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Fingerprint generation and authentication though Adaptive convolution generative adversarial network (ADCGAN) 7

Fingerprint generation and authentication though Adaptive co...

引用

7th International Multi-Topic ICT Conference: IMTIC 2023

作者： Mustafa, Syed Muhammad Nabeel Zehra, Syeda Sundus Baber, Alina Siddiqui, Maria Andleeb Ned University of Engineering and Technology Dept. of Computer Science & Information Technology Karachi Pakistan Sir Syed University of Engineering and Technology Dept of Software Engineering Karachi Pakistan

ISBN: (纸本)9798350338461

Fingerprints are crucial in identification of humans. The uniqueness of finger prints makes it an interesting subject. Fingerprints are termed as a technique used to define, assess, and quantify a person's physical and behavioral property. Deep learning has made its application in all the major fields such as natural language processing, computer vision and speech processing. Deep learning has also found its application in the important subject of fingerprint synthesis and biometric. The ever-growing complexity of fingerprint authentication issues, from cellphone authentication to airport security systems, seems to be best handled by these models. In recent years, deep learning-based models have been used more and more to raise the accuracy of various fingerprint recognition systems. The persuasive capacity of Generative Adversarial Networks (GANs) to generate believable instances can be credibly taken from an existing distribution of samples. GAN exhibits exceptional performance on data generation-based tasks and also encourages study in privacy and security. In this work, using Adaptive Deep Convolution Generative Adversarial Networks (ADCGAN), we develop a model that generates and authenticate the fingerprints. A Socofing dataset was trained on ADGAN model. The model gave 92% accuracy. The conduct of fingerprint research has been made possible due to ADGAN, without restrictions related to the confidential nature of biometric data. © 2023 IEEE.

关键词： Generative adversarial networks

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Synergizing Measurement Science and Artificial Intelligence in Smart Agriculture

Synergizing Measurement Science and Artificial Intelligence ...

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Lamonaca, Francesco Carni, Domenico Luca University of Calabria Dept. of Computer Science Modelling Eletctronics and Systems Engineering Arcavacata di Rende Italy

ISBN: (纸本)9798350324457

This paper would provide a comprehensive review of the synergistic integration of measurement science and artificial intelligence (AI) in the realm of smart agriculture. As agriculture undergoes a transformative phase toward precision and data-driven practices, the paper explores the critical role of measurement science in providing accurate data, while AI processes and analyzes this data to facilitate intelligent decision-making in smart agricultural systems. © 2023 IEEE.

关键词： Artificial Intelligence Distributed Measurement System Measurement Smart Agriculture

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AirWave: Enhancing UAV Connectivity in Cellular Networks through an Integrated Simulation Framework

AirWave: Enhancing UAV Connectivity in Cellular Networks thr...

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2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Zor, Emirhan Taskiran, Yusuf Dael, Fares A. Shayea, Ibraheem Rzayeva, Leila Syzdykova, Zuleikha Istanbul Technical University Electronic and Communication Engineering Istanbul Turkey İzmir Bakirçay University Management Information Systems İzmir Turkey Astana IT University Dept. of Intelligent Systems and Cybersecurity Astana Kazakhstan Astana IT University Dept. of Computer Engineering Astana Kazakhstan

ISBN: (纸本)9798350374100

This study presents AirWave, an innovative modeling framework tailored to address the challenges associated with integrating Unmanned Aerial Vehicles (UAVs) into 5G and future cellular networks. With the rising prominence of UAVs across various industries, ensuring uninterrupted connectivity and efficient mobility management within cellular networks becomes paramount. AirWave offers a comprehensive simulation environment, amalgamating network and physics simulations using ns-3 for network simulation, Gazebo for physics simulation, and integrating PX4 autopilot for realistic UAV flight dynamics. This framework facilitates in-dept. investigations into UAV mobility and handover management, including a trajectory optimization technique based on reinforcement learning for cellular-connected drones. Extensive simulations demonstrate the versatility and effectiveness of AirWave, showcasing notable improvements in UAV range, connectivity, and management within cellular networks. These findings hold significant promise for advancing research and practical applications in UAV communications. © 2024 IEEE.

关键词： Reinforcement learning

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A Power Quality Event Classifier Based on Convolutional Neural Network

A Power Quality Event Classifier Based on Convolutional Neur...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Lamonaca, Francesco Carni, Domenico Luca University of Calabria Dept. of Computer Science Modelling Eletctronics and Systems Engineering Arcavacata di Rende Italy

ISBN: (纸本)9798350324457

This article introduces a novel and accurate automatic classifier for Power Signal (PS) alterations. The efficient detection and accurate classification represent crucial steps in developing an automatic power quality (PQ) measurement system, which has become essential in contemporary settings. The objective of this article is to enhance alteration classification accuracy. This is accomplished by combining the Hilbert-Huang transform (HHT) and convolutional neural network (CNN). The HHT is employed to extract PS features and is resilient to the non-stationarity introduced by alterations. Meanwhile, the CNN is adept.at extracting information from the bidimensional characteristics of PS features and is robust against noise. Numerical tests demonstrate promising results showing high classification accuracy also in the case of PSs affected by high level of noise. © 2023 IEEE.

关键词： Classifier CNN Convolutional Neural Network Distributed Measurement systems Hilbert-Huang transform Measurement Measurement System Power Quality Power Signal

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Feature Relevance in NAT Detection Using Explainable AI 28

Feature Relevance in NAT Detection Using Explainable AI

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Nassar, Reem Elhajj, Imad H. Kayssi, Ayman Salam, Samer American University of Beirut Dept. of Electrical and Computer Engineering Beirut Lebanon Cisco Systems Beirut Lebanon

ISBN: (纸本)9798350300482

Network Address Translation (NAT) was developed to overcome the IPv4 address exhaustion problem. NAT allows multiple devices on a local network to share a single public IP address. However, NAT can result in communication concerns, security flaws, network administration issues, and difficulties locating network faults. This paper addresses the limitations of existing NAT detection techniques through adoption of an innovative machine learning-based approach. The NAT detection model achieves promising results by utilizing novel traffic features. The current techniques used in NAT detection face challenges in terms of explainability and transparency. To address this issue, our proposed method integrates explainable artificial intelligence (XAI) techniques to increase the transparency and interpretability of NAT detection models, thereby improving their efficiency and effectiveness. The results obtained from explain ability highlight the significance of incorporating new features in NAT detection. Furthermore, explainable results indicate that the presence of NAT is closely correlated with specific features. © 2023 IEEE.

关键词： Transparency

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Facial Shape-Based Eyeglass Recommendation Using Convolutional Neural Networks

Facial Shape-Based Eyeglass Recommendation Using Convolution...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Rifat, Rakib Hossain Siddique, Sunzida Das, Laxmi Rani Haque, Mohd Ariful Brac University Dept. of Computer Science and Engineering Mohakhali Dhaka1212 Bangladesh Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Noakhali Science and Technology University Dept. of Computer Science and Telecommunication Engineering Sonapur Bangladesh Clark Atlanta University Dept. of Cyber-Physical Systems AtlantaGA United States

ISBN: (纸本)9781665430654

Eyeglasses are not only used to protect our vision and prevent dust from getting into our eyes. Additionally, glass that fits properly can give a person an elegant appearance. However, people often find it difficult to choose eyeglasses that fit their face shape;to address this issue, we have proposed a novel architecture in this paper. In order to do this, we created a pipeline that can recommend eyeglasses based on the form of the eyes using multiple transfer learning architecture to predict the face shape from a given image. We utilized InceptionV4 [17], InceptionV3[18], Vit Small [12], DenseNet121 [10], ResNet50 [9], and VGG16 [16] to predict the facial shape from the image and achieve a test accuracy of 75%. We used 5500 photos with five different face shapes (Heart, Oblong, Oval, Round, Square) for this experiment, and two distinct datasets were gathered from Kaggle [2] and GitHub [1]. By simply uploading the photograph to our recommendation system, our proposed solution can assist users in selecting the appropriate eyewear. © 2023 IEEE.

关键词： CNN DenseNet121 Eye Glass InceptionV3 InceptionV4 ResNet50 Trasnfer Learning VGG16 Vit Small

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Forecasting Breakdowns of Electric Motors of a Sugar Factory Using Machine Learning Methods 4

Forecasting Breakdowns of Electric Motors of a Sugar Factory...

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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Zaiets, Nataliia Vlasenko, Lidiia Lutska, Nataliia Zhyltsov, Andrii Dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine Dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine Dept. of Electromagnetic Systems The Institute of Electrodynamics The National Academy of Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798350395532

The paper demonstrates the process of developing mathematical models for identifying breakdowns of electric motors using machine learning methods. The authors have developed three mathematical models for identifying breakdowns of electric drives with a power of 55 kW, 1500 rpm on the example of sugar production: model for quadratic discriminant analysis;binary classification decision tree;feedforward neural network model. The best structures and parameters of the models were determined by machine learning methods. The performance of the models on the training and test sets was more than 96% by F-score. It was proposed to use all three models independently for diagnosing breakdowns of electric drives, while the decision maker makes the final decision on the replacement or repair of electrical equipment. This approach is justified and effective because of the diversity of models. The developed models, except for assessing the state of the engine, can also be used for simulation modelling, forecasting, and serve as information support for process operators in organizing equipment maintenance. The availability of real-time information about the state of equipment in production will allow timely repair or replacement of equipment, thereby reducing the risks of stopping production and increasing the resource efficiency of technological processes. © 2023 IEEE.

关键词： Decision trees

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A Generalizable Machine Learning Model for NAT Detection

A Generalizable Machine Learning Model for NAT Detection

引用

2023 International Conference on Intelligent Computing, Communication, Networking and Services, ICCNS 2023

作者： Nassar, Reem Elhajj, Imad H. Kayssi, Ayman Salam, Samer American University of Beirut Dept. of Elecrtical and Computer Engineering Beirut Lebanon Cisco Systems Beirut Lebanon

ISBN: (纸本)9798350339291

Network Address Translation (NAT) aimed originally at resolving the issue of IPv4 address exhaustion. NAT allows multiple devices on a local network to share a single public IP address. This enables efficient utilization of the limited number of public IP addresses and allows devices on a local network to connect to the internet. However, NAT can lead to communication problems, security vulnerabilities, difficulties in network management, and challenges in identifying network issues. This paper proposes a novel approach for NAT detection using machine learning that overcomes the limitations of traditional NAT detection methods by building a generalizable model. The proposed method incorporates techniques to achieve generalization in the presence of obfuscation, a technique used by attackers to evade detection. The experimental results show the superiority of the proposed method over existing NAT detection techniques in terms of accuracy and generalization, even in the presence of obfuscation. A decline in the classification F1 score below 70% is observed when traffic data has multiple features obfuscated and when environment changes. To tackle this issue, transfer learning was applied to enhance the model's efficiency and effectiveness. Transfer learning resulted in more promising results in new network environments and in case of obfuscation. © 2023 IEEE.

关键词： Internet protocols

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A Novel Architecture of CXL Protocol Data Link Layer for Low Latency Memory Access

A Novel Architecture of CXL Protocol Data Link Layer for Low...

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2023 International Conference on Microelectronics, ICM 2023

作者： Mohamed, Basma H. Hany, Esmail El-Tahawy, Mahmoud Abdel-Salam, Mohamed El-Kharashi, M. Watheq Safar, Mona Ain Shams University Computer and Systems Engineering Dept. Cairo Egypt Siemens Eda Cairo Egypt

ISBN: (纸本)9798350380828

With the increase in memory-intensive applications, there is a need to provide low latency paths for memory access and coherent caching between host processors and storage devices. This paper proposes a novel architecture for the Data Link Layer of Compute Express Link (CXL) protocol, as defined in CXL Specification Revision 2.0. The architecture presented here contains the transmission and reception chains, which ensures the reliable conveyance of the packets between two CXL components using the CXL protocol. This paper explains how the proposed architecture ensures reliable data conveyance, responses and requests, through the addition of a 16-bit Cyclic Redundancy Check (CRC) to each 512-bit FLow control unIT (FLIT) in the transmitter side, and how the packets are processed in receiver side. The architecture is intended to have simple implementation in addition to having minimum datapath delays. The whole architecture is designed using HDL then verified using test-benches for different protocol scenarios to make sure that the functionality of the system is achieved. In addition, the whole design is synthesized using United Microelectronics Corporation (UMC) 130 nm [1] and Taiwan Semicomductors Manufactor Company (TSMC) 28 nm [2] technologies to get the maximum achieved frequency and calculate the corresponding power and area. The proposed hardware implementation manages to work up to 1 GHz using TSMC 28 nm technology and 438.59 MHz using UMC 130 nm technology. Its estimated dynamic and leakage power consumption by UMC 130 nm technology at 1 GHz are 95.3698 mW and 171.17 mW, respectively. In addition, its area cost extracted after synthesis is 242124.8 μ m2. © 2023 IEEE.

关键词： Microelectronics

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Deep Learning-Based Stock Price Prediction: A Comprehensive Approach Using Bi-LSTM 4

Deep Learning-Based Stock Price Prediction: A Comprehensive ...

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4th International Conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Chy, Md. Kalim Amzad Masum, Abdul Kadar Muhammad Alam, Mohammed Shamsul Azad, Md. Abul Kalam Hassan, Md. Maruf Bhuiyan, Touhid International Islamic University Dept. of Computer Science and Engineering Chittagong Bangladesh Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Islamic University of Technology Dept. of Business and Technology Management Gazipur Bangladesh School of It Washington University of Science and Technology VA United States

ISBN: (纸本)9798331529635

This study delves into the prediction of stock prices in the Bangladesh stock market using advanced deep learning frameworks, with a particular focus on the Bidirectional Long Short-Term Memory (Bi-LSTM) model. Traditional statistical models often struggle to effectively capture the complex, non-linear characteristics of financial data, leading to inconsistent results. The Bi-LSTM model, however, addresses this challenge by processing data in both forward and backward sequences, making it particularly suitable for analyzing the intricate dynamics of the Bangladesh stock market. A rigorous feature engineering process is undertaken, incorporating 64 distinct features that include market trends, stock-specific indicators, technical and fundamental variables, as well as crucial macroeconomic factors like gold prices and foreign exchange reserves. The dataset spans historical stock market data from 2012 to 2022, with comprehensive preprocessing steps taken to ensure data quality and consistency. Hyperparameter tuning is executed through a grid search to optimize model performance. The evaluation of the Bi-LSTM model is carried out using metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and the R2 score, which demonstrate its enhanced predictive power compared to traditional models. This research contributes to the growing field of deep learning in financial forecasting, offering new perspectives for stock price prediction in emerging markets, while also addressing ethical issues to minimize risks like market manipulation. © 2024 IEEE.

关键词： Mean square error

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