2023 IEEE Long Island Systems, Applications and Technology Conference (LISAT)最新文献

Effect of Silica nanoparticles (Laponite) on 3D printed Gelatin Methacryloyl Cell-based Scaffolds 二氧化硅纳米颗粒(拉脱石)对3D打印明胶甲基丙烯酰细胞支架的影响

2023 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Pub Date : 2023-05-05 DOI: 10.1109/LISAT58403.2023.10179559

Naomi Edwards, S. Tharakan, M. Hadjiargyrou, A. Ilyas

引用次数: 0

Smart Acquiring Platform in Contactless Payments using Advanced Machine Learning : Security Controls using Device Recognition, Geo Fencing and Customer on File 使用先进机器学习的非接触式支付智能收单平台:使用设备识别、地理围栏和客户档案的安全控制

2023 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Pub Date : 2023-05-05 DOI: 10.1109/LISAT58403.2023.10179552

Bharathan Kasthuri Rengan

{"title":"Smart Acquiring Platform in Contactless Payments using Advanced Machine Learning : Security Controls using Device Recognition, Geo Fencing and Customer on File","authors":"Bharathan Kasthuri Rengan","doi":"10.1109/LISAT58403.2023.10179552","DOIUrl":"https://doi.org/10.1109/LISAT58403.2023.10179552","url":null,"abstract":"Acquiring platform (Merchant Bank) today faces greater Merchant Fraud cases (catering to small medium business) post pandemic as new methods of payments are evolving. In many cases, the fraud merchants, present the legitimate Know Your Customer (KYC) documents and complete onboarding like a legitimate merchant profile. Acquirers need to mitigate the merchant risk in Payments (multiple method of payments – card Present, card Not Present, contactless Payments (Tap to Pay, Apple Pay, Samsung Pay, using Near-Field Communication (NFC) and biometric based payments), in Point of Sale (PoS) and in large ecommerce portals). To address this, acquiring platform needs to build a comprehensive risk management platform with large set of data from multiple sources (device activity, merchant activity over time, analyze merchant portfolio for a specific geography and finally line of business). Acquirer needs to also analyze KYC data as well as payment data (over period of time). While doing so, risk platform needs to address both account fraud and payments fraud. It also needs to balance accuracy in fraud detection and without compromising high authorization rates. As new payment methods emerge, it is critical that risk management platform gets data from various sources in addition to just KYC and payment data. These are device /peripherals – Internet of Things (IoT) data, leveraging estate management, tracking geo location, analyzing transaction pattern (over period of twenty hours), profiling merchants and merchant segmentation. Risk management platform needs to address PoS3 needs specifically.","PeriodicalId":250536,"journal":{"name":"2023 IEEE Long Island Systems, Applications and Technology Conference (LISAT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116231127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mitigating Man-In-The-Middle Attack Using Quantum Key Distribution 利用量子密钥分发缓解中间人攻击

2023 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Pub Date : 2023-05-05 DOI: 10.1109/LISAT58403.2023.10179560

S. Alhazmi, Prashanna Kandel, Jayne Sabovik, Nadine Matondo-Mvula, K. Elleithy

引用次数: 0

Towards ARSPI-Net: development of an efficient hybrid deep learning framework 迈向ARSPI-Net:一个高效混合深度学习框架的开发

2023 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Pub Date : 2023-05-05 DOI: 10.1109/LISAT58403.2023.10179592

Andre C. Lane, Wendy Tang, Brady D. Nelson

{"title":"Towards ARSPI-Net: development of an efficient hybrid deep learning framework","authors":"Andre C. Lane, Wendy Tang, Brady D. Nelson","doi":"10.1109/LISAT58403.2023.10179592","DOIUrl":"https://doi.org/10.1109/LISAT58403.2023.10179592","url":null,"abstract":"In this paper, we implement and experiment with a portion of our overall hybrid deep learning framework, An Affective hYbrid SPIking Neural Network [ARSPI-NET]. In order to motivate and show the usage of liquid state machines as an efficient, feature extraction framework we perform experiments on connection architecture as well as neuron model and their effect on overall classification performance. We perform our initial experimentation on the MNIST dataset and achieve a 87% classification using a liquid state machine and logistic regression classifier. Our results suggest that our framework can compare with current models in terms of accuracy, however, we outperform traditional deep learning methods in terms of energy consumption and the potential to move to energy-efficient neuromorphic platforms. In addition, our framework has the advantage of being more interpretable, as it allows us to model the spatiotemporal dynamics of signals through the usage of a liquid reservoir and an interpretable readout vector. This paper sets precedence for future experimentation and development of ARSPI-Net as a hybrid deep learning framework.","PeriodicalId":250536,"journal":{"name":"2023 IEEE Long Island Systems, Applications and Technology Conference (LISAT)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132204521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Key Vulnerabilities in SSL & TLS That Needs To Be Mitigated 需要缓解的SSL和TLS中的关键漏洞

2023 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Pub Date : 2023-05-05 DOI: 10.1109/LISAT58403.2023.10179546

S. Alhazmi, K. Elleithy

引用次数: 0

A cloud-based fire safety system for emergency responders and civic community 一个基于云的消防安全系统，用于应急人员和公民社区

2023 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Pub Date : 2023-05-05 DOI: 10.1109/LISAT58403.2023.10179600

Swarnamouli Majumdar, Sonny E. Kirkley

引用次数: 0

Enhancement of CNNHQ with an Activation Function 用激活函数增强CNNHQ

2023 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Pub Date : 2023-05-05 DOI: 10.1109/LISAT58403.2023.10179557

Abdulrahman Kariri, Mohammed Aljamal, Jinan Al Aridhee, Ismaiel Albukhari, Nadine Matondo-Mvula, K. Elleithy

引用次数: 0

Subspace-Classification Approach for Simulated Tuple Class Assignment 模拟元组类分配的子空间分类方法

2023 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Pub Date : 2023-05-05 DOI: 10.1109/LISAT58403.2023.10179509

Victor Robila, R. Haralick

{"title":"Subspace-Classification Approach for Simulated Tuple Class Assignment","authors":"Victor Robila, R. Haralick","doi":"10.1109/LISAT58403.2023.10179509","DOIUrl":"https://doi.org/10.1109/LISAT58403.2023.10179509","url":null,"abstract":"We developed a subspace classifier for measurement classification to provide an alternative to current deep learning approaches. Many modern neural networks cannot provide an understandable explanation for their classification. The subspace classifier provides a decomposition of the classification problem making it computationally simpler. We first use a Bayesian method in which all the class conditional probabilities for the entire measurement space can be stored in memory.Then we made experiments with simulated class conditional distributions and defined a subspace classifier that only stores the class conditional probabilities for the subspaces. This can use much larger distributions than the previous model as it uses much less memory so we expanded to cases where the measurement space is generated sequentially and everything does not have to be in the memory at the same time.For cases with distributions that fit in the memory we also compared a Bayesian approach with the subspace approach. The Bayesian subspace classifiers consistently outperformed the subspace classifiers without Bayes rule by a large margin. We also compare the subspace classifier with 3 Python Machine Learning Models, namely a Ridge Classifier, a Multi-Layer Perceptron (MLP) classifier (neural network), and a Support Vector Machine (SVM) on a set of tuples and class conditional probability distributions with 4 classes. The subspace classifier had an average probability of correct identification of 0.25172, the SVM model had an average accuracy of 0.20987, the neural network MLP classifier had an average accuracy of 0.2140 and the Ridge Classifier had an average accuracy of 0.2798 over 10,000 trials.","PeriodicalId":250536,"journal":{"name":"2023 IEEE Long Island Systems, Applications and Technology Conference (LISAT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126683709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vehicle Monitoring System using SIM7600CE-T controlled over Arduino platform 车载监控系统采用SIM7600CE-T在Arduino平台上控制

2023 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Pub Date : 2023-05-05 DOI: 10.1109/LISAT58403.2023.10179505

Eden Yasin Ibraim, L. Szolga

引用次数: 1

High Voltage Transmission Line Response to Traveling Waves 高压输电线路对行波的响应

2023 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Pub Date : 2023-05-05 DOI: 10.1109/LISAT58403.2023.10179537

L. Durante, C. Burns, Mirjavad Hashemi Gavgani, R. Grabovickic, E. Seiter, T. Bujanovic

{"title":"High Voltage Transmission Line Response to Traveling Waves","authors":"L. Durante, C. Burns, Mirjavad Hashemi Gavgani, R. Grabovickic, E. Seiter, T. Bujanovic","doi":"10.1109/LISAT58403.2023.10179537","DOIUrl":"https://doi.org/10.1109/LISAT58403.2023.10179537","url":null,"abstract":"Fault occurrence, location, and system performance information, based on the analysis of traveling waves is gaining importance as new levels of high frequency measurement equipment are being introduced to the power grid. In this work Continuous Wavelet Transformations with the complex Morlet wavelet analyzing functions was used to design a signal processing scheme to extract time and frequency characteristics from measured and recorded transient events that launched traveling waves on a 115 kV transmission system. The primary motivation was to extract baseline system performance information employing the traveling waves launched as a result of line re-energizations. This work is a proof of concept aimed extracting and baselining a system’s line specific characteristics or ‘fingerprints’ and determining if a useful level of fingerprint consistency exists for typical line energizations. Implementation of the proposed scheme on these specific signals, measured at 1.5 MHz sampling frequency, successfully extracted consistent fingerprints of the system’s time-frequency behavior. A database of these fingerprints could be used to determine a dynamic high-frequency model for the system as well as track changes in the physical system as the response changes in different source and system operational scenarios.","PeriodicalId":250536,"journal":{"name":"2023 IEEE Long Island Systems, Applications and Technology Conference (LISAT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126123322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0