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Pseudonymous Decentralised Reputation System

Pseudonymous Decentralised Reputation System

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International Conference on Smart Computing & Communications (ICSCC)

作者： N Nalini Anshul Kumar Megha Sharma Aniruddha Sil Womika Khan Dept. of Computer Science & Engineering Nitte Meenakshi Institute of Technology Bangalore India

In this paper, we propose a novel pseudonymous decentralized reputation system that aims to establish trust within decentralized networks. The system allows users to maintain pseudonymous identities while ensuring their reputation scores are transparent, trustworthy, and resistant to manipulation. We review existing reputation systems and discuss their limitations. Our proposed system incorporates cryptographic techniques, a consensus algorithm, and distributed data storage to ensure data integrity, privacy, and scalability, specifically designed for the Decentralized Finance (DeFi) ecosystem. We present an implementation of our system, focusing on its application in DeFi platforms, and evaluate its performance against various attack vectors. Finally, we discuss the implications of our system on decentralized financial applications, such as lending and borrowing protocols, and future research directions. Lastly, we explore the broader implications of our decentralized reputation system for the DeFi ecosystem, identifying potential applications in areas such as decentralized exchanges, insurance, and asset management.

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Multivariate Time Series Forecasting for Cloud Machine Utilization

Multivariate Time Series Forecasting for Cloud Machine Utili...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Archana Naik Kavitha Sooda Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The cloud machine utilization prediction using a multivariate time series model is explored in this study. The cloud machine usage data set samples are selected with varied patterns. The long sequence forecasting model Informer is investigated for the forecasting. The model results are benchmarked against LSTM, GRU, Prophet, and XGBoost across the diverse datasets. The performance is evaluated using RMSE. Experimental results show that the Prophet model performs best overall due to its ability to capture both seasonal and irregular patterns accurately. The RMSE value for Informer is 0.13, Prophet is 0.097, XGBoost is 0.096, LSTM is 0.1725, and GRU is 0.1547.

关键词： Cloud computing Accuracy Scalability Time series analysis Predictive models Benchmark testing Data models Internet of Things Forecasting Long short term memory

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Hybrid Convolutional Neural Network with Bi-Long Short-Term Memory Method for Detection and Classification of Transmission Line Faults 2

Hybrid Convolutional Neural Network with Bi-Long Short-Term ...

引用

2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Devi, T. Jagadish, Sripelli Suganya, E. Alkhayyat, Ahmad Soni, Mukesh Saveetha Institute of Medical And Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Sr University School of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India The Islamic University Department of Computers Techniques Engineering College of Technical Engineering Najaf Iraq Department of Cse Uttarakhand Dehradun India

ISBN: (纸本)9798350372892

In recent years of technology, classification and detection of Transmission line fault plays an important role because faults have various adverse effects on transmission lines, power storage. So, to avoid all these problems a proposed method Hybrid Convolutional Neural Network -Bi-Long Short-Term Memory (CNN -Bi-LSTM) was introduced in this paper. The main advantage of combining CNN, Bi-LSTM algorithms to grasp long term dependencies, and to perform well than individual algorithms. With this algorithm an Attention layer is included which focuses on the relevant data and ignoring the irrelevant data and also combined with activation functions like Rectified Linear Unit (ReLU), tanh, sigmoid, and Adam, so that it improved its classification process and fault detection is done effectively. In this research, the proposed method is implemented on PTL-AI Furnas dataset and data preprocessing is done by using Auto Encoders (AE) consisting of two significant phases encoders and decoders are used to reduce the dimensionality of the images. A machine learning algorithm is used to extract the features from the images to improve model interpretability, accuracy, model performance and this algorithm also prevents overfitting. The proposed method achieved high accuracy of 99.99% when compared with other existing algorithms like Capsule Setwork with Sparse Filtering (CNSF), LSTM -Window Regression (LSTM-WR), SoftMax, CatBoost, and YOLOv8. © 2024 IEEE.

关键词： Convolutional neural networks

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Cooperrative Spectrum Sensing in Cognitive Radio Network Using Flying Fox Optimization Based AlexNet 2

Cooperrative Spectrum Sensing in Cognitive Radio Network Usi...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Devi, T. Al-Farouni, Mohammed Pradeep Kumar, S. Sathishkumar, B.R. Nagendar, Y. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India Sri Ramakrishna Engineering College Department of Ece Coimbatore India Sr University School of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350372892

Nowadays, availability of spectrum becomes an essential task among the users for preventing the channel interference and effective utilization of the available spectrum in the scenario of Cognitive Radio (CR) sensing. The need and increasing of radio spectrum are due to the growth of wireless communication and its uses. To identify distribute free spectrum channels technology, CR is designed, where it also manages the available spectrum difficulties. Result of test statistics designing is still a complex task in CR networks even, if there are different spectrum sensing methods. To increase the utilization of spectrum and solve the problem scarcity of spectrum, Alex net based on the Flying Foxes Optimization (FFO-based AlexNet) is developed. In this paper, parameters like signal energy and Eigen signal statistics with approaches of deep learning are used to model test statistics. The FFO-based AlexNet results like false alarm probability for Rician channel using developed spectrum sensing method compared to Honey Badger Remora Optimization-based AlexNet (HBRO-based AlexNet) have gained 0.899 at 5dB by considering Signal-To-Noise Ratio (SNR) respectively. © 2024 IEEE.

关键词： Signal to noise ratio

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Predicting Earthquake Damage and Rehabilitation Intervention using Adaptive Fuzzy C-Means-Based Support Vector Machine 2

Predicting Earthquake Damage and Rehabilitation Intervention...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Kumar, Pradeep S. Jasim, Laith Basha, C.H. Hussaian Rafikiran, Shaik Ramulu, Kothuri Parashu Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India The Islamic University of Babylon Babylon Iraq Department of Electrical & Electronics Engineering School of Engineering SR University Warangal India Department of Electrical & Electronics Engineering Sri Venkateswara College of Engineering Tirupati India Department of Computer Science and Engineering Indur Institute of Engineering and Technology Nizamabad India

ISBN: (纸本)9798331504960

Earthquakes are the greatest rates of human loss among natural disasters in a past 20 years. Predicting rehabilitation intervention and damage grades is significant, especially in the moderate aftermath of a strong earthquake as prioritized in post-earthquake housing rescue requires data about damage extent. However, building collapses caused by earthquakes lead to huge loss of property and life. Therefore, an Adaptive Fuzzy C-means-based Support Vector Machine (AFCM-SVM) is proposed to predict earthquake damage and rehabilitation intervention using Machine Learning (ML). Initially, the xBD dataset is employed to evaluate the proposed technique and min-max normalization is established which enhances numerical stability. The AFCM is utilized to segment the building effectively and Gray-Level Co-occurrence Matrix (GLCM) is employed for extraction. Finally, SVM is performed to predict the earthquake damage and rehabilitation intervention. When compared with existing approaches like Convolutional Neural Networks (CNN) and Siamese Neural Networks, the AFCM-SVM achieves a better f1-score of 0.945 respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Classification of Lung diseases using Convolutional Neural Network algorithms 2

Classification of Lung diseases using Convolutional Neural N...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Sidheeque, Mohammed Abdul Gafur, M. Sumathy, P. Kalshetty, Jagadevi. N. Bharathidasan University School of Computer Science Engineering & Applications Tamil Nadu Trichy India Ilahia College of Engineering and Technology Kerala Ernakulam India Nitte Meenakshi Institute of Technology Department of Computer Science & Engineering Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Since the publication of the book Covid-19, several investigations of varying kinds have been carried out all across the globe to see how well it predicted future events. The early lung illness known as pneumonia is intimately linked to the virus known as Covid-19, which causes severe inflammation of the chest (pneumonic condition). It is difficult for doctors and other medical professionals to differentiate Covid-19 from other lung diseases including pneumonia. As a consequence of this, we need an independent diagnostic platform that is able to provide clinical results in a timely and efficient manner. Chest X-ray screening is the method that provides the most reliable diagnosis of lung disease. The purpose of this investigation was to offer a condensed CNN (RMNet) model for COVID-19 classification. When compared to prior models, the solution that has been developed requires less memory and requires fewer processing resources. When it comes to COVID-19 classification, the performance of the recommended RMNet model ensemble that makes use of ResNet18, Inceptionv3, and MobileNetV2 is superior to that of previously cutting-edge methodologies. Additionally, the ensemble model makes less of a demand on available memory and is straightforward to incorporate into the backend of a smart device. Lung cancer is produced by the unrestrained growth of aberrant cells. This proliferation may begin in either of the lungs, but it most often originates in the cells that border the airways. Lung cancer can be prevented by maintaining a healthy immune system. These abnormal cells do not develop into lung tissue that is healthy;instead, they rapidly multiply and produce tumours. This is because of how they behave. The process by which cancer cells spread from the primary site of the disease to other parts of the body is referred to as "metastasis."Once the disease has spread to other areas of the body, it is much more challenging to treat it in an appropriate manner. Primary or second

关键词： Cells

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Structural Health Monitoring and Damage Detection using Namib-Beetle Optimization Algorithm with 1D Convolutional Neural Network 2

Structural Health Monitoring and Damage Detection using Nami...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Pramodhini, R. Alzubaidi, Laith H. Rao, Kandukuri Seshagiri Deepa, M. Sheeba, B. Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Maisammaguda Hyderabad India Department of Computer Science and Engineering Nandha Engineering College Erode India Department of Physics New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (纸本)9798331504960

Structural Health Monitoring and damage detection in structures, buildings and roadways are an eminently vital field due to the widespread utility of the method, ranging from public security to civil application. Numerous architectures are used for extraction, selection and classification to detect the location, extent of the damage and monitor the structure. However, traditional dimensionality reduction methods exhibit poor-accuracy in dealing with non-linear distributions which manifest at higher Degrees of Freedom in vehicle-bridge interactions. This work proposes a Namib-Beetle Optimization Algorithm (NBOA) with 1D-Convolutional Neural Network (CNN) which helps to minimize the feature cost function and effectively capture sequential time series data in classification in structural health monitoring and damage detection. In the preprocessing step, frequency transformation, signal averaging and normalization procedures are considered to eliminate the stochastic nature of time domain processes and then, an Adversarial Auto-Encoder (AAE) architecture is used to perform feature extraction and understand the latent space in the architecture. NBOA is used to perform feature selection and 1D-CNN performs classification with reduced computation and an increased attention to features. The proposed NBOA with 1D-CNN achieves an Area Under the Curve (AUC) value of 0.987 compared to the existing methods like Convolutional Auto- Encoder module and U-Net based Auto-Encoder module. © 2024 IEEE.

关键词： Feature Selection

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Deep Steganography Using SteganoGAN

Deep Steganography Using SteganoGAN

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Devidas Bhat Suraj B Madagaonkar Vinayak G Pai Deepthi J. Shetty Information Science & Engineering NMAMIT Nitte Karnataka India Computer Science and Engineering IIT Dharwad Karnataka India Computer Science and Engineering Manipal Institute of Technology Manipal Karnataka India Department of CSE Nitte Meenakshi Institute of Technology Bangalore Karnataka India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

An improved method for covertly embedding secret messages within images is deep steganography utilizing SteganoGAN, which combines steganography and deep learning. SteganoGAN employs Generative Adversarial Networks (GANs) to create realistic images that can safely conceal secret data. The concealed message is integrated into the image pixels and encoded as a binary stream, which can be retrieved using a decoder, ensuring the final image maintains visual integrity. This technique provides strong security, as it is difficult to detect the hidden data, making it ideal for applications like digital water-marking, copyright protection, and private data transfer. The deep learning architecture enhances processing speed, increases data embedding capacity, and improves security. Experimental results using the Div2k dataset showed that models trained with a data depth of 4 achieved the highest bits per pixel (RS-BPP), demonstrating a balance between data embedding capacity and image quality (PSNR and SSIM). Despite its strengths, SteganoGAN faces challenges, such as vulnerability to attacks by adversaries familiar with the technique, emphasizing the need for continuous innovation to improve security.

关键词： Deep learning Training Steganography Ethics Visualization Technological innovation Watermarking Generative adversarial networks Data models Security

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An Effective Hand Gesture Recognition using Convolutional Neural Network with Long Short-Term Memory

An Effective Hand Gesture Recognition using Convolutional Ne...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Sunil, G. Saranya, A. Duraimurugan, N. Dineshkumar, R. Ramachandra, A.C. Sr University School of Computer Science & Artificial Intelligence Warangal India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Department of Ece Thandalam India Engineering Nitte Meenakshi Institute of Technology Department of Electronics and Communication Bengaluru India

ISBN: (纸本)9798350313413

A Hand Gesture (HG) constitutes a mode of nonverbal communication or non-vocal interaction, where discernible bodily movements convey specific messages, either independently or in combination with spoken language. Gesture recognition techniques are employed to create systems capable of transmitting information among individuals with disabilities for operating devices. In this paper, we propose the use of Electromyography (EMG) signals for classifying hand gestures through Employing both a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in combination is being used. The main problem lies in the noise signals produced by the EMG sensor. In this work, we employ a Low Pass Filter for preprocessing the signal images, followed by feature extraction using the CNN model. To reduce the high-dimensional signal and normalize the features for the classification process, we utilize LSTM. This experiment was conducted using the Ninapro DB1 dataset. This model successfully addresses the previous issues and attains an overall accuracy of 95.56%, Precision 91.90%, Recall 89.50%, and F1 score 85.50% 91.90%, then the accurate classification compares to other existing models like Bidirectional Convolutional Gated Recurrent Unit, and Deformable convolutional network models. © 2023 IEEE.

关键词： Convolutional Neural Network Electromyography Hand Gesture Long Short-Term Memory Low Pass Filter

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Session-based Personalized Recommender System for Online Shopping 6th

Session-based Personalized Recommender System for Online Sho...

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6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020

作者： Sreenivasa, B.R. Nirmala, C.R. Manoj Kumar, M.V. Department of Information Science and Engineering Bapuji Institute of Engineering and Technology Davangere India Department of Computer Science and Engineering Bapuji Institute of Engineering and Technology Davangere India Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (纸本)9789811613418

The recommendation system (RS) plays an important role in aiding e-commerce customers in overcoming information overload. In the last decades, the recommendation system has made significant advances. In specific, user-dynamic behavioural context concerning the item. Modelling user-behavioural context is complex and has been employed by state-of-the-art methods. However, this model fails to address the several problems occurring in future real-time online shopping portal environment. This paper aimed at addressing the issues of online recommendation considering continuous data (i.e. streaming environment) in an e-commerce environment. Customers and products may rely on a certain unexpected scenario and can arise variously at varied rates. This paper presents an efficient RS by continuously identifying (i.e. using session-specific information) and adapts to changing scenarios in customer items views, customer favourites (i.e. preferences) and product portrayals. Further shows the significance of proposed time-centric prediction/recommendation (TCP) when compared with the existing recommendation model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recommender systems

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