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Hierarchical Federated Learning-Based Method for privacy-preserving in the Healthcare Environment

Hierarchical Federated Learning-Based Method for privacy-pre...

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2023 International conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Manoj Kumar, D.P. Babu. J, Ananda Nagasireesha, Jaladi Venkatesh, Bhattu Siddiqua, Ayesha Kalpataru Institute of Technology Department of Computer Science and Engineering Tiptur India Malnad College of Engineering Department of Information Science and Engineering Hassan India Malla Reddy Engineering College for Women Autonomous India Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

The COVID-19 pandemic has led to increase the demand for medical equipment with internet of things (IoMT) capabilities forcing doctors to diagnose and treat patients remotely. This has increased awareness of health, leading to a surge in purchasing of IoMT-capable medical equipment. However, medical data is sensitive and valuable on the dark web, making it vulnerable to cybercriminals. Due to limited storage and processing capabilities of network devices, system administrators struggle to enhance security measures. This work aims to investigate rapidly spreading attacks before they compromise wellness systems. Hierarchical federated learning is (HFL) proposed using a model built on Dew-cloud, which offers improved data privacy due to the increased availability of IoMT critical applications. Dew servers with distributed architecture and a cloud computing-backed backend implement hierarchical long-term memory (HLSTM) method. The implemented HFL-HLSTM method's performance is evaluated using NSL-KDD dataset. Implemented method achieved better performance than the existing intrusion detection methods including MiTed, LS2DNN, and MetaCID and the metrics are 98.62% of f-measure, 99.53% of accuracy, 99.21% of precision, and 99.10% of recall. © 2023 IEEE.

关键词： and Intrusion detection Data privacy Dew computing Federated Learning Hierarchical Long Short-Term Memory Internet of Medical Things (IoMT)

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48th IEEE European Solid State Circuits conference (ESSCIRC)

作者： Kramnik, D. Wang, I. Cabanillas, J. M. Fargas Ramesh, A. Buchbinder, S. Zarkos, P. Adamopoulos, C. Kumar, P. Popovic, M. A. Stojanovic, V. Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Boston Univ Dept Elect & Comp Engn Photon Ctr Boston MA 02215 USA Northwestern Univ Dept Elect & Comp Eng Evanston IL 60208 USA

ISBN: (数字)9781665484947

ISBN: (纸本)9781665484947

Integrated photonics provides scalability needed for useful photonic quantum information processing. Many optical resonators must be aligned to the same pump wavelength to produce sources of quantum-correlated photon pairs that drive such systems, but existing solutions rely on manual alignment or offline tuning based on external photodiodes and bulky off-chip electronics, limiting scalability. Here we demonstrate feedback control of four-wave mixing (stimulated and spontaneous) in a silicon microring using circuits integrated alongside photonics in a standard 45 nm CMOS foundry process. The carrier-sweepout-generated feedback signal enables in-situ operation in the photon-pair generation regime, which is a key building block enabling large-scale CMOS quantum-photonic systems-on-chip.

关键词： Scalability Four-wave mixing Process control information processing Foundries Silicon Feedback control

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Network Asset Sensitive information Management System Based on Golang

Network Asset Sensitive Information Management System Based ...

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algorithms, Computing and Data processing (ACDP), International conference on

作者： Shichen Fu Kunshan Zhang State Grid Zhangzhou Electric Fuzhou Fujian

With the swift upsurge in the quantity of IoT devices in the power Internet of Things, as well as their intrinsic heterogeneity, limited computing power, and weak network load capacity, collecting IoT assets becomes challenging, and these devices are highly susceptible to external attacks. To address this issue, a network asset sensitive information management and control tool is designed specifically for organizations such as power grids that possess numerous IoT devices and other network assets. This tool automates the collection and pre-warning of vulnerabilities associated with assets like IoT devices and domain names in the power network. By doing so, it significantly reduces the need for human resources, material costs, and potential economic losses, while also minimizing the risk of network attacks on the power grid. However, many asset-sensitive information collection systems are initially designed without sufficient consideration of corresponding security protection mechanisms, making them highly vulnerable to attacks and exploits by hackers. Consequently, these systems often suffer from incomplete information collection, leakage of sensitive information, and other abnormalities. To address these shortcomings, this study focuses on the development of a fast and accurate Internet asset high concurrency scanning tool based on the Go programming language. Additionally, it investigates device fingerprint collection methods for ports and leverages YAML integration to achieve efficient and precise device fingerprint collection. Moreover, the study explores the use of Echarts for visualizing big data, enabling data analysis visualization, and facilitating the rapid identification of risk assets by relevant personnel. Furthermore, it examines the implementation of a precise risk warning and notification function based on containerized deployment, assisting relevant personnel in promptly responding to and defending against potential threats.

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Strategies for the Creation and Implementation of Business Intelligence Frameworks

Strategies for the Creation and Implementation of Business I...

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2023 Annual International conference on Emerging Research Areas: International conference on Intelligent systems, AICERA/ICIS 2023

作者： Hassan, Ahmed R. Ahmed, Ibrahem Ghali, Fatima Mohsin, Sarah Kadhim Majed, Safa Jawad, Israa Abed National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Medical Instruments Engineering Techniques Al-farahidi University Baghdad Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq Computer Technologies Engineering Al-Turath University College Baghdad Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Computer Engineering Techniques Al-Nisour University College Baghdad Iraq

ISBN: (纸本)9798350303452

Organizations in the modern era are under increasing pressure from the socioeconomic environment to seek out various technological methods/tools that might encourage the efficient collection, processing, and evaluation of massive volumes of data from disparate and scattered sources to provide guidelines for exploring actual ideas. Thus, this study focuses on how firms use modern instruments in their BI system development processes. This article also suggests the machine intelligence-based demand forecasting system (MI-DFS) for smarter choices in the digital sphere. This method uses big data analytics and a machine learning algorithm to predict the future demand for a product or service. This system gauges the level of acceptability of business intelligence by administering organized questionnaires and interviews with customers and service providers. Using properly classified test data, the results of many experiments show the effect of various forms of machine intelligence. Several learning algorithms were compared to the MI-DFS in terms of efficiency. Recurrent Neural Networks (RNNs) are a supervised learning technique for one-dimensional time series used for demand forecasting. The RNN model achieves the highest F1-measure and prediction accuracy. The proposed model will need to incorporate the financial decision assistance system. © 2023 IEEE.

关键词： Data Analytics

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Two-step Timing Synchronization Algorithm for OFDM systems

Two-step Timing Synchronization Algorithm for OFDM Systems

引用

IEEE International conference on information Communication and Signal processing (ICICSP)

作者： Xianliang Wu Miao Xu Ran Chu Guangzu Liu School of Electronic Engineering and Optical Engineering Nanjing University of Science and Technology Nanjing Jiangsu

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

In order to improve the accuracy of timing estimation, the paper proposed a Two-step timing synchronization algorithm based on the Schmidl & Cox algorithm for Orthogonal Frequency Division Multiplexing (OFDM) systems. The first step is to design a new training symbol frame structure without cyclic prefixes, that makes the timing metrics without peak plateau and reduces the ambiguity of the timing estimation. Secondly, the timing method that utilizes the receiving sequence and local sequence cross-correlation operation makes the timing estimation more accurate. Simulation results for mean square error (MSE) and bit error rate (BER) demonstrate that the proposed algorithm reduces the timing estimation error and improves the OFDM systems performance.

关键词： Training Accuracy OFDM System performance Estimation Signal processing algorithms Symbols Timing Synchronization Demodulation

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Predicting Software Energy Consumption Using Time Series-Based Recurrent Neural Network with Natural Language processing on Stack Overflow Data 1

Predicting Software Energy Consumption Using Time Series-Bas...

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1st Asian conference on Communication and Networks, ASIANComNet 2024

作者： Deepajothi, S. Dasari, Kalyankumar Krishnaveni, N. Juliana, R. Shrivastava, Neeraj Muppavaram, Kireet SRM Institute of Science and Technology Kattankulathur Department of Computing Technologies Tamil Nadu Chengalpattu India Chalapathi Institute of Technology Mothadaka Department of CSE-CS Guntur India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Loyola ICAM College of Engineering and Technology Loyola Campus Nungambakkam Department of Information Technology Chennai India Medicaps University Indore India Gitam Deemed To be University Department of CSE Hyderabad India

ISBN: (纸本)9798350367003

In recent years, there has been an increasing number of software solutions presented to tackle the issue of energy usage at the application level. Nevertheless, there is little knowledge about the level of concern among software developers over energy use, the specific areas of energy consumption that they deem significant, and the potential solutions they propose for enhancing energy efficiency. Especially, the increasing amount of data and IoT devices require more storage space and computational power, which results in higher energy consumption. In order to address this problem, academics and professionals have been investigating several strategies to enhance energy efficiency in computer systems. It may be an interesting project to use deep learning algorithms, especially those that make use of natural language processing (NLP) methods, to estimate software energy usage based on Stack Overflow data. This NLP techniques can analyze the text of questions and answers. This involves tokenization, lemmatization, and named entity recognition to identify terms and phrases related to energy consumption. This study examines the concerns of practitioners about energy consumption on Stack Overflow via the utilization of lexicon-based sentiment analysis, a concept in NLP, combined with RNNs. The objective is to improve energy efficiency by forecasting time series data. The results of this study indicate that the practitioners' desire to start conversations in the field of energy is closely linked to the utilization of ideas. This analysis of software energy consumption issues may assist academics in identifying the most significant concerns for software developers and end users. © 2024 IEEE.

关键词： Application programs

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Analysis and design of testing supporting system of flight track processing software

Analysis and design of testing supporting system of flight t...

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2022 International conference on Intelligent systems, Communications, and Computer Networks, ISCCN 2022

作者： Li, Wenxing Fang, Yu He, Xiaojun Peng, Fei Xichang Satellite Launch Center Xichang China

ISBN: (纸本)9781510657342

This paper aiming at the functions completed by the air target track processing system. The test requirements to support this type of system are analyzed. The method of interpolation and extrapolation is used to realize the simulation of the whole theoretical trajectory of the track. We select different working states of multiple devices to generate test data and realize the ability to send test data packets and manage test data. According to the designed test case. Through the control of the software interface. The test data is sent according to the requirements of the test case. And the testing supporting system is able to manage test data can test and reuse test data. The test cases in similar software systems to shorten the test cycle. It well improve the efficiency of software testing and the coverage of software testing and ensure the reliability of the system under test. © 2022 SPIE.

关键词： Software testing

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Channel State information based Device Free Wireless Sensing for IoT devices Employing TinyML 4

Channel State Information based Device Free Wireless Sensing...

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4th IEEE Middle East and North Africa COMMunications conference, MENACOMM 2022

作者： Hayajneh, Ali M. Aldalahmeh, Sami Zaidi, Syed Ali R. McLernon, Des Obeidollah, Haitham Alsakarnah, Rashed The Hashemite University Faculty of Engineering Department of Electrical Engineering Zarqa13133 Jordan Al-Zaytoonah University of Jordan Department of Communication and Computer Engineering Amman Jordan University of Leeds School of Electronic and Electrical Engineering LeedsLS2 9JT United Kingdom

ISBN: (纸本)9781665494113

The channel state information (CSI) of the sub-carriers employed in orthogonal frequency division multiplexing (OFDM) systems has been employed traditionally for channel equalisation. However, the CSI intrinsically is a signature of the operational RF environment and can serve as a proxy for certain activities in the operational environment. For instance, the CSI gets influenced by scatterers and therefore can be an indicator of how many scatterers or if there are mobile scatterers etc. The mapping between the activities whose signature CSI encodes and the raw data is not deterministic. Nevertheless, machine learning (ML) based approaches can provide a reliable classification for patterns of life. Most of these approaches have only been implemented in lab environments. This is mainly because the hardware requirements for capturing CSI, processing it and performing signal-processing algorithms are too complex to be implemented in commercial devices. The increased proliferation of IoT sensors and the development of edge-based ML capabilities using the TinyML framework opens up possibilities for the implementation of these techniques at scale on commercial devices. Using RF signature instead of more invasive methods e.g. cameras or wearable devices provide ease of deployment, intrinsic privacy and better usability. The design space of device-free wireless sensing (DFWS) is complex and involves device, firmware and ML considerations. In this article, we present a comprehensive overview and key considerations for the implementation of such solutions. We also demonstrate the viability of these approaches using a simple case study. © 2022 IEEE.

关键词： Channel state information

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On the Evaluation of Machine Learning algorithms for Hyperspectral Image Classification on a Heterogeneous Computing Device 16

On the Evaluation of Machine Learning Algorithms for Hypersp...

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16th Iberian conference on information systems and Technologies (CISTI)

作者： Pereira, Nuno Plaza, Javier Haut, Juan M. Plaza, Antonio Polytech Inst Beja Dept Math & Phys Sci Beja Portugal Univ Extremadura Hyperspectral Comp Lab Badajoz Spain Univ Extremadura Escuela Politecn Dept Technol Comp & Commun Hyperspectral Comp Lab Badajoz Spain Natl Distance Educ Univ Higher Sch Comp Engn Dept Commun & Control Syst Madrid 28015 Spain

ISBN: (纸本)9789895465910

Hyperspectral imaging (HSI) systems collect electromagnetic radiation, emitted or reflected by the scene under observation at, typically, hundreds of contiguous and regularly spaced narrow spectral bands, from the visible to the infra-red region of the spectrum. The result is a 3D data cube with two spatial dimensions and a wavelength dimension, composed of hundreds of co-registered images containing information on the spectral signature of the materials present in the field of view of the sensor at the time of capture. Retrieving this information in an efficient way, requires compute systems capable of dealing with a huge amount of data and complex algorithms. The recent development of single board computers (SBC) with enough compute power to deal with HSI processing using machine learning (ML) algorithms opens new opportunities for embedded HSI systems for Earth Observation remote sensing, laboratory setups and industrial applications. In this work, we evaluate the performance of the ODROID-XU4, a heterogeneous computing device, running ML algorithms for HSI classification. The experimental results show the potential of SBC as alternative hardware platforms for applications requiring low power and low cost devices, such as onboard processing units and edge computing.

关键词： hyperspectral imaging machine learning open hardware onboard processing edge computing air & space borne platforms

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Enhancing Bangla Language Text Recognition in OCR Using Levenshtein Distance and Character Grouping Strategy

Enhancing Bangla Language Text Recognition in OCR Using Leve...

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IEEE International conference on Signal processing, information, Communication & systems (SPICSCON)

作者： Md. Sajid Hasan Avi Pal Sk. Md. Masudul Ahsan Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (数字)9798331510213

ISBN: (纸本)9798331510220

One crucial technology for text digitization from image is optical character recognition (OCR). Character recognition in languages with complex scripts, like Bangla, is still difficult despite recent advancements. This research proposes an innovative approach to enhance OCR's Bangla text recognition capabilities. In order to improve recognition performance, OCR errors are efficiently corrected and performance is improved by first applying the Levenshtein distance measure. Experiments show a notable improvement, increasing the initial weighted F1-score of Tesseract OCR from 84.96% to 87.26%. In addition, a method of character grouping is demonstrated to address nuances specific to the Bangla script. Characters that share visual similarities are categorized into different groupings. Subsequently, priority is given to dictionary candidate words with characters that belong to the same class as those identified by OCR. Integrating the character grouping technique yields an astounding 90.92% F1-score.

关键词： Visualization Dictionaries Accuracy Text recognition optical character recognition Measurement uncertainty Signal processing algorithms Machine learning Signal processing Linguistics

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