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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Mirza Akhi Khatun Mangolika Bhattacharya Ciarán Eising Lubna Luxmi Dhirani Dept. Electronic & Computer Engineering SFI CRT Foundations in Data Science University of Limerick Limerick Ireland School of Information Technology Illinois State University Illinois USA Dept. Electronic & Computer Engineering University of Limerick Limerick Ireland

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

This research develops a new method to detect anomalies in time series data using Convolutional Neural Net-works (CNNs) in healthcare-IoT. The proposed method creates a Distributed Denial of Service (DDoS) attack using an loT network simulator, Cooja, which emulates environmental sensors such as temperature and humidity. CNNs detect anomalies in time series data, resulting in a 92 % accuracy in identifying possible attacks.

关键词： Temperature sensors Wireless sensor networks Temperature distribution Accuracy Time series analysis Medical services Humidity

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CULTURAL LEAF: A LOD PORTAL FOR EXPLORING THE CULTURAL HERITAGE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and computer Science 2024年第2期86卷 61-72页

作者： Dorobăț, Ilie Cristian Posea, Vlad Boncea, Radu Dept. of Computer Science National University of Science and Technology POLITEHNICA Bucharest Romania National Institute for Research and Development in Informatics-ICI Bucharest Romania

The paper introduces the Cultural Leaf Semantic Portal whose aim is to provide a consolidated user-friendly solution for exploring the 36,132 Romanian artifacts and the 1,086 cultural entities that house them. Using the eCHO framework, the data collected from official sources are subjected to a transformation process through which they are sanitized, temporal expressions are normalized, thus reducing the number of forms in which the centuries and millennia are expressed from 1,733 to only 67, and then they are transformed into semantic structures. Finally, the resulting data is accessible via both a SPARQL endpoint and a semantic portal. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Semantic Web

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NestedVGG: A Novel Deep Learning-based Architecture for Brain Tumour Diagnosis 3

NestedVGG: A Novel Deep Learning-based Architecture for Brai...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Maurya, Ritesh Thirwani, Parth Gopalakrishnan, T. Dutta, Malay Kishore Panchal, Soumyashree M Amity University Amity Centre for Artificial Intelligence Noida India Amity University Dept. of Artificial Intelligence Noida India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Manipal India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350384598

Brain tumors are sudden outbreak of cells in the brain or on its surface and are categorized into gliomas, meningiomas, and pituitary adenomas. Traditionally, tumors of the brain are categorized using a manual approach that includes visual examination by radiologists on Magnetic Resonance Imaging (MRI). This is arduous and subject to inter-expert variability. In view of the subtle and fine-grained compact variations that exist in MRI images of these three types of brain tumors, this paper proposes an automated brain tumor detection system using deep learning (DL) with MRI scans. This paper presents an automated system for brain tumor detection using DL on MRI scans. The "NestedVGG"model implements transfer learning(TL) with the outer architecture of "Outer VGG"for feature extraction and the inner architecture for fine-grained identification. This "Outer VGG"utilizes a pre-trained VGG16 model to acquire information about features from MRI images. While the inner "Inner VGG"can capture minute variations between tumor types to classify glioma, meningioma, no-tumour, and pituitary cases effectively. Publicly available MRI dataset consists of three classes of tumours were used for testing and this framework has achieves accuracy of 97.71% on the test set, thus outperforming the prior methods in this task. Moreover, it has maintained its high accuracy across various types of tumors, thus making the model flexible. This paper furthers brain tumor diagnosis through an effective architecture for brain tumor identification, which can lead to advanced diagnostic tools and better patient care. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Digital Currency Abnormal Detection Based on Graph Neural Network

Digital Currency Abnormal Detection Based on Graph Neural Ne...

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Electronics and Devices, Computational Science (ICEDCS), International Conference on

作者： Xingle Que Yang Cheng Yunfei Bai Dandan Lu School of Computer Science and Technology Southwest University of Science and Technology Mianyang China Dept. Engineering Technology Center Southwest University of Science and Technology Mianyang China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

In recent years, with the rapid development of the digital currency market, the security issues of digital currency transactions have become increasingly prominent, and abnormal transaction detection has become an important technical means to ensure the security of the financial system. Traditional detection methods face many challenges in dealing with complex blockchain data, as they cannot fully utilize the inherent structural information of transaction data, and are difficult to handle the problem of imbalanced categories. Accordingly, this study proposes a GNN (Graph Neural Network)-based abnormal transaction detection method, which models transaction data as a graph structure, extracts transaction features using Graph Convolutional Network (GCN) and uses small sample processing techniques to alleviate data imbalance. The proposed method showed that the experimental results could effectively improve the detection performance of abnormal transactions after multiple rounds of training, especially in terms of AUC evaluation indicators, which verifies its potential application in the detection of abnormal transactions in digital currencies. This study provides a novel and effective solution for anomaly detection in complex network data and points out the direction for future research.

关键词： Training Online banking Graph convolutional networks Sensitivity analysis Computational modeling Bitcoin Standardization Feature extraction Security Anomaly detection

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Design of receiver RF front end for mm-Wave 5G applications 21

Design of receiver RF front end for mm-Wave 5G applications

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21st International Learning and technology Conference, L and T 2024

作者： Hussein, Aziza I. Alamoudi, Nora Ali, Mirna Zakaria, Ahmed Mourad Mabrook, M. Effat University College of Engineering Energy and Technology Research Center Saudi Arabia Effat University Electrical and Computer Engineering Department Jeddah Saudi Arabia Telecom Egypt Telecommunication Engineer Asyut Egypt Beni-Suef University Faculty of Navigation Science & Space Technology Space Communication Dept. Egypt

ISBN: (纸本)9798350393569

Recently, the research emphasis has shifted towards 5G due to its potential to accommodate the increasing demand for data traffic, extensive interconnectivity of devices, and the emergence of numerous novel applications. Given the inadequacy of speed provided by spectrum resources in the lower frequency bands of 5G, a novel sub-generation utilizing millimeter wave frequencies has been introduced. The new sub-generation is called mm-wave 5G and provides higher bandwidth for faster speed and higher capacity. RF system architecture, circuits, and antenna innovation will be required to provide the requisite speed and capacity. In this paper the design challenges and trade-offs in RF front-end circuits and receiver sub-systems are discussed. Moreover, the massive multiple-input multiple-output (MIMO) techniques are examined. In addition, design, and simulation of key components in 5G mm-wave receiver, including the design of linear phased array antenna receiver and an analog-to-digital converter (ADC) is presented. Sigma-Delta (ΣΔM) Converters are a type of ADC with a technique that involves oversampling and shaping quantization noise to achieve a higher resolution which make it a best choice to be used in the design of the mm wave 5G communication applications. The proposed ΣΔM uses multistage architecture to provide high-order noise shaping and high resolution. The simulation results shows that the designed Cascade 2-2 MASH ΣΔ achieve a signal to noise ratio of 110(resolution of 16 bits). © 2024 IEEE.

关键词： Antenna phased arrays

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Comparative Study on Serial and Parallel Implementation of Face Detection

Comparative Study on Serial and Parallel Implementation of F...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Mallegowda M Theertha K Varsha S D Anita Kanavalli Dept. Of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India Prof. and Dept of CSE. Ramaiah Institute of Technology Bangalore India

Large-data set artificial neural network training takes a lot of time. Many ways for reducing effort have been proposed, many of which make use of parallelization techniques. This paper explores the implementation of face detection algorithms utilizing OPENMP to achieve greater efficiency through parallelization. We focus on specific OpenMP parallelization setups that run on a typical multi-threaded CPU. These frameworks are also available for CUDA, however utilizing CUDA is only possible if you have an NVIDIA graphics card, which is clearly not the case for everyone. OpenMP's release of a stable version in late 2015 facilitated parallel processing and broadened its development base.

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Harnessing ECU Data for Real-Time Fuel Consumption Forecasting

Harnessing ECU Data for Real-Time Fuel Consumption Forecasti...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： P. Kranthi Kumari V. Sravani Kumari M. Varshini B. Hari Krishna B. Prem Kumar V. Srinivas Department of Computer Science and Engineering Godavari Global University Rajamahendravaram Andhra Pradesh India Dept. of Computer Science and Engineering Godavari Institute of Engineering & Technology (Autonomous) Rajamahendravaram Andhra Pradesh India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

The Electronic Control Unit (ECU) is an integral part of a vehicle from which we can extract data which makes perfect sense regarding multiple aspects in the automotive sector. This is critical information for analysing driving behaviour. These capabilities are put together to work in the project, utilizing machine learning models to classify drivers based on their fuel efficiency and predict fuel consumption. The feature selection process enables to pinpoint the most noteworthy factors and utilize them to construct accurate fuel usage regression models. The proposed system implements algorithms like Random Forest Regression, AdaBoost Regression for prediction and K-Means Clustering for classification. With an in-dept. review of driving profiles such as Sporty, Eco, Calm, Normal and Aggressive this project enables a broader comprehension of your complete drives and furthermore enabling a smarter fuel-efficient drive, which is noteworthy way for better vehicle execution and lower natural impact.

关键词： Reviews Biological system modeling Predictive models Feature extraction Real-time systems Fuels Forecasting Driver behavior Random forests Vehicles

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Addressing Semantic Similarity Attacks in Online Social Networks

Addressing Semantic Similarity Attacks in Online Social Netw...

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

作者： Amardeep Singh Monika Singh Computer Science & Engg. Dept. SVIET Banur India Computer Science & Engg. Apex Institute of Technology Mohali India

An efficient technique for preserving user privacy of users while publishing data is anonymization. Banks, Social Network service providers and hospitals are examples of data owners/stakeholders who anonymize their customers' data before releasing it in order to protect users' privacy. However, trustworthy information consumers still value anonymous data. Numerous anonymization models and techniques have been developed for releasing data while preserving user privacy. These models/algorithms de-identify user data, which is typically presented as graphs. Giving clear perspectives on the entire information privacy field, including recent anonymization research, tabular and SN data, is crucial. In this article, we proposed an approach for anonymization to address privacy issues in social networks specifically addressing semantic similarity attacks. Far as we are aware, our proposed technique is capable of resolving the issues of semantic similarity between sensitive attributes efficiently. The results of the proposed approach have been evaluated using the dataset of around 6000 users of a real-time social network viz. Twitter. Evaluation metrics of APL (Average Path Length) show that our technique can protect privacy and also maintain the utility of data while publishing it for public use.

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DNS Tunnel Problem In Cybersecurity

DNS Tunnel Problem In Cybersecurity

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Güneş Gürsoy Asaf Varol Ahad Nasab Dept. of Computer Engineering College of Eng. and Natural Sciences Maltepe University İstanbul Turkey Dept. of Eng. Management and Technology College of Eng. and Computer Science The Uni. of Tennessee at Chattanooga Chattanooga TN US

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

The Domain Name System (DNS) is the most important building block of the Internet. Websites, file transfer applications, and e-mail services use the DNS service. Therefore, these services can rarely be blocked by firewalls to prevent their access from being affected. Since applications such as firewalls and Intrusion Detection Systems (IDS) do not check the allowed protocols, attackers can open a secret path called DNS Tunnel through the DNS protocol to access sensitive data and cause many attacks. In this study, DNS Tunnel, DNS Tunnel detection methods and detection methods, and DNS attack types are included.

关键词： Deep learning Protocols Machine learning algorithms Firewalls (computing) Tunneling Malware Behavioral sciences

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How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance

arXiv

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arXiv 2024年

作者： Li, Hongkang Zhang, Shuai Zhang, Yihua Wang, Meng Liu, Sijia Chen, Pin-Yu The Dept. of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute United States New Jersey Institute of Technology United States The Dept. of Computer Science and Engineering Michigan State University United States IBM Research United States

Group imbalance has been a known problem in empirical risk minimization (ERM), where the achieved high average accuracy is accompanied by low accuracy in a minority group. Despite algorithmic efforts to improve the minority group accuracy, a theoretical generalization analysis of ERM on individual groups remains elusive. By formulating the group imbalance problem with the Gaussian Mixture Model, this paper quantifies the impact of individual groups on the sample complexity, the convergence rate, and the average and group-level testing performance. Although our theoretical framework is centered on binary classification using a one-hidden-layer neural network, to the best of our knowledge, we provide the first theoretical analysis of the group-level generalization of ERM in addition to the commonly studied average generalization performance. Sample insights of our theoretical results include that when all group-level co-variance is in the medium regime and all mean are close to zero, the learning performance is most desirable in the sense of a small sample complexity, a fast training rate, and a high average and group-level testing accuracy. Moreover, we show that increasing the fraction of the minority group in the training data does not necessarily improve the generalization performance of the minority group. Our theoretical results are validated on both synthetic and empirical datasets, such as CelebA and CIFAR-10 in image classification. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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