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4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020

作者： Naqvi, Muhammad Raza Shahzad, Syed Khuram Iqbal, Muhammad Waseem Ahmed, Muhammad Tahir, Muhammad Usman Khan, Babar Ayub Superior College Department of Computer Science Lahore Pakistan University of Management Technology Department of Informatics and Systems Lahore Pakistan Superior College Department of Software Engineering Lahore Pakistan

ISBN: (纸本)9781728190907

Natural Language Processing (NLP) is an argument that provokes territory of figural manner for motorized scrutiny and depiction of humane language. Research in it unfolded from the term of punch cards till the age of intelligent examination engines which takes less than a second to Figure out millions of web pages and digital documents. In the web, pieces of evidence and end-users produced content to have formerly stimulated text. The obligation of advantageous and astute content driving is decisive forthwith. Consequently, the web is being treated as a foremost source of evidence brought about the extensive extent of a probe to perceive the compass of the machine. Considering the democratization of online content formulation, it has skeptically influenced the information resurgence and eradication. This paper pulls contemporary developments in the NLP probe to perceive the scope of machine learning in a modern era to avoid ramification of adverse instructions. © 2020 IEEE.

关键词： Computational and psycholinguistics models Cortana Google Assistant ALO Natural language processing Siri Web documents

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Experimental Researches of Biometric Authentication Using Convolutional Neural Networks and Histograms of Oriented Graphs 4

Experimental Researches of Biometric Authentication Using Co...

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4th IEEE International Conference on Advanced Information and Communication Technologies, AICT 2021

作者： Kuznetsov, Alexandr Datsenko, Serhii Gorbenko, Yuriy Chupilko, Tetiana Korneyev, Maxim Klym, Victoria V.N. Karazin Kharkiv National University Department of Information Systems and Technologies Security Kharkiv Ukraine University of Customs and Finance Department of Computer Science and Software Engineering Dnipro Ukraine University of Customs and Finance Faculty of Innovative Technologies Dnipro Ukraine University of Customs and Finance Department of Cybersecurity and Information Technologies Dnipro Ukraine

ISBN: (纸本)9781665406185

User authentication technologies are an important component of modern cybersecurity. These technologies are designed to verify the correctness and validity, for example, by verifying the authenticity of entered passwords, identifiers, certain biometric features, and so on. We consider biometric authentication techniques for the human face. Two techniques are used to process biometric features in our work: convolutional neural networks and histograms of directed gradients. We conduct experimental research to study these techniques and compare the results of both in terms of authentication accuracy and the speed of processing biometric images. The obtained results will be useful in the development and implementation of cybersecurity applications that use biometric authentication © 2021 IEEE.

关键词： Authentication

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Self-Healing Secure Blockchain Framework in Microgrids

arXiv

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arXiv 2022年

作者： Rath, Suman Nguyen, Lam Duc Sahoo, Subham Popovski, Petar The Department of Computer Science and Engineering University of Nevada RenoNV89557 United States Software Systems Research Group CSIRO's Data61 Sydney2015 Australia The Department of Energy Aalborg University Aalborg9220 Denmark The Department of Electronic Systems Aalborg University Aalborg9220 Denmark

Blockchain has recently been depicted as a secure protocol for information exchange in cyber-physical microgrids. However, it is still found vulnerable to consensus manipulation attacks. These stealth attacks are often difficult to detect as they use kernel-level access to mask their actions. In this paper, we firstly build a trusted and secured peer-to-peer network mechanism for physical DC microgrids' validation of transactions over Distributed Ledger. Secondly, we leverage from a physics-informed approach for detecting malware-infected nodes and then recovering from stealth attacks using a self-healing recovery scheme augmented into the microgrid Blockchain network. This scheme allows compromised nodes to adapt to a reconstructed trustworthy signal in a multi-hop manner using corresponding measurements from the reliable nodes in the network. Additionally, recognizing the possible threat of denial-of-service attacks and random time delays (where information sharing via communication channels is blocked), we also integrate a model-free predictive controller with the proposed system that can locally reconstruct an expected version of the attacked/delayed signals. This supplements the capabilities of Blockchain, enabling it to detect and mitigate consensus manipulation attempts, and network latencies. © 2022, CC BY.

关键词： Blockchain

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D-distance factor for high voltage transmission line risk and cost analysis 18

D-distance factor for high voltage transmission line risk an...

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18th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2021

作者： Suwanasri, Cattareeya Saribut, Surapol Luejai, Waraporn Suwanasri, Thanapong King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering 1518 Pracharat 1 Rd. Wongsawang Bangsue Bangkok Thailand King Mongkut's University of Technology North Bangkok The Sirindhorn International Thai-German Graduate School of Engineering Department of Electrical and Software Systems Engineering 1518 Pracharat 1 Rd. Wongsawang Bangsue Bangkok Thailand

ISBN: (纸本)9780738111278

Currently, thirty five percent of high voltage transmission lines in Thailand are aged and degraded. These transmission lines need some renovation work with major components;i.e. conductor, insulator, steel structure, foundation, grounding system and right of way according to the remaining performance and importance to the system. With known condition and importance, the renovation task can be properly setup. Therefore, this work focuses on a risk assessment by using d-distance risk factor based condition index and importance index of transmission line. In the condition index calculation, the conditions of transmission line and its components are calculated by taking several testing and visual inspection techniques into consideration. In the importance index calculation, various criteria i.e. line load, voltage rating, contingency, etc. are also considered. The weighting and scoring technique and analytical hierarchy process are applied in the analysis. Finally, the risk is determined. The renovation tasks could be prioritized. The renovation cost is also analysis according to the risk in order to preform effective maintenance and cost management for the transmission line facilities. © 2021 IEEE.

关键词： Risk analysis

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Basic Space Mapping: A Retrospective and its Application to Design Optimization of Nonlinear RF and Microwave Circuits

Basic Space Mapping: A Retrospective and its Application to ...

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European Conference on Microwave

作者： José E. Rayas-Sánchez John W. Bandler Department of Electronics Systems and Informatics ITESO The Jesuit University of Guadalajara Mexico Microwave Harmonica User’s Manual Compact Software Inc. Paterson Department of Electrical and Computer Engineering McMaster University Hamilton Canada OSA90 Optimization Systems Associates Dundas Canada Canada

ISBN: (数字)9782874870699

ISBN: (纸本)9781665458818

Space mapping (SM) is one of the most powerful and computationally efficient design optimization methodologies in RF and microwave engineering. Its impressive evolution in terms of algorithmic variations and diverse engineering applications is well-documented. Most of the SM-based design optimization cases, including those solved by advanced and sophisticated space mapping formulations, have been demonstrated for linear frequency-domain microwave circuits. In this paper, we provide a brief retrospective on the emergence of space mapping, including its initial impression on a worldwide authority on nonlinear microwave circuit simulation and design: Prof. Vittorio Rizzoli. We briefly review some of the most fundamental space mapping optimization concepts and emphasize their applicability to nonlinear transient-domain microwave circuit design optimization. We illustrate this by a typical problem of high-speed digital signal conditioning: the physical design of a set of CMOS inverters driving an FR4 printed circuit board interconnect.

关键词： Semiconductor device modeling Radio frequency Printed circuits Integrated circuit interconnections Voltage Microwave circuits Microwave theory and techniques

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Information Leakage from Optical Emanations

arXiv

引用

arXiv 2023年

作者： Loughry, Joe Umphress, David A. Lockheed Martin Space Systems Dept. 3740 Mail Stop X3741 P.O. Box 179 DenverCO80201 United States Auburn University Department of Computer Science and Software Engineering 215 Dunstan Hall AL36849 United States

A previously unknown form of compromising emanations has been discovered. LED status indicators on data communication equipment, under certain conditions, are shown to carry a modulated optical signal that is significantly correlated with information being processed by the device. Physical access is not required;the attacker gains access to all data going through the device, including plaintext in the case of data encryption systems. Experiments show that it is possible to intercept data under realistic conditions at a considerable distance. Many different sorts of devices, including modems and Internet Protocol routers, were found to be vulnerable. A taxonomy of compromising optical emanations is developed, and design changes are described that will successfully block this kind of "Optical Tempest" attack. Categories and Subject Descriptors: C.2.0 [computer systems Organization]: computer-COMMUNICATION NETWORKS—General, Security and protection (e.g., firewalls);D.4.6 [software]: OPERATING systems—Security and Protection, Invasive software (e.g., viruses, worms, Trojan horses);E.3 [Data]: DATA ENCRYPTION—Code breaking;K.6.5 [Computing Milieux]: MANAGEMENT OF COMPUTING AND INFORMATION systems—Security and Protection, Unauthorized Access (e.g., hacking, phreaking) Copyright © 2023, The Authors. All rights reserved.

关键词： Light emitting diodes

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An Intelligent Approach to Segment the Liver Cancer using Machine Learning Method

An Intelligent Approach to Segment the Liver Cancer using Ma...

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Electronics and Sustainable Communication systems (ICESC), 2020 International Conference on

作者： L Anand Mahesh Maurya J. Seetha D Nagaraju Ananda Ravuri R G Vidhya Department of networking and communications School of computing SRM Institute of science and Technology K. C. College of Engineering Management and Research University of Mumbai Mith Bunder Road Near Hume Pipe Thane Maharashtra India Department of Computer Science and Business Systems Panimalar Engineering College Chennai Department of CSE Sri Venkatesa Perumal College of Engineering and Technology Puttur Andhra Pradesh India Senior Software Engineer Intel corporation Hillsboro Oregon USA Department of ECE HKBK College of Engineering Bangalore

Cancer Liver cancer is difficult to detect and diagnose early. Machine learning has improved liver cancer diagnosis in recent years. This abstract describes a novel liver cancer detection and classification method using Autoencoder-Extreme Learning Machine (AE-ELM) and Convolutional Neural Network (CNN) technology. AE-ELM models extract features in the suggested method. The autoencoder-extreme learning machine AE-ELM technique learns high-level abstract characteristics from incoming data. AE-ELM extracts liver cancer-specific features by reducing the dimensionality of input data. A CNN model classifies the extracted features. CNNs are good at detecting spatial patterns in medical pictures like CT and MRI scans. The CNN model learns hierarchical representations of input information and classifies them into liver cancer subtypes or stages using various convolutional and pooling processes. AE-ELM and CNN are advantageous. First, AE-ELM decreases input data dimensionality to extract critical features without manual feature engineering. This simplifies feature selection and improves the model’s capacity to handle big data. Second, the CNN model uses hierarchical convolutional processes to capture subtle patterns and spatial correlations in input data, enhancing liver cancer classification accuracy. The proposed liver cancer diagnostic method works in experiments. AE-ELM and CNN models outperform classic machine learning approaches and standalone CNN models. Improved liver cancer detection accuracy, sensitivity, and specificity help healthcare practitioners make quick and precise diagnosis. Finally, AE-ELM and CNN technologies can identify and classify liver cancer. This unique strategy uses both models to extract relevant characteristics and capture complicated patterns, improving liver cancer diagnosis accuracy and efficiency. To evaluate this approach’s therapeutic promise and application in real-world healthcare settings, additional datasets are needed.

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An analysis of machine learning algorithms in rotating machines maintenance

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IFAC-PapersOnLine 2022年第2期55卷 252-257页

作者： Alexandre S. Roque Vinicius W. Krebs Iuri Castro Figueiro Nasser Jazdi Alexandre S. Roque Vinicius W. Krebs and Iuri C. Figueiro are members of the Research Group on Electronics and Power Systems (GPESP) of the Computer Science and Engineering Department Regional Integrated University of High Uruguay and Missions Brazil Institute of Industrial Automation and Software Engineering (IAS) University of Stuttgart Germany

The industrial maintenance activities represent an increase in production costs, mainly caused by unnecessary production stops. Recent technologies approaches are handling the continuous monitoring of industrial machines, storing sensors data, and also maintenance history. More data analysis is necessary specifically for rotating machines presenting methodologies to reduce the maintenance. In order to handle this problem, a comparative analysis of machine learning methods is presented. The strategy aims to predict failures and then indicates the maintenance necessity before a break occurs. Thus, it is applied and analyzed the specific machine learning algorithms, Gradient Boosting and Random Forest, using a dataset of rotation machines. The results show that both methods have an excellent performance (metrics accuracy, precision, and recall), with slightly better results in Gradient Boosting (hit rate of 99.93%) indicating the prominent application of these algorithms in this industrial scenario.

关键词： Maintenance Rotation machines Machine learning Intelligent inspection

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Collaborative Human-AI Decision-Making systems with Numerical Channels

Collaborative Human-AI Decision-Making Systems with Numerica...

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International Conference on Advanced computer Information Technologies (ACIT)

作者： Mules Oksana Mykola Kotsipak Serge Dolgikh Yurii Bilak Tamara Radivilova Oleksii Baranovskyi Department of Software Systems Uzhhorod National University Uzhhorod Ukraine Department of Network Engineering Solana Networks Ottawa Canada Department of V.V.Popovskyy Infocommunication Engineering Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of Computer Science Blekinge Institute of Technology Karlskrona Blekinge Sweden

ISBN: (数字)9781665410502

ISBN: (纸本)9781665466479

This work is devoted to the study of the features of functioning in Collaborative Human-AI Decision-Making systems with numerical channels. The system operates in automatic mode without external influences. The channels can be both artificial intelligence software and competent experts giving conclusions on the question under study. The cases considered are those in which an agreed solution must be numerical and logical. For each of these cases, decision rules for the system have been developed. The developed rules include the ability to take into account the reliability of the channels, which allows expanding their number in the system if necessary.

关键词： Decision making Collaboration software software reliability Artificial intelligence Information technology

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Pattern recognition in vital signs using spectrograms

arXiv

引用

arXiv 2021年

作者： Sribhashyam, Sidharth Srivatsav Salekin, Md Sirajus Goldgof, Dmitry Zamzmi, Ghada Last, Mark Sun, Yu Department of Computer Science and Engineering University of South Florida TampaFL United States Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel

— Spectrograms visualize the frequency components of a given signal which may be an audio signal or even a time-series signal. Audio signals have higher sampling rate and high variability of frequency with time. Spectrograms can capture such variations well. But, vital signs which are time-series signals have less sampling frequency and low-frequency variability due to which, spectrograms fail to express variations and patterns. In this paper, we propose a novel solution to introduce frequency variability using frequency modulation on vital signs. Then we apply spectrograms on frequency modulated signals to capture the patterns. The proposed approach has been evaluated on 4 different medical datasets across both prediction and classification tasks. Significant results are found showing the efficacy of the approach for vital sign signals. The results from the proposed approach are promising with an accuracy of 91.55% and 91.67% in prediction and classification tasks respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal reconstruction

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