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Blockchain Based Secure and Decentralized Smart Licensing of Charging Vehicles for Rajshahi City Corporation 2

Blockchain Based Secure and Decentralized Smart Licensing of...

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2nd International Conference on Information and Communication technology for Sustainable Development, ICICT4SD 2023

作者： Haque, Momenul Paul, Subrata Kumer Islam, Kamrul Della, Mursheda Nusrat Paul, Rakhi Rani Fahim, Sultan Bangladesh Army University of Engineering and Technology Dept. of Computer Science and Engineering 6431 Bangladesh University of Rajshahi Dept. of Computer Science and Engineering Rajshahi6205 Bangladesh Rabindra Maitree University Dept. of Computer Science and Engineering Kushtia7000 Bangladesh Bangladesh Army University of Engineering and Technology Dept. of Information and Communication Engineering Bangladesh

ISBN: (纸本)9798350358667

The adoption of charging vehicles in Rajshahi has emerged as a significant step towards reducing air pollution and promoting a healthy, green city. However, the current centralized licensing system poses challenges such as time-consuming processes and the potential for fraudulent activities. To address these issues, this paper proposes the implementation of a secure and decentralized smart licensing system for charging vehicles in Rajshahi City Corporation, leveraging the smart capabilities of blockchain technology. By utilizing Hyperledger Fabric, the implementation generates blockchain based smart contracts that automate and streamline the licensing process smartly and efficiently. This approach ensures the immutability, transparency, and integrity of license records, minimizing the risk of fraud and enhancing overall efficiency. Additionally, the system incorporates the use of QR codes and an inquiry system to enhance the credibility of paper-based licenses. Compared to other digital licenses, this blockchain-based method significantly reduces risks and contributes to the development of a greener and more sustainable Rajshahi city environment. Furthermore, the introduction of a sustainable business policy, utilizing the G2C model, facilitates revenue generation and distribution fairly and equitably for all stakeholders involved in the charging vehicle ecosystem. This implementation not only offers cost benefits and efficiency improvements for drivers but also promotes a sustainable and environmentally friendly city. © 2023 IEEE.

关键词： Smart contract

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Optimizing Privacy-Accuracy Trade-Off in IoT Intrusion Detection: An Analysis of FedAvg and FedProx with Differential Privacy

Optimizing Privacy-Accuracy Trade-Off in IoT Intrusion Detec...

引用

2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Anwar, Sayeda Suaiba Hoque, Mohammed Moshiul Chittagong University of Engineering & Technology Dept. of Computer Science and Engineering Bangladesh East Delta University Chattagram Bangladesh

ISBN: (纸本)9798331535476

Ensuring the privacy and accuracy of interconnected network devices is crucial in the Internet of Things (IoT) landscape, especially when deploying Intrusion Detection Systems (IDS) on devices with constrained resources. This study offers a method to improve intrusion detection in IoT environments by combining Federated Learning with Differential Privacy. The proposed approach builds an efficient intrusion detection model by fusing Bidirectional Long Short-Term Memory (BiLSTM) with Convolutional Neural Networks (CNN). We evaluate this model on the Bot-IoT dataset, meticulously curated by the University of New South Wales (UNSW). This work aims to balance high detection accuracy with robust privacy protection, a crucial yet often neglected aspect of IoT security. We use federated learning to distribute intrusion detection tasks across various IoT devices while protecting data privacy, using differential privacy algorithms to assess and limit information leakage. This study evaluates two aggregation strategies, Federated Averaging (FedAvg) and Federated Proximal (FedProx), and examines how varying epsilon values and noise multipliers affect model accuracy. The findings indicate t hat a noise multiplier o f roughly 0.5 yields a steady accuracy curve, with FedProx achieving higher accuracy than FedAvg. © 2024 IEEE.

关键词： Network intrusion

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Advancement in Image Plagiarism Detection using Feature Extraction and Deep Learning Approaches 5

Advancement in Image Plagiarism Detection using Feature Extr...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Bhandari, Sonam Chaudhary, Sumit Malhotra, Sonal Joshi, Kapil Kirola, Madhu Rajkumar Uttaranchal Institute Of Technology Uttaranchal University Dept. of Computer Science and Engineering Dehradun India Graphic Era Hill University Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Dev Bhoomi Uttarakhand University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350367171

With the widespread use of digital media, amount of visual content on the internet and the incidence of image plagiarism has also increased. In various domains including academia, journalism and creative industries, unauthorized use or reproduction of images without proper attribution has become significant concern. Scaling, rotation, cropping, filtering due to these diverse range of transformations detecting image plagiarism is a challenging task now. This study investigates the use of deep learning approach to image plagiarism detection that combines methods for similarity measurement, clustering and feature extraction. A comprehensive solution is being proposed by combining Scale Invariant Feature Transform (SIFT) for extracting features, Bag of Visual Words (BoVW) model and Convolutional Neural Networks (CNNs) for image analysis. The effectiveness of the proposed approach through extensive experiments is demonstrated, outperforming the baseline methods and showcasing superior performance metrics. © 2024 IEEE.

关键词： Contrastive Learning

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SroX: Accelerating Hidden Weight Creation in Neural Networks via Spectral Relaxation and X-Iteration 2

SroX: Accelerating Hidden Weight Creation in Neural Networks...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Agarwal, Harshit Middha, Parth Thakur, Ayush Singh, Sofia Saraswat, Shipra Bhatia, Neha Amity University Dept. of Computer Science & Engineering Noida India Amity Institute of Information Technology Amity university Noida India Panipat Institute of Engineering & Technology Dept. of Information Technology Panipat India

ISBN: (纸本)9798331519056

This paper proposes SroX, a novel approach for accelerating Hidden Weight creation in neural networks, which is a critical component of neural network training that can significantly impact the overall efficiency of the training process. SroX leverages spectral relaxation and X-Iteration to reduce the computational time required for Hidden Weight creation, enabling faster training times without sacrificing accuracy. Our approach addresses the long-standing challenge of efficient Hidden Weight creation, which has been a major bottleneck in neural network training. We demonstrate the efficacy of SroX through extensive experiments on benchmark datasets, including MNIST, CIFAR-10, and ImageNet, where SroX outperforms existing methods, including Random Projection and Gradient-Based Optimization, in terms of training time and accuracy. The proposed approach provides a promising solution for large-scale neural network applications, where efficient training is crucial, and has the potential to revolutionize the field of neural network research and applications, driving innovation and breakthroughs in areas such as computer vision, natural language processing, and beyond. By providing a faster and more efficient way to create Hidden Weights, SroX can enable researchers and practitioners to tackle complex problems with unprecedented speed and efficiency, leading to significant advancements in the field of artificial intelligence. © 2024 IEEE.

关键词： Accelerated Training Hidden Weights Neural Networks Spectral Relaxation X-Iteration

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Early Discovery of Emerging Entities in Persian Twitter with Semantic Similarity 9

Early Discovery of Emerging Entities in Persian Twitter with...

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9th International Conference on Web Research, ICWR 2023

作者： Yousefi, Shahin Hooshmand, Mohsen Afsharchi, Mohsen Dept. of Computer Science and Information Technology Zanjan Iran University of Zanjan Dept. of Electrical & Computer Engineering Zanjan Iran

ISBN: (纸本)9798350399691

Discovering emerging entities (EEs) is the problem of finding entities before their establishment. These entities can be critical for individuals, companies, and governments. Many of these entities can be discovered on social media platforms, e.g. Twitter. These identities have been the spot of research in academy and industry in recent years. Similar to any machine learning problem, data availability is one of the major challenges in this problem. This paper proposes EEPT that is an online clustering method able to discover EEs without any need for training on a dataset. Additionally, due to the lack of a proper evaluation metric, this paper uses a new metric to evaluate the results. The results show that EEPT is promising and finds significant entities before their establishment. © 2023 IEEE.

关键词： Semantics

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Global Asymptotic Stabilty Analysis of DC-DC Buck Converters 1

Global Asymptotic Stabilty Analysis of DC-DC Buck Converters

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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Kannabiran, Kanimozhi Mohamed Rabi, B. Raja Jayapalan, Booma Krismadinata Lakshmanan, Thangapalani Rajalakshmi, K. Npr College of Engineering & Technology Dept. of Electrical and Electronics Engg. Natham India Dept. of Mechanical Engineering Rathinam Technical Campus Coimbatore India Psna College of Engineering and Technology Dept. of Electronics and Communication Engg. Dindigul India Universitas Negeri Centre for Energy and Power Electronics Research Pedang Indonesia Sri Sairam Institute of Technology Dept. of Computer Science and Engineering Chennai India Npr College of Engineering & Technology Dept. of Computer Science and Engineering Natham India

ISBN: (纸本)9798350348637

Stability of power electronic converters is one of the most important problems related to their dynamics. The problems related to global asymptotic stability is discussed here. The design example used is DC-DC buck converter with Adaptive sliding mode control. An averaged small signal model is derived and transfer functions are obtained. Stability is also verified using conventional techniques by MATLAB simulation. Simulation results and mathematical proof shows that closed loop system designed is stable and a fast response is obtained. Simulated results show that the simulated converter system acquires the excellent robustness feature of achieving a stable steady-state and fast transient response under variable operating points. A prototype model has been developed and simulated results are compared with experimental results. The results validate the stability analysis of Dc-Dc buck converter © 2024 IEEE.

关键词： Buck converter

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Enhanced Convolutional Neural Networks for Early Detection and Classification of Ophthalmic Diseases 2

Enhanced Convolutional Neural Networks for Early Detection a...

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2nd International Conference on Information and Communication technology for Sustainable Development, ICICT4SD 2023

作者： Shojol, Md. Shojeb Hossain Siddique, Md Abu Ismail Haque, Fariha Rajshahi University of Engineering & Technology Dept. of Electronics & Telecommunication Engineering Rajshahi Bangladesh Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350358667

Eye diseases are an important public health concern that affects millions of people around the world. The detection and diagnosis of these disorders at an early stage is crucial for timely intervention and treatment. This study focuses on building a Custom convolutional neural network (CNN) model alongside well-known architectures such as SqueezeNet, VGG16, and ResNet50 with Long Short-Term Memory (LSTM) to identify common eye illnesses. Photos of four common eye illnesses, including squint, cataract, chalazion, keratitis, and normal eyes, were collected and preprocessed to boost the dataset's diversity and quality. To select the best effective model, the evaluation compared the models using several metrics such as the confusion matrix, classification report, accuracy, precision, recall, and F1-score. After thorough analysis, we determined the ideal configuration for each of these CNN models and got the highest test accuracy of 98.16% for SqueezeNet architecture. © 2023 IEEE.

关键词： Convolutional neural networks

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High-Performance Face Identification Using Randomly Selected Channels from Hyperspectral Images

High-Performance Face Identification Using Randomly Selected...

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Furusawa, Sota Premachandra, Chinthaka Graduate School of Engineering and Science Shibaura Institute of Technology Dept. of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350378214

With the recent development of information technology, the importance of protecting personal information has increased. Because of the vulnerability in passwords, biometric authentication is now being used as a method of personal information protection. However, biometric authentication has the possibility of malicious authentication due to the emergence of technology that can generate biometric data that is difficult to duplicate. Therefore, this study aimed to solve the security problems posed by Generative Adversarial Networks (GANs) in biometric authentication by using Hyperspectral Images (HSI). We searched for solutions to the problems identified in previous studies, such as the inability to correctly identify individuals who have not been previously learned and the length of time required for identification. In this paper, we used three channels of data randomly selected from hyperspectral face images for learning to identify individuals by binary classification into 'learned subject (class)' and 'unlearned subjects (classes)'. In this case, discriminators based on the least-squares generative adversarial network (LSGAN) model was developed for each class using three randomly selected channels of data from the HSI for two class classification. The proposed method significantly reduced the identification time and achieved very high discrimination accuracy in all classes. In addition, the results showed high accuracy throughout multiple face identification experiments. © 2024 IEEE.

关键词： Generative adversarial networks

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Augmented Memory Replay-based Continual Learning Approaches for Network Intrusion Detection 37

Augmented Memory Replay-based Continual Learning Approaches ...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Amalapuram, Suresh Kumar Channappayya, Sumohana S. Tamma, Bheemarjuna Reddy Dept. of Computer Science and Engineering Indian Institute of Technology Hyderabad India Dept. of Electrical Engineering Indian Institute of Technology Hyderabad India

ISBN: (纸本)9781713899921

Intrusion detection is a form of anomalous activity detection in communication network traffic. Continual learning (CL) approaches to the intrusion detection task accumulate old knowledge while adapting to the latest threat knowledge. Previous works have shown the effectiveness of memory replay-based CL approaches for this task. In this work, we present two novel contributions to improve the performance of CL-based network intrusion detection in the context of class imbalance and scalability. First, we extend class balancing reservoir sampling (CBRS), a memory-based CL method, to address the problems of severe class imbalance for large datasets. Second, we propose a novel approach titled perturbation assistance for parameter approximation (PAPA) based on the Gaussian mixture model to reduce the number of virtual stochastic gradient descent (SGD) parameter computations needed to discover maximally interfering samples for CL. We demonstrate that the proposed approaches perform remarkably better than the baselines on standard intrusion detection benchmarks created over shorter periods (KDDCUP'99, NSL-KDD, CICIDS-2017/2018, UNSW-NB15, and CTU-13) and a longer period with distribution shift (AnoShift). We also validated proposed approaches on standard continual learning benchmarks (SVHN, CIFAR-10/100, and CLEAR-10/100) and anomaly detection benchmarks (SMAP, SMD, and MSL). Further, the proposed PAPA approach significantly lowers the number of virtual SGD update operations, thus resulting in training time savings in the range of 12 to 40% compared to the maximally interfered samples retrieval algorithm. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Intrusion detection

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Post Quantum Code-Based Cryptosystems with Dual Inverse Matrix 13

Post Quantum Code-Based Cryptosystems with Dual Inverse Matr...

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13th International Conference on Information technology in Asia, CITA 2023

作者： Makoui, Farshid Haidary Gulliver, T. Aaron Dakhilalian, Mohammad University of Victoria Dept. of Electrical & Computer Engineering VictoriaBC Canada Isfahan University of Technology Dept. of Electrical & Computer Engineering Isfahan Iran

ISBN: (纸本)9798350326208

The Shor algorithm demonstrates the significant risk that quantum attacks pose to the security of widely used cryptographic primitives. However, code-based cryptography has been shown to be resistant to these attacks. To date, no polynomial-time attack exists that can break code-based cryptosystems such as the McEliece cryptosystem. Despite this, these cryptosystems are not employed in practical applications in domains such as online banking, blockchains, and e-commerce platforms. The primary reason is the large sizes of the public and private keys associated with code-based cryptosystems. In this paper, a new code-based cryptosystem is introduced which employs a dual matrix based on the transpose and inverse of the parity check matrix to reduce the key size compared to the McEliece cryptosystem. © 2023 IEEE.

关键词： Public key cryptography

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