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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Prakash, Harshitha Yadav, Ayush Ushashree, P. Jha, Chandranshu Sah, Gopi Kumar Naik, Archana Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350398465

This electronic Counterfeit currency is a major concern for governments, banks, and businesses around the world. The detection of counterfeit currency notes is a challenging task that requires sophisticated techniques to analyse various physical and security features on the notes. Traditional methods of detection are time-consuming and prone to human error. This paper proposes the use of deep learning approaches for the automated detection of counterfeit bank notes. The use of Convolutional neural networks (CNNs) to analyse currency note image features and Recurrent Neural Networks (RNNs) to analyse security features such as serial numbers is investigated. An ensemble of CNNs and RNNs is proposed to improve detection accuracy. The proposed method is tested on a dataset of genuine and counterfeit currency notes. This method outperforms traditional methods of detection in terms of accuracy and precision. Our proposed method can be integrated into existing systems to enhance the security of banknotes and protect against counterfeiting. © 2023 IEEE.

关键词： Recurrent neural networks

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DREaD: Decision Tree-Aided Random Early Detection - An Intelligent Active Queue Management Technique 3

DREaD: Decision Tree-Aided Random Early Detection - An Intel...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Kesiezie, Kevikhietuo Murry, Lithungo K. National Institute of Technology Nagaland Department of Computer Science and Engineering Chumukedima India

ISBN: (纸本)9798350319125

As the number of packets exceeds the capacity of the network, network resources get overwhelmed, resulting in congestion - affecting the performance of the network. In order to ensure optimal network performance, minimize data loss, and improve packet delivery ratio., it is imperative that the onset of network congestion be identified., analyzed, and mitigated. In this paper, we propose Decision tree-aided Random Early Detection (DREaD) technique, an improved version of Random Early Detection (RED) for queue management at the router to detect congestion in the network a-priori so as to handle the incoming packets. The simulation results show an improvement in the packet delivery ratio, throughput, and a decrease in the packet loss ratio over the existing RED, Adaptive RED (ARED), Nonlinear RED (NLRED), Gentle RED (GRED), and RED-Quadratic Linear (RED-QL) algorithms for active queue management. © 2023 IEEE.

关键词： Active Queue Management congestion avoidance congestion control machine learning Random Early Detection

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Improved Yolov7 Tiny with Global Attention Mechanism for Camouflage Object Detection

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Journal of The Institution of Engineers (India): Series B 2024年 1-16页

作者： Mahajan, Chaitali N. Jadhav, Ashish Department of Computer Engineering Ramrao Adik Institute of Technology Nerul D Y. Patil University Maharashtra Mumbai400008 India Department of Information Technology Ramrao Adik Institute of Technology Nerul D Y. Patil University Maharashtra Mumbai400008 India

Detecting camouflaged objects presents a greater challenge in comparison to normal object detection as an object is highly similar to a complex background. Due to the similarities of an object with the background, it is very difficult to extract complete feature information of that target, causing an increase in false and missed detection. To analyse these issues, this research article introduces an enhanced algorithm built upon Yolov7 tiny. Firstly, a GAM was introduced in the backbone of Yolov7 tiny for the extraction of intricate features of an object. Also, three attention layers were introduced in the head section to increase prediction confidence. The parameters of the GAM module were adjusted and the position of the GAM was properly selected to ensure that the proposed method offered the best accuracy in camouflage detection. For training purposes, a Moving Camouflaged Animal dataset was considered which was initially available in video format and was later converted into images. It may be noted that seven classes have been considered to have a high camouflage environment. The problem of false and missed detection for camouflage images with complex dynamic backgrounds was improved giving better detection results by the introduced approach. The findings obtained from the experimentation revealed that the proposed model has attained an increase in mean Average Precision by 10.7% and 7.2% for mAP at 0.5 and mAP at 0.5:0.95 respectively for validation images for all classes and testing images there was an increase in mAP by 10.5% and 9.2% respectively. © The Institution of Engineers (India) 2024.

关键词： Camouflage

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Efficient Regular Expression Matching and Hardware-Accelerated Finite Automata Pattern Recognition in NIDS 6

Efficient Regular Expression Matching and Hardware-Accelerat...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Thota, Krishna Kishore Retna Raj, R. Jeberson Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798350394702

To protect network infrastructure from the ever-changing cyber threats, Intrusion Detection Systems (IDS) perform important duties. This study introduces a sophisticated algorithm aimed at overcoming key challenges in IDS. These challenges include efficient regular expression matching, hardware implementation, and improved pattern recognition. The algorithm employs a unique technique to reduce memory specifications by using controlled faults into the pattern matching process. It builds upon perfect hashing, ensuring precise and efficient regular expression matching, making it ideal for high-speed processing in IDS applications. Additionally, the central component of a hardware-based Network Intrusion Detection System (NIDS), the technique comprises a signature match processor that is based on a field-programmable gate array (FPGA). For effective string matching, this processor makes use of a CAM-based cellular architecture. For multiple signature matches, it generates priority-encoded addresses using a binary tree structure. To enhance IDS capabilities further, the algorithm introduces an efficient method for detecting regular expression matches within network traffic text using Field-Programmable Gate Arrays (FPGAs). It presents a hardware implementation approach based on Deterministic Finite Automata (DFA), enhancing both speed and accuracy in pattern matching. This proposed algorithm provides a comprehensive solution to optimize intrusion detection by integrating innovative techniques in regular expression matching, hardware acceleration, and pattern recognition, thereby enhancing network security against springing cyber threats. © 2023 IEEE.

关键词： Cryptography

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Learned, uncertainty-driven adaptive acquisition for photon-efficient scanning microscopy

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Optics Express 2025年第6期33卷 12269-12287页

作者： Ye, Cassandra Tong Han, Jiashu Liu, Kunzan Angelopoulos, Anastasios Griffith, Linda Monakhova, Kristina You, Sixian The Department of Electrical Engineering and Computer Science CambridgeMA02139 United States Fu Foundation School of Engineering and Applied Science Columbia University in The City of New York New YorkNY10027 United States University of California Berkeley Department of Electrical Engineering and Computer Science BerkeleyCA94720 United States Department of Biological Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

Scanning microscopy systems, such as confocal and multiphoton microscopy, are powerful imaging tools for probing deep into biological tissue. However, scanning systems have an inherent trade-off between acquisition time, field of view, phototoxicity, and image quality. This often results in noisy measurements when fast, large field of view, and/or gentle imaging is needed. Deep learning could be used to denoise noisy microscopy measurements, but these algorithms can be prone to hallucination, which can be disastrous for medical and scientific applications. We propose a method to simultaneously denoise and predict pixel-wise uncertainty for scanning microscopy systems, improving algorithm trustworthiness and providing statistical guarantees for deep learning predictions. Furthermore, we propose to leverage this learned, pixel-wise uncertainty to drive an adaptive acquisition technique that rescans only the most uncertain regions of a sample, saving time and reducing the total light dose to the sample. We demonstrate our method on experimental confocal and multiphoton microscopy systems, showing that our uncertainty maps can pinpoint hallucinations in the deep learned predictions. Finally, with our adaptive acquisition technique, we demonstrate up to 16× reduction in acquisition time and total light dose while successfully recovering fine features in the sample and reducing hallucinations. We are the first to demonstrate distribution-free uncertainty quantification for a denoising task with real experimental data and the first to propose adaptive acquisition based on reconstruction uncertainty. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Pixels

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Sentiment Analysis of IMDB Movies Dataset Using Deep Learning Based GRU Model

Sentiment Analysis of IMDB Movies Dataset Using Deep Learnin...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Sen, Muskan Chaturvedi, Kapil Sagar Institute of Research Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350313987

Sentiment Analysis is the need of the hour for every big or small companies, be it manufacturing or entertainment industries, educational institutions, media houses, political parties or even individual. All organizations want to identify churn and to get their customers retained with them by mean of sentiment analysis on social media. Increasing number of Internet and social media users contribute in petabytes of unstructured data generated every day. The continuous advancement in technology sector, in terms of high storage capability, computational processing power of the computers, cloud technology etc., has enabled processing of these high-volume unstructured data with ease, in a very less time, which otherwise would have taken even days to process. Many sentiment analysis systems are modelled by using deep learning, by using GRU (gate recurrent unit) architecture, has showed significant improvements with high tendency to reveal the underlying semantic meaning in the input text. GRU model seems to perform even better with lowest loss 0.0783, variable loss 0.4728, highest accuracy 97.20% and variable accuracy 88.19%. © 2023 IEEE.

关键词： Accuracy Deep Learning Gate Recurrent Unit Loss Sentiment Analysis (SA)

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SentiMatch: Sentiment Analysis of Multi-modal Social Media Posts 10

SentiMatch: Sentiment Analysis of Multi-modal Social Media P...

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10th International Conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Yadav, Abhishek Jain, Nisarg Sharma, Lakshay Singh, Preety The Lnm Institute of Information Technology Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9781665490993

Social media users are increasingly using images and videos to communicate their feelings. However, researchers usually rely on textual data for sentiment analysis. In this paper, we focus on Twitter posts having both text and image data and analyze the match between their sentiments, when predicted independently. We also propose a method to predict the sentiment of a social media post as a collective entity from the individual sentiments of text and image. Comparing our proposed model to a naive approach where the sentiment of the text is assigned to the image also, we see an improvement of around 20% in prediction of sentiment of Twitter images associated with text. © 2023 IEEE.

关键词： Sentiment analysis

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Distributed Asynchronous Discrete-Time Feedback Optimization

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IEEE Transactions on Automatic Control 2024年第6期70卷 3968-3983页

作者： Behrendt, Gabriel Longmire, Matthew Bell, Zachary I. Hale, Matthew The School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States The Department of Mechanical and Aerospace Engineering the University of Florida GainesvilleFL United States AFRL RW at Eglin AFB

In this article, we present an algorithm that drives the outputs of a network of agents to jointly track the solution of a time-varying, strongly convex optimization problem. This algorithm is robust to asynchrony in the agents’ operations, namely in (1) computations of control inputs, (2) linear measurements of network outputs, and (3) communications of agents’ inputs and outputs. We first show that our distributed asynchronous algorithm converges to the solution of a time-invariant feedback optimization problem in linear time. Next, we show that our algorithm tracks the solution of a time-varying feedback optimization problem within a bounded error dependent upon the movement of the minimizers and degree of asynchrony, which we make precise. These convergence results are extended to quantify agents’ asymptotic behavior as the length of their time horizon approaches infinity. Then, to ensure satisfactory network performance we specify the timing of agents’ operations relative to changes in the objective function that ensure a desired error bound. Numerical experiments verify these developments and show the utility of feedback optimization under asynchrony. ©1963-2012 IEEE.

关键词： Optimization algorithms

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High-Frequency Performance with Microwave and Millimeter-Wave Methods 1

High-Frequency Performance with Microwave and Millimeter-Wav...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Poornima, D. Jany Shabu, S.L. Refonaa, J. Karunya, K. Bobby, M.P. Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

Modern wireless communication, radar systems, and the realization of new technologies like 5G and beyond all rely heavily on high-frequency performance. The use of microwave and millimeter-wave techniques (MMWT) could provide revolutionary new uses for vast amounts of data at extremely low latency. High-frequency performance optimization is still a challenging and essential problem. Interference Mitigation, Antenna Design, Signal Processing, and Regulatory Compliance are all considered to be sizeable obstacles towards accomplishing High-Frequency Performance. The motive of the research is to advocate the use of Regulatory Compliance Frequency Band Analysis (RCFBA) for the purpose of figuring out target frequencies in accordance with utility necessities and acting spectrum scanning and evaluation with the intention to assess regulatory constraints and available channels. Maximizing signal energy even as keeping off interference via cautiously choosing and positioning antennas. Enabling higher data throughput, decrease latency, and a wider variety of applications for high-frequency systems by way of easing their adoption and deployment in fields like telecommunications, radar systems, satellite communique, and 5G networks. High-frequency performance can be modelled and evaluated with the help of present day modeling tools, that may then be used to optimize gadget parameters, propagation, and antenna designs. It equips users with the way for thorough simulation evaluation, optimization, and fulfillment in excessive-frequency packages, taking into account the deployment of excessive-frequency structures that healthy the desires of contemporary packages and regulatory standards. © 2023 IEEE.

关键词： Regulatory compliance

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Game Statistics Forecast Based on Sports Using Machine Learning

Game Statistics Forecast Based on Sports Using Machine Learn...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Devi, I Mridula Juliet, Sujitha Karunya Institute of Technology and Sciences Department of Computer Science and Engineering TamilNadu India

ISBN: (纸本)9798350333244

Cricket is a popular sport worldwide, with millions of fans and players. One of the critical aspects of cricket is analyzing player statistics to forecast future performances. In order to examine player performance data and forecast future results, machine learning (ML) techniques have been used in a variety of fields, including sports. In this study, we present a machine-learning model that predicts cricket player performance using data from actual cricket matches. We use a dataset collected from international cricket matches and analyze various statistical features, including batting and bowling averages, strike rates, and fielding performance. We use this data to train a machine-learning model that predicts a player's future performance. We evaluate our model's performance by comparing its predictions to actual gameplay data and show that our model achieves high accuracy in predicting player performance. Our proposed model can be used to provide insights into a player's performance and help players improve their skills. It can also be used by cricket coaches and selectors to evaluate player skills and select teams. Overall, our work demonstrates the potential of machine learning techniques in predicting player performance in cricket and can have significant implications for the game's future. © 2023 IEEE.

关键词： Sports

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