentation is a separation of a market into multiple distinct groups of consumers who share the similar characteristics. Segmentation of market is an effective way to define and meet Customer needs and also to identify the future business plan. Unsupervised machine learning algorithms are suitable to analyze and identify the possible set of customers when the labeled data about the customers are no available. In this research work the spending of different customers who have credit cards are analyzed to segment them into different clusters and also to plan further business improvements based on the different characteristics of these identified clusters.

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Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection

arXiv

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arXiv 2023年

作者： Zheng, Yu Koh, Huan Yee Jin, Ming Chi, Lianhua Phan, Khoa T. Pan, Shirui Phoebe Chen, Yi-Ping Xiang, Wei Department of Computer Science and Information Technology La Trobe University Melbourne Australia The Department of Data Science and AI Faculty of IT Monash University ClaytonVIC3800 Australia School of Information and Communication Technology Griffith University Australia

Multivariate time-series anomaly detection is critically important in many applications, including retail, transportation, power grid, and water treatment plants. Existing approaches for this problem mostly employ either statistical models which cannot capture the non-linear relations well or conventional deep learning models (e.g., CNN and LSTM) that do not explicitly learn the pairwise correlations among variables. To overcome these limitations, we propose a novel method, correlation-aware spatial-temporal graph learning (termed CST-GL), for time-series anomaly detection. CST-GL explicitly captures the pairwise correlations via a multivariate time series correlation learning module based on which a spatial-temporal graph neural network (STGNN) can be developed. Then, by employing a graph convolution network that exploits one- and multi-hop neighbor information, our STGNN component can encode rich spatial information from complex pairwise dependencies between variables. With a temporal module that consists of dilated convolutional functions, the STGNN can further capture long-range dependence over time. A novel anomaly scoring component is further integrated into CST-GL to estimate the degree of an anomaly in a purely unsupervised manner. Experimental results demonstrate that CST-GL can detect and diagnose anomalies effectively in general settings as well as enable early detection across different time delays. Our code is available: https://***/huankoh/CST-GL © 2023, CC BY.

关键词： Anomaly detection

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TransformerFusionNet: A Real-Time Multimodal Framework for ICU Heart Failure Mortality Prediction Using Big data Streaming

TransformerFusionNet: A Real-Time Multimodal Framework for I...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Saleh, Hager McCann, Michael El-Sappagh, Shaker Breslin, John G. Hurghada University Faculty of Computers and Artificial Intelligence Hurghada Egypt University of Galway Insight Research Ireland Centre for Data Analytics GalwayH91 TK33 Ireland Atlantic Technological University Letterkenny Ireland Atlantic Technological University Department of Computing Letterkenny Ireland Galala University Faculty of Computer Science and Engineering Suez Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt School of Engineering University of Galway GalwayH91 TK33 Ireland

ISBN: (纸本)9798350367560

This paper presents a real-time multimodal frame-work to enhance ICU mortality prediction for heart disease patients by integrating structured data, clinical notes, and big data streaming platforms. The proposed framework comprises two components: an offline multimodal model and a real-time pipeline. In the offline component, we proposed the Transformer-FusionNet model leverages BioBERT-Transformer for clinical notes and a Recurrent Neural Network (RNN) for structured data. The model integrates outputs through a concatenation layer to make predictions and is rigorously evaluated using the MIMIC-III dataset. It demonstrates superior performance across evaluation metrics and achieves an accuracy of 91.720%, precision of 91.85%, recall of 91.720%, and F1-score of 91.693%, outperforming other single and multimodal models. The real-time pipeline component incorporates Apache Spark and Apache Kafka for ingesting, preprocessing, and streaming structured data and clinical notes. This integration enables real-time mor-tality prediction with TransformerFusionNet, addressing critical gaps in the utilization of clinical notes in healthcare analytics. The framework significantly advances multimodal deep learning applications in healthcare by combining state-of-the-art models with scalable streaming technologies, offering a practical solution for improving clinical decision support systems and patient outcomes in real-time, especially in intensive care unit settings. © 2024 IEEE.

关键词： Recurrent neural networks

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Stochastic Security Ephemeral Generation Protocol for 5G Enabled Internet of Things 1

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7th EAI International Conference on the Internet of Things as a Service, IoTaaS 2021

作者： Al Sibahee, Mustafa A. Nyangaresi, Vincent Omollo Ma, Junchao Abduljabbar, Zaid Ameen College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China Computer Technology Engineering Department Iraq University College Basrah Iraq Faculty of Biological and Physical Sciences Tom Mboya University College Homabay Kenya Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq Shenzhen Institute Huazhong University of Science and Technology Shenzhen China

ISBN: (数字)9783030959876

ISBN: (纸本)9783030959869

To ensure secure access to the data held in internet of things, many lightweight authentication schemes have been developed using approaches such as symmetric cryptography or hashing operations. Although these schemes achieve forward key secrecy and user anonymity, de-synchronization is a major problem in these protocols. As such, many other schemes have been presented to address this pertinent security challenge. However, some of these schemes are still susceptible to smart card loss attacks among others. In this paper, stochastic security ephemeral generation protocol for 5G enabled internet of things is presented. It is demonstrated to offer mutual authentication and session key agreement. It is also robust against packet replays, eavesdropping and man-in-the-middle attacks. In terms of performance, it has the lowest computation and communication overheads. © 2022, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Internet of things

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Distantly Supervised Relation Extraction Based on Multi-head Self-attention and Gate Mechanism 14th

Distantly Supervised Relation Extraction Based on Multi-head...

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14th International Conference on Genetic and Evolutionary Computing, ICGEC 2021

作者： Xu, Fengzhu Li, Meijing Ryu, Keun Ho College of Information Engineering Shanghai Maritime University Shanghai201306 China Data Science Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh700000 Viet Nam Biomedical Engineering Institute Chiang Mai University Chiang Mai50200 Thailand Department of Computer Science College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of

ISBN: (纸本)9789811684296

In the field of distant supervision relation extraction, PCNN (piecewise convolution neural network) is normally involved to trap local features of sentences, and has achieved good results. However, the existing PCNN-based methods are unable to capture the features of long-distance interdependence in sentences and cannot distinguish the influence of the three segments of PCNN on relation classification, which probably results in missing some vital information. To address the above two issues, we come up with a new model with multi-head self-attention and gate mechanism for distant supervised relation extraction. Firstly, to capture the features of long-distance interdependence in sentences, we employ an internal multi-head self-attention in PCNN model, which can grab information in different representation subspaces. Secondly, to distinguish the influence of the three segments of the piecewise max-pooling output on the relation classification, the gate mechanism is introduced to assign different weights to the three segments and highlight important segments. After a series of experiments, it is proven that the model we presented is better than the previous approaches on each evaluation criterion. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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Feature Aggregation Network for Memory-Based Industrial Anomaly Detection

Feature Aggregation Network for Memory-Based Industrial Anom...

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IEEE International Conference on data science in Cyberspace (DSC)

作者： Min Li Delong Han Rui Shao Jinghui He Faculty of Data Science City University of Macau China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

In the process of large-scale industrial production, complex defects of different shapes and sizes can occur in industrial products and their components, and these defects seriously affect the performance of industrial products and their components, and being able to quickly and accurately identify defective industrial products and their components is an important task. Memory-based methods are usually used for anomaly detection, and these methods often rely on pre-trained models for feature extraction, but have high requirements on feature quality. In addition, patch-based methods mainly focus on local differences in feature embedding when detecting anomalies without explicitly exploiting broader contextual information, thus greatly affecting the robustness and accuracy of anomaly detection. Aiming at the above problems and requirements, this paper proposes a Feature Aggregation Network for Memory-Based Industrial Anomaly Detection (FANet), which is based on the pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-level and multi-scale features, and the FANet provides advanced performance in industrial image anomaly detection and localisation by pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-layered and multi-scale features, and introduction of environmental information in the anomaly score computation to perform industrial anomaly detection tasks, and using core set subsampling to reduce storage requirements. On the MVTec AD, FANet has an image-level AUROC score of 99.1% and a pixel-level AUROC score of 98.2%. Similarly high detection results were achieved on the ViSA dataset, further demonstrating the robustness of FANet.

关键词： Training Runtime Shape Memory management Training data Production Feature extraction Robustness Anomaly detection Testing

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Scheduling Space Expander: An Extension of Concurrency Control for data Ingestion Queries

arXiv

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arXiv 2023年

作者： Nakazono, Sho Uchiyama, Hiroyuki Fujiwara, Yasuhiro Kawashima, Hideyuki NTT Computer and Data Science Laboratories Tokyo Japan Recruit Co. Ltd. Tokyo Japan NTT Communication Science Laboratories Kanagawa Japan Faculty of Environment and Information Studies Keio University Kanagawa Japan

With the continuing advances of sensing devices and IoT applications, database systems needs to process data ingestion queries that update the sensor data frequently. To process data ingestion queries with transactional correctness, we propose a novel protocol extension method, scheduling space expander (SSE). The key idea of SSE is that we can safely omit an update if the update becomes outdated and unnecessary. SSE adds another control flow to conventional protocols to test the transactional correctness of an erasing version order, which assumes that a transactions' updates are all outdated and unnecessary. In addition, we present an optimization of SSE called epoch-based SSE (ESSE), which generates, tests, and maintains the erasing version order more efficiently than SSE. Our approach makes the performance of data ingestion queries more efficient. Experimental results demonstrate that our ESSE extensions of Silo and MVTO improve 2.7× and 2.5× performance on the TATP benchmark on a 144-core machine, and the extensions achieved performance comparable to that of the original protocol for the TPC-C benchmark. © 2023, CC BY.

关键词： Concurrency control

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Network Access Selection for URLLC and eMBB Applications in Sub-6GHz-mmWave-THz Networks: Game Theory Versus Multi-Agent Reinforcement Learning

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IEEE Transactions on Communications 2024年

作者： Thanh Van, Nguyen Thi Le Tuan, Nguyen Luong, Nguyen Cong Nguyen, Tien Hoa Feng, Shaohan Gong, Shimin Niyato, Dusit In Kim, Dong Faculty of Electrical and Electronic Engineering PHENIKAA University Vietnam Hanoi12116 Viet Nam Faculty of Computer Science PHENIKAA University Hanoi12116 Viet Nam School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi100000 Viet Nam Zhejiang Gongshang University Hangzhou310018 China School of Intelligent Systems Engineering Sun Yat-sen University China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

We investigate a heterogeneous network (HetNet) including sub-6GHz base stations (BSs), mmWave BSs, and THz BSs to support enhanced mobile broadband (eMBB) users and ultra-reliable low-latency communication (URLLC) users. We particularly investigate a user-centric network in which the users locally and dynamically select and switch among BSs over time to achieve their highest utility. Two types of users have different Quality of Service (QoS) requirements. Thus, we design two types of utility functions specifically for the eMBB users and URLLC users. Then, to model the dynamic selection behavior of the users, we propose to use a fractional game with the power-law memory. The fractional game allows the eMBB users and the URLLC users to incorporate their past strategies into their current selection, thus improving their utility. Furthermore, we consider the case that the BSs communicate the system state with each other, and we model the network selection of the users as a multi-agent problem. Then, we propose to use a multi-agent deep reinforcement learning (MADRL) algorithm that enables the URLLC users and eMBB users to make their network selection decision online to achieve their long-term utility. Various simulation results are provided to demonstrate the scalability and effectiveness of the proposed approaches. Particularly, compared with the classical game, the fractional game is able to achieve a higher utility but incurs a higher network adaptation cost. Moreover, the different types of URLLC users (in terms of latency and reliability requirements) and the number of URLLC users in the network significantly affect the total utility and the network selection strategies of the eMBB users. Importantly, given the full observations, the MADRL outperforms both classical and fractional games in terms of total network utility. © 2024 IEEE.

关键词： Deep reinforcement learning

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HCA-NET: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling

HCA-NET: Hierarchical Context Attention Network for Interver...

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IEEE International Symposium on Biomedical Imaging

作者： Afshin Bozorgpour Bobby Azad Reza Azad Yury Velichko Ulas Bagci Dorit Merhof Faculty of Informatics and Data Science University of Regensburg Germany Electrical Engineering and Computer Science Department South Dakota State University USA Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Machine and Hybrid Intelligence Lab Northwestern University Chicago IL USA Fraunhofer Institute for Digital Medicine MEVIS Germany

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Accurate and automated segmentation of intervertebral discs (IVDs) in medical images is crucial for assessing spine-related disorders, such as osteoporosis, vertebral fractures, or IVD herniation. We present HCA-Net, a novel contextual attention network architecture for semantic labeling of IVDs, with a special focus on exploiting prior geometric information. Our approach excels at processing features across different scales and effectively consolidating them to capture the intricate spatial relationships within the spinal cord. To achieve this, HCA-Net models IVD labeling as a pose estimation problem, aiming to minimize the discrepancy between each predicted IVD location and its corresponding actual joint location. In addition, we introduce a skeletal loss term to reinforce the model’s geometric dependence on the spine. This loss function is designed to constrain the model’s predictions to a range that matches the general structure of the human vertebral skeleton. As a result, the network learns to reduce the occurrence of false predictions and adaptively improves the accuracy of IVD location estimation. Through extensive experimental evaluation on multi-center spine datasets, our approach consistently outperforms previous state-of-the-art methods on both MRI T1w and T2w modalities. The code-base is accessible to the public on GitHub.

关键词： Adaptation models Accuracy Spinal cord Semantics Pose estimation Predictive models Skeleton

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