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1st IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2022

作者： Shinde, Rohan Chorghe, Sahil Dhanani, Keval Salunke, Abhijeet Sardar Patel Institute of Technology Dept. of Computer Engineering Maharashtra Mumbai India

ISBN: (数字)9781665428323

ISBN: (纸本)9781665428323

In today's digital world there are a lot of issues in verifying the legitimacy of documents that an individual provides such as School/College mark sheets, Course certificates, etc. Companies are concerned whether a certificate provided to them is legit or forged. Verifying these documents manually or by offline visits is a tedious procedure and takes days to complete. The use of physical documents is also inconvenient and inefficient in today's fast paced digital world. In this paper, to counter this forgery issue a platform using blockchain technology is suggested. Blockchain provides immutability, once the document is stored in the blockchain it cannot be changed. An overview of the working and implementation of our platform and the functionalities provided are presented. © 2022 IEEE.

关键词： Cryptography

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Deep Learning based Indigenous Herbal Medicinal Plants Recognition: A Comprehensive Review 6

Deep Learning based Indigenous Herbal Medicinal Plants Recog...

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6th International Conference on Computing Methodologies and Communication, ICCMC 2022

作者： Gracelin, S. Raimond, Kumudha Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9781665410281

In this modern medicinal world, numerous medicines with various chemical compositions have been discovered. Many individuals consume them these days because they are quick in healing. However, they have a variety of drawbacks, including the failure of internal organs, which may even lead to demise. As a result, some alternative treatment that is both effective and free of adverse effects is required. Medicinal herbs have been utilized to cure practically every disease since ancient times. It is quite effective, and it is also free of side effects. Nowadays, Machine Learning (ML) and Deep Learning (DL) algorithms are used frequently to solve many real-time problems. They predict results with much accuracy. In this paper, a systematic review is devised for identifying therapeutic plants using ML and DL models. The performance of various models as well as the features used by each model is compared to choose the best performing model. © 2022 IEEE.

关键词： Deep learning

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Short-term Wind Power Ramp Forecasting Using Sequential Approach

Short-term Wind Power Ramp Forecasting Using Sequential Appr...

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Modeling, Simulation & Intelligent Computing (MoSICom), International Conference on

作者： Leechita Gopalakrishnan Julian L Cardenas Barrera N. Sabiyath Fatima Dept. of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Dept. of Electrical and Computer Engineering University of New Brunswick Fredericton Canada

ISBN: (数字)9798331533311

ISBN: (纸本)9798331533328

As wind energy becomes a key player in the global transition to renewable energy, its variability presents significant challenges. Accurate wind ramp forecasting is crucial for managing such rapid fluctuations in wind power output, ensuring grid stability, and enhancing the reliability of renewable energy integration. This paper presents a short-term wind ramp forecasting model using a sequential approach: initially forecasting wind power through the integration of Variational Mode Decomposition (VMD) and the XGBoost algorithm, and subsequently detecting wind ramps using the Definition Based Sign Indicator (DSI) method. The historical wind speed and power data for Turkey was obtained from Kaggle. The dataset comprises readings from a wind turbine’s SCADA system in Turkey, with measurements recorded at 10minute intervals. Variational Mode Decomposition (VMD) was applied to deconstruct and restore the historical wind power of the wind field, resulting in the formation of three training datasets. Wind power projections for the subsequent 8 hours, at 10 -minute intervals, are generated using historical wind power, wind speed, and wind direction as inputs. The DSI algorithm is employed to identify ramps. To assess the predictive capability of the proposed model, two algorithms, SVR and XGBoost, were employed for forecasting and the resulting error is minimal. The proposed model, VMD + XGBOOST is also compared without VMD to evaluate the performance. Taking mean square error (MSE), root mean square error (RMSE) mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE) as evaluation indicators, the results show that the forecasted accuracy of wind power is significantly improved after VMD processing. The performance of XGBOOST is better than SVR in the two evaluation indicators. Precision, Recall, and F1-score are calculated to evaluate the accuracy of the predicted wind ramps.

关键词： Renewable energy sources Accuracy Wind speed SCADA systems Wind power generation Predictive models Prediction algorithms Wind turbines Wind forecasting Forecasting

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Privacy and Data Control on Social Networks using Deep Learning 4

Privacy and Data Control on Social Networks using Deep Learn...

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4th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2022

作者： Parimala, A. Vijayalata, Y. Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Telangana Hyderabad India

ISBN: (数字)9781665462464

ISBN: (纸本)9781665462464

Online social networks are huge data exchange platforms that help to promote and share a lot of good information about products, news, education, tourism, health care, etc., also there is a great risk involved to individual's privacy and security. Online posted photos can be shared, tagged, and reposted again without having any consent from the people present in the images. In this project, we are proposing a facial recognition-based system that detects every single individual and checks their online account relationship between the individual. The post or image that is reposted, tagged, or copied by known or unknown individuals will be checked. If it is an unknown individual the images convolved through a low-pass filter kernel which removes the high-frequency content like noise and edges to smooth the images that make the image blur. For this mechanism, we proposed to use deep learning based VGG19 architecture for image classification and Haar cascade viola jones algorithm for detection, and computer vision-based blurring techniques used. algorithm performance as the area under the ROC curve (AUC) accuracy of 88% obtained. This proposed model achieved 91.53% classification accuracy and further we evaluated the model using Accuracy, Precision, Recall and F1-Score metrices and achieved 70.83%, 82%, 71% and 0.68%. © 2022 IEEE.

关键词： Deep learning

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A lightweight deep learning pipeline with DRDA-Net and MobileNet for breast cancer classification

arXiv

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arXiv 2024年

作者： Ahmadi, Mahdie Karimi, Nader Samavi, Shadrokh Dept. of Electrical and Computer Engn Isfahan University of Technology Iran Dept. of Computer Science Seattle University United States

Accurate and early detection of breast cancer is essential for successful treatment. This paper introduces a novel deep-learning approach for improved breast cancer classification in histopathological images, a crucial step in diagnosis. Our method hinges on the Dense Residual Dual-Shuffle Attention Network (DRDA-Net), inspired by ShuffleNet's efficient architecture. DRDA-Net achieves exceptional accuracy across various magnification levels on the BreaKHis dataset, a breast cancer histopathology analysis benchmark. However, for real-world deployment, computational efficiency is paramount. We integrate a pre-trained MobileNet model renowned for its lightweight design to address computational. MobileNet ensures fast execution even on devices with limited resources without sacrificing performance. This combined approach offers a promising solution for accurate breast cancer diagnosis, paving the way for faster and more accessible screening procedures. © 2024, CC BY-NC-ND.

关键词： Diseases

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Design and Performance Analysis of a Compact Two-Port Circularly Polarized MIMO Antenna for Sub-6 GHz 5G Applications 2

Design and Performance Analysis of a Compact Two-Port Circul...

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2nd International Conference on Power, electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Islam, Akib Jayed Das, Nobel Salehin, Sultanus Chakraborty, Swarup Pranta, Swapnil Alam, Sayem Ui Norwegian University of Science and Technology Computational Colour and Spectral Imaging GjOvik Norway Port City International University Dept. of Eee Chattogram Bangladesh The University of Alabama Dept. of Ece Tuscaloosa United States University of Vermont Dept. of Electrical and Biomedical Engineering Burlington United States

ISBN: (纸本)9798331517984

This research presents the design of a compact, dual-port, circularly polarized (CP) multiple-input-multiple-output (MIMO) antenna operating within the sub-6 GHz band designated for 5G cellular networks. This design emphasizes achieving smaller dimensions while preserving the functionalities of circularly polarized (CP) radiation and multiple-input-multiple-output (MIMO) technology. Utilizing a single radiating slot fed by an elliptical ring slot, the single antenna achieves circular polarization between 3.67 GHz and 3.81 GHz. The proposed MIMO antenna provides an impedance bandwidth of 340 MHz (reflection coefficient ≤,-10 dB) spanning from 3.57 GHz to 3.91 GHz and a 3 dB axial ratio bandwidth ranging from 3.65 GHz to 3.76 GHz. This configuration effectively covers the prospective 5G band with right-handed circular polarization. The performance of the MIMO antenna was validated through the evaluation of key metrics like the envelope correlation coefficient (ECC) and diversity gain (DG). The results demonstrate a low ECC value (ECC © 2024 IEEE.

关键词： Bandwidth

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Disruption of Commercial Solar Inverter System by TLS Proxy Man-in-the-Middle Attack

Disruption of Commercial Solar Inverter System by TLS Proxy ...

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Industrial Cyber-Physical Systems (ICPS)

作者： BoHyun Ahn Alycia Jenkins Joaquin Massa Lauren M Silva Taesic Kim Seungdeog Choi Dept. of Electrical Engineering and Computer Science Texas A&M University-Kingsville Kingsville TX USA Dept. of Electrical Engineering and Computer Engineering Mississippi State University Starksville MS USA

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

Transport Layer Security (TLS) is a cryptographic protocol that encrypts communication data, providing end-to-end communication encryption and authentication. Currently, TLS is widely adopted for securing communication between servers and end devices, including solar inverter systems. Therefore, users/operators can securely access the solar inverters through a web user interface (WebUI) application programmable interface (API) on a PC or server over TLS-enabled Wi-Fi or Ethernet. However, the security of the TLS-based network becomes compromised if it is breached by a TLS proxy man-in-the-middle (MITM) exploit. This paper explores potential vulnerabilities in a commercial solar inverter system that leverages a TLS proxy MITM and discusses the impacts through assume-breached penetration testing. Furthermore, the paper explores recommended mitigation methods against the TLS proxy MITM exploit in solar inverters.

关键词： Quantum computing Prevention and mitigation Ethernet User interfaces Cyber-physical systems Inverters Encryption

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Behind Closed Doors: Process-Level Rootkit Attacks in Cyber-Physical Microgrid Systems

Behind Closed Doors: Process-Level Rootkit Attacks in Cyber-...

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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： Rath, Suman Zografopoulos, Ioannis Vergara, Pedro P. Nikolaidis, Vassilis C. Konstantinou, Charalambos School of Electrical & Computer Engineering Oklahoma State University United States Cemse Division Saudi Arabia Netherlands Democritus University of Thrace Dept. of Electrical and Computer Engineering Greece

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

Embedded controllers, sensors, actuators, advanced metering infrastructure, etc. are cornerstone components of cyber-physical energy systems such as microgrids (MGs). Harnessing their monitoring and control functionalities, sophisticated schemes enhancing MG stability can be deployed. However, the deployment of 'smart' assets increases the threat surface. Power systems possess mechanisms capable of detecting abnormal operations. Furthermore, the lack of sophistication in attack strategies can render them detectable since they blindly violate power system semantics. On the other hand, the recent increase of process-aware rootkits that can attain persistence and compromise operations in undetectable ways requires special attention. In this work, we investigate the steps followed by stealthy rootkits at the process level of control systems pre- and post-compromise. We investigate the rootkits' precompromise stage involving the deployment to multiple system locations and aggregation of system-specific information to build a neural network-based virtual data-driven model (VDDM) of the system. Then, during the weaponization phase, we demonstrate how the VDDM measurement predictions are paramount, first to orchestrate crippling attacks from multiple system standpoints, maximizing the impact, and second, impede detection blinding system operator situational awareness. © 2022 IEEE.

关键词： Semantics

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An IRS-Assisted Secure Dual-Function Radar-Communication System

arXiv

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arXiv 2023年

作者： Li, Yi-Kai Petropulu, Athina Dept. of Electrical and Computer Engineering Rutgers University PiscatawayNJ United States Dept. of Electrical and Computer Engineering and Technology Minnesota State University MankatoMN United States

In dual-function radar-communication (DFRC) systems the probing signal contains information intended for the communication users, which makes that information vulnerable to eavesdropping by the targets. We propose a novel design for enhancing the physical layer security (PLS) of DFRC systems, via the help of intelligent reflecting surface (IRS) and artificial noise (AN), transmitted along with the probing waveform. The radar waveform, the AN jamming noise and the IRS parameters are designed to optimize the communication secrecy rate while meeting radar signal-to-noise ratio (SNR) constrains. Key challenges in the resulting optimization problem include the fractional form objective, the SNR being a quartic function of the IRS parameters, and the unit-modulus constraint of the IRS parameters. A fractional programming technique is used to transform the fractional form objective of the optimization problem into more tractable non-fractional polynomials. Numerical results are provided to demonstrate the convergence of the proposed system design algorithm, and also show the impact of the power assigned to the AN on the secrecy performance of the designed system. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Fusion of Images to Detect Brain Tumors Using DWT, IDWT and VGG19 Algorithms 5

Fusion of Images to Detect Brain Tumors Using DWT, IDWT and ...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Noorul Julaiha, G.A. Vasudevan, B. Ruhit Krishnan, M. Suriya, S.M. Rajalakshmi Institute of Technology Computer Science and Engineering Chennai India St. Joseph's College of Engineering Dept. of Electronics & Communication Engineering Chennai600119 India

ISBN: (纸本)9798350321425

Detecting brain tumors from medical images is a complex task in medical image analysis and is crucial for accurate diagnosis and treatment planning. Over the past few years, there has been significant development in the field of medical image processing. One such technique that has been employed is the multi-scale decomposition of brain images, which is achieved through the discrete wavelet transform (DWT). DWT decomposes an image into two different components, structural and textural information, without compressing the image. As a result, it provides high accuracy details and it preserves edge and texture details when reconstructing the image from its original frequency, and reduces problems like blocking and ringing artifacts. The low frequency sub-band coefficients are fused by selecting the coefficient with the maximum spatial frequency, indicating the overall active level of an image, while the high frequency subband coefficients are fused by selecting the coefficient with the maximum code value. Brain tumor detection using wavelet transform involves the use of both CT scan and MRI scan images. CT scan uses X-ray images taken from different angles of a specific part of the human body to provide detailed information about its internal structure, while MRI scan employs strong magnetic fields, radio waves, and field gradients to generate images of the inner human body. Finally, the fused image is reconstructed by taking the inverse Inverse Discrete Wavelet Transformation (IDWT) of two varied frequency sub-bands. The methods such as Visual Geometry Group (VGG19) which is a sub class of Convolutional Neural Network (CNN), watershed algorithm, Procrustes Analysis Algorithm, data argumentation are used in registration, fusion and segmentation process to detect brain tumor. Overall, the application of DWT and the fusion of multi-scale components of brain images has significant potential in improving the accuracy and quality of medical image processing, especially in t

关键词： Discrete wavelet transforms

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