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2023 IEEE Applied Sensing Conference, APSCON 2023

作者： Samaddar, Narendranath Wani, Mohamad Idris Maharshi, Vikram Sonkusale, Sameer Malik, Shahid Indian Institute of Technology Centre for Sensors Instrumentation and Cyber Physical System Engineering Delhi New Delhi India Indian Institute of Technology Department of Electrical Engineering Delhi New Delhi India Tufts University Department of Electrical and Computer Engineering Boston Medford United States

ISBN: (纸本)9781665461634

Resistive pressure sensors such as strain gauge's have immense industrial applications due to low-cost and moderate to high sensitivity. Their applications have grown recently with the advancement in flexible resistive pressure sensors. However, the resistance of these sensors is highly affected by drift and offset resistance. Conventionally, voltage dividers and Wheatstone bridge-based circuits have been used for interfacing such sensors. However such interfacing circuits need calibration for offset resistance compensation. But the problem due to drift in resistance is still present. In this paper, a novel circuit is proposed to compensate for the offset and drift of these sensors. The proposed circuit detects the change in resistance using auto-zeroing feedback loop. The proposed circuit is tested with a fabricated thread-based pressure sensor for touch detection. Experimental results show that R variations as low as 10 Ω can be detected with a wide range of sensor resistance measurement. The error of measurement in sensor resistance is less than 1%. © 2023 IEEE.

关键词： Pressure sensors

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TSTFL-CGAN: Fuzzy Logic-Based Two-Stage Training Federated Learning Conditional Generative Adversarial Network Under Non-IID Data

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IEEE Transactions on Consumer Electronics 2025年

作者： Hou, Gaopan Guan, Yu Yang, Weidong Liu, Zhiquan Miao, Yinbin Shen, Yulong Ma, Jianfeng Xidian University Hangzhou Institute of Technology Hangzhou311231 China Jinan University College of Cyber Security Guangzhou510632 China Xidian University School of Cyber Engineering Xi'an710071 China Xidian University School of Computer Science and Technology Xi'an710071 China

With the proliferation of Internet of Things devices, federated Generative Adversarial Networks (GAN) have become an effective tool for handling data deficiencies in distributed architectures. However, in federated learning scenarios, due to the non-independently and identically distributed (non-IID) nature of data, the training of federated GAN faces multiple challenges. Existing works on federated GAN training still face limitations when confronted with highly heterogeneous data. To address these issues, this paper proposes a federated data augmentation and aggregation scheme based on Conditional GAN (CGAN), named TSTFL-CGAN. The method divides federated GAN training into two stages: pre-training and formal training. It uses fuzzy logic to handle uncertain data and allows the global server to flexibly allocate generators to supplement the missing samples on client. Additionally, we introduce a time factor to dynamically adjust each client's model contribution to global aggregation based on the model training loss and data volume, which accelerates convergence and enhances the performance of the global model. Extensive experiments show that TSTFL-CGAN achieves convergence within 3 rounds of aggregation across 4 different data distribution scenarios, and it demonstrates an improvement of 10 to 30 in the evaluation of generated sample quality. © 1975-2011 IEEE.

关键词： Generative adversarial networks

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Optimizing Hyperspectral Anomaly Detection with a Scalable Quantum Snow Geese Non-Local Neural Network Algorithm 4

Optimizing Hyperspectral Anomaly Detection with a Scalable Q...

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

作者： Ramkumar, M.Siva Emayavaramban, G. Latha, G.Charlyn Pushpa Giri, Jayant Al-Qawasmi, Khaled Sns College of Technology Department of Ece India Jct College of Engineering and Technology Department of Ece India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Dept of Information Technology Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Saveetha University Department of Vlsi Microelectronics TN Chennai602105 India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331529635

Hyperspectral Anomaly Detection (HAD) focuses on identifying unusual objects or patterns in hyperspectral imagery (HSI) that differ from the normal background or surrounding pixels. In the past two decades, anomaly detection (AD) has become essential in hyperspectral image analysis, offering a novel approach to differentiate targets from the background without requiring prior information. Current methods face multiple drawbacks, either by neglecting the problem of low resolution or by depending on super-resolution models that aim to reconstruct the entire image, which can result in ineffective detection of abnormal pixels. To address these challenges, a Scalable Quantum Snow Geese Non-local Neural Network Algorithm (ASQSGNon-LNNet) is introduced for hyperspectral anomaly detection, aimed at achieving high accuracy. In this approach, input images are sourced from the ABU-urban dataset. First, the features of these input images are extracted using the Memory Efficient Vision Transformer (ME-ViT) method. Subsequently, the extracted features undergo Hyperspectral Super Resolution using the Deform-Mamba Network. After that, anomaly detection is conducted using a Scalable Quantum Non-local Neural Network (SQNonL-NNet), optimized by the Snow Geese Algorithm (SGA), leading to automated hyperspectral anomaly detection with improved efficiency and accuracy. The performance of the proposed ASQSGNon-LNNet is assessed using the ABU-urban dataset, achieving an accuracy of 99.8% and surpassing existing methods. The results of this technique reveal unusual patterns or objects in hyperspectral images by analyzing their spectral signatures and enhancing accuracy in detecting hidden or subtle anomalies in remote sensing, environmental monitoring, and military applications. This technology provides greater accuracy than traditional methods. © 2024 IEEE.

关键词： Snow

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Pioneering Advanced Deep Multi-Task Learning Model for Precise COVID-19 Diagnosis 2

Pioneering Advanced Deep Multi-Task Learning Model for Preci...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Kordnoori, Shirin Alqammaz, Abdullah Sabeti, Maliheh Boostani, Reza Mostafaei, Hamidreza Agha Banihashemi, Saeed Seyed Islamic Azad University North Tehran Branch Department of Computer Engineering Tehran Iran Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Shiraz University Faculty of Electrical and Computer Engineering Cse and It Departmnet Shiraz Iran Islamic Azad University Shiraz University North Tehran Branch Department of Statistics Tehran Iran Islamic Azad University North Tehran Branch Department of Mathematics Tehran Iran

ISBN: (纸本)9798350373363

To address the urgent need for intelligent systems capable of simultaneously identifying COVID-19 cases and delineating lung infection regions in CT scan images, we introduce a multi-task architecture. Initially, our model, an enhanced U-Net with a classification branch, aims to balance segmentation and classification tasks using binary preprocessing algorithms. The CBAM attention module further improves task coherence. Rigorous evaluation demonstrates our model's superiority over previous studies, achieving an impressive 95% classification accuracy and a Dice index of 88.37 ± 0.01 for segmentation, supported by robust metrics. We also extend our multi-task framework to complex U-Net++ and ResUNet architectures with positive outcomes. Future exploration includes integrating ensemble learning within this framework. Our work signifies significant progress in enhancing diagnostic capabilities in complex medical image analysis, elevating standards for COVID-19 detection and lung infection analysis through multi-task learning and shared feature extraction. © 2023 IEEE.

关键词： COVID-19

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Syn-BERT: Bi-level Synonymous Data Augmented Self-supervised Embedding-based Information Retrieval with BERT 5

Syn-BERT: Bi-level Synonymous Data Augmented Self-supervised...

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Yu, Zhaojian Huang, Tianlin Chen, Rong Chen, Haosheng Lin, Shuyuan College of Cyber Security Jinan University Guangzhou510632 China School of Mathematics and Computer Science Quanzhou Normal University Quanzhou362000 China Information Engineering College Xizang Minzu University Shanyang712082 China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Image Cognition Chongqing400065 China

ISBN: (纸本)9798350355925

Information retrieval, which seeks to locate the most relevant documents from a document pool given a user query, is critical in a wide range of applications. However, current embedding-based retrieval models require a high quantity of labeled documents as training data to construct appropriate target training tasks, which can be expensive and difficult to obtain in practical applications. In this paper, we present a Synonymous BERT (Syn-BERT) model with strong training data augmentation with the aim of building an effective and efficient retrieval system on unlabeled documents. Unlike most existing embedding-based retrieval models, which require high-quality labeled documents to learn the semantic connections between original documents and labels, the proposed Syn-BERT generates synonymous queries and irrelevant queries for each original document in unlabeled documents and exploits the distances between them to obtain better embeddings. We evaluated the proposed Syn-BERT on two publicly-available Frequently Asked Question datasets, with the experimental results demonstrating that the proposed Syn-BERT achieved state-of-the-art results in embedding-based retrieval tasks with unlabeled documents. © 2024 IEEE.

关键词： Semantics

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Lattice-Based Cryptography and NTRU: Quantum-Resistant Encryption Algorithms 4

Lattice-Based Cryptography and NTRU: Quantum-Resistant Encry...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Nisha, F. Lenin, J. Saravanan, S.K. Rohit, V. Robin Selvam, P.D. Rajmohan, M. Cmr University School of Engineering and Technology Computer Science & Engineering Karnataka Bengaluru India Alliance University Alliance College of Engineering and Design Department of Computer Science & Engineering Karnataka Bengaluru India Simats Saveetha School of Engineering Department of BigData and Network Security Tamil Nadu Chennai India Presidency University Computer Science & Engineering School of Computer Science & Engineering and Information Science Karnataka Bengaluru India Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350349856

The quantum-resistant qualities of various encryption methods, such as N-th degree Truncated polynomial Ring Units (NTRU) Encrypt, NTRU Sign, Ring-Lizard, and Kyber protocols are becoming more important in light of the ever-evolving nature of digital threats. An-depth analysis of the mathematical derivation of these algorithms, as well as a determination of their computing efficiency and resistance to quantum assaults is discussed in this paper. Techniques of the Ring-Lizard and Kyber algorithms to lattice-based encryption are analyzed. The study gives an overview of quantum-resistant capabilities of these algorithms and their performance measures. Strengths and weaknesses of the NTRU Encrypt, NTRU Sign, Ring-Lizard, and Kyber algorithms are discussed. The results give useful insights for cybersecurity practitioners, academics, and policymakers, aiding them in making educated choices to protect digital infrastructures against quantum attacks. © 2024 IEEE.

关键词： Polynomials

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Cancelable Speaker Identification System Based on Optical-Like Encryption Algorithms

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computer Systems Science & engineering 2022年第10期43卷 87-102页

作者： Safaa El-Gazar Walid El-Shafai Ghada El-Banby Hesham F.A.Hamed Gerges M.Salama Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department of Electrical Engineering Faculty of EngineeringEgyptian-Russian UniversityCairoEgypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Biometric authentication is a rapidly growing trend that is gaining increasing attention in the last *** achieves safe access to systems using biometrics instead of the traditional *** utilization of a biometric in its original format makes it usable only ***,a cancelable biometric template should be used,so that it can be replaced when it is *** biometrics aims to enhance the security and privacy of biometric *** encryption is an efficient technique to be used in order to generate cancelable biometric *** this paper,a highly-secure encryption algorithm is proposed to ensure secure biometric data in verification *** considered biometric in this paper is the speech *** speech signal is transformed into its ***,the spectrogram is encrypted using two cascaded optical encryption *** first algorithm is the Optical Scanning Holography(OSH)for its efficiency as an encryption *** OSH encrypted spectrogram is encrypted using Double Random Phase Encoding(DRPE)by implementing two Random Phase Masks(RPMs).After the two cascaded optical encryption algorithms,the cancelable template is *** verification is implemented through correlation estimation between enrolled and test templates in their encrypted *** the correlation value is larger than a threshold value,the user is *** threshold value can be determined from the genuine and imposter correlation distribution curves as the midpoint between the two *** implementation of optical encryption is adopted using its software rather than the optical *** efficiency of the proposed cancelable biometric algorithm is illustrated by the simulation *** can improve the biometric data security without deteriorating the recognition *** results give close-to-zero This values for the Equal Error Rate(EER)and close-to-one values for the Area under Receiver Operator Charact

关键词： Cancelable biometrics spectrogram OSH DRPE EER AROC

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A robust and automatic method for the recognition of speech category in online learning discourse

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Journal of Ambient Intelligence and Humanized Computing 2024年第12期15卷 3913-3924页

作者： Jiang, Dazhi He, Zhihui Chen, Yifei Xu, Linyan Lin, Jiali Guangdong Provincial Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510399 China Department of Computer Science Shantou University Shantou515063 China Department of Management Economics and Industrial Engineering Politecnico di Milano MI Milan20156 Italy School of Business Shantou University Shantou515063 China

It is a rapid and irresistible developing trend that the traditional classroom learning mode is transformed into an online learning mode. In the online learning process, it is important to achieve effective voice separation and provide a good lecture experience for students. In this paper, a robust and automatic method for the recognition of speech categories (teacher speech, student speech, silence speech and overlapping speech) is presented. The system, which is part of artificial intelligence in education (AIED), is a hybrid speech processing system that includes GMM modeling, GMM clustering, silence processing and overlapping processing. The online learning environment we are based on is not a customized environment that defines the roles of teachers or students, but a more casual, broader and more general online environment. The method proposed and designed based on this situation has better adaptability and scalability. The experimental results show that our automatic detection of speech categories fares well when compared to the results from professional coders. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Contrastive Learning

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A comparative analysis of machine learning models developed from homomorphic encryption based RSA and paillier algorithm 5

A comparative analysis of machine learning models developed ...

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5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021

作者： Kiratsata, Harsh J. Panchal, Mahesh GTU-Graduate School of Engineering and Technology Cyber Security Department Ahmedabad India

ISBN: (纸本)9781665412728

Nowadays, the use of Auto ML for development of machine learning (ML) models is increasing day by day. In which users need to upload their dataset to develop machine learning models. Where security of user's data is a very big problem. Which can be solved using Homomorphic encryption. In this work, first the spiderman correlation of the machine learning models developed using encrypted dataset is compared with each other. In which the algorithms used to encrypt dataset are Homomorphic Encryption (HE) based Rivest Shamir Adleman (RSA) and Paillier algorithm. Where the homomorphic encryption based Rivest Shamir Adleman (RSA) is a multiplicative operation and the HE-based Pailler supports additive operations. These algorithms are used to encrypt data and store it on cloud servers. An in-house key generator is developed to generate keys for HE-based RSA algorithm and used this algorithm to encrypt data. Then a comparison between the time taken by these algorithms to encrypt the data and develop the machine learning model using Azure auto Machine Learning is done where the HE-based RSA algorithm is winner. © 2021 IEEE.

关键词： Cryptography

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Fuzzy-Based Weighted Federated Machine Learning Approach for Sustainable Energy Management with IoE Integration

Fuzzy-Based Weighted Federated Machine Learning Approach for...

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2024 Systems and Information engineering Design Symposium, SIEDS 2024

作者： Ghazal, Taher M. Iqbal Janjua, Jamshaid Abbas, Sagheer Fatima, Areej Saleem, Muhammad Khan, Muhammad Adnan Alqarafi, Abdulrahman Khalifa University Centre for Cyber Physical Systems Computer Science Department Abu Dhabi United Arab Emirates Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Selangor Malaysia National College of Business Administration and Economics School of Computer Sciences Lahore Pakistan Al-Khawarizmi Institute of Computer Science Lahore Pakistan Bahria University Department of Computer Science Lahore Pakistan LGU Department of Computer Science Lahore Pakistan Minhaj University School of Computer Science Lahore Pakistan Skyline University College School of Computing Sharjah United Arab Emirates Riphah International University Riphah School of Computing and Innovation Lahore Pakistan Taibah University College of Computer Science and Engineering Medina42353 Saudi Arabia

ISBN: (纸本)9798350385144

The considerable surge in energy consumption due to population growth and the use of new technology has posed significant challenges to energy security and the environment. Managing energy consumption on the consumer side is crucial as the demand for energy continues to increase. Consumers need to monitor their daily usage and understand consumption standards to prepare themselves for energy efficiency and cost savings. To address these challenges, a Smart Energy Management System (SEMS) based on the Internet of Energy (IoE) has been developed. This system uses day-Ahead planning and accurate energy availability forecasting to manage energy demand in a smart environment with high sustainable energy penetration. The Fuzzy based Weighted Federated Learning (FWFML) approach is integrated into the system to optimize energy management. The SEMS system monitors energy usage at the user level, and a review of management plans has been introduced to improve energy efficiency in terms of 93.5% accuracy, and 6.5% miss-rate. This research work also highlights future challenges in developing SEMS to increase energy efficiency in a more robust manner. © 2024 IEEE.

关键词： Energy management systems

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